Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix remaining 100Hz -> 19Hz mentions #440

Open
wants to merge 2 commits into
base: main
Choose a base branch
from
Open
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion docs/ingestion.md
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ There are two ways to ingest data into Parca.

## Push based

The recommended way to use for generic profiling is Parca Agent. Parca Agent is an always-on sampling profiler that uses [eBPF](https://ebpf.io/) to capture raw profiling data with very low overhead. It observes user-space, and kernel-space stack traces 100 times per second and builds [pprof](https://github.com/google/pprof) formatted profiles from the extracted data. For in-depth detail and explanation refer to the the [Parca Agent Design](/docs/parca-agent-design) documentation.
The recommended way to use for generic profiling is Parca Agent. Parca Agent is an always-on sampling profiler that uses [eBPF](https://ebpf.io/) to capture raw profiling data with very low overhead. It observes user-space, and kernel-space stack traces 19 times per second per logical CPU and builds [pprof](https://github.com/google/pprof) formatted profiles from the extracted data. For in-depth detail and explanation refer to the the [Parca Agent Design](/docs/parca-agent-design) documentation.

The collected data can be [sent to a Parca server](https://buf.build/parca-dev/parca/docs/main/parca.profilestore.v1alpha1), where it can be queried and analyzed over time.

Expand Down
2 changes: 1 addition & 1 deletion docs/parca-agent-design.md
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Design

Parca Agent implements a sampling profiler, to sample stack traces [19 times per second](#cpu-sampling-frequency) via eBPF. It tracks user space as well as kernel-space stack traces. From the raw data, it builds a [pprof](https://github.com/google/pprof) formatted profile and optionally sends it to a Parca server where it is stored and can be queried and analyzed over time.
Parca Agent implements a sampling profiler, to sample stack traces [19 times per second](#cpu-sampling-frequency) per logical CPU via eBPF. It tracks user space as well as kernel-space stack traces. From the raw data, it builds a [pprof](https://github.com/google/pprof) formatted profile and optionally sends it to a Parca server where it is stored and can be queried and analyzed over time.

Parca Agent is a whole-system profiler. It collects stack traces from all the processes that run on the host system. This provides more insights about all the aspects of the system to the user. Please see our [blog post](https://www.polarsignals.com/blog/posts/2022/08/24/system-wide-profiling/) about internals of this mechanism.

Expand Down
2 changes: 1 addition & 1 deletion docs/parca-agent.md
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
# Overview

Parca Agent is an always-on sampling profiler that uses [eBPF](https://ebpf.io/) to capture raw profiling data with very low overhead. It observes user-space, and kernel-space stack traces 100 times per second and builds [pprof](https://github.com/google/pprof) formatted profiles from the extracted data. For in-depth detail and explanation refer to the the [Parca Agent Design](/docs/parca-agent-design) documentation.
Parca Agent is an always-on sampling profiler that uses [eBPF](https://ebpf.io/) to capture raw profiling data with very low overhead. It observes user-space, and kernel-space stack traces 19 times per second per logical CPU and builds [pprof](https://github.com/google/pprof) formatted profiles from the extracted data. For in-depth detail and explanation refer to the the [Parca Agent Design](/docs/parca-agent-design) documentation.

The collected data can be viewed locally via HTTP endpoints and optionally be configured to be sent to a Parca server, where it can be queried and analyzed over time.

Expand Down