In this lab you will deploy and test the IBM Stock Trader application on Red Hat OpenShift.
The IBM Stock Trader application is a simple stock trading sample, where you can create various stock portfolios and add shares of stock to each for a commission. It keeps track of each porfolio’s total value and its loyalty level, which affect the commission charged per transaction. It also lets you submit feedback on the application, which can result in earning free (zero commission) trades, based on the tone of the feedback. (Tone is determined by calling the Watson Tone Analyzer).
The architecture of the app is shown below:
The portfolio microservice sits at the center of the application. This microservice;
- persists trade data using JDBC to a MariaDB database
- invokes the stock-quote service that invokes an API defined in API Connect in the public IBM Cloud to get stock quotes
- invokes the Tone Analyzer service in the public IBM Cloud to analyze the tone of submitted feedback
- sends trades to Kafka so that they can be recorded in Mongo by the event-consumer microservice
- calls the trade-history service to get aggregated historical trade data.
Tradr is a Node.js UI for the porfolio service
The event-consumer service serves as a Kafka consumer and stores trade data published by the portfolio service in the Mongo database.
The trade-history service exposes an API to query the historical data in Mongo and is called by the portfolio to get aggregated historical data.
The stock-quote service queries an external service to get real time stock quotes via an API Connect proxy.
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Login into the OpenShift web console using the user credentials provided to you.
- Access the OpenShift console via the url link provided by the workshop instructors
- Select
dragonslayer-ldaplogin option
- Login with the username and password provided by the workshop instructors
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From the OpenShift web console click on your username in the upper right and select Copy Login Command
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You are prompted to login to the OpenShift console again. Repeat the same login procedure above to login.
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Click Display Token link.
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Copy the contents in the field Log in with this token. It provides a valid login command with an alive token.
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Paste the login command in a terminal window and run it (Note: leave the web console browser tab open as you'll need it later on in the lab)
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If prompted with
Use insecure connections? (y/n):, enter y. -
Set an environment variable for your studentid based on your user identifier from the instructor (e.g. user001)
export STUDENTID=userNNN -
Create a new OpenShift project for this lab
oc new-project mcsvcs-$STUDENTID
Like a typical Kubernetes app, Stock Trader use secrets and ConfigMaps to store information needed by one or more microservices to access external services and other microservices. We've provided that info in a file hosted in Cloud Storage and there is a script that you'll use to retrieve it.
1.1 From a terminal window clone the Github repo that has everything needed to deploy the aggregated Stock Trader app.
git clone https://github.com/IBMStockTraderLite/stocktrader-openshift4.git
cd stocktrader-openshift41.2 Retrieve credentials and other details needed to create secrets and/or ConfigMaps. Ask you instructor for the SETUPURL for the command below.
# Note you must be in the scripts sub folder or this command will fail
cd scripts
# Your instructor will provide your with values for SETUPURL adn STUDENID
./setupLab.sh SETUPURL $STUDENTID1.3 Verify that the output looks something like the following:
Script being run from correct folder
Validating URL to setup files ...
Validating student id ...
Retrieving setup files ...
Getting application subdomain for cluster ...
Updating OpenShift template with shared host for all routes: stocktrader-microservices.apps.ocp.kubernetes-workshops.com
Using stocktrader-user001 as Kafka topic name ...
Updating variables.sh with Kafka topic : stocktrader-user001
Setup completed successfully1.4 Also verify that there is now a file called variables.sh in the current folder
In this part you'll install the prereqs step by step before installing the Stock Trader application.
2.1 Install MariaDB by running the following command. Verify that no errors are displayed by the installation script.
./setupMariaDB.sh2.2 Install Mongo by running the following command. Verify that no errors are displayed by the installation script.
./setupMongo.sh
2.3 Create the DNS Proxy and store all the access information as secrets for the external Kafka installation. Verify that no errors are displayed by the script.
./setupKafka.sh
2.4 Store all the access information as secrets for the API Connect proxy to the external realtime stock quote . Verify that no errors are displayed by the script.
./setupAPIConnect.sh
2.5 Store all the access information as secrets for the external Watson Tone Analyzer service . Verify that no errors are displayed by the script.
./setupWatson.sh
2.6 Verify your progress so far. Run the following to see the pods you have so far
oc get podsThe output should show pods for MariaDB and Mongo and they both should be running and in the READY state
NAME READY STATUS RESTARTS AGE
mariadb-1-shzjl 1/1 Running 0 2m
mongodb-1-gqpln 1/1 Running 0 2m2.7 Initialize the MariaDB transactional database with some data. Verify that no errors are displayed by the script.
./initDatabase.sh
2.8 Next look at your services
oc get svc2.9 Verify that the output shows services for Mongo, MariaDB and your DNS proxy to Kafka
NAME TYPE CLUSTER-IP EXTERNAL-IP
kafka-dns-proxy ExternalName <none> broker-0-0mqz41lc21pr467x.kafka.svc01.us-south.eventstreams.cloud.ibm.com
mariadb ClusterIP 172.30.103.15 <none>
mongodb ClusterIP 172.30.235.11 <none>In this part you'll install all the Stock Trader microservices using a template for all the microservices. Note that all the microservices require some of the information stored via secrets in the scripts you ran in the previous section.
3.1 Go back to the top level folder of the cloned repo
cd ..3.2 Install the microservices chart. Verify that no errors are displayed
oc process -f templates/stock-trader.yaml | oc create -f -3.3 Verify that all the pods are running and are in the READY state. Note you may have to run this command multiple times before all the pods become READY.
oc get pods3.4 Keep running the command until the output looks something like this:
NAME READY STATUS RESTARTS AGE
event-streams-consumer-1-455pj 1/1 Running 0 2m
mariadb-1-shzjl 1/1 Running 0 2d
mongodb-1-gqpln 1/1 Running 0 2d
portfolio-1-vkxnp 1/1 Running 0 2m
stockquote-1-zck9n 1/1 Running 0 2m
trade-history-1-5pngp 1/1 Running 0 2m
tradr-1-qdps9 1/1 Running 0 2m3.5 The app uses OpenShift routes to provide access outside of the cluster. Use the following command to get the external hostnames you'll need to access Stock Trader.
oc get routes3.6 Verify the output looks something like the following. The value in the HOST/PORT column is the common hostname used for all the microservices. The value in the PATH column is the unique path for each microservice.
NAME HOST/PORT PATH SERVICES
portfolio stocktrader-microservices.apps.ocp.kubernetes-workshops.com /portfolio portfolio
stockquote stocktrader-microservices.apps.ocp.kubernetes-workshops.com /stock-quote stockquote
trade-history stocktrader-microservices.apps.ocp.kubernetes-workshops.com /trade-history trade-history
tradr stocktrader-microservices.apps.ocp.kubernetes-workshops.com /tradr tradrIn this example the URL for the tradr UI is http://stocktrader-microservices.apps.ocp.kubernetes-workshops.com/tradr (the common hostname plus the PATH for tradr).
In this part you'll verify that the various microservices are working as designed.
4.1 Bring up the tradr web application using the hostname you noted at the end of the previous section
4.2 Log in with the following credentials (note these are the only values that will work)
username: stock
password: trader
4.3 Click Add Client and name the client Client2. Click OK
4.4 Click on the link in the Name column to see the details of Client2
4.5 Do 3 or 4 "Buy" operations with different stock tickers (e.g. STT, T, GOOG, IBM).
4.6 Sell part of one of the holdings you just bought and verify that the table is updated appropriately
4.7 Click on Feedback and submit some client feedback. If the client sounds really angry they'll get 3 free trades otherwise they'll get one free trade.
4.8 Verify that the data flow of portfolio->Kafka->event-consumer->trade-history-Mongo works by querying the trade-history service via an endpoint that makes it do a Mongo query. Add the path /trades/Client2 to the route for the trade-history microservice. For example http://stocktrader-microservices.apps.ocp.kubernetes-workshops.com/trade-history/trades/Client2 for the route used in the example above.
4.9 Enter the URL in another browser tab and verify that the history has captured all the trades you did while testing. A partial screen shot of what you should get back is shown below:
Free up resources for subsequent labs by deleting the Stock Trader app.
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Run the following commands to cleanup (note: you can copy all the commands at once and post then into you command window)
cd scripts oc delete all,routes --selector app=stock-trader ./cleanupWatson.sh ./cleanupAPIConnect.sh ./cleanupKafka.sh ./cleanupMongo.sh ./cleanupMariaDB.sh cd -
You installed and then tested the Stock Trader microservices sample application and got some insight into the challenges of deploying microservices apps in an OpenShift cluster.