user avatar
Update caniuse-lite
Vihang Mehta authored
Summary: `browserlist` was complaining about an outdatted db.

Test Plan: Ran `yarn test` no more messages about outdated `cauniuse`

Reviewers: michelle, nlanam, #third_party_approvers

Reviewed By: michelle, nlanam, #third_party_approvers
Signed-off-by: default avatarVihang Mehta <vihang@pixielabs.ai>

Differential Revision: https://phab.corp.pixielabs.ai/D10155

GitOrigin-RevId: 8000862a83ae9bdaae9a556e22a670b6ff3ed251
360388c0
Name Last commit Last update
.github Apply text linter fixes to files
.idea Don't typecheck `.pnp.js`; allow typecheck watch to keep going
.readme_assets Update README.md
.vscode Apply text linter fixes to files
bazel Switch to using prometheus metrics instead of custom library
ci Add ARM64 CLI support on mac
demos Apply text linter fixes to files
k8s Annotate vzmgr with prometheus config
scripts Upgrade minukube
skaffold Add boilerplate for metrics server
src Update caniuse-lite
styleguide Add and enable text linter
third_party Switch to using prometheus metrics instead of custom library
tools Fix licenses
.arcconfig Move linters into tools
.arclint Fix yamllint document-start warnings
.arcunit Rewrite generated file checker tests
.bazel_fix_commands.json Adding ibazel fix commands
.bazelignore Move chef under tools
.bazelproject Clion with bazel setup
.bazelrc Fix coverage build config to not run coverage for targets with no_gcc.
.clang-format Clang-format: Make pointer bind to type
.clang-tidy Silence some more clang-tidy rules that we never seem to follow
.flake8rc Allow 120 chars line length
.fossa.yml Move operator apis to location expected by most tooling
.gitattributes
.gitignore
.gitmodules
.golangci.yaml
.graphqlconfig
.pxl.flake8rc
.snyk
.yamllint
AUTHORS
BUILD.bazel
CLA.md
CODEOWNERS
CODE_OF_CONDUCT.md
CONTRIBUTING.md
DEVELOPMENT.md
Doxyfile
GOVERNANCE.md
Jenkinsfile
LICENSE
Makefile
OWNERS
README.md
WORKSPACE
codecov.yml
docker.properties
go.mod
go.sum
go_deps.bzl
pixielabs.sublime-project
prototool.yaml
workspace.bzl

Pixie!


Docs Slack Twitter Mentioned in Awesome Kubernetes Mentioned in Awesome Go Build Status codecov FOSSA Status


Pixie is an open source observability tool for Kubernetes applications. Use Pixie to view the high-level state of your cluster (service maps, cluster resources, application traffic) and also drill-down into more detailed views (pod state, flame graphs, individual full-body application requests).

Why Pixie?

Three features enable Pixie's magical developer experience:

  • Auto-telemetry: Pixie uses eBPF to automatically collect telemetry data such as full-body requests, resource and network metrics, application profiles, and more. See the full list of data sources here.

  • In-Cluster Edge Compute: Pixie collects, stores and queries all telemetry data locally in the cluster. Pixie uses less than 5% of cluster CPU, and in most cases less than 2%.

  • Scriptability: PxL, Pixie’s flexible Pythonic query language, can be used across Pixie’s UI, CLI, and client APIs.

Use Cases

Network Monitoring

Network Flow Graph

Use Pixie to monitor your network, including:

  • The flow of network traffic within your cluster.
  • The flow of DNS requests within your cluster.
  • Individual full-body DNS requests and responses.
  • A Map of TCP drops and TCP retransmits across your cluster.

For more details, check out the tutorial or watch an overview.


Infrastructure Health

Infrastructure Monitoring

Monitor your infrastructure alongside your network and application layer, including:

  • Resource usage by Pod, Node, Namespace.
  • CPU flamegraphs per Pod, Node.

For more details, check out the tutorial or watch an overview.


Service Performance

Service Performance

Pixie automatically traces a variety of protocols. Get immediate visibility into the health of your services, including:

  • The flow of traffic between your services.
  • Latency per service and endpoint.
  • Sample of the slowest requests for an individual service.

For more details, check out the tutorial or watch an overview.


Database Query Profiling

Database Query Profilling

Pixie automatically traces a number of different database protocols. Use Pixie to monitor the performance of your database requests:

  • Latency, error and throughput (LET) rate for all pods.
  • LET rate per normalized query.
  • Latency per individual full body query.
  • Individual full-body requests and responses.

For more details, check out the tutorial or watch an overview.


Request Tracing

Request Tracing

Pixie makes debugging this communication between microservices easy by providing immediate and deep (full-body) visibility into requests flowing through your cluster. See:


For more details, check out the tutorial or watch an overview.


Continuous Application Profiling

Continuous Application Profiling

Use Pixie's continuous profiling feature to identify performance issues within application code.


For more details, check out the tutorial or watch an overview.


Distributed bpftrace Deployment

Use Pixie to deploy a bpftrace program to all of the nodes in your cluster. After deploying the program, Pixie captures the output into a table and makes the data available to be queried and visualized int he Pixie UI. TCP Drops pictured. For more details, check out the tutorial or watch an overview.

Dynamic Go Logging

Debug Go binaries deployed in production environments without needing to recompile and redeploy. For more details, check out the tutorial or watch an overview.


Get Started

Request Tracing

It takes just a few minutes to install Pixie. To get started, check out the Install Guides.


Once installed, you can interact with Pixie using the:


Get Involved

Pixie is a community driven project; we welcome your contribution! For code contributions, please read our contribution guide.


About Pixie

Pixie was contributed by New Relic, Inc. to the Cloud Native Computing Foundation as a Sandbox project in June 2021.

License

Pixie is licensed under Apache License, Version 2.0.