diff --git a/openai_pgvector_helloworld/blog_data_and_embeddings.csv b/openai_pgvector_helloworld/blog_data_and_embeddings.csv
index 4a371d1..c21fa3a 100644
--- a/openai_pgvector_helloworld/blog_data_and_embeddings.csv
+++ b/openai_pgvector_helloworld/blog_data_and_embeddings.csv
@@ -1,6 +1,6 @@
 title,content,url,tokens,embeddings
 "How to Build a Weather Station With Elixir, Nerves, and TimescaleDB","This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition,Alexander Koutmos, author of the Build a Weather Station with Elixir and Nerves book, joins us to share how he uses Grafana and TimescaleDB to store and visualize weather data collected from IoT sensors.About the teamThe bookBuild a Weather Station with Elixir and Nerveswas a joint effort between Bruce Tate, Frank Hunleth, and me.I have been writing software professionally for almost a decade and have been working primarily with Elixir since 2016. I currently maintain a few Elixir libraries onHexand also runStagira, a software consultancy company.Bruce Tateis a kayaker, programmer, and father of two from Chattanooga, Tennessee. He is the author of more than ten books and has been around Elixir from the beginning. He is the founder ofGroxio, a company that trains Elixir developers.Frank Hunlethis an embedded systems programmer, OSS maintainer, and Nerves core team member. When not in front of a computer, he loves running and spending time with his family.About the projectIn the Pragmatic Bookshelf book,Build a Weather Station with Elixir and Nerves, we take a project-based approach and guide the reader to create a Nerves-powered IoT weather station.For those unfamiliar with the Elixir ecosystem,Nervesis an IoT framework that allows you to build and deploy IoT applications on a wide array of embedded devices. At a high level, Nerves allows you to focus on building your project and takes care of a lot of the boilerplate associated with running Elixir on embedded devices.The goal of the book is to guide the reader through the process of building an end-to-end IoT solution for capturing, persisting, and visualizing weather data.Assembled weather station hooked up to development machine.One of the motivating factors for this book was to create a real-world project where readers could get hands-on experience with hardware without worrying too much about the nitty-gritty of soldering components together. Experimenting with hardware can often feel intimidating and confusing, but with Elixir and Nerves, we feel confident that even beginners get comfortable and productive quickly. As a result, in the book, we leverage a Raspberry Pi Zero W along with a few I2C enabled sensors to",https://www.timescale.com/blog/how-to-build-a-weather-station-with-elixir-nerves-and-timescaledb/,501,"[-0.015646429732441902, 0.00886395014822483, 0.008715273812413216, -0.026379454880952835, 0.04576403647661209, 0.0017416387563571334, -0.010010883212089539, 0.014811009168624878, -0.05593067780137062, -0.0042337398044764996, 0.04627378284931183, 0.0006354149663820863, -0.032935380935668945, -0.027937019243836403, 0.04703840613365173, 0.04400824010372162, 0.009635651484131813, -0.019455378875136375, -0.04344185069203377, 0.0310946237295866, -0.02814941294491291, -0.017671260982751846, 0.043810002505779266, -0.04114798456430435, -0.001835446571931243, -0.024113908410072327, -0.029622018337249756, 0.06796639412641525, 0.045933954417705536, -0.032340675592422485, -0.002100940328091383, -0.024623656645417213, -0.016057059168815613, 0.006676281802356243, 0.020913824439048767, 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-"How to Build a Weather Station With Elixir, Nerves, and TimescaleDB","capture weather and environmental data. In all, the reader will capture and persist into TimescaleDB the current: altitude, atmospheric pressure, temperature, CO2 levels,TVOClevels, and the ambient light.Once the environmental data is captured on the Nerves device, it is published to a backend REST API and stored in TimescaleDB for later analytics/visualization. Luckily, TimescaleDB is an extension on top of PostgreSQL, allowing Elixir developers to use existing database tooling likeEctoto interface with time-series enabled tables.After the time-series weather data is stored in TimescaleDB, we walk the reader through how to visualize this data using the popular open-source visualization toolGrafana.Using Grafana for visualizing the weather was an easy choice given that Grafana natively supports TimescaleDB and is able to easily plot time-series data stored in TimescaleDB hypertables.✨Editor’s Note:Check outGrafana 101 video seriesandGrafana tutorialsto learn everything from building awesome, interactive visualizations to setting up custom alerts, sharing dashboards with teammates, and solving common issues.The diagram shows all of the various components of the weather station system and how they interact with one another.By the end of the book, readers have a fully-featured IoT application and API backend that can power a live Grafana dashboard in order to plot their TimescaleDB data published from their Nerves weather station.Screenshot of Grafana dashboard, showing various graphs for various weather data📖If you are interested in learning about how to build an end-to-end IoT weather monitoring solution, be sure tocheck out the book and the accompanying code. If you are interested in learning more about Nerves and Elixir,check out the Nerves documentation.Choosing (and using!) TimescaleDBFrom the onset of the book, we knew that we wanted to use a purpose-built time-series database to persist the weather station data. We wanted the project to be as realistic as possible and something that could possibly be expanded for use in the real world.With that goal in mind, TimescaleDB was an obvious choice given that PostgreSQL has become a ubiquitous database and it has great support in the Elixir community. In addition,leveraging TimescaleDB on top of PostgreSQL does not add a tremendous amount of overhead or complexity and allows new users to easily leverage the benefits of a time-series database without having to learn any new query languages or databases. Specifically, all it took for readers to start leveraging TimescaleDB was to run a single SQL",https://www.timescale.com/blog/how-to-build-a-weather-station-with-elixir-nerves-and-timescaledb/,512,"[-0.017784500494599342, -0.0019238684326410294, 0.025660494342446327, -0.03254697471857071, 0.03372369334101677, 0.019522834569215775, 0.004018227569758892, -0.013853190466761589, -0.03728059306740761, 0.009079455398023129, 0.028669150546193123, 0.013672670349478722, -0.0029066961724311113, -0.039099156856536865, 0.02694418653845787, 0.010202687233686447, 0.01444154977798462, -0.0009025968611240387, -0.026034904643893242, 0.014735729433596134, -0.025593634694814682, 0.028294740244746208, 0.0181321669369936, -0.0209135040640831, 0.0044996123760938644, -0.01009571272879839, -0.0021027163602411747, 0.07734251767396927, 0.061938200145959854, -0.010944822803139687, -0.001898796297609806, -0.02842845767736435, -0.0254866611212492, 0.009166372008621693, 0.03671897575259209, 0.01826588623225689, 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+"How to Build a Weather Station With Elixir, Nerves, and TimescaleDB","capture weather and environmental data. In all, the reader will capture and persist into TimescaleDB the current: altitude, atmospheric pressure, temperature, CO2 levels,TVOClevels, and the ambient light.Once the environmental data is captured on the Nerves device, it is published to a backend REST API and stored in TimescaleDB for later analytics/visualization. Luckily, TimescaleDB is an extension on top of PostgreSQL, allowing Elixir developers to use existing database tooling likeEctoto interface with time-series enabled tables.After the time-series weather data is stored in TimescaleDB, we walk the reader through how to visualize this data using the popular open-source visualization toolGrafana.Using Grafana for visualizing the weather was an easy choice given that Grafana natively supports TimescaleDB and is able to easily plot time-series data stored in TimescaleDB hypertables.✨Editor’s Note:Check outGrafana 101 video seriesandGrafana tutorialsto learn everything from building awesome, interactive visualizations to setting up custom alerts, sharing dashboards with teammates, and solving common issues.The diagram shows all of the various components of the weather station system and how they interact with one another.By the end of the book, readers have a fully-featured IoT application and API backend that can power a live Grafana dashboard in order to plot their TimescaleDB data published from their Nerves weather station.Screenshot of Grafana dashboard, showing various graphs for various weather data📖If you are interested in learning about how to build an end-to-end IoT weather monitoring solution, be sure tocheck out the book and the accompanying code. If you are interested in learning more about Nerves and Elixir,check out the Nerves documentation.Choosing (and using!) TimescaleDBFrom the onset of the book, we knew that we wanted to use a purpose-built time-series database to persist the weather station data. We wanted the project to be as realistic as possible and something that could possibly be expanded for use in the real world.With that goal in mind, TimescaleDB was an obvious choice given that PostgreSQL has become a ubiquitous database and it has great support in the Elixir community. In addition,leveraging TimescaleDB on top of PostgreSQL does not add a tremendous amount of overhead or complexity and allows new users to easily leverage the benefits of a time-series database without having to learn any new query languages or databases. Specifically, all it took for readers to start leveraging TimescaleDB was to run a single SQL",https://www.timescale.com/blog/how-to-build-a-weather-station-with-elixir-nerves-and-timescaledb/,512,"[-0.017861494794487953, -0.0019201774848625064, 0.025669213384389877, -0.032567813992500305, 0.03363736346364021, 0.019519297406077385, 0.004057607613503933, -0.013890787959098816, -0.03735405206680298, 0.009031017310917377, 0.028663955628871918, 0.013636769726872444, -0.002934579271823168, -0.03917228803038597, 0.026925934478640556, 0.01025431603193283, 0.01442556269466877, -0.0009926765924319625, -0.02601681649684906, 0.014733059331774712, -0.025575628504157066, 0.028262874111533165, 0.018128881230950356, -0.020882975310087204, 0.004468715749680996, -0.01014067605137825, -0.002065569395199418, 0.07748893648386002, 0.06187349930405617, -0.010942839086055756, -0.0019235197687521577, -0.028423305600881577, -0.025455303490161896, 0.009191449731588364, 0.036765798926353455, 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 "How to Build a Weather Station With Elixir, Nerves, and TimescaleDB","command in their database migration:SELECT create_hypertable('weather_conditions', 'timestamp').""It’s this kind of pragmatism and ease of use that makes TimescaleDB a great time-series database for projects both small and large. ""-Alexander KoutmousAll in all, leveraging TimescaleDB as the time-series database for the project worked out great and allowed us to show readers how they can set up a production-ready IoT project in a relatively short amount of time.✨Editor’s Note:To start with TimescaleDB today,sign up for a free 30-day trialorinstall TimescaleDB on your own server.Getting started advice & resourcesAny time we had questions about the inner workings of TimescaleDB, how to set it up, or what the various configuration options are, we turned to the official TimescaleDB docs. Some of the articles that helped us get started included:•Using TimescaleDB via Docker• Understanding some ofthe fundamental TimescaleDB concepts• Getting an overview of some of theTimescaleDB best practicesWe’d like to thank Alex, Bruce, and Frank for sharing their story, as well as for writing a book that makes building full-stack IoT solutions accessible for complete beginners. We congratulate them and the entire Nerves community on their success, and we cannot wait to read the final version of their book that will be released in January 2022 🎊We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Lucie Šimečková), and we’ll go from there.Additionally, if you’re looking for more ways to get involved and show your expertise, check out theTimescaleHeroes program.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/how-to-build-a-weather-station-with-elixir-nerves-and-timescaledb/,374,"[0.0011837596539407969, -0.0051201689057052135, 0.045665442943573, -0.007441266439855099, 0.07460522651672363, 0.00011884878040291369, -0.015102607198059559, 0.02995762974023819, -0.02295651286840439, -0.010405393317341805, 0.028279563412070274, -0.008851118385791779, 0.012647401541471481, -0.04170410335063934, 0.044014882296323776, 0.05014945939183235, -0.01202156487852335, 0.003933830186724663, 0.0033440995030105114, 0.04313458502292633, -0.0007844449719414115, 0.0018224912928417325, 0.015432718209922314, 0.01390595268458128, -0.030012648552656174, 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 CloudQuery on Using PostgreSQL for Cloud Assets Visibility,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition,Ron Eliahu, CTO and co-founder at CloudQuery, joins us to share how they transform data about cloud assets into PostgreSQL tables to give developers visibility into the health of cloud infrastructure. Thanks toTimescaleDB, CloudQuery users manage their data more transparently while maintaining scalability.CloudQueryis an open-source cloud asset inventory powered by SQL. CloudQuery extracts, transforms, and loads cloud assets intonormalizedPostgreSQL tables, enabling developers to assess, audit, and monitor the configurations of their cloud assets.Cloud asset inventory is a key component to solve various challenges in the cloud:Cloud Infrastructure Monitoring, Visibility, and Search: Give developers, SREs, DevOps, and security engineers a streamlined way to gain visibility and perform a wide range of tasks. These tasks include security analytics, fleet management auditing, governance, and cost.Security & Compliance: Turn security and compliance tasks to data problems and solve them with the best tools and practices in DataOps. UseCloudQuery Policiesto codify, version control, and automate security and compliance rules with SQL.Infrastructure as Code (IaC) Drift Detection: CloudQuery leverages its asset inventory to quickly detect drift against IaC (Terraform, more to come) which you can run both in the CI and locally.📖Read CloudQuery´sannouncement blogabout releasing CloudQuery History in Alpha and adding support for TimescaleDB.About the teamWe started CloudQuery a year ago, to solve the cloud asset visibility problem, and quickly gained traction. We are currently a small but mighty team of open-source and cloud security enthusiasts, spread all around the world!A little about myselfRon Eliahu, I am the CTO and co-founder at CloudQuery, I am an engineer at heart, I love building open source projects and working with anything database-related.About the projectQueryable cloud asset inventory is key in solving a lot of core challenges in the cloud such as security, compliance, search, cost, and IaC drift detection. That is why we started CloudQuery and followed a few key decisions:PostgreSQL- The most used database in the world with a huge ecosystem of business intelligence and visualization tools.Open-source- To cover a huge amount of API and cloud providers we decided to make this open-source where everyone can contribute without being blocked by a vendor.Pluggable",https://www.timescale.com/blog/cloudquery-on-using-postgresql-for-cloud-assets-visibility/,519,"[0.00983363389968872, 0.013287304900586605, 0.04509708657860756, 0.01903698965907097, 0.04067227244377136, -0.01110705453902483, -0.03249151259660721, 0.04273032397031784, -0.05013931542634964, -0.005141916684806347, 0.014483547769486904, -0.016683092340826988, 0.02086351253092289, -0.028992820531129837, 0.017210468649864197, 0.05335502326488495, -0.018895499408245087, -0.012071766890585423, -0.03588729724287987, 0.040620818734169006, 0.008257937617599964, -0.006836594548076391, -0.009981556795537472, 0.006791574880480766, 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 CloudQuery on Using PostgreSQL for Cloud Assets Visibility,"Architecture with CloudQuery SDK- Writing plugins, extracting configuration data, transforming it, and loading it to PostgreSQL requires a lot of boilerplate code. To scale this and improve the developer’s experience both internally and externally we releasedCloudQuery SDK.Normalized PostgreSQL tables inDataGripcontaining data about cloud assets from Microsoft AzureAnother key observation and requirement that we saw early on for a cloud asset inventory is the ability to not only query the current state but also go back in time. This is super useful for tasks such as forensics, post-mortems, compliance, and more.This feature required us to maintain historical snapshots in PostgreSQL and we started to look out for a solution, which was quite a journey for us.Choosing (and using!) TimescaleDBFirst attempt: PostgreSQL PartitioningWith some good experience ofPostgreSQLunder our belt, the first thing we tried isPostgreSQL partitioningPretty quickly it turned out to be not as easy as expected, hard to maintain and manage, lacking easily creatable retention policies, and bucketing queries. Given our philosophy is to integrate with best-in-class tools and focus our development efforts on our core business use-cases we started looking for an alternative solution.✨Editor’s Note:For more comparisons and benchmarks, seehow TimescaleDB compares to InfluxDB, MongoDB, AWS Timestream, vanilla PostgreSQL, and other time-series database alternativeson various vectors, from performance and ecosystem to query language and beyond.Second attempt: TimescaleDBGiven CloudQuery uses PostgreSQL under the hood, supporting historical snapshots in a scalable way usually involves using partitioning. TimescaleDB´screate_hyperfunctionsfeature allows us to do just that in a simple and effective way giving our users a transparent and automated way to manage their data while still maintaining scalability.Current CloudQuery architecture diagramCloudQuery transforms cloud resources into tables, some of these resources have a complex data structure, and are split into multiple relational tables, some of which are hypertables. In order to retain data integrity, we use foreign key relationships (withON DELETE CASCADE) to the root resource table. With these foreign keys in place, if a reference to a cloud resource is deleted (for instance a S3 bucket), the downstream data is removed.While TimescaleDB hypertablesdosupport using foreign keys that reference a regular PostgreSQL table, hypertable cannot be thesource reference of a foreign keyIn our case, some of our ""root"" reference tables are hypertables which meant that we had to come up with another way to do the cascading deletes to retain",https://www.timescale.com/blog/cloudquery-on-using-postgresql-for-cloud-assets-visibility/,511,"[-0.006330333184450865, 0.0187095794826746, 0.05791430175304413, -0.0007694573723711073, 0.050720300525426865, 0.026912812143564224, 0.01298413798213005, 0.00670232530683279, -0.030173402279615402, 0.019925832748413086, 0.02206074260175228, -0.033822160214185715, 0.02459675818681717, -0.01009877398610115, -0.002225482603535056, 0.05879414454102516, -0.019045989960432053, -0.028284329921007156, -0.012880627065896988, 0.04833954945206642, 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@@ -27,7 +27,7 @@ How WsprDaemon Combines TimescaleDB and Grafana to Measure and Analyze Radio Tra
 How WsprDaemon Combines TimescaleDB and Grafana to Measure and Analyze Radio Transmissions,"path with less loss over 1000km than 16km for over-the-ground waves.The time-series view also allows us to see day-to-day variations, such as the shorter period of high SNR on 23rd June compared to prior days.They hear IU0ICC from Italy via the ionosphere from early evening to about 0300 local time each day, with a consistent shape to the rise and fall of SNR.While SNR is the main measurement, our users are also interested in aggregate metadata functions, which provide an overview of daily changes in the ionosphere.Our project allows them to run these more complex queries, and we bring in complementary public domain data, such as the case below where we pull in data from theUS Space Weather Prediction Center.WsprDaemon also runs complex transmission data queries and visualizationsIn this example, the top panel of the Grafana dashboard uses the WsprDaemon dataset to display a simple count of the “spots” in each 10 minute time bucket with, on the second y-axis, a measure of the planetary geomagnetic disturbance index (kp) from the US Space Weather Prediction Center. In 2020, we’re at the minimum of the11-year sunspot cycle, so our current space weather is generally very quiet, but we’re anticipating disturbances - as well as more WSPR spots - as the sun becomes more active over the next four years.The second panel is a heat map that shows the variation in distance between the receiver in Belgium and the transmitters it’s heard over time.The third panel shows the direction of arrival at the receiver, while the bottom panel helps the user interpret all of this data, showing the local noise level and instances of overload.Editor’s note: see ourGrafana Series Overrideblog post to learn how (and why) to use two y-axes to more accurately plot your data and understand trends.Using TimescaleDBAs evidenced above, a big advantage for our users is our ability to bring disparate datasets together into one database, with one graphical visualisation tool.Our initial need was for a database and visualisation tool for radio noise measurements from a handful of stations. A colleague suggestedInfluxandGrafana, and kindly set up a prototype for us. We were hooked.We sought to expand to cover a larger range of data sets from several thousand sources. The Influx documentation was great, and we had an extended application running quickly. 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 How WsprDaemon Combines TimescaleDB and Grafana to Measure and Analyze Radio Transmissions,"time performance was excellent, but, as we accumulated weeks of data we hit thecardinalityissue.Query times became unacceptably long, and we looked for an alternative time-series database solution.We quickly came across an objectivearticleon how to solve the cardinality problem that led us to adopt TimescaleDB.The biggest factor in choosing TimescaleDB was that it solved our cardinality problem, but there were also “nice to have” features, such as the easy-to-usetool to migrate our data from Influxand the underlying use of PostgreSQL. But, we did miss Influx’s comprehensive single-source documentation.Editor’s Note: Because we think it’s important to remain balanced and let our community members’ voice shine through, we don’t edit mentions of alternative technologies (favorable or unfavorable🙂).Current deployment & future plansOur initial TimescaleDB implementation is on a DigitalOcean Droplet (2 cores, 4GB memory 100GB SSD disk), but we are moving to our own 16 core, 192GB memory Dell server and a back-up (we’re evaluating query performance as our user base grows).As noted above,the way TimescaleDB has solved the issue of cardinality was a big selling point for us, and it’s what makes the WsprDaemon site performant for our users.When we wereusing Influx, a typical query that returned 1,000 results from a table of 12 million records and a cardinality of about 400,000 took 15-25 seconds.Now,running TimescaleDB on the same Digital Ocean Droplet(albeit with 4GB rather than the previous 2GB of memory),those same queries overwhelmingly return results in under 2s*.*as long as the data requested is within the chunk that is in memory. That’s why we’ve recently increased our Dell server memory from 24 to 192GB, to handle one-month chunks, and why it will become our primary machine.We use bash Linux shell scripts with Python to gather the data that populates our database tables. We find that batch upload usingpsycopg2.extras.execute_batchworks well for us, and our users use a variety of methods to access WsprDaemon, including Node.js andpsqlvia its command line interface.Simplified WsprDaemon architecture, showing data routes fromwsprnetand 3rd party interfacesWe already make extensive use of Grafana dashboards, and we expect to expand our capabilities - adding global map panels is just one example. But, even after extensive searching, it’s not always straightforward or obvious how to obtain the best, streamlined end result.For example, creating an animation that shows the global geographic distribution of receivers and transmitters by hour",https://www.timescale.com/blog/wsprdaemon-combines-timescaledb-grafana-analyze-radio/,547,"[-0.00170712114777416, -0.00047701760195195675, 0.08444420248270035, -0.008508116938173771, 0.06497107446193695, 0.012747010216116905, -0.02064090594649315, 0.0011782074579969049, -0.023901591077446938, 0.00299528194591403, 0.017183059826493263, -0.016015280038118362, -0.01414228044450283, -0.030665621161460876, 0.05596248432993889, 0.033729150891304016, -0.0021421953570097685, -0.0019658906385302544, -0.030923442915081978, 0.000767777964938432, -0.006995310541242361, -0.0032455206383019686, 0.03118126466870308, -0.0033990764059126377, -0.001379156718030572, -0.0095090726390481, -0.0050540645606815815, 0.05399090424180031, 0.00198105676099658, 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 How WsprDaemon Combines TimescaleDB and Grafana to Measure and Analyze Radio Transmissions,"requires us to export data to CSV using psql, import the file intoOctave, generate maps on anazimuthal equidistantprojection, save these map files as PNG, and then import intoImageJto generate an AVI file.Editor’s Note: To learn more about building Grafana visualizations with TimescaleDB, check out ourstep-by-step tutorials,blogs, and “Guide to Grafana” webinar series.Our future path includes collaboration, both with others in the global amateur radio community and more data sources. We continually learn about people who have neat solutions for handling and visualising data, and, by sharing knowledge and experience, we can collectively grow and improve the tools we offer this great community. We’re keen to expand our connections to other third party data sources, such as space weather, to help our users better interpret their results.Getting started advice & resourcesWe’re non-professional database users, so we only feel qualified to speak to others with a similar minimal level of prior familiarity.As you evaluate time-series database options, of course, readindependent comparisonsand the links they provide, but also look carefully at insightful, fair-minded comparisons from TimescaleDB, e.g., onSQL vs Flux. Try to assess the advantages of different approaches foryourapplication, current and future skill sets, and requirements.Parting thoughtsWe believe that data analytics for radio amateurs is in its infancy. We’re detailing our approach and experience with TimescaleDB and Grafana in a paper at the 39th gathering of theDigital Communications Conferencein September 2020 (for the first time, the Conference will be completely virtual, which is likely to enable a larger-than-usual participation from around the world). We’ll feature some nice examples of how self inner joins help pull out features and trends from comparisons, as well as many other capabilities of interest to our users.We’d like to thank Rob & Gwyn for sharing their story, as well as for their work to create open-source, widely distributed queries, graphs, and tools. Their dedication to making transmission data accessible and consumable for the global amateur radio community is yet another testament to how technology and creativity combine to breed amazing solutions.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@lacey butler), and we’ll go from there.Additionally, if you’re looking for more ways to get involved and show your expertise, check out theTimescale Heroesprogram.The",https://www.timescale.com/blog/wsprdaemon-combines-timescaledb-grafana-analyze-radio/,509,"[-0.02971036173403263, -0.002695000497624278, 0.08556338399648666, -0.010234082117676735, 0.007077740505337715, 0.004840236157178879, 0.0024739413056522608, -0.00042529875645413995, -0.027419036254286766, 0.0033312668092548847, 0.013794097118079662, -0.0381375253200531, 0.002823791466653347, -0.028603143990039825, 0.045026883482933044, 0.0501631498336792, -0.022021345794200897, 0.010195637121796608, -0.019760774448513985, 0.04453478753566742, -0.02328234352171421, 0.0036503609735518694, 0.03727635368704796, 0.014601443894207478, -0.03266294673085213, -0.033585626631975174, -0.03450831025838852, 0.06975476443767548, 0.036230649799108505, -0.04198203235864639, 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-How WsprDaemon Combines TimescaleDB and Grafana to Measure and Analyze Radio Transmissions,open-source relational database for time-series and analytics.Try Timescale for free,https://www.timescale.com/blog/wsprdaemon-combines-timescaledb-grafana-analyze-radio/,14,"[0.0006815252127125859, 0.011646680533885956, 0.04602117836475372, -0.019381679594516754, 0.031811874359846115, -0.00023895724734757096, -0.030115241184830666, 0.006963265594094992, -0.01385583821684122, -0.02790019102394581, 0.02196197584271431, -0.03565286472439766, 0.028607122600078583, -0.016506828367710114, 0.05165305733680725, 0.028324350714683533, 0.013773363083600998, -0.004097251687198877, -0.011522967368364334, 0.06918493658304214, -0.023929597809910774, 0.02455405332148075, -0.017319798469543457, -0.000562598870601505, 0.0310578141361475, -0.013478809036314487, -0.016518609598279, 0.01356128416955471, 0.028937023133039474, -0.05108751356601715, -0.0034433407709002495, -0.041166920214891434, -0.029149102047085762, 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+How WsprDaemon Combines TimescaleDB and Grafana to Measure and Analyze Radio Transmissions,open-source relational database for time-series and analytics.Try Timescale for free,https://www.timescale.com/blog/wsprdaemon-combines-timescaledb-grafana-analyze-radio/,14,"[0.0006947529036551714, 0.01165800541639328, 0.0459958091378212, -0.019333792850375175, 0.03185775503516197, -0.00022385256306733936, -0.030090495944023132, 0.006992447189986706, -0.01385529525578022, -0.027828406542539597, 0.02197289653122425, -0.03565146401524544, 0.028629563748836517, -0.016470836475491524, 0.05169815942645073, 0.028323238715529442, 0.013772822916507721, -0.004094145260751247, -0.0114989522844553, 0.06922934949398041, -0.023846188560128212, 0.0245530903339386, -0.01734268292784691, -0.0005703085917048156, 0.03105659782886505, -0.013490062206983566, -0.01656509004533291, 0.013560752384364605, 0.028935888782143593, -0.05108550935983658, -0.0034873872064054012, -0.04118886962532997, -0.029147960245609283, 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 How Ndustrial Is Providing Fast Real-Time Queries and Safely Storing Client Data With 97 % Compression,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Michael Gagliardo, technical lead and software architect at Ndustrial, shares how Ndustrial is helping clients save energy and money by collecting several types of data and offering on-demand analytics quickly and seamlessly using TimescaleDB. And the best part is that they don’t need to worry about keeping vast amounts of raw data—TimescaleDB handles that, too.About the CompanyNdustrialis based out of Raleigh, North Carolina, in the United States. Our company has been around since 2011, and we work exclusively in the industrial sector.We aim to help our customers—who are large industrial energy users—save energy. And by doing so also save money and improve sustainability. A lot of that is collecting data from very disparate devices, such as industrial IoT equipment, utility meters, utility bills, and even external data, and ultimately trying to use and combine that data to give our customers a better view of what we would call production-normalized KPIs.The company’s high-level data architectureThese KPIs are essentially metrics that we try to get down to the point of the single unit that our customers are producing, and that changes based on their manufacturing process, what sector they're in, and what their actual product is. But if we can get it down to that point, we can understand how much energy consumption is required for a single product or unit. And then help them find and even completely automate these opportunities to save energy.We’re talking about customers that might have multiple facilities, even in the hundreds across the US and the world, so our goal in this space is to be able to aggregate all that data into a single view, understand what’s happening, and where you may have a lack of efficiencies in order to find those opportunities and save energy and money.About the TeamThe Ndustrial team building more than data applicationsWe currently have around 18 engineers working at the company, split into two main teams. We have an Integrations team that's more focused on different types of ingestion and where we get that data from (which sometimes gets very specific with our customers). 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 How Ndustrial Is Providing Fast Real-Time Queries and Safely Storing Client Data With 97 % Compression,"other team is focused on building our platform's more generalized extensibility aspect and a front end,our energy intensity user interface (UI), called Nsight.I am the technical lead and software architect for the Platform team. We focus on building the functionality to turn the customers’ data into meaningful metrics based on their organization as well as building out features for Nsight, making that easier to consume as a customer.From a background perspective, most of our engineers come from a mix of experiences. Some of them have actually been in the energy world, whereas others come from the B2B SaaS world, such as myself. So it's a nice foundation to build on where you have both perspectives and merge them toward one scalable product.About the ProjectMore than 80 percent of our data is time series across the board. Now the facets of where it comes from could be standard IoT devices just setting up their data points. It could be a time series of line items on a utility bill associated with a statement per day, per month, or per year. But it always comes down to data points associated with a timestamp that we can aggregate together to give a perspective of what’s happening.✨Editor’s Note:Learn more about the features of the best database for time-series data.Then, we have the other side: non-time series, to give a digital perspective of the client’s organization so you can start associating different elements by those groupings or categories to provide even more focused metrics in our UI.The company’s UI, NsightI joined Ndustrial in June 2020, and we already had some applications to store and manage time-series data. This data is very unique, frequent, and needs to be retrieved quickly. But as far as leaning on databases specific to time-series data, that wasn’t really a factor in the beginning. It was more focused on, “How can we store this and at least get to it quickly?”“Compression was a game-changer from our perspective: not having to worry about getting databases on the order of 5, 10, or 15 TB to store this information was a massive factor for us”But then you start considering other aspects: now, I want to aggregate that data in unique ways or across arbitrary windows. 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 How Ndustrial Is Providing Fast Real-Time Queries and Safely Storing Client Data With 97 % Compression,"can handle this data and not just store it efficiently but query it efficiently” comes in. And along with it came Timescale.Compression was a game-changer from our perspective as a company: not having to worry about getting databases on the order of 5, 10, or 15 TB to store this information was a massive factor for us. But then we want the dashboarding that we have, Nsight, which is our energy intensity platform, to be very dynamic.We don’t always know ahead of time what our customers want to visualize. We’re not trying to hard code a bunch of metrics we can report on quickly. We tried to build a much more dynamic platform, and most of our querying is actually on demand. There is some downsampling in between to make things more efficient, but the queries we are providing in our dashboard and arbitrary metrics are all things our customers are creating, and they happen on demand.Getting data back efficiently without feeling like the UI is slow was a major win for us in using TimescaleDB. Before, we had a lot more pre-aggregation. That was the biggest power I've seen from it, along with not having to deal with having an entire team of database administrators (DBAs) to go through and make sure that's possible [Ndustrial uses Managed Service for TimescaleDB]. It’s also one of the more valuable benefits for our customers, whether or not they realize this is even happening.Choosing (and Using!) TimescaleDBWhen I joined the company, Ndustrial had a proof-of-concept (PoC) running for TimescaleDB, so I think the company as a whole was moving in that direction. I had some experience with TimescaleDB and other time-series databases. This was only three years ago, but even then, Timescale was very prominent in the world of “Hey, we need a time-series database to be stored somewhere.”“For one of our larger customers, we normally store about 64 GB of uncompressed data per day. With compression, we’ve seen, on average, a 97 percent reduction”Other alternatives we researched were InfluxDB and Prometheus for storing this data. But what brought it home for us is that PostgreSQL is a foundation in our company. It is a standard of what we expect from developers and, ultimately, the main reason the PoC started to test out how we could work",https://www.timescale.com/blog/how-ndustrial-is-providing-fast-real-time-queries-and-safely-storing-client-data-with-97-compression/,485,"[0.002135609509423375, -0.0047034695744514465, 0.05277849733829498, 0.01563427411019802, 0.09108027070760727, 0.03120993822813034, -0.01806659810245037, 0.016264334321022034, -0.003553244052454829, 0.0029469947330653667, 0.03759844973683357, -0.00208066264167428, 0.005630243569612503, -0.03991355374455452, 0.04923257976770401, 0.021158287301659584, -0.010139566846191883, -0.032352838665246964, -0.04299059137701988, 0.0038902529049664736, -0.011524233035743237, 0.04296128451824188, 0.020645447075366974, 0.008395913057029247, -0.005923294462263584, -0.020103303715586662, -0.02728305384516716, 0.05647093802690506, 0.03226492181420326, -0.0927799642086029, -0.0077731795608997345, -0.04184769093990326, -0.04955493286252022, 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@@ -45,15 +45,15 @@ How I Power a (Successful) Crypto Trading Bot With TimescaleDB,"today.time, toda
 How I Power a (Successful) Crypto Trading Bot With TimescaleDB,"all Bitcoin markets. When it detects that a market is doing well, based on the metrics I track, that market enters the main bot instance and starts live trading. The same applies to those that are performing poorly; as soon as the main instance of my bot detects things are going badly, the market is removed from the main instance and the second instance begins tracking it. If it improves, it's added back in.As every developer likely knows all too well, the process of building a software is to always improve it. Right now, I’m trying to improve my capital management system usingKelly Criterion, assuggested by a userin my Reddit post (thanks, btw :)).Getting started advice & resourcesFor my use case, I’ve found TimescaleDB is a powerful and solid choice: it’s fast with reliable ingest rates, efficiently stores and compresses a huge dataset in a way that’s manageable and cost-effective, and gives me real-time aggregation functionality.TheTimescale website,""using TimescaleDB"" core documentation, andthis blog post about about managing and processing huge time-series datasetsis all pretty easy to understand and follow – and the TimescaleDB team is responsive and helpful (and they always show up in community discussions, likemine on Reddit).It’s been easy and straightforward to scale, without adding any new technologies to the stack. And, as an SQL user, TimescaleDB adds very little maintenance overhead, especially compared to learning or maintaining a new database or language.We’d like to thank Felipe for sharing his story, as well as for his work to evangelize the power of time-series data to developers everywhere. His success with this project is an amazing example of how we can use data to fuel real-world decisions – and we congratulate him on his success 🎉.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@lacey butler), and we’ll go from there.Additionally, if you’re looking for more ways to get involved and show your expertise, check out theTimescale Heroesprogram.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/how-i-power-a-successful-crypto-trading-bot-with-timescaledb/,443,"[-0.013979515060782433, -0.018599925562739372, 0.06263059377670288, 0.025001967325806618, 0.05263571813702583, -0.0008293637074530125, -0.0037554698064923286, 0.053049709647893906, -0.010918954387307167, 0.022458892315626144, 0.029171425849199295, -0.023212943226099014, -0.028062526136636734, -0.03820525109767914, 0.06564679741859436, 0.049323808401823044, -0.02053680084645748, -0.002896073507145047, -0.04550919681787491, 0.030147256329655647, 0.0020828810520470142, 0.03796868771314621, 0.01192435622215271, 0.031463149935007095, 0.007388961035758257, 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 "Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","This is an installment of our “Community Member Spotlight” series, where we invite our customers or users to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, David Bailey, an intern at the Wendelstein 7-X fusion reactor experiment at theMax Planck Institute for Plasma Physics, explains how he’s using TimescaleDB and Grafana to monitor the reliability of the reactor’s heating system. Currently working on the designs of the sensor boards that measure the microwaves feeding the reactor, David needs a tight grasp on his systems and data to prevent the experiment from being aborted due to excessive heating—and is successfully achieving it with a little help from TimescaleDB.About the UserI am David Bailey, and I am currently completing an internship at the Wendelstein 7-X fusion reactor experiment, which is operated by the Max Planck Institute for Plasma Physics in Greifswald, Germany. The experiment aims to help us understand, and someday utilize, the power of fusion for further scientific endeavors and power generation. It does this by using a novel way of containing the hot gasses needed for the experiments and tweaking its design to reach longer and longer experiment runs.David BaileyThe end goal is to reach 30 minutes of uninterrupted containment of plasma heated to millions of degrees. To be able to do this, there is a lot of data to collect and process and a number of problems left to solve—one of which I can help with!About the ProjectMy specific task at the internship is to help with the heating system. Because the experiment is not set up for self-sustaining fusion, where the gasses would heat themselves, we constantly have to feed in energy to keep it hot: 10 megawatts, to be precise!We do this with various microwave sources—similar to scaled-up versions of household microwaves—and a big array of mirrors to guide the energy to the reactor vessel.Schematic of the W7-X microwave heating system and mirror assembly. Credit: Torsten Stange, David Bailey; Max Planck Institute GreifswaldThere are dangers in using these highly powerful microwaves: if dust or a fine droplet of water gets in the way of the energy, an arc can form—a lightning bolt of sorts. If the energy is not turned off fast enough, this arc can",https://www.timescale.com/blog/using-iot-sensors-timescaledb-and-grafana-to-control-the-temperature-of-the-nuclear-fusion-experiment-in-the-max-planck-institute/,482,"[0.004719009157270193, 0.045747339725494385, 0.025608891621232033, -0.002158495830371976, 0.016772020608186722, 0.031650424003601074, -0.012631615623831749, 0.020288733765482903, -0.03315329551696777, -0.017313053831458092, 0.031830769032239914, -0.037090811878442764, -0.01249635685235262, -0.014367429539561272, 0.051338013261556625, 0.044575098901987076, -0.019387012347579002, -0.009347846731543541, -0.047460608184337616, 0.038112763315439224, -0.019326897338032722, -0.002552998950704932, 0.043913837522268295, 0.020303763449192047, -0.010099280625581741, -0.039675746113061905, -0.033183351159095764, 0.049744971096515656, 0.050857093185186386, -0.05798069387674332, -0.010730486363172531, -0.037180982530117035, -0.052900996059179306, 0.0018428933108225465, 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 "Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","do a lot of damage, and it also means that we need to abort the experiment to wait for things to cool off again—not good if you want to run everything reliably for long periods of time!Image of a forming microwave arc, taken at the Max Planck IPP Greifswald experiment siteMy task is to design sensor boards to precisely track the amount of energy coming out of the microwave array. The ultimate goal is to detect changes in the power output on the microsecond scale, so the power can be turned off before the arc fully forms. If done right, we can turn the heating back on without a pause and continue the experiment!One of the key aspects of what I am designing is that it needs to be reliable. If it's not sensitive enough, or too sensitive, or if there are issues with the communication with the rest of the system, it can severely impact the rest of the experiment in a negative way.One of the sensors that David is working onThe only way to ensure something is reliable is through data—a lot of it. A problem might present itself only after hundreds of hours or in subtle ways that are only apparent across days of data, but it can still be relevant and important to know about.​Writing a program to handle this amount of data myself would've been an unnecessary effort. It needs a tool that has the necessary functionality in it already, such as statistical operators, compression, etc., and you can get all of this in time-series databases, such as TimescaleDB!To track that the sensors and system are working as expected, I collect and handle several types of data using TimescaleDB:I am recording the general health metrics of the board in question: temperature, voltage levels, etc. These shouldn't change, but the harsh environment close to the reactor might cause issues, which would be very important to know about!Log messages of the boards themselves to understand what the software was doing.And finally, detection events: every time the board sees something suspicious, it sends a measurement series of this event, about 1,000 samples taken over a millisecond. We can use this during the initial development phase to ensure everything is working correctly. I can use a function generator to send a predetermined reference",https://www.timescale.com/blog/using-iot-sensors-timescaledb-and-grafana-to-control-the-temperature-of-the-nuclear-fusion-experiment-in-the-max-planck-institute/,450,"[0.020964955911040306, 0.048945169895887375, -0.008825648576021194, -0.017117878422141075, -0.021563030779361725, 0.04823394492268562, 0.007593128830194473, -0.05531385913491249, 0.004473439883440733, -0.0038733442779630423, 0.047975316643714905, -0.03979623690247536, -0.018168549984693527, -0.017376504838466644, 0.018427178263664246, 0.01259190496057272, -0.006809165235608816, 0.009423723444342613, -0.031116066500544548, 0.014054764062166214, -0.00015292840544134378, -0.0072092292830348015, 0.03966692462563515, -0.008760991506278515, 0.011323015205562115, -0.05007666349411011, 0.018168549984693527, 0.06148857995867729, 0.05463496595621109, -0.03266783058643341, 0.012793957255780697, -0.03076045587658882, -0.08508829772472382, -0.003974370192736387, 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-"Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","signal and compare that to what the board tells me it saw. If there are discrepancies, those might point to errors in the soft- or hardware and help me fix them.When the system is deployed, we can use these measurements to refine the detection system, to be able to set it as fast as possible without too many false positives, and gather data about how the actual physical events look to the board.Choosing (and Using!) TimescaleDB✨Editor's Note:See how you can get started with Grafana and TimescaleDB with ourdocsorvideos.I started using Timescale to track data formy self-built smart homeas a small playground for me—that's still running! Aside from that, I only use it for the monitoring system mentioned above, though I hope to motivate others at my research institute to use it! It is also my go-to for other projects, should I need a way to store measurements efficiently.The top factors in my decision to use TimescaleDB were, first, the open-source nature of it. Open-source software can be much more beneficial to a wider range of people, mature faster, generally has more flexible features, and isn't so locked into a specific environment. It's also much more approachable to individuals because of no fussy licensing/digital rights management.Then, the documentation. You can read up on a lot of things that TimescaleDB can do, complete with great examples! It doesn't feel like you're alone and have to figure it out all by yourself—there's rich, easy-to-understand documentation available, and if that doesn't have what you need, the community has been very helpful, too.✨Editor's Note:Need help with TimescaleDB? Join ourSlack CommunityorForumand ask away!And lastly,it's still all just SQL! You don't need to learn another database-specific format, but you can start using it immediately if you've worked with any standard database before! That was helpful because I already knew the basics of SQL, giving me a great starting point. Plus, you can use PostgreSQL's rich relational database system to easily store non-time-series data alongside your measurements.Because the Wendelstein is fairly old, database tools didn’t exist when it started. As such, they decided to write their own tool,called ArchiveDB, and never quite brought it to the modern age. It can only store time-series data in a particular format and has no relational or statistical tools aside from minimum/maximum aggregates, no continuous aggregates,",https://www.timescale.com/blog/using-iot-sensors-timescaledb-and-grafana-to-control-the-temperature-of-the-nuclear-fusion-experiment-in-the-max-planck-institute/,510,"[-0.013684406876564026, -0.0032614502124488354, 0.04762173444032669, -0.016512516885995865, 0.05516336113214493, 0.038468386977910995, -0.008537549525499344, -0.018625997006893158, -0.03871166706085205, 0.04871648550033569, 0.002731179352849722, -0.015843501314520836, 0.0024460875429213047, -0.02572668343782425, 0.06890858709812164, 0.03107880800962448, -0.016512516885995865, 0.0058044688776135445, -0.020283332094550133, 0.0049339886754751205, -0.003322269767522812, 0.03682625666260719, 0.013053403235971928, -0.003987484145909548, 0.00412052683532238, -0.05856925994157791, -0.012376785278320312, 0.06367810815572739, 0.01229315809905529, 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+"Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","signal and compare that to what the board tells me it saw. If there are discrepancies, those might point to errors in the soft- or hardware and help me fix them.When the system is deployed, we can use these measurements to refine the detection system, to be able to set it as fast as possible without too many false positives, and gather data about how the actual physical events look to the board.Choosing (and Using!) TimescaleDB✨Editor's Note:See how you can get started with Grafana and TimescaleDB with ourdocsorvideos.I started using Timescale to track data formy self-built smart homeas a small playground for me—that's still running! Aside from that, I only use it for the monitoring system mentioned above, though I hope to motivate others at my research institute to use it! It is also my go-to for other projects, should I need a way to store measurements efficiently.The top factors in my decision to use TimescaleDB were, first, the open-source nature of it. Open-source software can be much more beneficial to a wider range of people, mature faster, generally has more flexible features, and isn't so locked into a specific environment. It's also much more approachable to individuals because of no fussy licensing/digital rights management.Then, the documentation. You can read up on a lot of things that TimescaleDB can do, complete with great examples! It doesn't feel like you're alone and have to figure it out all by yourself—there's rich, easy-to-understand documentation available, and if that doesn't have what you need, the community has been very helpful, too.✨Editor's Note:Need help with TimescaleDB? Join ourSlack CommunityorForumand ask away!And lastly,it's still all just SQL! You don't need to learn another database-specific format, but you can start using it immediately if you've worked with any standard database before! That was helpful because I already knew the basics of SQL, giving me a great starting point. Plus, you can use PostgreSQL's rich relational database system to easily store non-time-series data alongside your measurements.Because the Wendelstein is fairly old, database tools didn’t exist when it started. As such, they decided to write their own tool,called ArchiveDB, and never quite brought it to the modern age. It can only store time-series data in a particular format and has no relational or statistical tools aside from minimum/maximum aggregates, no continuous aggregates,",https://www.timescale.com/blog/using-iot-sensors-timescaledb-and-grafana-to-control-the-temperature-of-the-nuclear-fusion-experiment-in-the-max-planck-institute/,510,"[-0.013446396216750145, -0.003635394386947155, 0.047518711537122726, -0.016306037083268166, 0.054941609501838684, 0.03854430839419365, -0.008495262823998928, -0.018602875992655754, -0.0391223207116127, 0.049100641161203384, 0.003211391856893897, -0.015537888742983341, 0.00237479479983449, -0.025858454406261444, 0.06881390511989594, 0.03064987249672413, -0.016518989577889442, 0.005475908052176237, -0.01969805732369423, 0.0051260581240057945, -0.0028805555775761604, 0.03735785931348801, 0.013514844700694084, -0.003954822197556496, 0.0041867876425385475, -0.05831841751933098, -0.012259948998689651, 0.06358137726783752, 0.012267555110156536, 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 "Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","and probably no compression.“In the future, I want to motivate my research institute to install a proper multi-node TimescaleDB cluster. It could bring many much-needed modern features to the table, and distributed hypertables and the matching distributed computing of statistics could be incredibly useful”Storing all my measurement data in it would have been possible, but I would have had to write all of the statistical processing myself. Using Timescale was a major time-saver! I tried other tools, but none of them quite fit what I needed. I didn't want to spend too much time on an unfamiliar tool with sparse documentation.First, I tried the company’s standard database, ArchiveDB, but it didn’t offer much more functionality than simply saving a CSV file. I also tried another PostgreSQL extension,Citus. It didn’t lend itself nicely to my use case because the documentation was not as clear and easy, and it seemed much more tailored to a distributed setup from the start, making trying it out on my local machine a bit tricky. I believe I gaveInfluxDBa try, but writing in PostgreSQL felt more natural. Additionally, it was helpful to have the regular relational database from PostgreSQL and the rich set of aggregate functions.I might have used PostgreSQL without TimescaleDB,or perhaps InfluxDB had it not been for Timescale.The Grafana panels observing the sample measurements. A single event is plotted above for inspection using a scatter plot, while the accuracy of the individual measurements is plotted in a time-series belowOne of the things I am verifying right now is the measurements’ consistency. This means running thousands of known reference signals into the boards I am testing to see if they behave as expected. I could automate this using a query that JOINs a regular table containing the reference data with aTimescaleDB hypertablecontaining the measurements.With a bit of clever indexing, I was able to use the corr(x, y) function to check if things lined up. Thanks to the rich data types of PostgreSQL, I can cache metadata such as this correlation in a JSONB field. This speeds up later analysis of the data and allows me to store all sorts of extra values for individual events.The resulting data I then downsampled usingminto find the worst offenders.WITH missing_bursts AS ( SELECT burst_id FROM adc_burst_meta WHERE NOT metadata ? 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-"Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","raw_correlations AS ( SELECT burst_id, corr(value, reference_value) AS ""correlation"" FROM adc_burst_data INNER JOIN missing_bursts USING(burst_id) INNER JOIN adc_burst_reference USING(burst_offset) GROUP BY burst_id ) update_statement AS ( UPDATE adc_burst_meta SET metadata = jsonb_set(metadata, '{correlation}', correlation::text::jsonb) FROM raw_correlations WHERE adc_burst_meta.burst_id = raw_correlations.burst_id ) SELECT $__timeGroup(time, $__interval) AS “time”, min((metadata->>’correlation’)::numeric) FROM adc_burst_meta WHERE $__timeFilter(time) GROUP BY “time”That made even small outliers very easy to spot across hundreds of thousands of samples and made analyzing and tagging data a breeze! This insight is incredibly useful when you want to ensure your system works as intended. I could easily find the worst measurements in my sample set, which allowed me to quickly see if, how, and in what way my system was having issues.Current Deployment and Future Plans​Right now, I deploy TimescaleDB on a small, local computer.Running the Docker image has been very useful in getting a fast and easy setup, too!In the future, I want to motivate my research institute to install a proper multi-node TimescaleDB cluster. It could bring many much-needed modern features to the table, and distributed hypertables and the matching distributed computing of statistics could be incredibly useful! My queries could work orders of magnitude faster than my local machine with little extra effort.For the most part, I interact with TimescaleDB through a simple Ruby script, which acts as an adapter between the hardware itself and the database. I also used Jupyter notebooks and theJupyter IRuby Kernelto do more in-depth data analysis.✨Editor's Note:Check out this videoto learn how to wrap TimescaleDB functions for the Ruby ecosystem.“Grafana and Timescale really go hand in hand, and both of them are excellent open-source tools with rich documentation”I do use Grafana extensively! Grafana and Timescale really go hand in hand, and both of them are excellent open-source tools with rich documentation. Both have Docker images and can be set up quickly and easily. It is great to plot the measurements with just a few SQL queries.​Without Grafana, I would have had to write a lot of the plots myself, and correlating different time-series events with each other would have been much harder. Either I would have had to spend more time implementing that myself, or I wouldn't have gotten this level of information from my measurements.​The main benefit of using TimescaleDB is that you get a well-balanced mixture. 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+"Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","raw_correlations AS ( SELECT burst_id, corr(value, reference_value) AS ""correlation"" FROM adc_burst_data INNER JOIN missing_bursts USING(burst_id) INNER JOIN adc_burst_reference USING(burst_offset) GROUP BY burst_id ) update_statement AS ( UPDATE adc_burst_meta SET metadata = jsonb_set(metadata, '{correlation}', correlation::text::jsonb) FROM raw_correlations WHERE adc_burst_meta.burst_id = raw_correlations.burst_id ) SELECT $__timeGroup(time, $__interval) AS “time”, min((metadata->>’correlation’)::numeric) FROM adc_burst_meta WHERE $__timeFilter(time) GROUP BY “time”That made even small outliers very easy to spot across hundreds of thousands of samples and made analyzing and tagging data a breeze! This insight is incredibly useful when you want to ensure your system works as intended. I could easily find the worst measurements in my sample set, which allowed me to quickly see if, how, and in what way my system was having issues.Current Deployment and Future Plans​Right now, I deploy TimescaleDB on a small, local computer.Running the Docker image has been very useful in getting a fast and easy setup, too!In the future, I want to motivate my research institute to install a proper multi-node TimescaleDB cluster. It could bring many much-needed modern features to the table, and distributed hypertables and the matching distributed computing of statistics could be incredibly useful! My queries could work orders of magnitude faster than my local machine with little extra effort.For the most part, I interact with TimescaleDB through a simple Ruby script, which acts as an adapter between the hardware itself and the database. I also used Jupyter notebooks and theJupyter IRuby Kernelto do more in-depth data analysis.✨Editor's Note:Check out this videoto learn how to wrap TimescaleDB functions for the Ruby ecosystem.“Grafana and Timescale really go hand in hand, and both of them are excellent open-source tools with rich documentation”I do use Grafana extensively! Grafana and Timescale really go hand in hand, and both of them are excellent open-source tools with rich documentation. Both have Docker images and can be set up quickly and easily. It is great to plot the measurements with just a few SQL queries.​Without Grafana, I would have had to write a lot of the plots myself, and correlating different time-series events with each other would have been much harder. Either I would have had to spend more time implementing that myself, or I wouldn't have gotten this level of information from my measurements.​The main benefit of using TimescaleDB is that you get a well-balanced mixture. 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 "Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","built on top of PostgreSQL and still giving you access to all regular relational database features, you can use TimescaleDB for a much larger variety of tasks.​You can also dive right in, even with minimal SQL knowledge—and if you ever do get stuck, theTimescaleandPostgreSQL documentationare well written and extensive, so you can almost always find a solution!​Lastly, there is no ""too small"" for TimescaleDB either. Being open source, quick to set up, and easy to use while performing well even on a spare laptop or PC, it can be a valuable tool for any sort of data acquisition—even if it feels like a ""small"" task!​I learned a lot about my system already by using TimescaleDB and can be much more confident in how to proceed thanks to it. In a way, it has even changed my developer experience. I have the data acquisition and TimescaleDB running continuously while working on new software features or modifying the hardware. If I were to introduce a bug that could mess up the measurements, I might see those much sooner in the data. I can then react appropriately and quickly while still developing.​I can worry less about ensuring everything works and focus more on adding new features!Advice and ResourcesGive TimescaleDB a try whenever you need to record any kind of time-series data. It's much easier to use than writing your own script or dumping it into a CSV file. The built-in functionality can take care of a lot of number crunching very efficiently, allowing you to benefit from years of finely developed functions. It's well worth the trouble, and there's no such thing as ""too small"" a use case for it!​TimescaleDB has helped me be more confident in developing my hardware, and I want to motivate others in my field to try it out themselves. A lot can be gained from a few days or months of data, even if at first you don't think so—some insights only pop out at larger scales, no matter what you're working on.I am very excited to share my use case of TimescaleDB. Whenever I think of databases, I am drawn to these larger web services and big tech companies—but that doesn't have to be the case! That can also be helpful advice for others looking for the right database for their",https://www.timescale.com/blog/using-iot-sensors-timescaledb-and-grafana-to-control-the-temperature-of-the-nuclear-fusion-experiment-in-the-max-planck-institute/,470,"[-0.004970492795109749, -0.03404538333415985, 0.03534868359565735, -0.002164408564567566, 0.05165322870016098, -0.0113240797072649, -0.00902335625141859, 0.02138475514948368, -0.035109300166368484, 0.03662538528442383, 0.02915135957300663, -0.02012135274708271, 0.009781397879123688, -0.03848724067211151, 0.0776660293340683, 0.04229074716567993, -0.0012634028680622578, -0.004870750475674868, 0.0015526555944234133, 0.02709001675248146, 0.0007073393790051341, 0.02470950037240982, 0.009362480603158474, 0.01884465105831623, 0.039364974945783615, -0.05428642779588699, -0.03987033665180206, 0.05359487980604172, 0.037476517260074615, -0.04367384314537048, -0.0037469868548214436, -0.031119607388973236, -0.08601778745651245, 0.01758124865591526, 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 "Using IoT Sensors, TimescaleDB, and Grafana to Control the Temperature of the Nuclear Fusion Experiment at the Max Planck Institute","use case.We’d like to thank David and all of the folks at the Max Planck Institute for Plasma Physics for sharing their story on how they’re monitoring the Wendelstein 7-X fusion reactor’s heating system using TimescaleDB.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/using-iot-sensors-timescaledb-and-grafana-to-control-the-temperature-of-the-nuclear-fusion-experiment-in-the-max-planck-institute/,106,"[0.006297644227743149, 0.02305917628109455, 0.04174111410975456, -0.005775522440671921, 0.032185573130846024, 0.013939930126070976, -0.030840933322906494, 0.012580983340740204, -0.007560033816844225, -0.036820296198129654, 0.019125383347272873, -0.03404518589377403, -0.02089916728436947, -0.019454391673207283, 0.05547362565994263, 0.058563441038131714, 0.0034527969546616077, 0.010907333344221115, -0.04511702433228493, 0.04056812822818756, -0.0192970409989357, 0.01014203205704689, 0.03101258911192417, 0.01360376924276352, -0.009720043279230595, -0.019325649365782738, -0.04691941663622856, 0.047949355095624924, 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 How Trading Strategy Built a Data Stack for Crypto Quant Trading,"This is an installment of our “Community Member Spotlight” series, where we invite TimescaleDB community members to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Mikko Ohtamaa, CEO at Trading Strategy, joins us to share how they give traders and investors direct access to high-quality trading strategies and real-time control over their assets by integrating market data about thousands of crypto assets, algorithms, profitability simulation, and more into one solution. Thanks to TimescaleDB, they can focus on solving business problems without a need to build the infrastructure layer.Trading Strategyis a protocol for algorithmic trading of crypto-assets in decentralized markets. Cryptocurrency traders and strategy developers can utilize the protocol to easily access and trade on next-generation markets with sophisticated tools that have been traditionally available only for hedge funds.Users don’t need to have a deep understanding of blockchain technology, as the Trading Strategy protocol is designed to make a complex topic easier to understand and approach. This is accomplished by integrating market data feeds for thousands of crypto assets, algorithm development, profitability simulation, trade execution, and smart contract-based treasury management tools into one vertically integrated package.About the teamMy name isMikko Ohtamaa. I am the CEO of Trading Strategy. I have been a software developer for 25 years. For the last decade, I have been CTO of various cryptocurrency and fintech companies. I am also one of the firstEthereum Dappdevelopers and Solidity smart contract auditors.Trading Strategy is a remote-first company with five people. We have offices in London and Gibraltar. We use a combination of remote working tools (Discord, Github, Google Workspace) and more intense get-together sprint weeks to manage the software development.As our work is highly technical, all team members have backgrounds in software development, quantitative finance, or blockchain technologies.About the projectTrading Strategy operates on market data feeds, for which raw data is collected directly from the blockchains. Most market data is heavily time-series, though there are also elements of product catalog features like different trading pairs, tokens, and exchanges.Because of the powerful combination of PostgreSQL and TimescaleDB, we can store the data in a single database,making it simple for software developers to build on top of this and this, in turn, saves us a lot of software development costs.We",https://www.timescale.com/blog/how-trading-strategy-built-a-data-stack-for-crypto-quant-trading/,484,"[0.009817959740757942, -0.008470667526125908, 0.05029891058802605, 0.012782002799212933, 0.05063055083155632, 0.026241106912493706, -0.02865932323038578, 0.048999980092048645, -0.015476587228477001, 0.004573884420096874, 0.035209234803915024, -0.03338520973920822, 0.03357866778969765, -0.04095768183469772, 0.03863619640469551, 0.02555018663406372, -0.029294969514012337, -0.009016494266688824, -0.06157470867037773, 0.031201904639601707, 0.011738714762032032, 0.006335727870464325, 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 How Trading Strategy Built a Data Stack for Crypto Quant Trading,"have two kinds of workloads:historical datasets, that are challenging sizewise, andreal-time data feedsfor algorithms that are challenging latency-wise. TimescaleDB offers vertical scaling as a data lake, but also offers continuous real-time aggregations for time-series data, making it a good fit for real-time needs.✨Editor’s Note:We’ve put together resources about TimescaleDB’scontinuous aggregatesto help you get started.Data from TimescaleDB is feeding bothon our market information websiteand feeds, but also on the trading algorithms themselves, which make the trading decisions based on the data input. Our applications include OHLCV, or so-called candle charts, market summaries information like daily top trades and liquidity, and risk information for technical trade analysis.Collage of charts from Trading Strategy websiteChoosing (and using!) TimescaleDBPostgreSQL has been the open-source workhorse of databases for the last three decades and offers the most well-known, solid, foundation to build your business on.We chose TimescaleDB over other time-series databases because of its solid PostgreSQL foundation, easy, out-of-the-box functionality, and true open-source nature with an active TimescaleDB community.Moreover, TimescaleDB comes with well-documented code examples on how to use it for stock-market chart data, allowing us to take these examples and build our first MVP based on TimescaleDB example code.✨Editor’s Note:Check out ourthree-step tutorialto learn how to collect, store, and analyze intraday stock data.For example, we heavily utilize the continuous aggregate view feature of TimescaleDB, to upsample our 1-minute candle data to 15 minutes, 1 hour, and daily candles.We can fully offload this work to TimescaleDB, with only a minimal 10-20 lines of SQL code describing how to upsample different columns.Current deployment & future plansBesides TimescaleDB, our other major components in the software development stack are Svelte/SvelteKitweb frontend framework and Python, Pyramid, andSQLAlchemybackend.I invite everyone evaluating TimescaleDB toread our blog post about our software architecture.The current architecture diagramAt the moment, we have trading data from 1000 decentralised exchanges (aka dexes), from three blockchains (Ethereum, Polygon and Binance Smart Chain), featuring 800k trading pairs. For reference,NASDAQhas only 3000 trading pairs, giving a reference point for the massive diversity of blockchain and cryptocurrency markets! Currently, we are fitting everything on one 1 TB database, but we are still early on what kind of data points we collect. We expect the database to grow dozens of terabytes over the next year.Architecture diagram of trading data sourcesTrading Strategy is completing its seed round. 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 How Trading Strategy Built a Data Stack for Crypto Quant Trading,"the team has been lean. We expect to start growing as a business now, as our business is finding a product-market fit. We are looking to launch user-accessible trading strategies later this year, as soon as we are confident the software stack is well-behaving and related smart contracts are secure.We are at the bleeding edge of blockchain technology. Many of the components we built and many of the problems we solve we do as the first in the world.TimescaleDB allows us to focus on solving these business problems without us needing to build the infrastructure layer ourselves.🗯️If you are interested in crypto trading strategies, please come to ask any questions in ourpublic Discord chat.Advice & resourcesIf you are generally interested in algorithmic trading and machine-based solutions on financial markets, pleaseread our announcement blog postto learn about our vision for decentralised finance and decentralised protocol.To see TimescaleDB in action, youcan explore our public real-time API endpoints, view ourreal-time market data charts, ordownload historical market datasets generatedout from TimescaleDB.Trading Strategy contributes heavily to open source. You canstudy our Trading Strategy Python clientand our100% open source SvelteKit frontend.If you have software development questions or questions about trading strategies, please come to ask any questions inour public Discord chat.We also love coffee brewing tips shared by the TimescaleDB. Due to increased brewing activity, our team is now 150% caffeinated.It matters, and is one of the major reasons I came to Timescale to work in#DevRel.And for the record, I'm totally up for answering any coffee questions you want to throw our way. ☕️😉https://t.co/f74kT9eHWF— Ryan Booz (@ryanbooz)January 19, 2022We’d like to thank Mikko and the entire Trading Strategy team for sharing their story! We applaud your effort to give access to high-quality trading strategies to users around the world.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Lucie Šimečková), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/how-trading-strategy-built-a-data-stack-for-crypto-quant-trading/,466,"[-0.0004356809367891401, 0.005361652467399836, 0.03688936308026314, 0.004726916551589966, 0.055050283670425415, -0.0032912930473685265, -0.005824636667966843, 0.04331139847636223, -0.026270603761076927, -0.011193756014108658, 0.0382036417722702, -0.03572443500161171, -0.011962907388806343, -0.019325844943523407, 0.0612930990755558, 0.02794332057237625, -0.014479449018836021, -0.011320703662931919, -0.059650249779224396, 0.04537242278456688, 0.006526579614728689, 0.012814200483262539, 0.01070090290158987, 0.014994705095887184, -0.017070665955543518, 0.0051450952887535095, -0.040145184844732285, 0.03246861323714256, -0.01877325214445591, -0.06153205782175064, 0.01283660251647234, 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-Automated Mocking Using API Traffic: Speedscale's Story,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Ken Ahrens, co-founder and CEO of Speedscale, joins us to share how they modernize and automate testing practices for cloud infrastructure by providing API traffic data going into and out various microservices. Thanks to TimescaleDB, Speedscale´s UI loads quickly and provides the ability to drill deep down into high-fidelity data.Speedscaleis one of the first commercial technologies utilizingactual API trafficin order to generate tests and mocks. Speedscale helps Kubernetes engineering teams validate how new code will perform under production-like workload conditions.Speedscale service mapSpeedscale provides unparalleled visibility, collects and replays API traffic, introduces chaos, and measures the golden signals of latency, throughput, saturation, and errors before the code is released.Screenshot of Speedscale UISpeedscale Traffic Replayis a modern load, integration, and chaos testing framework -- an alternative to legacy scripting tools that can take days or weeks to run and do not scale well for modern architectures.If your organization provides a SaaS product that is critical for revenue, or your team is responsible for performant infrastructure, Speedscale is for you. Speedscale enables engineering leaders to generate quality automation quickly without the need for manual scripting. Since actual API calls (not scripts) are the primary ingredient, tests and mocks can be built and regenerated quickly to keep pace with the speed of changing business requirements.As microservices become more separated logically, they are highly dependent on each other to deliver the expected functionality. This means performance problems become distributed across multiple services and can be difficult to trace. Multiple contributing factors affect the state of an application in microservices and Kubernetes environments. A testing harness that closely mirrors the production setup and incoming traffic has become a requirement for highly distributed and containerized environments.By leveraging traffic to automatically generate sophisticated mocks, engineers and testers are granted the ability to isolate and contract/performance test smaller components in the context of a tightly coupled architecture. This superpower enables rapid testing iterations. Moreover, without the need for scripting, testing can finally move as fast as development.About the foundersSpeedscale’s leadership team comes from companies like New Relic, Observe Inc, Wily Introscope (bought by CA",https://www.timescale.com/blog/automated-mocking-using-api-traffic-speedscales-story/,474,"[0.032025836408138275, -0.006828018929809332, 0.03429953008890152, 0.0002900608233176172, 0.03083353117108345, -0.013101478107273579, -0.07453285157680511, 0.009850370697677135, -0.0162347424775362, 0.025468165054917336, 0.016414973884820938, -0.028670748695731163, 0.00024240334460046142, -0.002018944825977087, 0.03255266696214676, 0.024428363889455795, 0.007777702994644642, -0.02892030030488968, -0.054762791842222214, 0.022917188704013824, 0.030805803835392, 0.0019478917820379138, 0.006068964954465628, 0.004155733622610569, -0.023222196847200394, -0.042007915675640106, -0.0568978488445282, 0.03346769139170647, 0.04098197817802429, -0.09438610076904297, -0.013441146351397038, 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+Automated Mocking Using API Traffic: Speedscale's Story,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Ken Ahrens, co-founder and CEO of Speedscale, joins us to share how they modernize and automate testing practices for cloud infrastructure by providing API traffic data going into and out various microservices. Thanks to TimescaleDB, Speedscale´s UI loads quickly and provides the ability to drill deep down into high-fidelity data.Speedscaleis one of the first commercial technologies utilizingactual API trafficin order to generate tests and mocks. Speedscale helps Kubernetes engineering teams validate how new code will perform under production-like workload conditions.Speedscale service mapSpeedscale provides unparalleled visibility, collects and replays API traffic, introduces chaos, and measures the golden signals of latency, throughput, saturation, and errors before the code is released.Screenshot of Speedscale UISpeedscale Traffic Replayis a modern load, integration, and chaos testing framework -- an alternative to legacy scripting tools that can take days or weeks to run and do not scale well for modern architectures.If your organization provides a SaaS product that is critical for revenue, or your team is responsible for performant infrastructure, Speedscale is for you. Speedscale enables engineering leaders to generate quality automation quickly without the need for manual scripting. Since actual API calls (not scripts) are the primary ingredient, tests and mocks can be built and regenerated quickly to keep pace with the speed of changing business requirements.As microservices become more separated logically, they are highly dependent on each other to deliver the expected functionality. This means performance problems become distributed across multiple services and can be difficult to trace. Multiple contributing factors affect the state of an application in microservices and Kubernetes environments. A testing harness that closely mirrors the production setup and incoming traffic has become a requirement for highly distributed and containerized environments.By leveraging traffic to automatically generate sophisticated mocks, engineers and testers are granted the ability to isolate and contract/performance test smaller components in the context of a tightly coupled architecture. This superpower enables rapid testing iterations. Moreover, without the need for scripting, testing can finally move as fast as development.About the foundersSpeedscale’s leadership team comes from companies like New Relic, Observe Inc, Wily Introscope (bought by CA",https://www.timescale.com/blog/automated-mocking-using-api-traffic-speedscales-story/,474,"[0.031810663640499115, -0.006728912238031626, 0.03425123915076256, 0.0003390889905858785, 0.030950916931033134, -0.01322902087122202, -0.07449297606945038, 0.009831630624830723, -0.01636294089257717, 0.025556692853569984, 0.016487741842865944, -0.028676746413111687, 0.00025502091739326715, -0.002085234737023711, 0.032614946365356445, 0.02447507530450821, 0.007654527667909861, -0.02895408309996128, -0.05485745146870613, 0.02296358346939087, 0.03078451380133629, 0.0019049651455134153, 0.005883031524717808, 0.004121935460716486, -0.023310255259275436, -0.04201670363545418, -0.056909751147031784, 0.033585626631975174, 0.04093508422374725, -0.09457224607467651, -0.013346889987587929, 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 Automated Mocking Using API Traffic: Speedscale's Story,"Technologies), and iTKO (bought by CA Technologies).My name isKen Ahrens. I am co-founder and CEO of Speedcale. Much of my career has been focused on helping companies develop and manage complex web applications. I previously ran North America teams for New Relic and CA/Broadcom. Previous startups included Pentaho (acquired by Hitachi), ITKO (acquired by CA/Broadcom), and ILC (acquired by General Dynamics). My first foray into programming started with a brand new language called Java at Georgia Tech and has grown into a lifetime interest.Matthew LeRay, co-founder and CTO at Speedscale, has invested the past 20 years in improving the performance of applications across multiple generations of technology. Previously, he was head of product at Observe, SVP at CA Technologies (acquired by Broadcom), and engineering leader at ILC (acquired by General Dynamics). He is an alumnus of Georgia Tech in both Computer Engineering and Business. His first love is debugging Golang code but he occasionally takes a break to craft hand-carved guitars.Nate Lee, co-founder and VP of Sales & Marketing, has served a variety of roles within the software industry. Most recently, he was in enterprise sales for the digital transformation consultancy Contino (acquired by Cognizant). Prior to Contino, he served as Product Manager at CA Technologies, by way of iTKO where he was a Presales Engineer for 6 years. Before iTKO, he spent time as a support leader at IBM Internet Security Systems, and engineer at ILC (acquired by General Dynamics). He graduated from Georgia Tech with an MBA in Technology, and a BS in Computer Science. You’ll most likely find him outdoors on 2 wheels when he’s not innovating with his Speedscale buddies.As a small, nimble startup, our normal workweek is comprised mostly of helping customers scope their use cases and deciding where to start with our testing framework (recording traffic, generating traffic replay “scenarios” comprised of tests and mocks of auto-identified dependencies). We are an engineering startup with a heavy emphasis on Kubernetes and Golang. We also are improving the protocol and version support of both our Enterprise Kubernetes version and Desktop version called theSpeedscale CLI.About the projectWe noticed the application of outdated testing practices to modern cloud infrastructure (eg. UI testing, manual testing, API test tools with no mocking). 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 Automated Mocking Using API Traffic: Speedscale's Story,"for quality automation increases exponentially. As a result, the popularity ofcanary releases and blue green deploymentshave risen, but we believe this is due to a lack of robust quality automation alternatives.Speedscale uses a transparent proxy to capture API transaction data from which to model robust API tests and realistic mocks of backend dependencies. Our traffic replay framework iscapable of showing engineering teamscomplex headers, authentication tokens, message bodies, and associated metadata (with sensitive data redaction when necessary). In addition, the platform is able toautomatically identify backend dependenciesthat are needed to operate your service, allowing new developers on old services to get up to speed quickly. This data is streamed to AWS S3 and filtered for test and mock scenario creation. These scenarios can then be replayed as part of validation test suites and integrated into CI or GitOps workflows.Our customers need to be able to understand the API traffic going into and out of their various microservices over time. They want to see the sequence of API calls as well as the trend of the overall volume of calls.Data is ingested by our platform into our cloud data warehouse. As new data arrives, we determine the index where that API call can be found and write the index to TimescaleDB. Then we can use the data from TimescaleDB to find the original value. Because the indexes are much smaller than the original data, we are able to calculate aggregates on the fly and plot them in our user interface. The Traffic Viewer graph shows inbound and outbound calls, backend dependencies, and an “infinite scroll” list of traffic. All of these components are powered by TimescaleDB queries.Speedscale Traffic ViewerChoosing (and using!) TimescaleDBWe knew right from the start that we have a time-series problem. There was always new data flowing in, and users wanted to focus on data from certain periods of time, they didn't just want all the data presented to them. We decided to use a time-series database to store the data.We wanted a technology that could run inside Kubernetes, is easy to operate (we are a startup after all) and scale for our needs. We initially implementedElasticsearchand exposed the data throughKibana. It let us quickly prototype the use cases and worked great for lower volumes of data. But it scaled poorly for our use",https://www.timescale.com/blog/automated-mocking-using-api-traffic-speedscales-story/,467,"[0.019009429961442947, -0.0009787932503968477, 0.05178431048989296, -0.02458116039633751, 0.05933678150177002, -0.026490652933716774, -0.05318080633878708, -0.005699979141354561, -0.027545148506760597, 0.01587444171309471, 0.011542458087205887, -0.015703443437814713, -0.036821864545345306, -0.02740265056490898, 0.05933678150177002, -0.01620219089090824, 0.009447715245187283, -0.027630649507045746, -0.05403580144047737, 0.00167971255723387, 0.009782589040696621, 0.02027767524123192, -0.0007018099422566593, -0.012639704160392284, -0.005789041519165039, -0.024053912609815598, -0.014534946531057358, 0.034000374376773834, 0.01220508012920618, -0.08624068647623062, 0.0038011735305190086, -0.0408688522875309, -0.04115384817123413, 0.028528396040201187, 0.017384937033057213, -0.024011163040995598, 0.030922386795282364, 0.002210523234680295, 0.016544189304113388, 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 Automated Mocking Using API Traffic: Speedscale's Story,"case and we had very little control over the look and feel of the UI. Then we evaluated TimescaleDB, Influx, Prometheus and Graphite.We selected TimescaleDB because we were already using PostgreSQL as part of our technology stack, and also the paper evaluation looked like TimescaleDB would scale well at our load ranges.✨Editor’s Note:For more comparisons and benchmarks, seehow TimescaleDB compares to InfluxDB, MongoDB, AWS Timestream, vanilla PostgreSQL, and other time-series database alternativeson various vectors, from performance and ecosystem to query language and beyond.We useSQiurreLto issue SQL queries like this one that powers the inbound throughput graph.rrPairsQuery := sq.Select( fmt.Sprintf(""time_bucket(INTERVAL '%s', time) AS bucket"", durationToSQLInterval(interval)), ""is_inbound"", ""COUNT(id)""). From(rrPairsTableName). GroupBy(""is_inbound"", ""bucket""). OrderBy(""bucket"")We deploy TimescaleDB on theKubernetes operatorviaFlux. Our core services are currently written in Golang which we use to connect TimescaleDB with microservices inside Kubernetes as well as AWS Lambda.Currently, our architecture that touches TimescaleDB looks like this:Current Speedscale architecture diagramAfter implementing TimescaleDB, our AWS cloud costs went down about 35% because it is cheaper to run than the AWS OpenSearch we used before. In addition, the query performance improved dramatically, a majority of queries take under 100ms to complete.Advice & resourcesHaving aKubernetes operatorwas a big help for us because it was proof that this was built for our architecture.We’ve made a version of our traffic capture capability available as a free CLI which you can find here:https://github.com/speedscale/speedscale-cliWe’d like to thank Ken and all folks from Speedscale for sharing their story. We applaud your efforts to modernize and automate testing practices for modern cloud infrastructure.🙌We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Lucie Šimečková), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/automated-mocking-using-api-traffic-speedscales-story/,464,"[0.0291631780564785, -0.010503806173801422, 0.022062210366129875, -0.02013581059873104, 0.049664583057165146, 0.017000136896967888, -0.04673982784152031, 0.008190719410777092, -0.02114822529256344, 0.004081298131495714, 0.023847999051213264, -0.022104395553469658, 0.001975615508854389, -0.03009122423827648, 0.07553741335868835, 0.03256601840257645, -0.013934767805039883, -0.04696480929851532, -0.05022703483700752, 0.02265278622508049, -0.012198194861412048, 0.012788770720362663, 0.025915013626217842, 0.0257743988186121, -0.012247409671545029, -0.006921684835106134, -0.017759447917342186, 0.029697507619857788, 0.023271484300494194, -0.06895671039819717, 0.01458158902823925, -0.03914671391248703, 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@@ -70,14 +70,14 @@ How Octave Achieves a High Compression Ratio and Speedy Queries on Historical Da
 How Octave Achieves a High Compression Ratio and Speedy Queries on Historical Data While Revolutionizing the Battery Market,"this entire process happens seamlessly.About the ProjectOctave distinguishes itself from traditional battery suppliers by leveraging the plethora of data and measurements from the battery system and building up an extensive history of each battery cell.We handle large streams of battery measurements with clear time-series characteristics: each data point is composed of a timestamp and a value. These data are collected from the battery systems (our IoT edge devices) and sent back to our cloud for further analysis.Among the information we collect, the most basic yet crucial data points are undoubtedly the voltage and temperature measurements from each battery cell in operation.One of Octave’s dashboards made with Grafana and Timescale""We initially used AWS Timestream in the early days of Octave, which at first seemed a natural choice to handle our time-series data since our cloud infrastructure was entirely built in AWS. However, we quickly realized we would need a more widely used, mature, and scalable database solution""The data are streamed back to ourBattery Cloud(our in-house developed cloud platform, hosted on AWS and relying on Timescale databases), where they are crunched and further processed. This allows us the following:Analyze the battery cells’ behavior and degradation based on their history and how they are cycled (the temperature, current levels, and depth of discharge significantly impact the lifetime of batteries!).Improve the safety of the system by immediately detecting anomalies.Implement a data-driven predictive maintenance process. The ultimate goal is to predict when to replace a used battery module, and by doing so, we can extend the lifetime of the entire system. This is a true game-changer for second-life battery systems.✨Editor's Note:Learn what time-series forecasting is, its applications, and its main techniques in this blog post.A diagram of Octave’s Battery CloudChoosing (and Using!) TimescaleDBI have been using PostgreSQL for some time, and we were keen to use tools and stacks we were familiar with for the sake of efficiency. So inevitably, I was immediately interested when I heard about an interesting PostgreSQL extension called TimescaleDB, specifically built to handle time-series data, which we knew we needed since we were dealing with a typical IoT use case.""Timescale has proven to be a key enabler of Octave’s data-driven Battery Cloud technology""We initially used AWS Timestream in the early days of Octave, which at first seemed a natural choice to handle our time-series",https://www.timescale.com/blog/high-compression-ratio-and-speedy-queries-on-historical-data-while-revolutionizing-the-battery-market/,485,"[0.00442163273692131, -0.0211044792085886, 0.05064532533288002, -0.00014421592641156167, 0.043972186744213104, 0.026990948244929314, 0.00010670495248632506, -0.027370719239115715, -0.022406555712223053, -0.00020747595408465713, 0.028428656980395317, -0.03504754230380058, -0.020263556391000748, -0.032226379960775375, 0.056531794369220734, 0.035698581486940384, -0.02555323950946331, 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 How Octave Achieves a High Compression Ratio and Speedy Queries on Historical Data While Revolutionizing the Battery Market,"data since our cloud infrastructure was entirely built in AWS. However, we quickly realized we would need a more widely used, mature, and scalable database solution if we ever wanted to scale our operations and deploy several dozen or hundred second-life battery systems in the field. So, we went looking for alternatives.After some research, Timescale quickly became our preferred option, given itsimpressive compression ratios, lightning-fast queries,unmissable continuous aggregates, friendlycommunityandextensive documentation, and most importantly, its plain PostgreSQL syntax.The cherry on the cake is Timescale's ease of use and user-friendly interface.We went immediately for Timescale because we were looking for a managed service, and Timescale seemed the recommended option and was nicely compatible with our preferred AWS region.✨Editor’s Note:Timescale’s user interface now offers an even friendlier and user-centered experience.The Timescale Team shared their redesign journey and lessons in this blog post.We have found TimescaleDB’s compression ratio to be absolutely phenomenal! We’re currently at a compression ratio of over26, drastically reducing the disk space required to store all our data.A narrow table modelOctave’s compression ratio with TimescaleThe power of continuous aggregates is hard to overstate:they are basically materialized views that are continuously and incrementally refreshedand allow for lightning-fast queries on large historical datasets.I have found thedocumentationto be very clear and abundant. Every feature is very well explained. So it’s very easy toget started with TimescaleDB. And if we have more precise questions specifically related to our use case, we can always rely on our customer success manager or the very reactive Support Team. 🙂✨Editor’s Note:Read how our Support Team raises the bar on hosted database support.Current Deployment & Future PlansMost of our backend software is currently written in Python. We leverage AWS IoT to manage our battery fleet and circulate the data between our edge devices and our battery cloud viaMQTT.We rely on some ETL (extract, transform, load) pipelines interacting with Timescale that extract battery measurements and insert the processed data back into the database. We also use some dashboarding tools, such asGrafanaandStreamlit, as well as API endpoints connected to our Timescale database.It’s no secret that time-series data can grow very fast. We currently store close to one million data points per battery system daily. As we have already sold our first 28 battery cabinets, the size of our database is expected to increase quickly. 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 How Octave Achieves a High Compression Ratio and Speedy Queries on Historical Data While Revolutionizing the Battery Market,"only the start, as we expect to triple or quadruple sales by the end of 2023.So far, we have found Timescale to be powerful, scalable, and pretty well-suited for our application. Timescale has proven to be a key enabler of Octave’s data-driven Battery Cloud technology.We have started to leverage the power of continuous aggregates. For example, continuous aggregates are queried behind the scenes of our customer-facing applications. They enable our clients to quickly and seamlessly inspect and download historical data of their second-life battery systems.SELECT time_bucket(INTERVAL '15 min', time) AS bucket, bms.iot_thing, bms.iot_device, bms.name, bms.string, bms.module, bms.cell, avg(bms.value_real) AS value_real, last(bms.value_str, time) AS value_str FROM public.bms GROUP BY bucket, iot_thing, iot_device, name, string, module, cell;✨Editor’s Note:Here are three reasons you should upgrade to the new version of continuous aggregates.RoadmapWe are very proud and honored to recently have won the competitive call from theEIC Accelerator, led by the European Innovation Council! We’re now working towards industrializing and scaling our battery cloud technology.Advice & ResourcesI believe that getting started with a hands-on example is always a good way to evaluate something. You can try Timescale for free with a demo dataset to get acquainted with the service.So I recommend quickly spinning up a first TimescaleDBinstance and having fun playing around with the service.Also,there is very extensive documentation and tons of examples and tutorialsavailable on the website, which helps you quickly master Timescale! So make sure to have a look at the website.Anything else you'd like to add?Thanks to Timescale for this opportunity to share our story and our mission at Octave!Want to read more developer success stories?Sign up for our newsletterfor more Developer Q&As, technical articles, tips, and tutorials to do more with your data—delivered straight to your inbox twice a month.We’d like to thank Nicolas and all the folks at Octave for sharing how they’re leveraging continuous aggregates and Timescale’s compression powers to handle their millions of battery cell data points daily.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/high-compression-ratio-and-speedy-queries-on-historical-data-while-revolutionizing-the-battery-market/,524,"[0.018129948526620865, -0.003661799943074584, 0.06117277592420578, 0.01337627787142992, 0.02398601919412613, -0.0017993025248870254, -0.010981889441609383, -0.003753081662580371, -0.009703945368528366, -0.0029245249461382627, 0.004160338547080755, -0.04300069808959961, -0.01595322974026203, -0.0172592606395483, 0.06504873186349869, 0.054460059851408005, -0.018424857407808304, 0.00513635016977787, -0.025432482361793518, 0.01693626306951046, -0.009556490927934647, 0.012119399383664131, 0.05019088461995125, 0.04286026209592819, -0.030782993882894516, -0.049320198595523834, -0.024126451462507248, 0.03364783525466919, 0.03853491693735123, 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-How Density Manages Large Real Estate Portfolios Using TimescaleDB,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, part of the team atDensity—Shane Steidley, director of software, andBrock Friedrich, software engineer—explain how they are using TimescaleDB to query data from IoT devices to help companies with substantial real estate footprints make data-driven decisions to optimize their real estate usage while reducing carbon emissions.About the CompanyShane:Density builds technology that helps companies understand how people use physical space. Founded in 2014, our platform provides workplace analytics enabled by custom-built sensors which capture the complexities of how people move through and use space without invading privacy. These sensors feed raw data to Density’s analytics platform—a fast, data-rich system that provides comprehensive insights into how spaces are used, allowing companies to easily compare a space's performance against its intended purpose, another space, or portfolio benchmarks.The platform can reveal if employees are choosing focus space or collaborative space, which floors are the most popular (and why), and how one office’s occupancy rate compares to others in a portfolio or against industry benchmarks. Today, our technology is helping inform decisions for the workplaces of some of the largest companies in the world, spanning 32 countries, with more than 1.25 billion square feet under management. Our growing list of customers ranges from Fortune 100 to high-growth tech companies that see the value in understanding the performance of their office spaces to lower operational costs, improve employee experience, and reduce their carbon footprint.Our ultimate mission is to measure and improve humanity’s footprint on the world—optimizing commercial real estate portfolios is just the first step. Thirty-nine percent of CO2 emissions are directly or indirectly linked to real estate construction, so if we can help companies measure and optimize their use of real estate, we can have an outsized impact on the different issues that are impacting climate.A tweet from the Density Team on Earth DayWith Density, customers can understand how their space is used and right-size it. When we started, we needed to sell the problem and then sell our solution. Post-COVID, everybody understands the problem and the data that they could have access to, and how Density can provide value.Brock: Density",https://www.timescale.com/blog/density-measures-large-real-estate-portfolios-using-timescaledb/,472,"[0.03432324156165123, 0.017802394926548004, 0.04187323898077011, 0.056945376098155975, 0.06318596750497818, 0.041204605251550674, -0.02813837118446827, 0.043210506439208984, -0.036078404635190964, -0.02687075175344944, 0.013707008212804794, 0.00013298688281793147, -0.012411529198288918, -0.04100958630442619, 0.022413186728954315, 0.02205101028084755, 0.011882193386554718, -0.023221120238304138, -0.05154057964682579, 0.027107559144496918, -0.01255082804709673, 0.006985838059335947, 0.01879141665995121, 0.0006677639321424067, -0.01776060461997986, -0.0022444529458880424, -0.023861896246671677, 0.06179297715425491, 0.0493117980659008, -0.04490995407104492, -0.0006703757680952549, -0.04020165279507637, -0.016242247074842453, -0.003186461515724659, 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+How Density Manages Large Real Estate Portfolios Using TimescaleDB,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, part of the team atDensity—Shane Steidley, director of software, andBrock Friedrich, software engineer—explain how they are using TimescaleDB to query data from IoT devices to help companies with substantial real estate footprints make data-driven decisions to optimize their real estate usage while reducing carbon emissions.About the CompanyShane:Density builds technology that helps companies understand how people use physical space. Founded in 2014, our platform provides workplace analytics enabled by custom-built sensors which capture the complexities of how people move through and use space without invading privacy. These sensors feed raw data to Density’s analytics platform—a fast, data-rich system that provides comprehensive insights into how spaces are used, allowing companies to easily compare a space's performance against its intended purpose, another space, or portfolio benchmarks.The platform can reveal if employees are choosing focus space or collaborative space, which floors are the most popular (and why), and how one office’s occupancy rate compares to others in a portfolio or against industry benchmarks. Today, our technology is helping inform decisions for the workplaces of some of the largest companies in the world, spanning 32 countries, with more than 1.25 billion square feet under management. Our growing list of customers ranges from Fortune 100 to high-growth tech companies that see the value in understanding the performance of their office spaces to lower operational costs, improve employee experience, and reduce their carbon footprint.Our ultimate mission is to measure and improve humanity’s footprint on the world—optimizing commercial real estate portfolios is just the first step. Thirty-nine percent of CO2 emissions are directly or indirectly linked to real estate construction, so if we can help companies measure and optimize their use of real estate, we can have an outsized impact on the different issues that are impacting climate.A tweet from the Density Team on Earth DayWith Density, customers can understand how their space is used and right-size it. When we started, we needed to sell the problem and then sell our solution. Post-COVID, everybody understands the problem and the data that they could have access to, and how Density can provide value.Brock: Density",https://www.timescale.com/blog/density-measures-large-real-estate-portfolios-using-timescaledb/,472,"[0.034271880984306335, 0.01786038652062416, 0.04182283952832222, 0.05698048323392868, 0.06313827633857727, 0.04115412011742592, -0.028197677806019783, 0.043216004967689514, -0.03605513274669647, -0.026902033016085625, 0.013646059669554234, 0.0001965669944183901, -0.01244793739169836, -0.04098694026470184, 0.02245783433318138, 0.022081678733229637, 0.0118419099599123, -0.023224076256155968, -0.05151927471160889, 0.027124939486384392, -0.012566355988383293, 0.006931000389158726, 0.01873808167874813, 0.0006835220265202224, -0.017776796594262123, -0.0022569287102669477, -0.023850999772548676, 0.061745110899209976, 0.04934593662619591, -0.04494353011250496, -0.0006639306084252894, -0.04020676761865616, -0.01616072468459606, -0.0031293989159166813, 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 How Density Manages Large Real Estate Portfolios Using TimescaleDB,"also provides that value while maintaining the anonymity of each person that walks underneath our sensors.Shane: Privacy is very important to us; it’s one of our core tenets. And because of this, we don’t use cameras—we use other sensor types, namely infrared and radar, that don’t capture any personally identifiable information (PII).About the TeamShane: Density was born out of a web development consultancy. The consultancy founders got tired of walking to their favorite coffee shop during the cold, harsh winter in upstate New York only to find a long line. This unpleasant experience gave them the idea to create an occupancy sensor.As you might expect, given the company’s origin, the Density engineering team started with 4-5 engineers with a heavy web development background. We have since grown our engineering team into a team capable of performing what I call true full-stack development. We have an electrical engineering team that designs our hardware and lays out the boards, and our devices are manufactured in Syracuse, NY, where we also have an R&D lab. Our team includes experts in mechanical design and embedded software, and we now have a backend system with multiple pipelines (whose data typically ends up in TimescaleDB) and multiple React-based frontend applications.Brock: As a team, there are many different daily routines. It’s a mix of running production systems like Shane mentioned, live data pipelines, running historical analysis, or writing new features.Shane:And we’re hiring! If you are interested in joining the Density Team,please check out our jobs page!About the ProjectShane: We use different types of data at different stages. Our entry sensors generate infrared data that is very dense. It’s not feasible to send this dense data to the cloud, so we have to do all processing and machine learning at the edge (on the device). The results of this edge processing are +1/-1 counts that can be aggregated in our pipelines and TimescaleDB.Our radar-based sensors generate sparse data, so we do less processing at the edge and more processing in the backend. That data has to go through a pipeline and get transformed before it makes sense to insert it into TimescaleDB and perform any aggregations. TimescaleDB really provides value when it comes to querying the data, allowing customers to slice the data up in multiple dimensions. 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 How Density Manages Large Real Estate Portfolios Using TimescaleDB,"wasn’t easy before we started using TimescaleDB.Brock:Yeah, to tack onto that, in TimescaleDB we store counts of people in spaces over time, and a wealth of derivative metrics off of those people counts, things like how long were people in spaces, not specific people, but how long was what we call dwell time?How long was this space used continuously, or what was its usage compared to similar space types throughout the day?One of the parts I think Shane was trying to highlight is that there's a real dynamism to how those queries can take shape in that they can be sliced and diced and composed in a more or less arbitrary number of ways. And TimescaleDB’s flexibility—it is built on a relational model at the end of the day—and its ability to do the partitioning under the covers and thehypertablesto let us access all of the data back in time very quickly is the magic combination that we were looking for in a time-series database to meet our use case.Choosing (and Using!) TimescaleDBBrock: I found out about Timescale back in a previous life, circa early 2019. I worked for an oil and gas firm and was doing a lot of research into time-series storage and the options available in that space because we were developing a data software solution for supervisory control and data acquisition. Essentially, it boiled down to real-time remote sensing and monitoring for industrial controls.During that research, I happened across TimescaleDB, which was still pretty early on. It was right about the timecontinuous aggregatescame out, which was one of the big selling points for me. So when I came to Density, they were just beginning to evaluate options for time-series databases for our applications. I was able to contribute my previous experience with TimescaleDB to that process. As we evaluated the options, TimescaleDB came out as the clear winner, and the rest is history.✨Editor’s Note:Read our documentationto learn more about continuous aggregates.Shane:As an IoT company, we’ve had sensors since the very beginning. And when we started, a lot of the engineering staff came from a web consultancy, so I don’t think we did realize from the beginning thatwe needed a time-series database or even quite knew what a time-series database was.“I think moving forward, TimescaleDB, at least in my opinion, is",https://www.timescale.com/blog/density-measures-large-real-estate-portfolios-using-timescaledb/,487,"[0.008549651131033897, 0.006548901554197073, 0.04177885875105858, 0.006129801273345947, 0.09003008902072906, 0.026574619114398956, -0.009956371039152145, 0.016545360907912254, -0.022186819463968277, 0.005182269494980574, 0.018892323598265648, -0.03737648203969002, 0.019373377785086632, -0.04507335275411606, 0.044810958206653595, 0.02772623486816883, 0.01452638953924179, -0.0034329802729189396, -0.023396743461489677, 0.0066545880399644375, -0.028076093643903732, 0.036880847066640854, 0.022711604833602905, 0.00030157019500620663, -0.02858630195260048, -0.026195606216788292, -0.0016782244201749563, 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 How Density Manages Large Real Estate Portfolios Using TimescaleDB,"just going to be the default time-series database”Shane SteidleyWhat seems obvious to us now wasn’t obvious back in 2017, when we built an entire time-series database using stored procedures and vanilla PostgreSQL. It was pretty cool when we finally brought over TimescaleDB. We were like: “Oh, it just does all this for us! Look, there’s a bucket function, and it’s going to return the right size buckets that you need. And it handles all of the weirdness of time zones and daylight savings time.” And you can ingest all this data, whereas before, because of how we were using PostgreSQL, we would struggle with ingesting the amount of data we needed to ingest.I think moving forward, TimescaleDB, at least in my opinion, is just going to be the default time-series database. I think you're just going to have to have a reason not to use TimescaleDB because it's so simple and fits in with PostgreSQL.Brock:The top factors that led us to TimescaleDB specifically were its tolerance for high insert rates. It's just blown away all of our expectations, even based on benchmarks that we were able to see online at the time. It's built on top of PostgreSQL, as Shane talked about earlier. There's very little in the way of a special TimescaleDB domain-specific language, and it's operationally very familiar to operating vanilla PostgreSQL.Both of those were huge wins, just both operationally and development-wise. We always have the ability to fall back on core PostgreSQL principles or relational data models as we need to, but we also have the capability to dive deeper into TimescaleDB’s specific functionality to meet those big time-series use cases.“I get the most pride in doing plain SQL against TimescaleDB, getting time-series results at scale, and not having to do a bunch of backflips”Brock FriedrichShane:We use TimescaleDB like a time-series database should be used. We use it not just for the continuous aggregates of count data and other metrics that Brock's touched on, but the bucketing, the things that are so complicated if you push them to application code. When handled in TimescaleDB, it just gives you the right data the way that you want it. There are obviously some edge cases, but 99 % of the time, TimescaleDB just does what you want it to do.Brock: What would we",https://www.timescale.com/blog/density-measures-large-real-estate-portfolios-using-timescaledb/,494,"[-0.00672829570248723, 0.0026700966991484165, 0.04586651921272278, 0.0170747023075819, 0.09729936718940735, 0.02104070968925953, 0.0005949009791947901, 0.009274889715015888, -0.014820341020822525, -0.008794793859124184, 0.027539394795894623, -0.022181805223226547, 0.03696040064096451, -0.03504001721739769, 0.017394766211509705, 0.002602257067337632, 0.0003428943164180964, -0.0017577410908415914, -0.018257547169923782, 0.014848172664642334, -0.018730685114860535, 0.0091566052287817, 0.03932608664035797, 0.022515784949064255, -0.023267239332199097, -0.01399930752813816, -0.016364995390176773, 0.057555802166461945, 0.006853538099676371, -0.028722237795591354, 0.013296559453010559, -0.04956812784075737, -0.031895045191049576, -0.007180559448897839, 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 How Density Manages Large Real Estate Portfolios Using TimescaleDB,"use if TimescaleDB didn't exist is one of those topics that I like not to think about because it gives me anxiety. There are plenty of other time-series database options, but none fit the cross-section of requirements that at least Density has in quite the manner that TimescaleDB does. So whenever somebody else asks me that question, like in passing, I just say, “Let's just pretend like that's not possible and go about our business.”I get really proud of querying against TimescaleDB whenever we run really simple queries, like selecting the data from one of our sensors for a day from six years ago, and we run that at scale, and it runs like it would run in a much smaller scale like we only had a few months of data. And that goes back to one of the things I was appreciating earlier, which is that chunk exclusion –– that ability to operate what would be these really large query plans for vanilla PostgreSQL, and trim them down to something that’s predictable and reasonable, and operates at relatively low latencies. So all that’s to say, I get the most pride in doing plain SQL against TimescaleDB, getting time-series results at scale, and not having to do a bunch of backflips./* This query yields space count aggregates at 10-minute resolution for a given set of spaces over a three-day period, where each 10-minute bucket is represented in the output, even if that bucket contains no data.The query first selects data from a sparse one-minute cagg and unions that data to a set of empty records, generated with the Postgres generate_series function, then rolls up the unioned records into 10-minute aggregates. The union against the set of empty records ensures that all 10-minute intervals are represented in the final results. This step is necessary as the one-minute data is sparse, meaning a given 10-minute interval could contain no data, and the time_bucket_gapfill function does not register that a bucket needs to be injected if no records exist within an interval. */ select und.space_id, time_bucket('10m', und.inner_bucket) as bucket, min(und.occupancy_min) as occupancy_min, max(und.occupancy_max) as occupancy_max, first(und.first_occupancy, und.inner_bucket) filter (where und.first_occupancy is not null) as first_occupancy, last(und.last_occupancy, und.inner_bucket) filter (where und.last_occupancy is not null) as last_occupancy from (select c1m.bucket as inner_bucket, c1m.space_id as space_id, c1m.occupancy_min as occupancy_min, c1m.occupancy_max",https://www.timescale.com/blog/density-measures-large-real-estate-portfolios-using-timescaledb/,552,"[-0.006320243701338768, -0.011803660541772842, 0.09225647151470184, 0.010328203439712524, 0.029817456379532814, 0.020098522305488586, 0.011708233505487442, 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 How Density Manages Large Real Estate Portfolios Using TimescaleDB,"as occupancy_max, c1m.first_occupancy as first_occupancy, c1m.last_occupancy as last_occupancy from cav_space_counts_1m c1m where c1m.bucket between '2022-05-22 13:00:00+0000' and '2022-05-25 13:00:00+0000' and c1m.space_id in (997969122178368367, 997969123637986180) union select time_bucket_gapfill('10m', generate_series, '2022-05-22 13:00:00+0000', '2022-05-25 13:00:00+0000') as inner_bucket, space_id, null as occupancy_min, null as occupancy_max, null as first_occupancy, null as last_occupancy from generate_series('2022-05-22 13:00:00+0000'::timestamptz, '2022-05-25 13:00:00+0000'::timestamptz, '10m') join unnest(array [997969122178368367, 997969123637986180]) as space_id on true) as und group by und.space_id, bucket order by bucket;✨Editor’s Note:Learn more about chunk exclusionin this blog post about how we fixed long-running now ( ) queries (and made them lightning fast), orread our docs for more info on hypertables and chunks.Current Deployment and Future PlansBrock:For data visualization, we useTableauandGrafana.The primary languages we use that interact with TimescaleDB are Rust and Python. Bonus: big shout out to JetBrains for their database IDE,DataGrip. It is the best on the market by a wide margin.✨Editor’s Note:Slow Grafana performance?Learn how to fix using downsampling in one of our recent blog posts.We find TimescaleDB to be very simple to use, just flat-out, dead simple. Any TimescaleDB-specific semantics, all of the defaults always take you a long way toward meeting whatever use case you're setting out to achieve. The narrative and API documentation online is first class, in my opinion.But maybe the most telling point is that there’s very little shock value whenever you’re discovering new feature value or features within TimescaleDB. And by shock value, I mean whenever I discover something like a continuous aggregate, for example, it operates conceptually almost identically to how vanilla PostgreSQL materialized view operates, but there’s extra pizazz on top that TimescaleDB does to meet the real-time component of the materialized view, refresh in the background, and all that.So I don't really want to undersell whatTimescaleDB is doing under the covers to make the magic happen. But, from an end perspective, coming from a PostgreSQL background, many of the features align conceptually with what I would expect if I was just writing something against the vanilla PostgreSQL.RoadmapShane:When we started, we threw everything at TimescaleDB, and now we’re being more responsible users of TimescaleDB. We’re at that sweet spot within TimescaleDB where it’s found product-market fit within Density.Brock:It’s hard to name my favorite TimescaleDB feature, but continuous aggregates have been a game-changer in a variety of different ways. 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 How Density Manages Large Real Estate Portfolios Using TimescaleDB,"to TimescaleDB and deployed, it made development significantly faster, and we could just offload a lot of decision logic and complexity around time zone handling and bucketing (that’s a big one) to TimescaleDB and let it handle that complexity for us.Continuous aggregates come into play in that we were able to roll up multiple resolutions of our sensor account data, our people count data, and make that available in a much more efficient way with little-to-no effort so far as the code that we actually had to write to deliver those efficiencies.Continuous aggregates are becoming less important for us now than they were in our previous usage, just because as Shane was talking about earlier on, we're growing to such a size, and our use cases are becoming so complex that we're wanting to move to more the ETL (extract, transform, load) type of processing out of the database. So we're not monopolizing database resources to do some of those computations and move them upstream of the database into processing pipelines and still take advantage of continuous aggregates but to a lesser degree. We're doing less of the mathematical stuff in the database, but we're still using continuous aggregates to roll up high-resolution data to lower resolutions.Advice & ResourcesBrock:If I had to recommend resources, the first would probably be theTimescale blog. It’s been historically pretty informative over the years about the internals, like what’s happening in TimescaleDB underpinning a specific feature. One that I remember specifically is explaining thevarious compression algorithms in play for different data types within PostgreSQL. Being able to distill that knowledge down to a blog article that I could consume and then assimilate into my mental model of what was going on under the covers of the technology I was using has been helpful repeatedly.The advice that I would give for building a scalable database or a strategy around that is that when designing for an analytic workload specifically, don't direct any read load to the master or in standby notes. Always use a read replica for enough reasons that we probably don't have enough time to talk about here.The primary determinant of how efficient and scalable your solutions are going to be boils down to how many rows your queries have to touch for any given type of query",https://www.timescale.com/blog/density-measures-large-real-estate-portfolios-using-timescaledb/,452,"[0.02794598415493965, 0.014525596052408218, 0.0583607442677021, -0.00671378243714571, 0.06424561887979507, 0.01838664896786213, 0.015903517603874207, -0.0019107905682176352, -0.020754951983690262, 0.00733097642660141, 0.035194430500268936, -0.0009002245496958494, 0.004137354902923107, -0.01992245763540268, 0.03809380531311035, 0.034218400716781616, 0.010449242778122425, -0.002210416831076145, 0.005791579373180866, 0.038897596299648285, -0.002386245410889387, 0.013865342363715172, 0.04808374121785164, 0.01591787114739418, -0.014654777012765408, -0.024142345413565636, -0.004406480118632317, 0.06774783879518509, 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-How Density Manages Large Real Estate Portfolios Using TimescaleDB,"you submit. Touching in that context includes both the scanning of index tuples and heap access for the rows, the end of the indexed contents of the rows, so be smart with your indexes and only index what you need and design towards the end of needing as few indexes as possible, because those are how you reduce that overhead that your database has to work through to fulfill a particular request or query.Shane: So my advice, at least for an IoT company, is to consider your real-time use cases and your historical use cases and consider them separately. What we found out is we treated everything the same. Everything went through the same pipeline, and it was just queries that were different. When you're using TimescaleDB—and really any architecture—it's extremely useful to consider those use cases separately and architect them separately. They have different requirements. If you try to shoehorn all data, in real time, into any time-series database, whether it's something awesome like TimescaleDB or something else, it's going to cause problems and it's not going to scale very well. You can do a lot more by separating historical and real-time use cases.We’d like to thank Shane, Brock, and all the folks at Density for sharing their experience with TimescaleDB in measuring and optimizing real estate usage.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/density-measures-large-real-estate-portfolios-using-timescaledb/,321,"[0.02513016387820244, 0.021440021693706512, 0.06373364478349686, 0.021382808685302734, 0.07037018239498138, 0.011356488801538944, -0.005957150366157293, 0.0032431785948574543, -0.025144467130303383, -0.005728304386138916, 0.018994228914380074, -0.01256508193910122, 0.035013455897569656, -0.028462735936045647, 0.03352595493197441, -0.00013721827417612076, 0.008045371621847153, -0.03055095672607422, -0.030207687988877296, 0.0421362929046154, -0.0011487002484500408, 0.03710167855024338, 0.012793928384780884, -0.0026817903853952885, 0.005556670017540455, -0.023342303931713104, -0.0008657713769935071, 0.0655072033405304, 0.06510671973228455, -0.041135091334581375, -0.0030876346863806248, 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+How Density Manages Large Real Estate Portfolios Using TimescaleDB,"you submit. Touching in that context includes both the scanning of index tuples and heap access for the rows, the end of the indexed contents of the rows, so be smart with your indexes and only index what you need and design towards the end of needing as few indexes as possible, because those are how you reduce that overhead that your database has to work through to fulfill a particular request or query.Shane: So my advice, at least for an IoT company, is to consider your real-time use cases and your historical use cases and consider them separately. What we found out is we treated everything the same. Everything went through the same pipeline, and it was just queries that were different. When you're using TimescaleDB—and really any architecture—it's extremely useful to consider those use cases separately and architect them separately. They have different requirements. If you try to shoehorn all data, in real time, into any time-series database, whether it's something awesome like TimescaleDB or something else, it's going to cause problems and it's not going to scale very well. You can do a lot more by separating historical and real-time use cases.We’d like to thank Shane, Brock, and all the folks at Density for sharing their experience with TimescaleDB in measuring and optimizing real estate usage.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/density-measures-large-real-estate-portfolios-using-timescaledb/,321,"[0.025152739137411118, 0.02138984389603138, 0.06369738280773163, 0.021418457850813866, 0.07039333134889603, 0.011331609450280666, -0.0060056098736822605, 0.003242457052692771, -0.02516704611480236, -0.005776688456535339, 0.019014783203601837, -0.012612138874828815, 0.03508220613002777, -0.028529329225420952, 0.03359421715140343, -0.0001592836924828589, 0.00802655704319477, -0.030503777787089348, -0.03024624101817608, 0.04215015470981598, -0.0011463954579085112, 0.03711388260126114, 0.012798137031495571, -0.0026808842085301876, 0.005537036340683699, -0.023407213389873505, -0.0008115085074678063, 0.0655287504196167, 0.06507091224193573, -0.04114862158894539, -0.003145880764350295, 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 How METER Group Brings a Data-Driven Approach to the Cannabis Production Industry,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition,Paolo Bergantino, Director of Software for the Horticulture business unit at METER Group, joins us to share how they make data accessible to their customers so that they can maximize their cannabis yield and increase efficiency and consistency between grows.AROYAis the leading cannabis production platform servicing the U.S. market today. AROYA is part ofMETER Group, a scientific instrumentation company with 30+ years of expertise in developing sensors for the agriculture and food industries. We have taken this technical expertise and applied it to the cannabis market, developing a platform that allows growers to grow more efficiently and increase their yields – and to do so consistently and at scale.About the teamMy name isPaolo Bergantino. I have about 15 years of experience developing web applications in various stacks, and I have spent the last four here at METER Group. Currently, I am the Director of Software for the Horticulture business unit, which is in charge of the development and infrastructure of the AROYA software platform. My direct team consists of about ten engineers, 3 QA engineers, and a UI/UX Designer. (We’re also hiring!)About the projectAROYA is built as a React Single-Page App (SPA) that communicates with a Django/DRF back-end. In addition to usingTimescale Cloudfor our database, we use AWS services such as EC2+ELB for our app and workers,ElastiCache for Redis,S3for various tasks,AWS IoT/SQSfor handling packets from our sensors, and some other services here and there.✨Editor’s Note:""Timescale Cloud"" is known as ""Managed Service for TimescaleDB"" as of September 2021.As I previously mentioned, AROYA was born out of our desire to build a system that leveraged our superior sensor technology in an industry that needed such a system. Cannabis worked out great in this respect, as the current legalization movement throughout the U.S. has resulted in a lot of disruption in the space.The more we spoke to growers, the more we were struck by how much mythology there was in growing cannabis and by how little science was being applied by relatively large operations. As a company with deeply scientific roots, we found it to be a perfect match",https://www.timescale.com/blog/how-meter-group-brings-a-data-driven-approach-to-the-cannabis-production-industry/,501,"[0.0038191722705960274, -0.0066579002887010574, 0.009660035371780396, 0.027026819065213203, 0.026981215924024582, 0.015337491407990456, -0.025962769985198975, 0.026662001386284828, -0.015170283615589142, 0.014934672974050045, -0.00781315192580223, -0.058127421885728836, 0.01196293905377388, -0.022238602861762047, 0.00948522798717022, -0.003885675221681595, 0.0109140919521451, -0.022375408560037613, -0.06414689123630524, 0.026966014876961708, 0.017389584332704544, 0.022086596116423607, 0.030386170372366905, -0.007676346227526665, -0.023409055545926094, -0.04514603689312935, -0.05466166511178017, 0.06198839470744133, -0.0029945350252091885, -0.050770290195941925, -0.008421179838478565, -0.03690726310014725, 0.0031655426137149334, 0.011347311548888683, 0.014045433141291142, -0.03523518890142441, 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 How METER Group Brings a Data-Driven Approach to the Cannabis Production Industry,"and an area where we could bring some of our knowledge to the forefront.We ultimately believe the only survivors in the space are those who can use data-driven approaches to their cultivation to maximize their yield and increase efficiency and consistency between grows.As part of the AROYA platform, we developed a wireless module (called a “nose”) that could be attached to our sensors. Using Bluetooth Low Energy (BLE) for low power consumption and attaching a solar panel to take advantage of the lights in a grow room, the module can run indefinitely without charging.The AROYA nose in its natural habitat (aroya.ioInstagram)The most critical sensor we attach to this nose is called the TEROS 12, the three-pronged sensor pictured below. It can be installed into any growing medium (like rockwool, coconut coir, soil, or mixes like perlite, pumice, or peat moss) and give insights into the temperature, water content (WC), and electrical conductivity (EC) of the medium. Without getting too into the weeds (pardon the pun), WC and EC, in particular, are crucial in helping growers make informed irrigation decisions that will steer the plants into the right state and ultimately maximize their yield potential.The AROYA nose with a connected TEROS 12 sensor (aroya.ioInstagram)We also have an ATMOS 14 sensor for measuring the climate in the rooms anda whole suite of sensorsfor other use cases.An AROYA repeater with an ATMOS 14 sensor for measuring the climate (aroya.ioInstagram)AROYA’s core competency is collecting this data - e.g., EC, WC, soil temp, air temperature, etc. - and serving it to our clients in real-time (or, at least “real-time” for our purposes, as our typical sampling interval is 3 minutes).Growers typically split their growing rooms into irrigation zones. We encourage them to install statistically significant amounts of sensors into each room and its zones, so that AROYA gives them good and actionable feedback on the state of their room. For example, there’s a concept in cultivation called ""crop steering"" that basically says that if you stress the plant in just the right way, you can ""steer"" it into generative or vegetative states at will and drive it to squeeze every last bit of flower. How and when you do this is crucial to doing it properly.Our data allows growers to dial in their irrigation strategy, so they can hit",https://www.timescale.com/blog/how-meter-group-brings-a-data-driven-approach-to-the-cannabis-production-industry/,504,"[0.01867528259754181, 0.0229243915528059, 0.0007860295590944588, 0.04272880777716637, 0.022448968142271042, 0.029149485751986504, -0.00904792919754982, 0.002533123129978776, -0.01099419966340065, 0.010073063895106316, -0.014968453906476498, -0.045403074473142624, 0.008371934294700623, -0.05966581776738167, -0.012361045926809311, -0.021616972982883453, 0.04843390733003616, -0.010154778137803078, -0.03075404465198517, 0.03824198618531227, 0.024826090782880783, 0.04064882546663284, 0.024142667651176453, -0.03244774788618088, 0.0045796786434948444, -0.0232512466609478, -0.009961636736989021, 0.04483850672841072, 0.01358675118535757, -0.020250126719474792, 0.0013092755107209086, -0.03978711739182472, -0.00774051109328866, 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 How METER Group Brings a Data-Driven Approach to the Cannabis Production Industry,"their target ""dryback"" for the plant (this is more or less the difference between the water content at the end of irrigation until the next irrigation event). Optimizing dryback is one of the biggest factors in making crop steering work, and it's basically impossible to do well without good data. (We provide lots of other data that helps growers make decisions, but this is one of the most important ones.)Graph showing electrical conductivity (EC) and water content (WC) data related to a room in AROYA.This can be even more important when multiple cultivars (“strains”) of cannabis are grown in the same room, as the differences between two cultivars regarding their needs and expectations can be pretty dramatic. For those unfamiliar with the field, an example might be that different cultivars ""drink"" water differently, and thus must be irrigated differently to achieve maximum yields. There are also ""stretchy"" cultivars that grow taller faster than ""stocky"" ones, and this also affects how they interact with the environment. AROYA not only helps in terms of sensing, but in documenting and helping understand these differences to improve future runs.The most important thing from collecting all this data is making it accessible to users via graphs and visualizations in an intuitive, reliable, and accurate way, so they can make informed decisions about their cultivation.We also have alerts and other logic that we apply to incoming data. These visualizations and business logic can happen at the sensor level, at the zone level, at the room level, or sometimes even at the facility level.A typical use case with AROYA might be that a user logs in to their dashboard to view sensor data for a room. Initially, they view charts aggregated to the zone level, but they may decide to dig deeper into a particular zone and view the individual sensors that make up that zone. Or, vice versa, they may want to pull out and view data averaged all the way up to the room. So, as we designed our solution, we needed to ensure we could get to (and provide) the data at the right aggregation level quickly.Choosing and using TimescaleDBThe initial solutionDuring the days of our closed alpha and beta of AROYA with early trial accounts (late 2017 through our official launch December 2019), the amount of data",https://www.timescale.com/blog/how-meter-group-brings-a-data-driven-approach-to-the-cannabis-production-industry/,470,"[0.007557332515716553, 0.022263702005147934, 0.029705479741096497, 0.01754903607070446, 0.052724145352840424, 0.032447997480630875, 0.0006755184731446207, -0.039905183017253876, -0.019397923722863197, 0.025745775550603867, -0.008266073651611805, -0.022510221228003502, -0.0032336292788386345, -0.044219255447387695, 0.023419257253408432, -0.02241777628660202, 0.03599170222878456, -0.014791109599173069, -0.05901036411523819, 0.01506073959171772, 0.01856592483818531, 0.028673183172941208, 0.03002903424203396, -0.024405330419540405, 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@@ -87,12 +87,12 @@ How METER Group Brings a Data-Driven Approach to the Cannabis Production Industr
 How METER Group Brings a Data-Driven Approach to the Cannabis Production Industry,"thus: first, after consulting with Timescale staff, we were alerted we had way too many bucket sizes. We could useTimescaleDB’s time_bucket functionsto do some of this on the fly without affecting performance or keeping as many continuous aggregates. That was an easy win.Next, we split each of our current continuous aggregates into three separate components:First, we kept the original continuous aggregate.Then, we leveraged theTimescaleDB job schedulerto move and compress chunks from the original continuous aggregate into ahypertablefor that specific bucket/aggregation view.Finally, we created a plain old view that UNIONed the two and made it a transparent change for our application.This allowed us to compress everything but the last week of all of our continuous aggregates, and the results were as good as we could have hoped for.The 1.83TB database was compressed into 700 GB.We were able to take our ~1.83 TB database and compress it down to 700 GB. Not only that, about 300 GB of that is log data that’s unrelated to our main reading pipeline.We will be migrating out this data soon, which gives us a vast amount of room to grow. (We think we can even move back the 1TB plan at this point, but have to test to ensure that compute doesn’t become an issue.) The rate of incrementation in disk usage was also massively slowed, which bodes well for this solution in the long term. What’s more, there was virtually no penalty for doing this in terms of performance for any of the metrics we monitor.Our monitoring shows how long sampling of chart requests takes to serve.Ultimately TimescaleDB had wins across the board for my team.Performance was going to be the driving force behind whatever we went with, and TimescaleDB has delivered that in spades.Current deployment & future plansWe currently ingest billions of readings every month using TimescaleDB and couldn’t be happier.Our data ingest and charting capabilities are two of the essential aspects of AROYA’s infrastructure.While the road to get here has been a huge learning experience, our current infrastructure is straightforward and performant, and we’ve been able to rely on it to work as expected and to do the right thing. I am not sure I can pay a bigger compliment than that.The current architecture diagramWe’ve recently gone live with our AROYA Analytics release, which is building upon",https://www.timescale.com/blog/how-meter-group-brings-a-data-driven-approach-to-the-cannabis-production-industry/,485,"[0.0026104156859219074, -0.005973449442535639, 0.044777125120162964, 0.02002110332250595, 0.049599725753068924, 0.03729478642344475, -0.008373790420591831, 0.004325727466493845, -0.02216935344040394, 0.014109763316810131, 0.03492732718586922, -0.022768525406718254, 0.007781925145536661, -0.021570181474089622, 0.04685230553150177, 0.021365586668252945, 0.004117478616535664, 0.00012045086623402312, -0.02425914816558361, 0.01800437830388546, -0.007949985563755035, 0.007665013894438744, 0.056906700134277344, 0.0022615077905356884, -0.023338468745350838, -0.03226758912205696, -0.0342843122780323, 0.04398797079920769, -0.003967685624957085, -0.10025165975093842, -0.0031821862794458866, -0.04322804510593414, 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 How METER Group Brings a Data-Driven Approach to the Cannabis Production Industry,"what we’ve done to deliver deeper insights into the environment and the operations at the facilities using our service. Every step of the way, it’s been straightforward (and performant!) to calculate the metrics we need with our TimescaleDB setup.Getting started advice & resourcesI think it’s worth mentioning that there were many trade-offs and requirements that guided me to where AROYA is today with our use of TimescaleDB. Ultimately, my story is simply the set of decisions that led me to where we are now and people’s mileage may vary depending on their requirements.I am sure that the set of functionality offered means that, with a little bit of creativity, TimescaleDB can work for just about any time-series use case I can think of.The exercise we went through when iterating from our initial non-Timescale solution to Timescale was crucial to get me to be comfortable with that migration. Moving such a critical part of my infrastructure was scary, and it isstillscary.Monitoring everything you can, having redundancies, and being vigilant about any unexpected activity - even if it’s not something that may trigger an error - has helped us stay out of trouble.We have a bigGrafanadashboard on a TV in our office that displays various metrics and multiple times we’ve seen something odd and uncovered an issue that could have festered into something much more if we hadn’t dug into it right away. Finally, diligent load testing of the infrastructure and staging runs of any significant modifications have made our deployments a lot less stressful, since they instill quite a bit of confidence.✨ Editor’s Note:Check outGrafana 101 video seriesandGrafana tutorialsto learn everything from building awesome, interactive visualizations to setting up custom alerts, sharing dashboards with teammates, and solving common issues.I would like to give a big shout-out to Neil Parker, who is my right-hand man in anything relating to AROYA infrastructure and did virtually all of the actual work in getting many of these things set up and running. I would also like to thankMike FreedmanandPriscila Fletcherfrom Timescale, who have given us a great bit of time and information and helped us in our journey with TimescaleDB.We’d like to give a big thank you to Paolo and everyone at AROYA for sharing their story, as well as for their efforts to help transform the cannabis production",https://www.timescale.com/blog/how-meter-group-brings-a-data-driven-approach-to-the-cannabis-production-industry/,485,"[0.010616706684231758, -0.009144041687250137, 0.03083578310906887, 0.005909440573304892, 0.04529193416237831, 0.023427382111549377, -0.020512107759714127, 0.007784082088619471, -0.02733444981276989, 0.023983387276530266, 0.0236978717148304, -0.04916894808411598, 0.021969744935631752, -0.04384932667016983, 0.03357073292136192, 0.0024832424242049456, 0.007449727039784193, -0.019069498404860497, -0.027319421991705894, 0.04919900372624397, 0.01095481775701046, 0.027379531413316727, 0.037898559123277664, 0.002526445547118783, 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 How METER Group Brings a Data-Driven Approach to the Cannabis Production Industry,"industry, equipping growers with the data they need to improve their crops, make informed decisions, and beyond.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Lucie Šimečková), and we’ll go from there.Additionally, if you’re looking for more ways to get involved and show your expertise, check out theTimescale Heroesprogram.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/how-meter-group-brings-a-data-driven-approach-to-the-cannabis-production-industry/,109,"[-0.010468360967934132, 0.004885013215243816, 0.05078551545739174, 0.00627503078430891, 0.033041179180145264, -0.011419425718486309, 0.0051709976978600025, 0.02567209117114544, -0.018449321389198303, -0.0034417896531522274, 0.0034850197844207287, -0.024395138025283813, 0.004602354485541582, -0.02705545909702778, 0.044932812452316284, 0.016640303656458855, 0.01487119123339653, -0.011672155931591988, -0.02373005822300911, 0.08906752616167068, -0.005480260122567415, 0.04038366302847862, 0.04235230013728142, 0.005892609711736441, 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-How Flowkey Scaled Its AWS Redshift Data Warehouse With Timescale,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Nikola Chochkov, lead data scientist atflowkey, shares how his team migrated from Amazon Redshift to TimescaleDB and is driving rapid growth and experimentation by analyzing the users’ behavioral data using TimescaleDB along with Metabase or Jupyter/Rmarkedown Notebooks.About the CompanyFlowkeyis a leading app for learning to play the piano, with over 10 million registered users in more than 100 countries. The company was launched in 2015 and quickly became one of the global leaders in its category.Here is a video from our founder, Jonas Gößling, explaining how it all started.About the TeamWe are a team of around 40 people, with more than 10 of us working in Data and Engineering.We have a Marketing team (responsible for user acquisition, customer relationship management, collaborations, etc.), a Creative team (building all of our visual content, our design, and advertising), Course and Song teams (creating our in-app learning content—e.g., the courses series and the piano renditions of the songs in our library). We also have Customer Support, Product, Engineering, Data, and Operations teams.Many of us take on more than one role, and for many of us, flowkey has been the first significant career step. Not for me personally, though, but still.🙂About the ProjectThe flowkey appWe are a business that depends heavily on analytics for business decision-making. Our experimentation—a major driver of our growth—is powered by the data analysis of user behavioral data (app usage). This data consists of user events, which we track from our product.“We realized we needed to scale our previous Amazon Redshift data warehouse model into a more suitable solution for categorical, time-series data analysis. We found out about TimescaleDB from being part of the PostgreSQL community”For example, a Learn Session starts at a given timestamp for a user, and we record this event.✨Editor’s Note:Time-series data is a sequence of data points collected over time intervals, allowing us to track changes over time. To learn more, readWhat Is Time-Series Data (With Examples).When we launch a new feature, we typically A/B test it and evaluate its impact based on measuring key performance indicators (KPIs), which are predefined for",https://www.timescale.com/blog/how-flowkey-solved-its-time-series-data-problem-by-migrating-from-aws-redshift-to-timescale/,509,"[0.0032796163577586412, -0.0008828436839394271, 0.05047331377863884, 0.0013770671794191003, 0.07320928573608398, -0.007718334905803204, -0.02666948363184929, 0.04904048889875412, -0.054933998733758926, 0.003289754269644618, 0.013963292352855206, -0.031035546213388443, -0.027980653569102287, -0.015382600948214531, 0.051554691046476364, 0.045147527009248734, -0.026993896812200546, -0.03560436889529228, -0.05417703464627266, 0.03309016302227974, -0.01599087566137314, -0.013726741075515747, 0.02721017226576805, 0.04587745666503906, 0.0064950259402394295, -0.036550574004650116, -0.046147800981998444, 0.04344435781240463, -0.0009445160394534469, 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+How Flowkey Scaled Its AWS Redshift Data Warehouse With Timescale,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Nikola Chochkov, lead data scientist atflowkey, shares how his team migrated from Amazon Redshift to TimescaleDB and is driving rapid growth and experimentation by analyzing the users’ behavioral data using TimescaleDB along with Metabase or Jupyter/Rmarkedown Notebooks.About the CompanyFlowkeyis a leading app for learning to play the piano, with over 10 million registered users in more than 100 countries. The company was launched in 2015 and quickly became one of the global leaders in its category.Here is a video from our founder, Jonas Gößling, explaining how it all started.About the TeamWe are a team of around 40 people, with more than 10 of us working in Data and Engineering.We have a Marketing team (responsible for user acquisition, customer relationship management, collaborations, etc.), a Creative team (building all of our visual content, our design, and advertising), Course and Song teams (creating our in-app learning content—e.g., the courses series and the piano renditions of the songs in our library). We also have Customer Support, Product, Engineering, Data, and Operations teams.Many of us take on more than one role, and for many of us, flowkey has been the first significant career step. Not for me personally, though, but still.🙂About the ProjectThe flowkey appWe are a business that depends heavily on analytics for business decision-making. Our experimentation—a major driver of our growth—is powered by the data analysis of user behavioral data (app usage). This data consists of user events, which we track from our product.“We realized we needed to scale our previous Amazon Redshift data warehouse model into a more suitable solution for categorical, time-series data analysis. We found out about TimescaleDB from being part of the PostgreSQL community”For example, a Learn Session starts at a given timestamp for a user, and we record this event.✨Editor’s Note:Time-series data is a sequence of data points collected over time intervals, allowing us to track changes over time. To learn more, readWhat Is Time-Series Data (With Examples).When we launch a new feature, we typically A/B test it and evaluate its impact based on measuring key performance indicators (KPIs), which are predefined for",https://www.timescale.com/blog/how-flowkey-solved-its-time-series-data-problem-by-migrating-from-aws-redshift-to-timescale/,509,"[0.0024707484990358353, 0.0002519798872526735, 0.04822734370827675, -0.0013734097592532635, 0.07122978568077087, -0.0066465395502746105, -0.026641719043254852, 0.051394347101449966, -0.05575592443346977, 0.002654796000570059, 0.0144320884719491, -0.03186449781060219, -0.026725061237812042, -0.01494603231549263, 0.05044980347156525, 0.04619935154914856, -0.026975087821483612, -0.03628163039684296, -0.05611707270145416, 0.03442032262682915, -0.013550050556659698, -0.013827857561409473, 0.02964203804731369, 0.04458807036280632, 0.0067993332631886005, -0.03442032262682915, -0.04556039720773697, 0.040282055735588074, -0.003069770522415638, 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 How Flowkey Scaled Its AWS Redshift Data Warehouse With Timescale,"the test. Every day, we receive millions of incoming events, tracked around our product, in the format:(user_id, event, timestamp, properties)Thepropertiesfield is a schemaless object that depends on the particular event type that we track from the app. For example, a Learn Session event would have asongIdand alearnMode, while a Subscription Offer interaction event would have aproductId, etc.Choosing (and Using!) TimescaleDBWith time, we realized we needed to scale our previous Amazon Redshift data warehouse model into a more suitable solution for categorical, time-series data analysis.We found out about TimescaleDB from being part of the PostgreSQL community, and when we were faced with the problem at hand, it was a natural way forward for us.After doing our research, we realized that TimescaleDB suited our needs perfectly. Here's a list of our arguments:Our data analysts are well-versed in SQL and PostgreSQL.The events’ raw data is available in a PostgreSQL schema alongside all our other business intelligence data.TimescaleDB is an actively developed, open-source solution. It allowed us to deploy it on our self-hosted PostgreSQL data warehouse.A TimescaleDB hypertable model would allow us to accommodate the schemaless JSON structure of our events.select event, platform, time, jsonb_pretty(data) from events_today limit 5; event | platform | time | jsonb_pretty ------------------------------+----------+-------------------------+--------------------------------------------------------- SONG_OPEN_UI_ELEMENT_CLICKED | ios | 2022-11-03 00:00:00.034 | { + | | | ""songId"": ""E2kCpqHCwB2xYf7LL"", + | | | ""context"": ""song-cover"", + | | | ""listIndex"": 6, + | | | ""routeName"": ""Songs"", + | | | ""currentTab"": ""SongsTab"" + | | | } ONBOARDING_QUESTION_VIEWED | web | 2022-11-03 00:00:00.145 | { + | | | ""context"": ""preferredCategories"" + | | | } SONG_PLAYER_SCROLLED | ios | 2022-11-03 00:00:00.157 | { + | | | ""level"": 1, + | | | ""songId"": ""Lui27TDJ4vZBevxzc"", + | | | ""direction"": ""backwards"", + | | | ""songGroupId"": ""vhisAJBkPwvn6tdoq"", + | | | ""loopBoundsMs"": null, + | | | ""finalPositionMs"": 0, + | | | ""initialPositionMs"": 24751 + | | | } AB_TEST_VARIANT_ASSIGNED | ios | 2022-11-03 00:00:00.249 | { + | | | ""variant"": ""CONTROL"", + | | | ""experimentName"": ""player_onboarding_video_09_2022""+ | | | } ONBOARDING_ANSWER_SUBMITTED | web | 2022-11-03 00:00:00.314 | { + | | | ""answers"": [ + | | | ""dont-know"" + | | | ], + | | | ""context"": ""learningGoals"" + | | | }TimescaleDB offers a great set of",https://www.timescale.com/blog/how-flowkey-solved-its-time-series-data-problem-by-migrating-from-aws-redshift-to-timescale/,697,"[-0.02763054333627224, -0.018888676539063454, 0.04271594062447548, -0.011615613475441933, 0.055743589997291565, 0.01605040766298771, -0.005672988947480917, -0.02450844831764698, -0.013950088992714882, 0.020832890644669533, 0.04527037963271141, -0.0540122464299202, 0.006460608448833227, 0.01647614873945713, 0.03306582570075989, 0.03882751241326332, 0.0063790082931518555, 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 How Flowkey Scaled Its AWS Redshift Data Warehouse With Timescale,"SQL analytical functions.TimescaleDB offers continuous aggregates, which integrate very well with how we do analytics and real-time data monitoring.Data migration and update (e.g., renaming of events or JSON properties) are available.✨Editor’s Note:Faster queries, reduced storage costs, and greater flexibility. Learn more abouthierarchical continuous aggregates.“We use compression, which has cut our disk space usage by 28 percent”Current Deployment & Future PlansOur data warehouse is deployed on self-hosted machines and works well for us. We employ other PostgreSQL extensions that aren't currently supported by the Timescale cloud offering, which was important to us when we launched. These includeMongo FDWandAdjust’siStore extensionfor cohort analysis data storage.✨Editor's Note:We're working on expanding the catalog of PostgreSQL extensions offered in Timescale's cloud offering. Stay tuned!We employ TimescaleDB's awesome data retention (automated through a user action), and thanks to that, our most recent (and more relevant to our analytics) data is available to us on SSD chunks, while historical data is kept on HDDs.Furthermore, we use compression, which has cut our disk space usage by 28 percent. Our data contains JSONB fields, which are difficult to be compressed. We are pretty happy with it, though, so it's a win. 🙂When we do business analytics, we employMetabaseorJupyter/Rmarkdown Notebooksto derive insights. We established a workflow of writing custom continuous aggregates for the duration of experiments, which are then easy to keep and fully deploy or discard, depending on the decision made for the experiment.✨Editor's Note:Learn how toconnect to Timescale from a Jupyter notebookfor better data querying, cleaning, and analysis.This allows us to iterate our experiments quickly and increase the bandwidth of change, which we can successfully bring to the product.RoadmapWe just finished migrating our setup to a more powerful cluster of machines, which allowed us to benefit from the data tiering options mentioned above. Right now, our system is scalable, and we don't expect any major upgrades to this system to come up soon.Advice & ResourcesWe recommend theTimescale documentationas well as theSlack Community.Want more insider tips?Sign up for our newsletterfor more Developer Q&As, technical articles, and tutorials to help you do more with your data. We deliver it straight to your inbox twice a month.We’d like to thank Nikola and all of the folks at flowkey for sharing their story on how they’re improving their online piano lessons by analyzing millions of user events",https://www.timescale.com/blog/how-flowkey-solved-its-time-series-data-problem-by-migrating-from-aws-redshift-to-timescale/,528,"[0.002735680667683482, 0.014756875112652779, 0.05985059216618538, 0.008730661123991013, 0.04332994669675827, 0.015241911634802818, 0.0013072106521576643, 0.027529507875442505, -0.01203772984445095, 0.016197286546230316, 0.027632394805550575, -0.036627620458602905, -0.0046115233562886715, -0.025001438334584236, 0.06143798306584358, 0.07354920357465744, -0.02112114429473877, -0.008311765268445015, -0.02504553273320198, 0.024001969024538994, -0.011890748515725136, 0.009671337902545929, 0.013779452070593834, 0.031542085111141205, 0.00039133644895628095, -0.033188268542289734, -0.028146827593445778, 0.046504732221364975, 0.015873929485678673, 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 How Flowkey Scaled Its AWS Redshift Data Warehouse With Timescale,"daily using TimescaleDB.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/how-flowkey-solved-its-time-series-data-problem-by-migrating-from-aws-redshift-to-timescale/,64,"[0.01494413148611784, -0.009204450994729996, 0.06446197628974915, 0.010289503261446953, 0.048999983817338943, 0.025375431403517723, 0.012884997762739658, 0.016584040597081184, -0.035436827689409256, -0.02125716395676136, -0.007607697509229183, -0.04626268893480301, 0.009562024846673012, -0.01695394515991211, 0.0627850815653801, 0.013439854606986046, 0.0038439217023551464, -0.004216908477246761, -0.010234017856419086, 0.06539907306432724, -0.0019866940565407276, 0.011035476811230183, 0.008920857682824135, 0.029641659930348396, 0.008464641869068146, -0.02559737302362919, -0.024302707985043526, 0.03647255897521973, 0.038149457424879074, -0.05711321532726288, 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 How United Manufacturing Hub Is Introducing Open Source to Manufacturing and Using Time-Series Data for Predictive Maintenance,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, we speak with Jeremy Theocharis, co-founder and CTO ofUnited Manufacturing Hub, about how they are bringing open source to the world of manufacturing by combining information and operational tools and technologies in an open-source Helm chart for Kubernetes.The team uses TimescaleDB to store both relational and time-series data coming fromMQTTandKafkaand then visualizes it usingGrafanaand their own REST API. With the data, they can prevent and predict maintenance issues, analyze and optimize production losses such as changeovers or micro-stops and reduce resource consumption, and much more.About the CompanyWe believe that open source is the future: we live in a world where 100 percent of all supercomputers useLinux, 95 percent of all public cloud providers useKubernetes, and the Mars Helicopter, Ingenuity, usesf-prime.We are confident that open source will also find its place in manufacturing, and we were among the first to use it in this field, making us the experts.Our story goes back to 2016, when we had the pain of integrating and maintaining various costly Industrial IoT solutions (IIoT). Existing vendors focused only on the end result, which resulted in large-scale IIoT projects failing because they did not address the real challenges.After suffering for years, in 2021, we were fed up and decided to do something about it. Since then, all our products and services have been focused on efficiently integrating and operating large-scale IIoT infrastructures.About the TeamWe are a team of 11 people with different backgrounds, from mechanical engineering to business administration to cybersecurity.Me, personally? I am an IT nerd that learned programming at 14 and then studied Mechanical Engineering and Business Administration atRWTH Aachenin Germany. I did my internship first as a technical project manager in theDigital Capability Center Aachen, where I was responsible for the technical integration of various Industrial IoT solutions. I later did another internship at McKinsey & Company in Singapore and decided I needed a solid technical part in my future profession.I started my own business as a system integrator in the Industrial IoT sector, metChristianandAlex, and founded theUMH Systems GmbHtogether in 2021.About the ProjectUnited Manufacturing Hub's architectureTheUnited Manufacturing Hub(UMH)",https://www.timescale.com/blog/how-united-manufacturing-hub-is-introducing-open-source-to-manufacturing-and-using-time-series-data-for-predictive-maintenance/,508,"[-0.008383229374885559, 0.0046215238980948925, 0.050987228751182556, 0.012997588142752647, 0.03582576662302017, -0.0028015747666358948, -0.028660612180829048, 0.06414245069026947, -0.023258086293935776, -0.009379185736179352, 0.02032037451863289, -0.042847614735364914, -0.03854852542281151, -0.022068671882152557, 0.037287455052137375, 0.027313563972711563, -0.03447871655225754, -0.04720402881503105, -0.06259477883577347, 0.03393416479229927, 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-How United Manufacturing Hub Is Introducing Open Source to Manufacturing and Using Time-Series Data for Predictive Maintenance,"is an open-source Helm chart for Kubernetes. It combines state-of-the-art information and operational tools (IT/OT) and technologies, bringing them into the engineer's hands.I assume most of the readers here will come from traditional IT, so let me explain the world of manufacturing, especially OT, first, as it will help you understand our usage of TimescaleDB.OT means Operational Technology. OT is the hardware and software to manage, monitor, and control industrial operations like production machines. Due to different requirements, OT has its own ecosystems.OT comes originally from the field of electronics, and this background is still evident today. For example, the computer that controls the production machine is called aPLC (Programmable Logic Controller). It runs some peculiar flavors of Windows and Linux, which are completely hidden from the system's programmer. The programmer of the PLC will use programming languages likeladder logic, which is like drawing electrical schematics.Because OT is an entirely different world, it is pretty hard to integrate it with the traditional IT world. During system integration, we felt all these pains and decided to develop a tool that allows an easy combination between both fields—the United Manufacturing Hub (UMH).With UMH, one can now easily extract data from the shopfloor, from the PLC tovarious IO-link compatible or analog (4-20mA / 0-10V) sensorstobarcodereaderand different manufacturing execution or enterprise resource planning systems. Using aUnified Namespacebased onMQTTandKafka, the data is aggregated and can then be contextualized through tools likeNode-RED.From there on, the processed data is stored automatically in a TimescaleDB running either in the cloud or on-premise. To visualize the data, we useGrafanawith our ownREST APIfor manufacturing specific logics (also calledfactoryinsight) and our own Grafana data source.Choosing (and Using!) TimescaleDBManufacturing data is mainly relational: orders, products, production plans, and shifts are good examples of this. However, due to the growth of analytics, time-series data gets more and more important, e.g., for preventive or predictive maintenance.✨Editor's Note:Want to learn more about time-series forecasting?Check out this blog post.During one of those earlier system integrator projects, I realized that we needed a time-series database and a relational one.Due to the strong marketing, we chose InfluxDB at first. We did not scan vendors; we just started with whatever we knew from home automation. It sounded perfect: a beautiful user interface, continuous queries to process data, etc.We wanted to process raw sensor",https://www.timescale.com/blog/how-united-manufacturing-hub-is-introducing-open-source-to-manufacturing-and-using-time-series-data-for-predictive-maintenance/,541,"[-0.03820886090397835, 0.022254934534430504, 0.041229892522096634, -0.016644448041915894, 0.0442221537232399, 0.004768914543092251, -0.016644448041915894, 0.005811890121549368, -0.020571788772940636, 0.0017775535816326737, 0.04845159873366356, -0.019924424588680267, -0.004722160752862692, -0.02347773313522339, 0.06272237747907639, 0.029016291722655296, -0.013659380376338959, -0.022700896486639977, -0.04873931407928467, 0.05155894532799721, 0.0005677921581082046, 0.021665114909410477, 0.014774284325540066, 0.007081442512571812, -0.024053167551755905, -0.016371116042137146, -0.013551485724747181, 0.048365283757448196, -0.014378673397004604, -0.04819265007972717, -0.001619309070520103, -0.05020667240023613, 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+How United Manufacturing Hub Is Introducing Open Source to Manufacturing and Using Time-Series Data for Predictive Maintenance,"is an open-source Helm chart for Kubernetes. It combines state-of-the-art information and operational tools (IT/OT) and technologies, bringing them into the engineer's hands.I assume most of the readers here will come from traditional IT, so let me explain the world of manufacturing, especially OT, first, as it will help you understand our usage of TimescaleDB.OT means Operational Technology. OT is the hardware and software to manage, monitor, and control industrial operations like production machines. Due to different requirements, OT has its own ecosystems.OT comes originally from the field of electronics, and this background is still evident today. For example, the computer that controls the production machine is called aPLC (Programmable Logic Controller). It runs some peculiar flavors of Windows and Linux, which are completely hidden from the system's programmer. The programmer of the PLC will use programming languages likeladder logic, which is like drawing electrical schematics.Because OT is an entirely different world, it is pretty hard to integrate it with the traditional IT world. During system integration, we felt all these pains and decided to develop a tool that allows an easy combination between both fields—the United Manufacturing Hub (UMH).With UMH, one can now easily extract data from the shopfloor, from the PLC tovarious IO-link compatible or analog (4-20mA / 0-10V) sensorstobarcodereaderand different manufacturing execution or enterprise resource planning systems. Using aUnified Namespacebased onMQTTandKafka, the data is aggregated and can then be contextualized through tools likeNode-RED.From there on, the processed data is stored automatically in a TimescaleDB running either in the cloud or on-premise. To visualize the data, we useGrafanawith our ownREST APIfor manufacturing specific logics (also calledfactoryinsight) and our own Grafana data source.Choosing (and Using!) TimescaleDBManufacturing data is mainly relational: orders, products, production plans, and shifts are good examples of this. However, due to the growth of analytics, time-series data gets more and more important, e.g., for preventive or predictive maintenance.✨Editor's Note:Want to learn more about time-series forecasting?Check out this blog post.During one of those earlier system integrator projects, I realized that we needed a time-series database and a relational one.Due to the strong marketing, we chose InfluxDB at first. We did not scan vendors; we just started with whatever we knew from home automation. It sounded perfect: a beautiful user interface, continuous queries to process data, etc.We wanted to process raw sensor",https://www.timescale.com/blog/how-united-manufacturing-hub-is-introducing-open-source-to-manufacturing-and-using-time-series-data-for-predictive-maintenance/,541,"[-0.038243841379880905, 0.022256191819906235, 0.041148073971271515, -0.016634633764624596, 0.04413856938481331, 0.004744536709040403, -0.01664901152253151, 0.005801274441182613, -0.020602792501449585, 0.0017567366594448686, 0.04848054051399231, -0.019984563812613487, -0.004733753856271505, -0.02349264547228813, 0.06268540024757385, 0.028984807431697845, -0.013644138351082802, -0.02271626703441143, -0.04871057718992233, 0.051586054265499115, 0.0005643123295158148, 0.021666718646883965, 0.014837460592389107, 0.007084456272423267, -0.024096496403217316, -0.01636146381497383, -0.013593817129731178, 0.0483655221760273, -0.01437019556760788, -0.04819299280643463, -0.0016138614155352116, -0.05020582675933838, 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 How United Manufacturing Hub Is Introducing Open Source to Manufacturing and Using Time-Series Data for Predictive Maintenance,"data, e.g., converting the distance of a light barrier into the machine status (running/not running). We also needed to store shifts, orders, and products and model the data. We did that via InfluxDB as well.The project was a nightmare. To be fair, InfluxDB was not its main driver, but it definitely was in the top five. Modeling relational data into a time-series database is a bad idea. The continuous queries were failing too often without even throwing error messages. The system could not handle the data buffered somewhere in the system and arrived late.“The stability of TimescaleDB allows us to focus on developing our microservices instead of running around fixing breaking API changes”Additionally, Flux as a query language is comparatively new and not as easy to work with as SQL. It quickly reached the point where we had to implement Python scripts to process data because Flux had reached its limits in use cases that would work seamlessly using SQL. So we felt like InfluxDB was putting unnecessary obstacles in our way.We even wrote a blog article aboutwhy we chose TimescaleDB over InfluxDB for the field of Industrial IoT.One of the main factors for us to use TimescaleDB as our database is the reliability and fault tolerance [the ability of a system to continue operating properly in case of failure] it offers to our stack. Since PostgreSQL has been in development for over 25 years, it is already very robust.“The reliability also manifests in the ease of horizontal scaling across multiple servers, which we are very interested in”The stability of TimescaleDB allows us to focus on developing our microservices instead of running around fixing breaking API changes, which newer, less stable databases like InfluxDB have shown to bring forth.The reliability also manifests in the ease of horizontal scaling across multiple servers, which we are very interested in.Being based on SQL was also a factor for us as SQL is the most well-known query language for relational databases—making working with it much easier. Almost any possible problem is already documented and solved somewhere on the Internet.Now, TimescaleDB is used in our stack as our main database to store the data coming in via MQTT/Kafka. We are storing (among others) machine states, product states, orders, worker shifts, and sensor data. 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 How United Manufacturing Hub Is Introducing Open Source to Manufacturing and Using Time-Series Data for Predictive Maintenance,"TimescaleDB didn’t exist, we probably would have to employ a PostgreSQL-based relational database system in addition to InfluxDB for time-series data. That would mean a lot of additional effort as we would have to manage two separate databases and the creation of datasets that span the two. This would also make the system more prone to errors as we would have to employ multiple querying languages.Current Deployment & Future PlansAs I mentioned, the United Manufacturing Hub is an open-source Helm chart for Kubernetes, which combines state-of-the-art IT/OT tools and technologies and brings them into the hands of the engineer.This allows us to standardize the IT/OT infrastructure across customers and makes the entire infrastructure easy to integrate and maintain.We typically deploy it on the edge and on-premise usingk3sas light Kubernetes. In the cloud, we use managed Kubernetes services likeAKS. If the customer is scaling out and okay with using the cloud, we recommend services likeTimescale.We are using TimescaleDB with MQTT, Kafka, and Grafana. We have microservices to subscribe to the messages from the message brokers MQTT and Kafka and insert the data into TimescaleDB, as well as a microservice that reads out data and processes it before sending it to a Grafana plugin, which then allows for visualization.✨Editor’s Note:Learn how you can improve your Grafana performance using downsampling in TimescaleDB.RoadmapWe are currently positioning the United Manufacturing Hub with TimescaleDB as an open-source Historian. To achieve this, we are currently developing a user interface on top of the UMH so that OT engineers can use it and IT can still maintain it.We can recommend our blog articlefor a good comparison between Historians and Open-Source databases.Furthermore, we are developing a Management Console on top of the Helm chart, which makes a lot of the typical operation tasks (monitoring, logging, changing the configuration, etc.) easily accessible for the OT engineer, reducing the workload of maintaining all the edge devices, servers, and so on for the IT person.Advice & ResourcesFor manufacturing, we recommend the previously mentioned blog articles and the official TimescaleDB documentation. For data models and data ingestions from MQTT and Kafka into TimescaleDB, we can also recommend looking at the United Manufacturing Hub source code (or using it directly).One last piece of advice: I can strongly recommend the bookDesigning Data-Intensive Applicationsby Martin Kleppmann. It really helped me",https://www.timescale.com/blog/how-united-manufacturing-hub-is-introducing-open-source-to-manufacturing-and-using-time-series-data-for-predictive-maintenance/,502,"[-0.03870213031768799, 0.011219186708331108, 0.057285062968730927, -0.019808990880846977, 0.040888357907533646, 0.017992058768868446, -0.018848823383450508, 0.021271400153636932, -0.018819279968738556, 0.0002679697936400771, 0.031168509274721146, -0.03096170537173748, 0.005018720868974924, -0.023132648319005966, 0.05291260778903961, 0.023088332265615463, -0.030016308650374413, -0.015362678095698357, -0.0499582476913929, 0.05105135962367058, -0.01366392057389021, 0.025791572406888008, 0.037402212619781494, 0.029115227982401848, -0.009284080006182194, -0.009882338345050812, -0.029927678406238556, 0.05914631113409996, -0.00420257868245244, -0.04540853202342987, 0.006706399843096733, -0.04591077193617821, -0.04174512252211571, 0.025466592982411385, 0.042808692902326584, 0.041863296180963516, 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 How United Manufacturing Hub Is Introducing Open Source to Manufacturing and Using Time-Series Data for Predictive Maintenance,"understand the fundamental principles in designing large-scale architectures so you can join discussions on the technical level. It explains the fundamental choices behind databases (from log-based approaches over WAL to binary trees) and the problems and solutions for distributed systems.We’d like to thank Jeremy Theocharis and the folks and United Manufacturing Hub for sharing their story on how they are using TimescaleDB to store their data, and why they chose us over other databases.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/how-united-manufacturing-hub-is-introducing-open-source-to-manufacturing-and-using-time-series-data-for-predictive-maintenance/,146,"[0.01799076423048973, -0.0010476289317011833, 0.07532580941915512, 0.00450739124789834, 0.03761117160320282, 0.002007955452427268, -0.01818476803600788, 0.0091053182259202, -0.059339772909879684, 0.0063375080935657024, 0.023642785847187042, -0.016956068575382233, -0.011510984040796757, -0.020577501505613327, 0.046535417437553406, 0.0005197727004997432, 0.014278794638812542, 0.02409546449780464, -0.004158181603997946, 0.01528762187808752, 0.016981935128569603, 0.02397906221449375, 0.028764527291059494, 0.041517145931720734, -0.003128336276859045, -0.028169577941298485, -0.014731473289430141, 0.06109875440597534, 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@@ -108,17 +108,17 @@ How Everactive Powers a Dense Sensor Network With Virtually No Power at All,"Ope
 How Everactive Powers a Dense Sensor Network With Virtually No Power at All,"at once.TimescaleDB is clearly much faster than our previous OpenTSDB system. More importantly, nobody has ever crashed it.One not-very-carefully-benchmarked but huge performance increase we’ve seen?We have a frontend view that requires the last data point from all sensors: in OpenTSDB, it required nearly 10minutesto load (due to hard-to-fix tail latencies in HBase), and our first TimescaleDB deployment brought that down to around 7seconds. Further improvements to our schema and access patterns have brought these queries into the sub-second range.""We moved those schemas from Amazon RDS for PostgreSQL to Timescale. The migration was very simple: moving among PostgreSQL schemas was straightforward""We have been able to maintain sub-second responses even with the growth of data volume: that's very good for us. Also, thanks to compression andcontinuous aggregates, we have been keeping our table sizes in check and with great performance.✨Editor’s Note:For more comparisons and benchmarks, seehow Timescale compares to Amazon RDS for PostgreSQL. To learn more and try Timescale yourself, see ourstep-by-step Timescale tutorial.Timescale has been so good for us that it’s triggered a wave of transitions to managed solutions for other parts of our stack.We’ve recently moved our Amazon RDS data into Timescale to further simplify our data infrastructure and make it easier and faster to work with our data.About 20 % of our data is metadata kept in a relational database. We moved those schemas from Amazon RDS for PostgreSQL to Timescale. The migration was very simple: moving among PostgreSQL schemas was straightforward.We chose RDS from the beginning for simplicity. Eventually, once we had Timescale up and running, it became evident that we didn't need two separate PostgreSQL vendors when we were having such good results with Timescale.The capacity of Timescale to support both traditional schemas and time-series data in the same database allowed us to consolidate into one storage solution. The instances multiply because we keep three environments (development, staging, and production) for each database, so we went from six (three RDS plus three Timescale) to only three Timescale instances.As you’ll see in the below diagram, our sensors don’t talk directly to TimescaleDB; they pass packets of measurements to gateways via our proprietary wireless protocol. From there, we useMQTTto send those packets to our cloud.From our cloud data brokers,Kafkaprocesses and routes packets into TimescaleDB (and Timescale), and our TimescaleDB database powers our dashboard",https://www.timescale.com/blog/how-everactive-powers-a-dense-sensor-network-with-virtually-no-power-at-all/,519,"[0.013106008060276508, -0.016448145732283592, 0.06261365115642548, -0.0038743261247873306, 0.0833902582526207, 0.030228259041905403, -0.011388815939426422, 0.014255532994866371, -0.021514572203159332, -0.004637128207832575, 0.025034107267856598, -0.02415422350168228, -0.003510664450004697, -0.03420192748308182, 0.05086294934153557, 0.013843975029885769, -0.0007011572597548366, -0.007329998072236776, -0.04467537999153137, 0.01782473921775818, -0.014773529022932053, 0.014915445819497108, 0.048166532069444656, -0.0005831889575347304, -0.02052115648984909, -0.00032685190672054887, -0.021287506446242332, 0.025672731921076775, 0.023061465471982956, -0.07203692197799683, 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 How Everactive Powers a Dense Sensor Network With Virtually No Power at All,"and analytics tools. We also added a third component: outbound channels for our platform users.Everactive architecture diagramCompared to Amazon RDS for PostgreSQL, Timescale also consolidates a lot of the costs associated with operating our instance in AWS—we now have a simpler bill that makes it easier to forecast costs.From the operation point of view, dealing with fewer instances and relying more on the Timescale Support Team for infrastructure maintenance has reduced our database maintenance workload significantly. Our security operations also benefited from the migration, again thanks to the consolidation and the transfer of certain responsibilities to Timescale.✨Editor’s Note:Read how our Support Team is raising the baron hosted database support.We’ll continue to innovate on our technology platform and increase Everactive’s product offerings, including improving our sensors’ wireless range, lowering power requirements to increase energy harvesting efficiency, integrating with additional sensors, and shrinking device form factor. These successive chip platform enhancements will allow us to monitor the condition of more and more assets, and we’re also developing a localization feature to identifywhereassets are deployed.Ultimately, Everactive’s mission is to generate new, massive datasets from a wealth of currently un-digitized physical-world assets. Transforming that data into meaningful insights has the potential to fundamentally improve the way that we live our lives—impacting how we manage our workplaces, care for our environment, interact with our communities, and manage our own personal health.Advice & ResourcesIf you’re evaluating your database options, here are two recommendations based on our experiences. First, if you have enough time-series data that a general database won’t cut it (millions of rows), TimescaleDB should be your first choice. It’s easy to try out,the docs are great, and thecommunityis very helpful.Second, don’t underestimate the importance of using solutions that leverage a wide knowledge base shared by many/most backend developers. The increase in team throughput and decrease in onboarding time afforded by TimescaleDB—everyone knows at leastsomeSQL—in contrast to OpenTSDB—an esoteric thing built on HBase—has been a huge advantage. We expected this to some degree, but actually experiencing it firsthand has confirmed its value.Additionally, the use of schema-as-code tools and an internal/external schema separation discussed above have also been cornerstones of our success. We hadn’t been using these tools and patterns at Everactive previously but have since seen them catch on in other projects and teams.Want to read more developer success",https://www.timescale.com/blog/how-everactive-powers-a-dense-sensor-network-with-virtually-no-power-at-all/,498,"[0.02805035561323166, -0.0037357453256845474, 0.06149280443787575, -0.00044209606130607426, 0.07021193206310272, 0.03355717658996582, -0.00946484412997961, -0.0039902920834720135, -0.016979357227683067, 0.024536890909075737, 0.05466664582490921, -0.027175575494766235, 0.016878971830010414, -0.028107719495892525, 0.05848126485943794, 0.057391371577978134, 0.01332965586334467, -0.018284358084201813, -0.047066088765859604, -0.013437210582196712, -0.0002545469324104488, 0.016247982159256935, 0.036884211003780365, 0.027318982407450676, 0.0073997871950268745, -0.012963968329131603, -0.046119604259729385, 0.06206643208861351, 0.0035295982379466295, -0.06252533197402954, 0.005517574027180672, -0.05191323533654213, 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 How Everactive Powers a Dense Sensor Network With Virtually No Power at All,"stories?Sign up for our newsletterfor more Developer Q&As, technical articles, tips, and tutorials to do more with your data—delivered straight to your inbox twice a month.We’d like to thank Carlos, Dan, Clayton, and all of the folks at Team Everactive for sharing their story (special shoutout to Brian, Joe, Carlos, Greg, and Elise for your contributions to this piece!). We applaud your efforts to bring sustainable, cost-effective sensors and easily actionable data to organizations around the world 🙌.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/how-everactive-powers-a-dense-sensor-network-with-virtually-no-power-at-all/,167,"[0.03067782148718834, 0.010664195753633976, 0.030035048723220825, 0.004879234824329615, 0.04049472510814667, 0.014104493893682957, -0.025360332801938057, 0.004126897547394037, -0.053525496274232864, -0.03999803587794304, 0.019253985956311226, -0.042364612221717834, 0.008670137263834476, -0.017910005524754524, 0.05504477769136429, 0.07070507854223251, 0.02804829552769661, -0.007242157123982906, -0.030210351571440697, 0.04540317878127098, 0.016624458134174347, -0.003995421342551708, 0.02134299837052822, 0.02641214430332184, 0.00701572559773922, -0.03561549261212349, -0.06778337806463242, 0.07082194089889526, 0.014959090389311314, -0.06416047364473343, 0.012534081935882568, 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-How to Reduce Query Cost With a Wide Table Layout in TimescaleDB,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Florian Herrengt, co-founder ofNocodelytics,shares how he tracks, records, and monitors millions of user interactions per day to build dazzling analytics dashboards for website building and hosting platformWebflow. And the best part? The team is making queries up to six times less costly by using a wide table layout.About the CompanyA Nocodelytics dashboardNocodelyticsis an analytics product built specifically for the website building and hosting companyWebflow. We are processing millions of events from various websites and turning them into insightful dashboards.We’re a close-knit team of three based in London. We are two co-founders,Sarwechand myself (Florian), and the team recently expanded whenAlexjoined us as a frontend developer. You can find us on theWebflow Marketplace.As our company is growing fast, we found that a quick, reliable database is vital for our company to grow and thrive.About the ProjectOur goal is to build the number one analytics platform for Webflow.Like other analytics tools, we provide users with a tracking script that they add to their Webflow site. However, because of the nature of Webflow’s audience, we have to do things quite differently from other analytics tools—which presents challenges that no other analytics tool faces.First, one of the things that we do that adds complexity is that we automatically track every event a user does. Whether it’s clicking on a button or link, interacting with a form, or viewing a page, we need to be able to track all of this information with minimal impact on accuracy.Adding a new metric to the Nocodelytics dashboardWe also track events tied to the Webflow Content Management System (CMS) and other third-party tools likeJetboost,Wized,Memberstack, andOutseta, which we automatically integrate with and track.So, we tap into the power of the CMS and the Webflow ecosystem to record how users interact. We then output these interactions into valuable insights for our analytics customers. This means we need to be able to record and ingest millions of events into our database per day without it crashing down. Some of our customers will get publicity and see huge spikes in traffic, so we need to be able to handle",https://www.timescale.com/blog/how-to-use-wide-table-model-to-reduce-query-cost-in-timescaledb/,500,"[-0.0013261799467727542, 0.030341973528265953, 0.0103897824883461, 0.030019808560609818, 0.05998104438185692, -0.003219807520508766, -0.0305176991969347, 0.017762940376996994, -0.03163062781095505, -0.007343870587646961, 0.009115771390497684, -0.021775344386696815, -0.028584714978933334, -0.03157205134630203, 0.024630887433886528, 0.04152984172105789, -0.0196519922465086, -0.06952880322933197, -0.06296838074922562, 0.020882071927189827, 0.03939184546470642, -0.0025681580882519484, 0.008830216713249683, -0.012659572996199131, 0.023693682625889778, -0.01473167259246111, -0.051019031554460526, 0.029756220057606697, -0.01599103957414627, -0.05336204171180725, 0.012322765775024891, -0.032069943845272064, -0.020530620589852333, 0.02272719144821167, 0.016196053475141525, 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+How to Reduce Query Cost With a Wide Table Layout in TimescaleDB,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Florian Herrengt, co-founder ofNocodelytics,shares how he tracks, records, and monitors millions of user interactions per day to build dazzling analytics dashboards for website building and hosting platformWebflow. And the best part? The team is making queries up to six times less costly by using a wide table layout.About the CompanyA Nocodelytics dashboardNocodelyticsis an analytics product built specifically for the website building and hosting companyWebflow. We are processing millions of events from various websites and turning them into insightful dashboards.We’re a close-knit team of three based in London. We are two co-founders,Sarwechand myself (Florian), and the team recently expanded whenAlexjoined us as a frontend developer. You can find us on theWebflow Marketplace.As our company is growing fast, we found that a quick, reliable database is vital for our company to grow and thrive.About the ProjectOur goal is to build the number one analytics platform for Webflow.Like other analytics tools, we provide users with a tracking script that they add to their Webflow site. However, because of the nature of Webflow’s audience, we have to do things quite differently from other analytics tools—which presents challenges that no other analytics tool faces.First, one of the things that we do that adds complexity is that we automatically track every event a user does. Whether it’s clicking on a button or link, interacting with a form, or viewing a page, we need to be able to track all of this information with minimal impact on accuracy.Adding a new metric to the Nocodelytics dashboardWe also track events tied to the Webflow Content Management System (CMS) and other third-party tools likeJetboost,Wized,Memberstack, andOutseta, which we automatically integrate with and track.So, we tap into the power of the CMS and the Webflow ecosystem to record how users interact. We then output these interactions into valuable insights for our analytics customers. This means we need to be able to record and ingest millions of events into our database per day without it crashing down. Some of our customers will get publicity and see huge spikes in traffic, so we need to be able to handle",https://www.timescale.com/blog/how-to-use-wide-table-model-to-reduce-query-cost-in-timescaledb/,500,"[-0.0012726914137601852, 0.030273769050836563, 0.010408437810838223, 0.029980985447764397, 0.05993269383907318, -0.0032078044023364782, -0.030478717759251595, 0.017801210284233093, -0.03159129247069359, -0.007345195394009352, 0.009178749285638332, -0.02188553474843502, -0.028517071157693863, -0.031562015414237976, 0.024608416482806206, 0.04151663929224014, -0.019660383462905884, -0.0695066973567009, -0.06306547671556473, 0.020890071988105774, 0.03937932103872299, -0.002580150729045272, 0.00887132715433836, -0.01264822855591774, 0.023686150088906288, -0.01471966877579689, -0.05097353085875511, 0.029732121154665947, -0.01598595641553402, -0.053315795958042145, 0.0122968889772892, -0.032059747725725174, -0.020509453490376472, 0.022705327719449997, 0.0161909032613039, 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 How to Reduce Query Cost With a Wide Table Layout in TimescaleDB,"this too.Second, we provide our customers with a simple-to-use and customizable dashboard. This allows them to create metrics that go deep and answer almost any question (What are the most popular jobs on my site? How many signups came from Google? Which contact button is most effective?).To do this, we’re building a metric creator that is simple to use on the frontend but complex on the backend, with some advanced SQL being done to return the right answers depending on the question asked. So it’s important that we have the right tool to help us with this.Third, when our customers view their dashboard and look at the metrics, even a few seconds’ wait can cause frustration. As our customers can have several metrics on the dashboard at any time—some with fairly complex queries—there’s a lot of pressure on our database to read the data, crunch the numbers, and return the result quickly.On top of that, we also allow our customers to share and embed their dashboard onto a site, which means the number of users viewing the metrics goes up, and the number of read requests can increase at any time.Choosing (and Using!) TimescaleDBFirst, let’s talk about the previous setup we had and what problems this resulted in.Like many other companies,Nocodelyticsstarted with PostgreSQL. In the beginning, it worked. But the size of the database grew very, very fast. Eventually, with millions of rows, our dashboards became sluggish. Queries for customers with a lot of traffic would take several minutes or even time-out.As we needed a solution as quickly as possible, I had three things in mind when looking for an alternative to PostgreSQL:It had to be quick to learn.The change needed to involve a minimal amount of code.The migration path had to be simple.My first choice wasClickHouse, which seems to have better performance than Timescale for our use case—but keep reading as there's more to it.Not everything was great about ClickHouse: It does a lot, which can get confusing, and I’d rather stick with PostgreSQL, which I’ve used for years and know works.Amazon Athenawas another good option. It's serverless and queries compressed data directly from S3 (which Timescale is now offering in private beta too). It did have some weird limitations (e.g., a maximum of 20 concurrent queries, no way to update data, and",https://www.timescale.com/blog/how-to-use-wide-table-model-to-reduce-query-cost-in-timescaledb/,483,"[0.0034908608067780733, -0.009565507993102074, 0.03867250680923462, 0.0033222732599824667, 0.09106656908988953, 0.014044071547687054, -0.04005052521824837, 0.0008117854595184326, -0.008568642660975456, 0.013743545860052109, 0.03360022231936455, -0.046618107706308365, -0.008898488245904446, -0.019644107669591904, 0.04298248142004013, 0.006457634270191193, 0.002336402889341116, -0.03615102544426918, -0.04855319857597351, 0.0019900654442608356, -0.007208948023617268, 0.03216356411576271, 0.023675547912716866, 0.000905699678696692, 0.004060759674757719, 0.011896413750946522, -0.014234649017453194, 0.02855725586414337, 0.012138299643993378, -0.0813911110162735, 0.0137948552146554, -0.03368818014860153, -0.05280453711748123, 0.005431449506431818, 0.014711092226207256, 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 How to Reduce Query Cost With a Wide Table Layout in TimescaleDB,"dynamic partition columns must always be included in queries), which I found out the hard way. At that point, I was worried about the next issue I’d find, and I lost confidence in the product.Finally,InfluxDB. I spent a few hours with it, but it’s too different from what we already had. The migration would take forever.Also, I must stress that I had never heard about those tools before. I either worked on large projects with big money, where we used Redshift/BigQuery or specialized, small-scale projects, where the smallest PostgreSQL instance would be enough.I was about to use ClickHouse before I came across Timescale by chance while browsing databases.It’s just PostgreSQLThere you have it. The best feature of TimescaleDB: it's all PostgreSQL, always has been. All your tools, all the existing libraries, and your code already work with it. I’m using TimescaleDB because it’s the same as PostgreSQL but magically faster.Whatever technology is behind TimescaleDB, it’s truly impressive. Since theWebflow Conf, we have been inserting more than a million rows per day (without optimizations) in our tiny 8 GB memory instance. Sometimes, we have 3 K/IOPS. PostgreSQL would struggle. It’s like pulling an elastic until it snaps—but it never does, and we barely scratched the surface of what it can do. Also, the community is really nice.“I’m using TimescaleDB because it’s the same as PostgreSQL but magically faster""So, in sum, Timescale was a drop-in replacement that solved most of our issues. I installed the extension,created a hypertable, and everything became magically fast.✨Editor’s Note: Want to get started with TimescaleDB?Check out our documentation.But as I was reading the Timescale documentation, I realized it could be faster. A lot faster.Relational vs. Wide Table LayoutWhen you first learn about relational databases, you learn how to normalize your data with multiple tables and foreign key references. That’s a good, flexible way to store your data. However, it can be an issue when dealing with a large amount of data.That’s where the wide table layout becomes useful.Normalized data vs. Wide tableThe idea is to trade storage and schema flexibility for query performance by reducing the number ofJOINs. But this doesn’t stop you from combining both. You can still add foreign keys to a wide table.You will end up using more storage, but you can mitigate it with TimescaleDB’s compression.✨Editor’s Note:Learn",https://www.timescale.com/blog/how-to-use-wide-table-model-to-reduce-query-cost-in-timescaledb/,519,"[-0.030874356627464294, -0.004395721480250359, 0.053702306002378464, 0.023930605500936508, 0.057397693395614624, 0.0027845778968185186, -0.02747698500752449, -0.0026672345120459795, -0.03921877592802048, 0.026702145114541054, 0.022008409723639488, 0.004216912668198347, 0.020458731800317764, -0.02008621208369732, 0.05236123874783516, 0.031500186771154404, -0.020384227856993675, -0.047742005437612534, -0.04270555451512337, 0.02555478736758232, -0.010288968682289124, 0.011630035936832428, 0.004645308945327997, 0.02857964113354683, 0.016897451132535934, -0.02705976366996765, -0.02631472609937191, 0.05522218346595764, 0.03257304057478905, -0.04088766127824783, -0.010169762186706066, -0.03370549902319908, -0.0308445543050766, 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 How to Reduce Query Cost With a Wide Table Layout in TimescaleDB,"how to save space using compression.Show time: Setting Up the SchemaLet’s create the above schema with relationships and insert dummy data:-- Sequence and defined type CREATE SEQUENCE IF NOT EXISTS events_id_seq; CREATE SEQUENCE IF NOT EXISTS countries_id_seq; CREATE SEQUENCE IF NOT EXISTS browsers_id_seq; CREATE SEQUENCE IF NOT EXISTS devices_id_seq; -- Table Definition CREATE TABLE ""public"".""countries"" ( ""id"" int4 NOT NULL DEFAULT nextval('countries_id_seq'::regclass), ""name"" varchar, PRIMARY KEY (""id"") ); CREATE TABLE ""public"".""browsers"" ( ""id"" int4 NOT NULL DEFAULT nextval('browsers_id_seq'::regclass), ""name"" varchar, PRIMARY KEY (""id"") ); CREATE TABLE ""public"".""devices"" ( ""id"" int4 NOT NULL DEFAULT nextval('devices_id_seq'::regclass), ""name"" varchar, PRIMARY KEY (""id"") ); CREATE TABLE ""public"".""events"" ( ""id"" int4 NOT NULL DEFAULT nextval('events_id_seq'::regclass), ""name"" varchar, ""value"" int, ""country_id"" int, ""browser_id"" int, ""device_id"" int, PRIMARY KEY (""id"") ); create index events_country_id on events(country_id); create index events_browser_id on events(browser_id); create index events_device_id on events(device_id); create index countries_name on countries(name); create index browsers_name on browsers(name); create index devices_name on devices(name);Then create our new wide table:create table events_wide as select events.id as id, events.name as name, events.value as value, countries.name as country, browsers.name as browser, devices.name as device from events join countries on events.country_id = countries.id join browsers on events.browser_id = browsers.id join devices on events.device_id = devices.id create index events_wide_country on events_wide(country); create index events_wide_browser on events_wide(browser); create index events_wide_device on events_wide(device);ResultsNeat. But was it worth it? Well, yes. It would be a lot less interesting to read otherwise. Now that we have our wide table, let’s have a look at the query cost.-- cost=12406.82 explain select devices.name, count(devices.name) from events join countries on events.country_id = countries.id join browsers on events.browser_id = browsers.id join devices on events.device_id = devices.id where browsers.name = 'Firefox' and countries.name = 'United Kingdom' group by devices.name order by count desc; -- cost=2030.21 explain select device, count(device) from events_wide where browser = 'Firefox' and country = 'United Kingdom' group by device order by count desc;This is a significant improvement. The same query is six times less costly. For a dashboard with dozens of metrics, it makes amassivedifference.You can find the full SQLhere.Future PlansTimescale is packed with amazing features we want to start using. Things liketime_bucket_gapfill()orhistogram().I didn't dive into it yet, but theTimescale Toolkitseems to have a lot of valuable functionality, such asapproximate count distinctsorfunction pipelines, which we can’t wait to try out.We also want to see",https://www.timescale.com/blog/how-to-use-wide-table-model-to-reduce-query-cost-in-timescaledb/,672,"[-0.008502686396241188, 0.004644842818379402, 0.047003958374261856, 0.009652323089540005, 0.02456674538552761, 0.0030071886721998453, -0.02004535309970379, -0.009590597823262215, -0.007978019304573536, 0.006770514417439699, 0.032498471438884735, -0.009251107461750507, -0.006214985158294439, -0.009629175998270512, 0.050553176552057266, 0.01180499978363514, -0.02260695956647396, 0.0008212383254431188, 0.010616783984005451, 0.022159449756145477, 0.016526998952031136, -0.005798337981104851, 0.022807568311691284, 0.002713992493227124, -0.016418980434536934, -0.005775190889835358, -0.0028914534486830235, 0.06326862424612045, 0.05765160545706749, 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 How to Reduce Query Cost With a Wide Table Layout in TimescaleDB,"howcontinuous aggregatescould help us relieve some pressure on the database.Our goal is to keep growing and scaling the number of events we store. We will soon leveragetablespacesanddata tiering to save on storage space. We’re keen to further optimize and use TimescaleDB to help as we grow towards handling billions of rows!June 2023 update:We’re now dealing with more than 500 GB of data, and those wide tables just aren’t efficient anymore.So, we’ve gone ahead and separated the table again. We’re executing a count query first, retrieving the ids, then running another query for the labels. Essentially, it’s a two-query process.TimescaleDB is row-based and our wide table is heavy on strings. As a result, we’re hitting I/O limits. This wasn’t a problem before because we’re using a fast SSD and had fewer rows per site, but now with the data volume, it’s a different story.In retrospect, choosing the wide table structure at that time was good. It accelerated our development pace significantly. We centralized all the events, simplifying our queries for quite some time. Plus, it enabled us to compress all our data without effort. Looking back, it was a beneficial strategy for that stage of our project.We’d like to thank Florian and the folks at Nocodelytics for sharing their story on tracking millions of user events while reducing their query cost using TimescaleDB. Stay tuned for an upcoming dev-to-dev conversation between Florian and Timescale’s developer advocate,Chris Englebert, where they will expand on these topics.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/how-to-use-wide-table-model-to-reduce-query-cost-in-timescaledb/,380,"[0.01927044987678528, 0.014825587160885334, 0.07404354214668274, 0.0037204627878963947, 0.030045023187994957, 0.02111309953033924, 0.007152572274208069, -0.015317898243665695, -0.05128471553325653, 0.0033406801521778107, 0.03035447560250759, -0.014684926718473434, -0.013109533116221428, -0.01939704455435276, 0.0189469326287508, 0.02811797894537449, 0.008087962865829468, -0.0010549516882747412, -0.03960992023348808, 0.007349496707320213, -0.014248880557715893, 0.03935673087835312, 0.013193928636610508, 0.01426294632256031, -0.015275700017809868, -0.02505158632993698, -0.008383349515497684, 0.0627344623208046, 0.04174795374274254, -0.05632035434246063, 0.002932765521109104, 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-Processing and Protecting Hundreds of Terabytes of Blockchain Data: Zondax’s Story,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Ezequiel Raynaudo, data team leader at Zondax, explains how the software company consumes and analyzes hundreds of terabytes of blockchain data using TimescaleDB to create complete backend tech solutions.About the CompanyZondaxis a growing international team of more than 30 experienced professionals united by a passion for technology and innovation. Our end-to-end software solutions are widely used by many exchanges, hardware wallets, privacy coins, and decentralized finance (DeFi) protocols. Security is one of the highest priorities in our work, and we aim to provide the safest and most efficient solutions for our clients.Founded in 2018, Zondax has been consideredthe leader in the industry for Ledger apps development, with more than 45 applications built to date and more near production. Since its inception, our team has been building and delivering high-quality backend tech solutions for leading blockchain projects of prominent clients, such as Cosmos, Tezos, Zcash, Filecoin, Polkadot, ICP, Centrifuge, and more.“The database is a critical part of the foundation that supports our software services for leading blockchain projects, financial services, and prominent ecosystems in the blockchain space”We put a lot of emphasis on providing maximum safety and efficiency for the ecosystem. Our services can be categorized into security, data indexing, integration, and protocol engineering. Each category has designated project leaders that take the initiative in managing the projects with well-experienced engineers.About the TeamMy team (which currently has two people but is looking to grow by the end of the year) manages the blockchain data in various projects and ensures the tools needed for the projects are well-maintained. For example, we pay close attention to the dynamic sets of different blockchains and create advanced mathematical models for discovering insightful information via data analytics.The database is a critical part of the foundation that supports our software services for leading blockchain projects, financial services, and prominent ecosystems in the blockchain space. In conclusion, we take serious steps to ensure the work of processing blockchain data remains effective, and that the quality of the results meets Zondax's high standards.We welcome professionals from different cultures, backgrounds, fields of experience, and mindsets.",https://www.timescale.com/blog/processing-and-protecting-hundreds-of-terabytes-of-blockchain-data-zondaxs-story/,487,"[0.0659821406006813, 0.01507374458014965, 0.050218209624290466, 0.02526094578206539, 0.03627637401223183, 0.0012164594372734427, -0.01772407256066799, 0.060515839606523514, -0.036607664078474045, -0.026655128225684166, 0.030423564836382866, -0.030423564836382866, -0.03707699477672577, -0.04218439757823944, 0.0735466256737709, 0.06349746137857437, 0.000985245918855071, -0.016992472112178802, -0.052813321352005005, 0.024391306564211845, 0.005276500713080168, 0.02956772968173027, 0.04177028685808182, -0.006021905690431595, -0.0008920702966861427, -0.029843805357813835, -0.030810071155428886, 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+Processing and Protecting Hundreds of Terabytes of Blockchain Data: Zondax’s Story,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition, Ezequiel Raynaudo, data team leader at Zondax, explains how the software company consumes and analyzes hundreds of terabytes of blockchain data using TimescaleDB to create complete backend tech solutions.About the CompanyZondaxis a growing international team of more than 30 experienced professionals united by a passion for technology and innovation. Our end-to-end software solutions are widely used by many exchanges, hardware wallets, privacy coins, and decentralized finance (DeFi) protocols. Security is one of the highest priorities in our work, and we aim to provide the safest and most efficient solutions for our clients.Founded in 2018, Zondax has been consideredthe leader in the industry for Ledger apps development, with more than 45 applications built to date and more near production. Since its inception, our team has been building and delivering high-quality backend tech solutions for leading blockchain projects of prominent clients, such as Cosmos, Tezos, Zcash, Filecoin, Polkadot, ICP, Centrifuge, and more.“The database is a critical part of the foundation that supports our software services for leading blockchain projects, financial services, and prominent ecosystems in the blockchain space”We put a lot of emphasis on providing maximum safety and efficiency for the ecosystem. Our services can be categorized into security, data indexing, integration, and protocol engineering. Each category has designated project leaders that take the initiative in managing the projects with well-experienced engineers.About the TeamMy team (which currently has two people but is looking to grow by the end of the year) manages the blockchain data in various projects and ensures the tools needed for the projects are well-maintained. For example, we pay close attention to the dynamic sets of different blockchains and create advanced mathematical models for discovering insightful information via data analytics.The database is a critical part of the foundation that supports our software services for leading blockchain projects, financial services, and prominent ecosystems in the blockchain space. In conclusion, we take serious steps to ensure the work of processing blockchain data remains effective, and that the quality of the results meets Zondax's high standards.We welcome professionals from different cultures, backgrounds, fields of experience, and mindsets.",https://www.timescale.com/blog/processing-and-protecting-hundreds-of-terabytes-of-blockchain-data-zondaxs-story/,487,"[0.06592098623514175, 0.015141398645937443, 0.05013086646795273, 0.02532768063247204, 0.036245498806238174, 0.0011464743874967098, -0.017846697941422462, 0.060399964451789856, -0.036604367196559906, -0.026721738278865814, 0.03033800795674324, -0.030393216758966446, -0.037128861993551254, -0.04215298965573311, 0.07359520345926285, 0.06349173188209534, 0.0008971657953225076, -0.01705995388329029, -0.05300179496407509, 0.024402910843491554, 0.005265673156827688, 0.029592668637633324, 0.041794124990701675, -0.0060765729285776615, -0.0008199578151106834, -0.02986872009932995, -0.030752083286643028, 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 Processing and Protecting Hundreds of Terabytes of Blockchain Data: Zondax’s Story,"New ideas are always encouraged, and the diversity within the team has been helping us to identify various potential advancements we can make in leading blockchain projects.At the same time, our efforts in experimenting and searching for creative and efficient solutions led us to pay close attention to the latest technologies and innovations that we immediately incorporate into our work. We never get bored at Zondax!Since the Covid-19 pandemic, many companies and teams have switched to remote work temporarily or permanently, and for Zondax this is familiar ground. We adopted a culture of remote work from the start, which has been rewarding and encouraging as team members from around the globe often spark fun and constructive discussions despite nuanced cultural differences. And in terms of quality of work, the tools and platforms we have been using accommodate the needs for smooth communication and effective collaboration within different teams.About the ProjectZondax provides software services to various customers, including leading projects in the blockchain ecosystem and financial services. We consume and analyze blockchain data in numerous different ways and for multiple purposes:As input for other services and ecosystems provided by Zondax and for other third partiesTo apply data science and get value in the form of insights by using mathematical models for different blockchain dynamic sets of variablesFinancial servicesBlockchain data backupsThe minimal unit of division of a blockchain, a.k.a. a “block” on most networks, contains a timestamp field among several other sub-structures. It allows you to define blocks at a specific point in time throughout the blockchain history. So having a database engine that can leverage that property is a go-to option.Choosing (and Using!) TimescaleDB✨Editor’s Note:Want to know more about optimizing queries on hypertables with thousands of partitions using TimescaleDB?Check out this blog post.Our first encounter with TimescaleDB was througha blog post about database optimizations. We decided to go ahead and install it on our infrastructure since it’s built on top of PostgreSQL, and our main code and queries didn’t require to be updated. After installing the corresponding helm chart on our infrastructure, we decided to try it.The first tests denoted a substantial increase in performance when writing or querying the database, with no optimizations at all and without using hypertables. Those results encouraged us to keep digging into TimescaleDB’s configurations and optimizations, such as usingtimescaledb-tuneand",https://www.timescale.com/blog/processing-and-protecting-hundreds-of-terabytes-of-blockchain-data-zondaxs-story/,471,"[0.038066089153289795, 0.023348117247223854, 0.058761946856975555, 0.028171654790639877, 0.037076644599437714, -0.001310669002123177, -0.005424761213362217, 0.027965519577264786, -0.02183646708726883, 0.005967580713331699, 0.043975263833999634, -0.030892623588442802, -0.04845524579286575, -0.019788868725299835, 0.07107502222061157, 0.062170032411813736, -0.017755012959241867, -0.0290511604398489, -0.03776375949382782, 0.020173652097582817, 0.019362859427928925, 0.04188644140958786, 0.03828596696257591, -0.015762384980916977, 0.012361172586679459, -0.022922106087207794, -0.02940845862030983, 0.0538971833884716, 0.027333375066518784, -0.06706228107213974, 0.02819913811981678, -0.025340747088193893, -0.005850771442055702, 0.037598852068185806, 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 Processing and Protecting Hundreds of Terabytes of Blockchain Data: Zondax’s Story,"converting critical tables to hypertables.“If Timescale didn’t exist, we would have a problem and might need to wait a couple of weeks to process a few dozens of terabytes rather than waiting only 1-2 days”Long story short, we went from having to wait a couple of weeks to process a few dozens of terabytes to only 1-2 days. Among the top benefits of TimescaleDB, I would highlight having the best possible write and read performance. It is a critical part of our ecosystem because it helps us provide fast and responsive services in real time. Using TimescaleDB also allows our software to stay synced with the blockchain's nodes, which is one of the most significant acknowledged advantages of our software and services. Last but not least, we also use TimescaleDB for blockchain data backups to protect data integrity.Before finding out about TimescaleDB, we first used custom PostgreSQL modifications like indexing strategies and high-availability setups. Also, our team did some benchmarking using NoSQL databases like MongoDB, but with no substantial improvements on the write/read speeds that we needed.If Timescale didn’t exist, we would have a problem and might need to wait a couple of weeks to process a few dozens of terabytes rather than waiting only 1-2 days.We are glad that we chose Timescale and proud of the work that has been expedited and achieved. For example, despite many challenges, we didn't give up on experimenting with new approaches to process a tremendous amount of blockchain data. Instead, we continued exploring new ideas and tools until we eventually started using TimescaleDB, which drastically shortened the time to process data and accelerated our progress in delivering quality results for the projects.Current Deployment & Future Plans✨Editor’s Note:Read how you canadd TimescaleDB to your Kubernetes deployment strategyquickly and easily.We deploy TimescaleDB using a custom helm chart that fits our infrastructure needs. As far as programming languages, we mainly use Golang to interact with TimescaleDB; and Hasura as the main query engine for external users.Advice & Resources✨Editor’s Note:Want to learn how to create a hypertable using TimescaleDB?Check out our documentation on hypertables and chunks.I’d recommend reading the blog postson how to get a working deployment,hypertables, andTimescale vs. vanilla Postgres's performance using the same queries.A wise man (Yoda) once said, ""You must unlearn what you have learned."" 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 Processing and Protecting Hundreds of Terabytes of Blockchain Data: Zondax’s Story,"to encounter countless challenges when developing a scalable database strategy, but staying curious and willing to explore new solutions with caution can sometimes be rewarding.We’d like to thank Ezequiel and all of the folks at Zondax for sharing their story and efforts in finding a solution to process enormous amounts of data to build backend solutions for blockchain projects.We’re always keen to feature new community projects and stories on our blog. If you have a story or project you’d like to share, reach out on Slack (@Ana Tavares), and we’ll go from there.The open-source relational database for time-series and analytics.Try Timescale for free",https://www.timescale.com/blog/processing-and-protecting-hundreds-of-terabytes-of-blockchain-data-zondaxs-story/,126,"[0.08217412233352661, 0.0020726362708956003, 0.06488830596208572, 0.02997095137834549, 0.025569714605808258, -0.009307748638093472, -0.0006390766939148307, 0.03930529206991196, -0.045155879110097885, -0.032736681401729584, 0.042975205928087234, -0.04191146418452263, -0.024492675438523293, -0.029093362390995026, 0.04784182831645012, 0.050979867577552795, 0.02409377135336399, -0.014413713477551937, -0.02224551886320114, 0.03603428229689598, 0.0029402512591332197, 0.02215244248509407, 0.03560878708958626, -0.0017485270509496331, 0.002664342988282442, -0.024732017889618874, -0.030848538503050804, 0.05467637628316879, 0.02381454035639763, -0.07297275215387344, 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 How FlightAware Fuels Flight Prediction Models for Global Travelers With TimescaleDB and Grafana,"This is an installment of our “Community Member Spotlight” series, where we invite our customers to share their work, shining a light on their success and inspiring others with new ways to use technology to solve problems.In this edition,Caroline Rodewig, Senior Software Engineer and Predict Crew Lead at FlightAware, joins us to share how they’ve architected a monitoring system that allows them to power real-time flight predictions, analyze prediction performance, and continuously improve their models.FlightAwareis the world's largestflight tracking and data platform; we fuse hundreds of global data sources to produce an accurate, consistent view of flights around the world. We make this data available to users through web and mobile applications, as well as different APIs.Our customers cover a number of different segments, including:Travelers / aviation enthusiastswho use our website and mobile apps to track flights (e.g., using our “where’s my flight?” program).Business aviation providers(such asFixed Base Operatorsoraircraft operators) who use flight-tracking data and custom reporting to support their businesses.Airlinesthat use flight-tracking data or our predictive applications to operate more efficiently.Editor’s Note: for more information about FlightAware’s products (and ways to harness its data infrastructure), check outthis overview. Want to build your own flight tracking receiver and ground station? SeeFlightAware’s PiAware tutorial.About the teamThe Predictive Technologiescrewis responsible for FlightAware's predictive applications, which as a whole are called ""FlightAware Foresight."" At the moment, our small-but-mighty team is made up of only three people: our project managerJames Parkman, software engineer Andrew Brooks, and myself. We each wear many different hats; a day's work can cover anything from Tier 2 customer support to R&D, and everything in between.A former crew member, Diorge Tavares, wrote acool articleabout his experience as a site reliability engineer embedded in the Predict crew. He helped us design infrastructure and led our foray into cloud computing; now that our team is more established, he’s moved back to the FlightAware Systems team full-time.About the projectOur team's chief project is predicting flight arrival times, or ETAs; we predict both landing (EON) and gate arrival (EIN) times. And, ultimately, we need to monitor, visualize, and alarm on thequalityof those predictions. This is where TimescaleDB fits in.Not only should we track how our prediction error changes over the course of each flight, we also need to track how our error changes over months - or years!",https://www.timescale.com/blog/how-flightaware-fuels-flight-prediction-models-with-timescaledb-and-grafana/,527,"[0.032769825309515, -0.007906034588813782, 0.03459037095308304, -0.010475985705852509, 0.03377426415681839, 0.030290117487311363, -0.0066112494096159935, 0.02310209907591343, -0.01743643544614315, 0.03096497431397438, -0.008576968684792519, -0.032330382615327835, -0.020528225228190422, -0.0173579640686512, 0.006395452190190554, 0.020434057340025902, 0.020810723304748535, -0.012186671607196331, -0.06761130690574646, 0.0330837145447731, -0.035249534994363785, -0.027386659756302834, 0.021658217534422874, -0.011127302423119545, -0.012390698306262493, -0.03653647378087044, -0.05201111361384392, 0.07872291654348373, 0.01947670243680477, -0.08186178654432297, 0.04246894270181656, 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-How FlightAware Fuels Flight Prediction Models for Global Travelers With TimescaleDB and Grafana,"- to ensure we're continually improving our predictions. Our predictive models can have short bursts of inaccuracy - like failing to anticipate the impact of a huge storm - but they can also drift slowly over time as real-life behaviors change.As an example of the type of data we extract, the below is our ""Worst Flights"" dashboard, which we use for QA. (Looking through outliers is an easy way to spot bugs.) The rightmost column compares our error to third-parties', so we can see how we're doing relative to the rest of the industry.Our Grafana dashboard for tracking ""Worst Flights"" and our prediction quality vs. other data sourcesBut, we also go deep into specific flights, like the below ""Single Flight"" dashboard view.This is useful for debugging, as it gives a detailed picture of how our predictions changed over the course of asingleflight.Our Grafana dashboard for debugging and assessing our prediction quality at the individual flight levelChoosing (and using) TimescaleDBWe tested out several different monitoring setups before settling on TimescaleDB and Grafana. We recently published ablog postdetailing our quest for a monitoring system, which I’ve summarized below.First, we considered usingZabbix; it's widely used at FlightAware, where most software reports into Zabbix in one way or another. However, we quickly realized that Zabbix was not the tool for the job – our Systems crew had serious doubts that Zabbix would be able to handle the load of all the metrics we wanted to track:We make predictions for around 75,000 flights per day; if we only stored two error values per flight (much fewer than we wanted), it would require making 100 inserts per minute.After ruling out Zabbix, I started looking atGrafanaas a visualization and alerting tool, and it seemed to have all the capabilities we needed. For my database backend, I first pickedPrometheus, because it was near the top of Grafana's ""supported databases"" list and its built-in visualization capabilities seemed promising for rapid development.I didn't know much about time-series databases, and, while Prometheus is a good fit for some data, it really didn't fit mine well:No JOINs. My only prior database experience was with PostgreSQL, and it didn't occur to me that some databases just wouldn't support JOINs. While wecouldhave worked around this issue by inserting specific, already-joined error metrics, this would have limited the flexibility",https://www.timescale.com/blog/how-flightaware-fuels-flight-prediction-models-with-timescaledb-and-grafana/,499,"[-0.001869003288447857, 0.004607392009347677, 0.08717791736125946, -0.03719703480601311, 0.04119550809264183, 0.009552683681249619, 0.0038928769063204527, -0.01067197322845459, -0.025961898267269135, 0.04406764730811119, -0.023244628682732582, -0.02842574380338192, -0.00784910935908556, -0.02908746339380741, 0.007616803981363773, 0.027538759633898735, -0.011474483646452427, -0.012966870330274105, -0.06324341148138046, 0.010559340007603168, -0.03528227657079697, 0.02162552997469902, 0.029537994414567947, -0.01875338889658451, -0.011910935863852501, -0.009960977360606194, 0.006011784542351961, 0.04268789291381836, 0.027735866606235504, -0.0357891246676445, 0.033283039927482605, -0.02655322104692459, -0.031086698174476624, 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+How FlightAware Fuels Flight Prediction Models for Global Travelers With TimescaleDB and Grafana,"- to ensure we're continually improving our predictions. Our predictive models can have short bursts of inaccuracy - like failing to anticipate the impact of a huge storm - but they can also drift slowly over time as real-life behaviors change.As an example of the type of data we extract, the below is our ""Worst Flights"" dashboard, which we use for QA. (Looking through outliers is an easy way to spot bugs.) The rightmost column compares our error to third-parties', so we can see how we're doing relative to the rest of the industry.Our Grafana dashboard for tracking ""Worst Flights"" and our prediction quality vs. other data sourcesBut, we also go deep into specific flights, like the below ""Single Flight"" dashboard view.This is useful for debugging, as it gives a detailed picture of how our predictions changed over the course of asingleflight.Our Grafana dashboard for debugging and assessing our prediction quality at the individual flight levelChoosing (and using) TimescaleDBWe tested out several different monitoring setups before settling on TimescaleDB and Grafana. We recently published ablog postdetailing our quest for a monitoring system, which I’ve summarized below.First, we considered usingZabbix; it's widely used at FlightAware, where most software reports into Zabbix in one way or another. However, we quickly realized that Zabbix was not the tool for the job – our Systems crew had serious doubts that Zabbix would be able to handle the load of all the metrics we wanted to track:We make predictions for around 75,000 flights per day; if we only stored two error values per flight (much fewer than we wanted), it would require making 100 inserts per minute.After ruling out Zabbix, I started looking atGrafanaas a visualization and alerting tool, and it seemed to have all the capabilities we needed. For my database backend, I first pickedPrometheus, because it was near the top of Grafana's ""supported databases"" list and its built-in visualization capabilities seemed promising for rapid development.I didn't know much about time-series databases, and, while Prometheus is a good fit for some data, it really didn't fit mine well:No JOINs. My only prior database experience was with PostgreSQL, and it didn't occur to me that some databases just wouldn't support JOINs. While wecouldhave worked around this issue by inserting specific, already-joined error metrics, this would have limited the flexibility",https://www.timescale.com/blog/how-flightaware-fuels-flight-prediction-models-with-timescaledb-and-grafana/,499,"[-0.0018830857006832957, 0.004617959726601839, 0.0871780663728714, -0.03722526133060455, 0.0411955788731575, 0.009524541907012463, 0.0038823243230581284, -0.010686071589589119, -0.02594786509871483, 0.04406772553920746, -0.023244669660925865, -0.02842579409480095, -0.007849123328924179, -0.02908751368522644, 0.007609778083860874, 0.027538808062672615, -0.01146746426820755, -0.012966892682015896, -0.06329983472824097, 0.010573438368737698, -0.03528233617544174, 0.021625567227602005, 0.02956620417535305, -0.018767500296235085, -0.01191799622029066, -0.009975074790418148, 0.006015314720571041, 0.04268796741962433, 0.027721837162971497, -0.03581734374165535, 0.03328309953212738, -0.026539187878370285, -0.03108675219118595, 0.014226975850760937, 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 How FlightAware Fuels Flight Prediction Models for Global Travelers With TimescaleDB and Grafana,"and ""query-a-bility"" of the data.Number of labels to store. At the bare minimum, we wanted to store EON and EIN predictions for 600 airports, at least 10 times throughout each flight. This works out to 12,000 different label combinations, each stored as a time series – whichPrometheus is not currently designed to handle.And, that’s when I found TimescaleDB. A number of factors went into our decision to use TimescaleDB, but here are the top four:Excellent performance.This article comparing TimescaleDB vs. PostgreSQL performancereally impressed me. Getting consistent performance, despite the number of rows in the table, was critical to our goal of storing performance data over several years.Institutional knowledge.FlightAware uses PostgreSQL in a vast number of applications, so there was already a lot of institutional knowledge and comfort with SQL.Impressive documentation.I have yet to have an issue or question that wasn't discussed and answered in the docs. Plus, it was trivial to test out – I love one-line docker start-up commands (seeTimescaleDB Docker Installation instructions).Grafana support.I was pretty confident that I wanted to use Grafana to visualize our data and power our dashboards, so this was a potential dealbreaker.We use several Grafana dashboards, like this one, to view detailed performance over time (average error trends over one or more airports)Editor’s Note: To learn more about TimescaleDB and Grafana,see our Grafana tutorials(5 step-by-step guides for building visualizations, using variables, setting up alerts, and more) andGrafana how-to blog posts.To see how to use TimescaleDB to perform time-series forecasting and analysis,check out our time-series forecasting tutorial(includes two forecasting methods, best practices, and sample queries).Current deployment & use casesOur architecture is pretty simple (see diagram below). We run a copy of this setup in several environments: production, production hot-standby, staging, and test. Each environment has its own predictions database, which allows us to compare our predictions in staging to those in production and validate changesbeforethey get released.⭐Pro tip:we periodically sync Grafana configurations from production to each of the other environments, which reduces the manual work involved in updating dashboards across instances.FlightAware Predict team's system architecture, which uses custom Python programs, Docker, Grafana, and TimescaleDBAfter some trial and error, we’ve set up our TimescaleDB schema as follows:(1) Short term (1 week) tables for arrivals, our own predictions, and third-party predictions.The predict-assessor program reads our flight data feed, extracts ETA predictions",https://www.timescale.com/blog/how-flightaware-fuels-flight-prediction-models-with-timescaledb-and-grafana/,546,"[0.0018291049636900425, -0.03256893530488014, 0.10564620792865753, -0.03732098639011383, 0.032134294509887695, 0.02102203108370304, -0.0012676964979618788, 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 How FlightAware Fuels Flight Prediction Models for Global Travelers With TimescaleDB and Grafana,"and arrival times, and inserts them into the database. For scale, the arrivals table typically contains 500k rows, and the predictions tables each contain 5M rows.Each table is chunked: arrivals by arrival time and predictions by the time the prediction was made.We use archiving functions to copy some data into long-term storage, andadrop_chunkspolicyto ensure that rows older than one week are dropped to prevent unlimited table growth.(2) Long term (permanent) table for prediction and prediction-error data.Archiving functions move data to the long term table by joining the short terms tables together. They also ""threshold"" the data to reduce verbosity, by only storing predictions at predetermined intervals; i.e., predictions that were present 1 and 2 hours before arrival are migrated to long-term tables, but intermediate predictions (i.e., at 1.5 hours before arrival) are not kept.Between the join and the threshold, the archiving process reduces the average number rows per flight from25(across 3 short-term tables) to6!We haven’t enabled adrop_chunkspolicy on this table as of now; after ~9 months of running this setup, our database file is pretty manageable at 54GB. If we start having space issues, we'd opt to store fewer predictions per flight rather than lose any year-over-year historical data.Biggest ""Aha!"" momentContinuous aggregates are what well and truly sold me on TimescaleDB. We went from 6.4 seconds to execute a query to 30ms.Yes, milliseconds.I was embarrassingly late to the party when it comes to continuous aggregates. When I first set up our database, every query was fast because the database was small. However, as we added data over time, some queries slowed down significantly.The biggest offender was a query on our KPIs dashboard, visualized in Grafana below. This graph gives us a birds-eye view of error over time. The blue line represents the average error for all airports at a certain time before arrival; the red line shows the number of flights per day. (You can see the huge traffic drop when airlines stopped flights in March, due to the COVID-19 pandemic.)Our KPI dashboard includes various metrics, including our average error rate and total flights per day across all airportsBeforelearning about continuous aggregates, the query to extract this data looked like this:SELECT time_bucket('1 day', arr_time) AS ""time"", AVG(get_error(prediction_fa, arr_time)) AS on_error, count(*) AS on_count FROM prediction_history WHERE time_out = '02:00:00' AND arr_time BETWEEN '2020-03-01' AND",https://www.timescale.com/blog/how-flightaware-fuels-flight-prediction-models-with-timescaledb-and-grafana/,543,"[-0.0039991470985114574, -0.015174797736108303, 0.10065524280071259, -0.00410187104716897, 0.03196483850479126, 0.03349507227540016, 0.028989389538764954, -0.024072811007499695, -0.029981207102537155, 0.04953416809439659, -0.018759507685899734, -0.03743400052189827, 0.006429098546504974, -0.024313680827617645, -0.0005074205691926181, 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 How FlightAware Fuels Flight Prediction Models for Global Travelers With TimescaleDB and Grafana,"'2020-09-05' GROUP BY 1 ORDER BY 1It took 6.4 seconds and aggregated 1.6M rows, from a table of 147M rows.For what the query was doing, this runtime wasn't too bad – the table was chunked byarr_time, which the query planner could take advantage of.I considered adding indexes to make the query faster, but wasn't convinced they would help much and was concerned about the resulting performance penalties for inserts.I also considered creating a materialized view to aggregate the data and writing a cron job to regularly refresh it...but that seemed like a hassle, and after all, I could wait 10 seconds for something to load 🤷‍♀️.Then, I discovered TimescaleDB's continuous aggregations! For the unfamiliar, they basically implement that regularly-refreshing materialized view idea, but in a far smarter way and with a bunch of cool extra features.Here's the view for the continuous aggregate:CREATE VIEW error_by_time_out WITH (timescaledb.continuous) AS SELECT time_out, time_bucket(INTERVAL '1 hour', arr_time) AS bucket, AVG(get_error(prediction_fa, arr_time)) AS avg_error, COUNT(*) AS count FROM prediction_history GROUP BY time_out, bucket;The new data extraction query is a little bit harder to parse, because the error needs to be aggregated across continuous aggregate buckets:SELECT time_bucket('1 day', bucket) AS ""time"", SUM(avg_error * count) / SUM(count) AS error, SUM(count) AS count FROM error_by_time_out WHERE time_out = '02:00:00' AND bucket BETWEEN '2020-03-01' AND '2020-09-05' GROUP BY 1 ORDER BY 1...and I'll let you guess how long it takes....30ms.Yes, milliseconds. We went from 6.4 seconds to execute the query to 30ms.On top of that, unlike in a classic materialized view, the whole view doesn't have to be recalculated every time it needs to be updated -just the parts that have changed.This means refreshes are lightning fast too.Continuous aggregates are what well and truly sold me on TimescaleDB.The amazing developers at Timescale recently made continuous aggregates even better through ""real-time"" aggregates. These will automatically fill in data between the last view refresh and real-time when they're queried, so you always get the most up-to-date data possible. Unfortunately, our database is a few versions behind so we're not using real-time aggregates yet, but I can't wait to upgrade and start using them.Editor’s Note: To learn more about real-time aggregates and how they work, see our“Ensuring up-to-date results with Real-Time Aggregations” blog and mini-tutorial(includes benchmarks, example scenarios, and resources to get started).Getting started advice &",https://www.timescale.com/blog/how-flightaware-fuels-flight-prediction-models-with-timescaledb-and-grafana/,591,"[-0.01708628609776497, 0.015725431963801384, 0.04464703053236008, 0.031230928376317024, 0.03606952354311943, 0.013642911799252033, 0.03870875760912895, -0.017278730869293213, -0.03211067244410515, 0.024220464751124382, 0.009704681113362312, -0.02750576101243496, 0.04211776703596115, -0.01903821900486946, 0.04519687220454216, 0.059547703713178635, -0.02750576101243496, 0.00851565133780241, -0.024591606110334396, 0.043657317757606506, -0.035932064056396484, 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diff --git a/openai_pgvector_helloworld/openai_pgvector_helloworld.ipynb b/openai_pgvector_helloworld/openai_pgvector_helloworld.ipynb
index 3f025f6..195c889 100644
--- a/openai_pgvector_helloworld/openai_pgvector_helloworld.ipynb
+++ b/openai_pgvector_helloworld/openai_pgvector_helloworld.ipynb
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 190,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 195,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 196,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 197,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 198,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 199,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -363,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 200,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -398,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 201,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -408,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 202,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,7 +426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -450,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 204,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -462,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 205,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -482,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 206,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -509,14 +509,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 207,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Helper function: get text completion from OpenAI API\n",
     "# Note max tokens is 4097\n",
     "# Note we're using the latest gpt-3.5-turbo-0613 model\n",
-    "def get_completion_from_messages(messages, model=\"gpt-3.5-turbo\", temperature=0, max_tokens=1000):\n",
+    "def get_completion_from_messages(messages, model=\"gpt-4o\", temperature=0, max_tokens=1000):\n",
     "    response = openai_client.chat.completions.create(\n",
     "        model=model,\n",
     "        messages=messages,\n",
@@ -528,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 208,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -539,7 +539,7 @@
     "    register_vector(conn)\n",
     "    cur = conn.cursor()\n",
     "    # Get the top 3 most similar documents using the KNN <=> operator\n",
-    "    cur.execute(\"SELECT content FROM embeddings4 ORDER BY embedding <=> %s LIMIT 3\", (embedding_array,))\n",
+    "    cur.execute(\"SELECT content FROM embeddings ORDER BY embedding <=> %s LIMIT 3\", (embedding_array,))\n",
     "    top3_docs = cur.fetchall()\n",
     "    return top3_docs"
    ]
@@ -557,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 209,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 210,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -600,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 211,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,7 +611,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 212,
    "metadata": {},
    "outputs": [],
    "source": [