Generic manual memory management for golang

mm-go

No instrumentation tracing for Golang applications via eBPF

[![Pixie!](./.readme_assets/pixie-horizontal-color.png)](https://px.dev)

 

[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://docs.px.dev)
[![Slack](https://slackin.px.dev/badge.svg)](https://slackin.px.dev)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/pixie_run.svg?style=social&label=Follow%20%40pixie_run)](https://twitter.com/pixie_run)
[![Mentioned in Awesome Kubernetes](https://awesome.re/mentioned-badge.svg)](https://github.com/ramitsurana/awesome-kubernetes)
[![Mentioned in Awesome Go](https://awesome.re/mentioned-badge.svg)](https://github.com/avelino/awesome-go)
[![Build Status](https://github.com/pixie-io/pixie/actions/workflows/build_and_test.yaml/badge.svg?branch=main)](https://github.com/pixie-io/pixie/actions/workflows/build_and_test.yaml)
[![codecov](https://codecov.io/gh/pixie-io/pixie/branch/main/graph/badge.svg?token=UG7P3QE5PQ)](https://codecov.io/gh/pixie-io/pixie)
[![FOSSA Status](https://app.fossa.com/api/projects/custom%2B26327%2Fgithub.com%2Fpixie-io%2Fpixie.svg?type=shield)](https://app.fossa.com/projects/custom%2B26327%2Fgithub.com%2Fpixie-io%2Fpixie?ref=badge_shield)
[![Artifact HUB](https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/pixie-operator)](https://artifacthub.io/packages/olm/community-operators/pixie-operator)
[![Go Report Card](https://goreportcard.com/badge/px.dev/pixie)](https://goreportcard.com/report/px.dev/pixie)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5027/badge)](https://bestpractices.coreinfrastructure.org/projects/5027)
[![CLOMonitor](https://img.shields.io/endpoint?url=https://clomonitor.io/api/projects/cncf/pixie/badge)](https://clomonitor.io/projects/cncf/pixie)
[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/pixie-io/pixie/badge)](https://api.securityscorecards.dev/projects/github.com/pixie-io/pixie)

 

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](https://docs.px.dev/about-pixie/data-sources/).

- **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](https://docs.px.dev/reference/pxl/), Pixie’s flexible Pythonic query language, can be used across Pixie’s UI, CLI, and client APIs.

## Use Cases

### Network Monitoring

<img src=".readme_assets/net_flow_graph.png" alt="Network Flow Graph" width="525" align="right">

 

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](https://docs.px.dev/tutorials/pixie-101/network-monitoring/) or [watch](https://youtu.be/qIxzIPBhAUI) an overview.

 

### Infrastructure Health

<img src=".readme_assets/nodes.png" alt="Infrastructure Monitoring" width="525" align="right">

 

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

- Resource usage by Pod, Node, Namespace.
- CPU flame graphs per Pod, Node.

 

For more details, check out the [tutorial](https://docs.px.dev/tutorials/pixie-101/infra-health/) or [watch](https://youtu.be/2dFIpiBryu8) an overview.

 

### Service Performance

<img src=".readme_assets/service.png" alt="Service Performance" width="525" align="right">

 

Pixie automatically traces a [variety of protocols](https://docs.px.dev/about-pixie/data-sources/). 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](https://docs.px.dev/tutorials/pixie-101/service-performance/) or [watch](https://youtu.be/Rex0yz_5vwc) an overview.

 

### Database Query Profiling

<img src=".readme_assets/sql_query.png" alt="Database Query Profilling" width="525" align="right">

 

Pixie automatically traces several different [database protocols](https://docs.px.dev/about-pixie/data-sources/#supported-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](https://docs.px.dev/tutorials/pixie-101/database-query-profiling/) or [watch](https://youtu.be/5NkU--hDXRQ) an overview.

 

### Request Tracing

<img src=".readme_assets/http_data_filtered.png" alt="Request Tracing" width="525" align="right">

 

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

- Full-body requests and responses for [supported protocols](https://docs.px.dev/about-pixie/data-sources/#supported-protocols).
- Error rate per Service, Pod.

 

For more details, check out the [tutorial](https://docs.px.dev/tutorials/pixie-101/request-tracing/) or [watch](https://youtu.be/Gl0so4rbwno) an overview.

 

### Continuous Application Profiling

<img src=".readme_assets/pod_flamegraph.png" alt="Continuous Application Profiling" width="525" align="right">

 

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

 

For more details, check out the [tutorial](https://docs.px.dev/tutorials/pixie-101/profiler/) or [watch](https://youtu.be/Zr-s3EvAey8) an overview.

 

### Distributed bpftrace Deployment

Use Pixie to deploy a [bpftrace](https://github.com/iovisor/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 in the Pixie UI. TCP Drops are pictured. For more details, check out the [tutorial](https://docs.px.dev/tutorials/custom-data/distributed-bpftrace-deployment/) or [watch](https://youtu.be/xT7OYAgIV28) 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](https://docs.px.dev/tutorials/custom-data/dynamic-go-logging/) or [watch](https://youtu.be/aH7PHSsiIPM) an overview.

 

## Get Started

<img src=".readme_assets/http_data.svg" alt="Request Tracing" width="525" align="right">

It takes just a few minutes to install Pixie. To get started, check out the [Install Guides](https://docs.px.dev/installing-pixie/install-guides/).

 

Once installed, you can interact with Pixie using the:

- [Web-based Live UI](https://docs.px.dev/using-pixie/using-live-ui/)
- [CLI](https://docs.px.dev/using-pixie/using-cli/)
- [API](https://docs.px.dev/using-pixie/api-quick-start/)

 

## Get Involved

Pixie is a community-driven project; we welcome your contribution! For code contributions, please read our [contribution guide](CONTRIBUTING.md).

- File a [GitHub issue](https://github.com/pixie-io/pixie/issues) to report a bug or request a feature.
- Join our [Slack](https://slackin.px.dev) for live conversations and quick questions. We are also available on the [CNCF slack](https://slack.cncf.io/app_redirect?channel=pixie").
- Follow us on [Twitter](https://twitter.com/pixie_run) and [YouTube](https://www.youtube.com/channel/UCOMCDRvBVNIS0lCyOmst7eg).
- Add our community meeting [calendar](https://calendar.google.com/calendar/u/0?cid=Y181OTA0Y2ZmNzdhNzdhOGYzMzQxNmNmMjQzODI4ZjY4ZGY5MWMzYzEzZWU3YmQ5NGFjODUzMmRiODg2Y2VjMjkxQGdyb3VwLmNhbGVuZGFyLmdvb2dsZS5jb20).
- Provide feedback on our [roadmap](https://docs.px.dev/about-pixie/roadmap/).

 

## Latest Releases
We version separate components of Pixie separately, so what Github shows as the "latest" release will only be the latest for one of the components.
We maintain links to the latest releases for all components here:
- [CLI v0.8.8](https://github.com/pixie-io/pixie/releases/tag/release/cli/v0.8.8)
- [Cloud v0.1.8](https://github.com/pixie-io/pixie/releases/tag/release/cloud/v0.1.8)
- [Vizier v0.14.15](https://github.com/pixie-io/pixie/releases/tag/release/vizier/v0.14.15)
- [Operator v0.1.7](https://github.com/pixie-io/pixie/releases/tag/release/operator/v0.1.7)

## Changelog

The changelog is stored in annotated git tags.

For vizier:

```
git for-each-ref refs/tags/release/vizier/$tagname --format='%(tag) %(contents)'
```

For the CLI:

```
git for-each-ref refs/tags/release/cli/$tagname --format='%(tag) %(contents)'
```

These are also published on the [releases](https://github.com/pixie-io/pixie/releases) page.

## Adopters

The known adopters and users of Pixie are listed [here](ADOPTERS.md).

## Software Bill of Materials

We publish a list of all the components Pixie depends on and the corresponding
versions and licenses [here](https://storage.googleapis.com/pixie-dev-public/oss-licenses/latest.json).

## Acknowledgements

The Pixie project would like to thank [Equinix Metal](https://www.equinix.com/) via the [CNCF Community Infrastructure Lab](https://github.com/cncf/cluster) for graciously providing compute resources to run all the CI/CD for the project.

## About Pixie

Pixie was contributed by [New Relic, Inc.](https://newrelic.com/) to the [Cloud Native Computing Foundation](https://www.cncf.io/) as a Sandbox project in June 2021.

## License

Pixie is licensed under [Apache License, Version 2.0](LICENSE).

pixie

> # 🧶 tracer
>
> Dead simple, lightweight tracing.

[![Build][build.icon]][build.page]
[![Documentation][docs.icon]][docs.page]
[![Quality][quality.icon]][quality.page]
[![Template][template.icon]][template.page]
[![Coverage][coverage.icon]][coverage.page]
[![Awesome][awesome.icon]][awesome.page]

## 💡 Idea

The tracer provides API to trace execution flow.

```go
func Do(ctx context.Context) {
	defer tracer.Fetch(ctx).Start().Stop()

	// do some heavy job
}
```

A full description of the idea is available [here][design.page].

## 🏆 Motivation

At [Avito](https://tech.avito.ru), we use the [Jaeger](https://www.jaegertracing.io) - a distributed tracing platform.
It is handy in most cases, but at production, we also use sampling. So, what is a problem, you say?

I had 0.02% requests with a `write: broken pipe` error and it was difficult to find the appropriate one in
the [Sentry](https://sentry.io) which also has trace related to it in the [Jaeger](https://www.jaegertracing.io).

For that reason, I wrote the simple solution to handle this specific case and found the bottleneck in our code quickly.

## 🤼‍♂️ How to

```go
import (
	"context"
	"io"
	"net/http"
	"time"

	"github.com/kamilsk/tracer"
)

func InjectTracer(handler http.Handler) http.Handler {
	return http.HandlerFunc(func(rw http.ResponseWriter, req *http.Request) {
		req = req.WithContext(tracer.Inject(req.Context(), make([]*tracer.Call, 0, 10)))
		handler.ServeHTTP(rw, req)
	})
}

func Handle(rw http.ResponseWriter, req *http.Request) {
	ctx, cancel := context.WithTimeout(req.Context(), time.Second)
	defer cancel()

	call := tracer.Fetch(req.Context()).Start(req.Header.Get("X-Request-Id"))
	defer call.Stop()

	...

	call.Checkpoint("serialize")
	data, err := FetchData(ctx, req.Body)
	if err != nil {
		http.Error(rw, err.Error(), http.StatusInternalServerError)
		return
	}

	call.Checkpoint("store")
	if err := StoreIntoDatabase(ctx, data); err != nil {
		http.Error(rw,
			http.StatusText(http.StatusInternalServerError),
			http.StatusInternalServerError)
		return
	}
	rw.WriteHeader(http.StatusOK)
}

func FetchData(ctx context.Context, r io.Reader) (Data, error) {
	defer tracer.Fetch(ctx).Start().Stop()

	// fetch a data into a struct
}

func StoreIntoDatabase(ctx context.Context, data Data) error {
	defer tracer.Fetch(ctx).Start().Stop()

	// store the data into a database
}
```

Output:

```
allocates at call stack: 0, detailed call stack:
	call Handle [ca7a87c4-58d0-4fdf-857c-ef49fc3bf271]: 14.038083ms, allocates: 2
		checkpoint [serialize]: 1.163587ms
		checkpoint [store]: 2.436265ms
	call FetchData: 1.192829ms, allocates: 0
	call StoreIntoDatabase: 10.428663ms, allocates: 0
```

## 🧩 Integration

The library uses [SemVer](https://semver.org) for versioning, and it is not
[BC](https://en.wikipedia.org/wiki/Backward_compatibility)-safe through major releases.
You can use [go modules](https://github.com/golang/go/wiki/Modules) to manage its version.

```bash
$ go get github.com/kamilsk/tracer@latest
```

---

made with ❤️ for everyone

[awesome.page]:     https://github.com/avelino/awesome-go#performance
[awesome.icon]:     https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg
[build.page]:       https://travis-ci.com/kamilsk/tracer
[build.icon]:       https://travis-ci.com/kamilsk/tracer.svg?branch=master
[coverage.page]:    https://codeclimate.com/github/kamilsk/tracer/test_coverage
[coverage.icon]:    https://api.codeclimate.com/v1/badges/fb66449d1f5c64542377/test_coverage
[design.page]:      https://www.notion.so/octolab/tracer-098c6f9fe97b41dcac4a30074463dc8f?r=0b753cbf767346f5a6fd51194829a2f3
[docs.page]:        https://pkg.go.dev/github.com/kamilsk/tracer
[docs.icon]:        https://img.shields.io/badge/docs-pkg.go.dev-blue
[promo.page]:       https://github.com/kamilsk/tracer
[quality.page]:     https://goreportcard.com/report/github.com/kamilsk/tracer
[quality.icon]:     https://goreportcard.com/badge/github.com/kamilsk/tracer
[template.page]:    https://github.com/octomation/go-module
[template.icon]:    https://img.shields.io/badge/template-go--module-blue


tracer

Provides utilities for loading, compiling, and debugging eBPF programs

# eBPF

[![PkgGoDev](https://pkg.go.dev/badge/github.com/cilium/ebpf)](https://pkg.go.dev/github.com/cilium/ebpf)

![HoneyGopher](docs/ebpf/ebpf-go.png)

ebpf-go is a pure Go library that provides utilities for loading, compiling, and
debugging eBPF programs. It has minimal external dependencies and is intended to
be used in long running processes.

See [ebpf.io](https://ebpf.io) for complementary projects from the wider eBPF
ecosystem.

## Getting Started

Please take a look at our [Getting Started] guide.

[Contributions](https://ebpf-go.dev/contributing) are highly encouraged, as they highlight certain use cases of
eBPF and the library, and help shape the future of the project.

## Getting Help

The community actively monitors our [GitHub Discussions](https://github.com/cilium/ebpf/discussions) page.
Please search for existing threads before starting a new one. Refrain from
opening issues on the bug tracker if you're just starting out or if you're not
sure if something is a bug in the library code.

Alternatively, [join](https://ebpf.io/slack) the
[#ebpf-go](https://cilium.slack.com/messages/ebpf-go) channel on Slack if you
have other questions regarding the project. Note that this channel is ephemeral
and has its history erased past a certain point, which is less helpful for
others running into the same problem later.

## Packages

This library includes the following packages:

* [asm](https://pkg.go.dev/github.com/cilium/ebpf/asm) contains a basic
  assembler, allowing you to write eBPF assembly instructions directly
  within your Go code. (You don't need to use this if you prefer to write your eBPF program in C.)
* [cmd/bpf2go](https://pkg.go.dev/github.com/cilium/ebpf/cmd/bpf2go) allows
  compiling and embedding eBPF programs written in C within Go code. As well as
  compiling the C code, it auto-generates Go code for loading and manipulating
  the eBPF program and map objects.
* [link](https://pkg.go.dev/github.com/cilium/ebpf/link) allows attaching eBPF
  to various hooks
* [perf](https://pkg.go.dev/github.com/cilium/ebpf/perf) allows reading from a
  `PERF_EVENT_ARRAY`
* [ringbuf](https://pkg.go.dev/github.com/cilium/ebpf/ringbuf) allows reading from a
  `BPF_MAP_TYPE_RINGBUF` map
* [features](https://pkg.go.dev/github.com/cilium/ebpf/features) implements the equivalent
  of `bpftool feature probe` for discovering BPF-related kernel features using native Go.
* [rlimit](https://pkg.go.dev/github.com/cilium/ebpf/rlimit) provides a convenient API to lift
  the `RLIMIT_MEMLOCK` constraint on kernels before 5.11.
* [btf](https://pkg.go.dev/github.com/cilium/ebpf/btf) allows reading the BPF Type Format.
* [pin](https://pkg.go.dev/github.com/cilium/ebpf/pin) provides APIs for working with pinned objects on bpffs.

## Requirements

* A version of Go that is [supported by
  upstream](https://golang.org/doc/devel/release.html#policy)
* Linux (amd64, arm64): CI is run against kernel.org LTS releases. >= 4.4 should work but EOL'ed
  versions are not supported.
* Windows (amd64): CI is run against Windows Server 2022. Only the latest eBPF for Windows
  release is supported.
* Other architectures are best effort. 32bit arches are not supported.

## License

MIT

### eBPF Gopher

The eBPF honeygopher is based on the Go gopher designed by Renee French.

[Getting Started]: https://ebpf-go.dev/guides/getting-started/


ebpf-go

Automatically add spans to all methods and functions

# ⚡️ go-instrument


 ✨ Sponsored by <a href="https://apps.apple.com/app/id6738306589">NDX AI Shopping Assistant</a>


[![codecov](https://codecov.io/gh/nikolaydubina/go-instrument/branch/master/graph/badge.svg?token=pXGPrKmAbT)](https://codecov.io/gh/nikolaydubina/go-instrument)
[![Go Report Card](https://goreportcard.com/badge/github.com/nikolaydubina/go-instrument)](https://goreportcard.com/report/github.com/nikolaydubina/go-instrument)
[![Go Reference](https://pkg.go.dev/badge/github.com/nikolaydubina/go-instrument.svg)](https://pkg.go.dev/github.com/nikolaydubina/go-instrument)
[![Mentioned in Awesome Go](https://awesome.re/mentioned-badge.svg)](https://github.com/avelino/awesome-go#performance)
[![go-recipes](https://raw.githubusercontent.com/nikolaydubina/go-recipes/main/badge.svg?raw=true)](https://github.com/nikolaydubina/go-recipes)
[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/nikolaydubina/go-instrument/badge)](https://securityscorecards.dev/viewer/?uri=github.com/nikolaydubina/go-instrument)
[![Hits](https://hits.sh/github.com/nikolaydubina/go-instrument.svg?view=today-total)](https://hits.sh/github.com/nikolaydubina/go-instrument/)

Automatically add Trace Spans to Go functions

```bash
go install github.com/nikolaydubina/go-instrument@latest
```

```bash
find . -name "*.go" | xargs -I{} go-instrument -app my-service -w -filename {}
```

Functions with `context.Context` in arguments
```go
func (s Cat) Name(ctx context.Context) (name string, err error) {
 ...
```

will be instrumented with span
```go
func (s Cat) Name(ctx context.Context) (name string, err error) {
	ctx, span := otel.Trace("my-service").Start(ctx, "Cat.Name")
	defer span.End()
	defer func() {
		if err != nil {
			span.SetStatus(codes.Error, "error")
			span.RecordError(err)
		}
	}()
 ...
```

Example HTTP server [go-instrument-example](https://github.com/nikolaydubina/go-instrument-example) as it appears in Datadog.
![](./docs/fib-error.png)

This tool uses standard Go library to modify AST with instrumentation.
You can add new instrumentations by defining your own `Instrumenter` and invoking `Processor` like it is done in `main`.

## Motivation

It is laborious to add tracing code to every function manually.
The code repeats 99% of time.
Other languages can either modify code or have wrapper notations that makes even manual tracing much less laborious.

As of `2025-05-11`, official Go does not support automatic function traces[^2].
> Is there a way to automatically intercept each function call and create traces? 
> 
> Go doesn’t provide a way to automatically intercept every function call and create trace spans. You need to manually instrument your code to create, end, and annotate spans.

Thus, providing automated version to add Trace Spans annotation.

## Performance 

### Go Compiler Inlining

Since we are adding multiple functions calls, it affects Go compiler decisions on inlining.
It is expected that Go will less likely inline.

For example, can inline function
```bash
$ go build -gcflags="-m -m" ./internal/testdata 2>&1 | grep OneLine
internal/testdata/basic.go:80:6: can inline OneLineTypical with cost 62 as: func(context.Context, int) (int, error) { return fib(n), nil }
```

```bash
go-instrument -w -filename internal/testdata/basic.go
```

Can not inline after instrumentation
```bash
$ go build -gcflags="-m -m" ./internal/testdata 2>&1 | grep OneLine
internal/testdata/basic.go:132:6: cannot inline OneLineTypical: unhandled op DEFER
``` 

## Appendix A: Related Work

* https://github.com/hedhyw/otelinji — Very similar to current project. This tool gracefully handles code comments, so that its output can be tracked with normal code in version control. Main difference current project focuses on minimal code and dependencies.
* https://github.com/open-telemetry/opentelemetry-go-instrumentation — (in development) official eBPF based Go auto instrumentation
* https://github.com/keyval-dev/opentelemetry-go-instrumentation — eBPF based Go auto instrumentation of _pre-selected_ libraries
* https://developers.mattermost.com/blog/instrumenting-go-code-via-ast — Very similar. Instrumenting Go code for tracing.
* https://github.com/gobwas/gtrace — non-OTEL, custom tracing framework that uses code generation

## Appendix B: Other Languages

### Java

Java runtime modifies bytecode of methods on load time that adds instrumentation calls.
Pre-defined libraries are instrumented (http, mysql, etc).

✅ Very short single line decorator statement can be used to trace selected methods.

Datadog
```java
import datadog.trace.api.Trace

public class BackupLedger {
 @Trace
 public void write(List<Transaction> transactions) {
 for (Transaction transaction : transactions) {
 ledger.put(transaction.getId(), transaction);
 }
 }
}
```

OpenTelemetry
```java
import io.opentelemetry.instrumentation.annotations.WithSpan;

public class MyClass {
 @WithSpan
 public void myMethod() {
 <...>
 }
}
```

✅ Automatic instrumentation of all functions is also possible.

Datadog supports wildcard for list of methods to trace.

> dd.trace.methods 
> Environment Variable: DD_TRACE_METHODS 
> Default: null 
> Example: package.ClassName[method1,method2,...];AnonymousClass$1[call];package.ClassName[*] 
> List of class/interface and methods to trace. Similar to adding @Trace, but without changing code. Note: The wildcard method support ([*]) does not accommodate constructors, getters, setters, synthetic, toString, equals, hashcode, or finalizer method calls

```bash
java -javaagent:/path/to/dd-java-agent.jar -Ddd.service=web-app -Ddd.env=dev -Ddd.trace.methods="*" -jar path/to/application.jar
```

* [Java Auto-Instrumentation](https://docs.oracle.com/javase/8/docs/api/java/lang/instrument/package-summary.html)
* [Datadog Java Auto-Instrumentation](https://docs.datadoghq.com/tracing/trace_collection/dd_libraries/java/?tab=containers#automatic-instrumentation)
* [Datadog Java Tracing Config](https://docs.datadoghq.com/tracing/trace_collection/library_config/java/#ddtracemethods)
* [Datadog Instrumentation Business Logic](https://docs.datadoghq.com/tracing/guide/instrument_custom_method/?code-lang=java)
* [Javaassist](https://www.javassist.org)

### Python

Python monkeypatching of functions at runtime is used to add instrumentation calls.
Pre-defined libraries are instrumented (http, mysql, etc).

✅ Very short single line decorator statement can be used to trace selected methods.

Datadog
```python
from ddtrace import tracer

class BackupLedger:
 @tracer.wrap()
 def write(self, transactions):
 for transaction in transactions:
 self.ledger[transaction.id] = transaction
```

OpenTelemetry
```python
@tracer.start_as_current_span("do_work")
def do_work():
 print("doing some work...")
```

⚠️ Automatic instrumentation of all functions is also possible via monkeypatching (fidning stable library is pending).

* [OpenTelemetry Python Instrumentation](https://opentelemetry.io/docs/instrumentation/python/automatic/#overview)
* [Blog: Timescale: OpenTelemetry and Python: A Complete Instrumentation Guide](https://www.timescale.com/blog/opentelemetry-and-python-a-complete-instrumentation-guide/)
* https://github.com/harshitandro/Python-Instrumentation

### C++

❌ Only manual instrumentation.

### Rust

✅ Very short single line decorator statement can be used to trace selected functions with well-establisehd tokio framework.

```rust
#[tracing::instrument]
pub fn shave(yak: usize) -> Result<(), Box<dyn Error + 'static>> {
```

```rust
#[instrument]
async fn write(stream: &mut TcpStream) -> io::Result<usize> {
```

* https://github.com/tokio-rs/tracing
* https://opentelemetry.io/docs/instrumentation/rust
* https://docs.rs/opentelemetry/latest/opentelemetry
* https://github.com/open-telemetry/opentelemetry-rust/tree/main/examples
* https://docs.rs/datadog-apm/latest/datadog_apm

## Appendix C: Generating Many Spans

`1.97K` spans, fibbonaci

![](./docs/large_fib.png)

`3.7K` spans, go cover treemap

![](./docs/large_tree.png)

## ADR

- `2025-05-11` not using commands like `//instrument:exclude` because: in practice this tool is used to instrument everything; there is still mechanism to exclude whole file; there is already automatic detection of instrumented functions. therefore, to simplify not using commands.
- not using eBPF because: with eBPF we can track latency, but we would not be able to assign errors to spans; some platforms may not have access to eBPF;
- not wrapping internal functions. benefit of wrapping is to keep original code without modifications. however, manual step for switching would still be requied. given every single function is duplciated and is within same package, code will quickly become messy and hard to maintain by user.
- not wrapping exported functions. typically, packages are failry big and performs lots of logic. oftencase, business domains are split only in few large packages. low level packages are already likely to be traced with standard tracing (MySQL, `het/http`, etc). thus, it is doubtful how much benefit would be from tracing only exported functions and only on import
- not wrapping exported functions with separate package, because this would lead to circular dependency failure, since some even exported functions in original package may be called withing same package. thus, we would either skip those calls, or fail with circular dependency while trying to wrap those.

[^2]: https://go.dev/doc/diagnostics

go-instrument

[![Stand With Ukraine](https://raw.githubusercontent.com/vshymanskyy/StandWithUkraine/main/banner2-direct.svg)](https://stand-with-ukraine.pp.ua)

<img align="right" width="290" height="290" src="https://www.jaegertracing.io/img/jaeger-vector.svg">

[![Slack chat][slack-img]](#get-in-touch)
[![Unit Tests][ci-img]][ci]
[![Coverage Status][cov-img]][cov]
[![Project+Community stats][community-badge]][community-stats]
[![FOSSA Status][fossa-img]][fossa]
[![OpenSSF Scorecard][openssf-img]][openssf]
[![OpenSSF Best Practices][openssf-bp-img]][openssf-bp] 
[![CLOMonitor][clomonitor-img]][clomonitor]
[![Artifact Hub][artifacthub-img]][artifacthub]

<img src="https://raw.githubusercontent.com/cncf/artwork/main/other/cncf-member/graduated/color/cncf-graduated-color.svg" width="250">

# Jaeger - a Distributed Tracing System

💥💥💥 Jaeger v2 is out! Read the [blog post](https://medium.com/jaegertracing/jaeger-v2-released-09a6033d1b10) and [try it out](https://www.jaegertracing.io/docs/latest/getting-started/).

## Quick Start

Get Jaeger running in seconds with Docker:

```bash
# Run Jaeger all-in-one (includes UI, collector, query, and in-memory storage)
docker run --rm --name jaeger \
 -p 16686:16686 \
 -p 4317:4317 \
 -p 4318:4318 \
 jaegertracing/jaeger:latest

# Access the UI at http://localhost:16686
# Send traces via OTLP: gRPC on port 4317, HTTP on port 4318
```

For production deployments and more options, see the [Getting Started Guide](https://www.jaegertracing.io/docs/latest/getting-started/).

## Architecture

```mermaid
graph TD
 SDK["OpenTelemetry SDK"] --> |HTTP or gRPC| COLLECTOR
 COLLECTOR["Jaeger Collector"] --> STORE[Storage]
 COLLECTOR --> |gRPC| PLUGIN[Storage Plugin]
 COLLECTOR --> |gRPC/sampling| SDK
 PLUGIN --> STORE
 QUERY[Jaeger Query Service] --> STORE
 QUERY --> |gRPC| PLUGIN
 UI[Jaeger UI] --> |HTTP| QUERY
 subgraph Application Host
 subgraph User Application
 SDK
 end
 end
```

Jaeger is a distributed tracing platform created by [Uber Technologies](https://eng.uber.com/distributed-tracing/) and donated to [Cloud Native Computing Foundation](https://cncf.io).

See Jaeger [documentation][doc] for getting started, operational details, and other information.

Jaeger is hosted by the [Cloud Native Computing Foundation](https://cncf.io) (CNCF) as the 7th top-level project, graduated in October 2019. See the CNCF [Jaeger incubation announcement](https://www.cncf.io/blog/2017/09/13/cncf-hosts-jaeger/) and [Jaeger graduation announcement](https://www.cncf.io/announcement/2019/10/31/cloud-native-computing-foundation-announces-jaeger-graduation/).

## Get Involved

Jaeger is an open source project with open governance. We welcome contributions from the community, and we would love your help to improve and extend the project. Here are [some ideas](https://www.jaegertracing.io/get-involved/) for how to get involved. Many of them do not even require any coding.

## Version Compatibility Guarantees

Since Jaeger uses many components from the [OpenTelemetry Collector](https://github.com/open-telemetry/opentelemetry-collector/) we try to maintain configuration compatibility between Jaeger releases. Occasionally, configuration options in Jaeger (or in Jaeger v1 CLI flags) can be deprecated due to usability improvements, new functionality, or changes in our dependencies.
In such situations, developers introducing the deprecation are required to follow [these guidelines](./CONTRIBUTING.md#deprecating-cli-flags).

In short, for a deprecated configuration option, you should expect to see the following message in the documentation or release notes:
```
(deprecated, will be removed after yyyy-mm-dd or in release vX.Y.Z, whichever is later)
```

A grace period of at least **3 months** or **two minor version bumps** (whichever is later) from the first release
containing the deprecation notice will be provided before the deprecated configuration option _can_ be deleted.

For example, consider a scenario where v2.0.0 is released on 01-Sep-2024 containing a deprecation notice for a configuration option.
This configuration option will remain in a deprecated state until the later of 01-Dec-2024 or v2.2.0 where it _can_ be removed on or after either of those events.
It may remain deprecated for longer than the aforementioned grace period.

## Go Version Compatibility Guarantees

The Jaeger project attempts to track the currently supported versions of Go, as [defined by the Go team](https://go.dev/doc/devel/release#policy).
Removing support for an unsupported Go version is not considered a breaking change.

Starting with the release of Go 1.21, support for Go versions will be updated as follows:

1. Soon after the release of a new Go minor version `N`, updates will be made to the build and tests steps to accommodate the latest Go minor version.
2. Soon after the release of a new Go minor version `N`, support for Go version `N-1` will be removed and version `N` will become the minimum required version.

Note: All importable code has been moved to internal packages, so there is no need to maintain backward compatibility with older compilers (previously version `N-1` was used).

## Related Repositories

### Components

 * [UI](https://github.com/jaegertracing/jaeger-ui)
 * [Data model](https://github.com/jaegertracing/jaeger-idl)

### Documentation

 * Published: https://www.jaegertracing.io/docs/
 * Source: https://github.com/jaegertracing/documentation

## Building From Source

See [CONTRIBUTING](./CONTRIBUTING.md).

## Contributing

See [CONTRIBUTING](./CONTRIBUTING.md).

Thanks to all the people who already contributed!

<a href="https://github.com/jaegertracing/jaeger/graphs/contributors">
 <img src="https://contributors-img.web.app/image?repo=jaegertracing/jaeger" />
</a>

### Maintainers

Rules for becoming a maintainer are defined in the [GOVERNANCE](./GOVERNANCE.md) document.
The official maintainers of the Jaeger project are listed in the [MAINTAINERS](./MAINTAINERS.md) file.
Please use `@jaegertracing/jaeger-maintainers` to tag them on issues / PRs.

Some repositories under [jaegertracing](https://github.com/jaegertracing) org have additional maintainers.

## Project Status Meetings

The Jaeger maintainers and contributors meet regularly on a video call. Everyone is welcome to join, including end users. For meeting details, see https://www.jaegertracing.io/get-in-touch/.

## Roadmap

See https://www.jaegertracing.io/docs/roadmap/

## Get in Touch

Have questions, suggestions, bug reports? Reach the project community via these channels:

 * [Slack chat room `#jaeger`][slack] (need to join [CNCF Slack][slack-join] for the first time)
 * [`jaeger-tracing` mail group](https://groups.google.com/forum/#!forum/jaeger-tracing)
 * GitHub [issues](https://github.com/jaegertracing/jaeger/issues) and [discussions](https://github.com/jaegertracing/jaeger/discussions)

## Security

Third-party security audits of Jaeger are available in https://github.com/jaegertracing/security-audits. Please see [Issue #1718](https://github.com/jaegertracing/jaeger/issues/1718) for the summary of available security mechanisms in Jaeger.

## Adopters

Jaeger as a product consists of multiple components. We want to support different types of users,
whether they are only using our instrumentation libraries or full end to end Jaeger installation,
whether it runs in production or you use it to troubleshoot issues in development.

Please see [ADOPTERS.md](./ADOPTERS.md) for some of the organizations using Jaeger today.
If you would like to add your organization to the list, please comment on our
[survey issue](https://github.com/jaegertracing/jaeger/issues/207).

## Sponsors

The Jaeger project owes its success in open source largely to the Cloud Native Computing Foundation (CNCF), our primary supporter. We deeply appreciate their vital support. Furthermore, we are grateful to Uber for their initial, project-launching donation, and for the continuous contributions of software and infrastructure from 1Password, Codecov.io, Dosu, GitHub, Google Analytics, Netlify, and Oracle Cloud Infrastructure. Thank you for your generous support.

## License

Copyright (c) The Jaeger Authors. [Apache 2.0 License](./LICENSE).

[doc]: https://jaegertracing.io/docs/
[ci-img]: https://github.com/jaegertracing/jaeger/actions/workflows/ci-unit-tests.yml/badge.svg?branch=main
[ci]: https://github.com/jaegertracing/jaeger/actions/workflows/ci-unit-tests.yml?query=branch%3Amain
[cov-img]: https://codecov.io/gh/jaegertracing/jaeger/branch/main/graph/badge.svg
[cov]: https://codecov.io/gh/jaegertracing/jaeger/branch/main/
[fossa-img]: https://app.fossa.com/api/projects/git%2Bgithub.com%2Fjaegertracing%2Fjaeger.svg?type=shield
[fossa]: https://app.fossa.io/projects/git%2Bgithub.com%2Fjaegertracing%2Fjaeger?ref=badge_shield
[openssf-img]: https://api.securityscorecards.dev/projects/github.com/jaegertracing/jaeger/badge
[openssf]: https://securityscorecards.dev/viewer/?uri=github.com/jaegertracing/jaeger
[openssf-bp-img]: https://www.bestpractices.dev/projects/1273/badge
[openssf-bp]: https://www.bestpractices.dev/projects/1273
[clomonitor-img]: https://img.shields.io/endpoint?url=https://clomonitor.io/api/projects/cncf/jaeger/badge
[clomonitor]: https://clomonitor.io/projects/cncf/jaeger
[artifacthub-img]: https://img.shields.io/endpoint?url=https://artifacthub.io/badge/repository/jaegertracing
[artifacthub]: https://artifacthub.io/packages/search?repo=jaegertracing


[community-badge]: https://img.shields.io/badge/Project+Community-stats-blue.svg
[community-stats]: https://all.devstats.cncf.io/d/54/project-health?orgId=1&var-repogroup_name=Jaeger
[hotrod-tutorial]: https://medium.com/jaegertracing/take-jaeger-for-a-hotrod-ride-233cf43e46c2
[slack]: https://cloud-native.slack.com/archives/CGG7NFUJ3
[slack-join]: https://slack.cncf.io
[slack-img]: https://img.shields.io/badge/slack-join%20chat%20%E2%86%92-brightgreen?logo=slack

jaeger

OpenTelemetry auto-instrumentation tool for adding spans to functions

# otelinji

![Version](https://img.shields.io/github/v/tag/hedhyw/otelinji)
![Build Status](https://github.com/hedhyw/otelinji/actions/workflows/check.yml/badge.svg)
[![Go Report Card](https://goreportcard.com/badge/github.com/hedhyw/otelinji)](https://goreportcard.com/report/github.com/hedhyw/otelinji)
[![go-recipes](https://raw.githubusercontent.com/nikolaydubina/go-recipes/main/badge.svg?raw=true)](https://github.com/nikolaydubina/go-recipes)
[![Coverage Status](https://coveralls.io/repos/github/hedhyw/otelinji/badge.svg?branch=main)](https://coveralls.io/github/hedhyw/otelinji?branch=main)

OpenTelemetry auto-instrumentation tool. It injects a common tracing block to all exported functions.

![OpenTelemetry diff](./assets/diff.png)

Features:
- Custom templates are supported. Example: [template](./internal/pkg/assets/otel.tmpl).
- If the function contains a named `err` result parameter,
  then `err` will be recorded in the span.
- It supports different names of `ctx` parameter.
- This can also be used with OpenTracing, but will require a custom template.
- Using creativity, this can be used for any inserts at the beginning of the function.

## Installation

### MacOS/Linux HomeBrew

```sh
brew install hedhyw/main/otelinji
```

### Deb/Rpm

Latest DEB and RPM packages are available on [the releases page](https://github.com/hedhyw/otelinji/releases/latest).

### Go

```sh
go install github.com/hedhyw/otelinji/cmd/otelinji@latest
```

### Source

```sh
git clone git@github.com:hedhyw/otelinji.git
cd gherkingen
task build # Requires https://taskfile.dev/
cp ./bin/gherkingen /usr/local/bin
chmod +x /usr/local/bin
```

## Usage

### Basic usage

Inject the layer and rewrite the file (be careful, always commit all changes first).
- `otelinji -filename input_file.go > input_file.go`

  or

- `otelinji -w -filename input_file.go`

  or in docker

- ```sh
  docker run \
        --rm \
        --read-only \
        --network none \
        --rm \
        --volume $PWD:/host \
        hedhyw/otelinji:latest \
        -filename /host/internal/pkg/assets/assets_test.go
  ```

### Recursive run

Running for all Go files in the current directory.
```sh
# It will inject the layer to all exported functions.
# It will ignore vendor and .git folders, test and generated files.

find . -name "*.go" \
    | grep -v "vendor/\|.git/\|_test.go" \
    | xargs -n 1 -t otelinji -w -filename
```

The same in docker:
```sh
docker run \
      --rm \
      --read-only \
      --network none \
      --volume $PWD:/host \
      --entrypoint sh \
      hedhyw/otelinji:latest \
      -c " \
            find /host -name \"*.go\" \
            | grep -v \"vendor/\|.git/\|_test.go\" \
            | xargs -n 1 -t /app/otelinji -w -filename \
      "
```

### Help

```
otelinji --help

Usage of otelinji:
  -filename string
        golang file [required]
  -skip-generated DO NOT EDIT
        skip files with DO NOT EDIT comment (default true)
  -template string
        path to template file [optional] (default "@/otel")
  -version
        print application version and quit
  -w    write result to file [optional]
```

### IDE

### License

[MIT](./LICENSE).


otelinji

Live visualization of your Go application runtime statistics

<div align="center">
 <a href="https://github.com/arl/statsviz" title="Statsviz's Github repository.">
 <img src="https://raw.githubusercontent.com/arl/statsviz/readme-docs/logo.png?sanitize=true" width="100" height="auto"/>
 </a>
 
 
 


 <a href="https://pkg.go.dev/github.com/arl/statsviz" title="Statsviz on pkg.go.dev">
 <img src="https://pkg.go.dev/badge/github.com/arl/statsviz" alt="Go Reference">
 </a>
 <a href="https://img.shields.io/github/tag/arl/statsviz.svg" title="Latest tag">
 <img src="https://img.shields.io/github/tag/arl/statsviz.svg" alt="Latest tag">
 </a>
 <a href="https://awesome.re/mentioned-badge.svg" title="Mentioned in Awesome Go">
 <img src="https://awesome.re/mentioned-badge.svg" alt="Mentioned in Awesome Go">
 </a>


 <a href="https://github.com/arl/statsviz/actions">
 <img src="https://github.com/arl/statsviz/workflows/Tests-linux/badge.svg" alt="Linux CI">
 </a>
 <a href="https://github.com/arl/statsviz/actions">
 <img src="https://github.com/arl/statsviz/workflows/Tests-others/badge.svg" alt="Others CI">
 </a>
 <a href="https://codecov.io/gh/arl/statsviz">
 <img src="https://codecov.io/gh/arl/statsviz/branch/main/graph/badge.svg" alt="Codecov">
 </a>


</div>

# Statsviz


Visualize real time plots of your Go program [runtime/metrics](https://pkg.go.dev/runtime/metrics) including heap, objects, goroutines, GC pauses, scheduler and more.

<div align="center">
<img alt="statsviz ui" width="300px" height="auto" src="https://github.com/arl/statsviz/raw/readme-docs/window-light.png">
<img alt="statsviz ui" width="300px" height="auto" src="https://github.com/arl/statsviz/raw/readme-docs/window-dark.png">
</div>


- [Statsviz](#statsviz)
 - [Install](#install)
 - [Usage](#usage)
 - [Examples](#examples)
 - [How Does That Work?](#how-does-that-work)
 - [Documentation](#documentation)
 - [Go API](#go-api)
 - [Web User Interface](#web-user-interface)
 - [Plots](#plots)
 - [User Plots](#user-plots)
 - [Questions / Troubleshooting](#questions--troubleshooting)
 - [Contributing](#contributing)
 - [Changelog](#changelog)
 - [License: MIT](#license-mit)

## Install

Get the latest version:

```
go get github.com/arl/statsviz@latest
```


## Usage

Register `Statsviz` HTTP handlers with your application `http.ServeMux`.

```go
mux := http.NewServeMux()
statsviz.Register(mux)

go func() {
 log.Println(http.ListenAndServe("localhost:8080", mux))
}()
```

Open your browser at http://localhost:8080/debug/statsviz


## Examples

If you check any of the boxes below:
 - [ ] you use some HTTP framework
 - [ ] you want Statsviz to be located at `/my/path/to/statsviz` rather than `/debug/statsviz`
 - [ ] you want Statsviz under `https://` rather than `http://`
 - [ ] you want Statsviz behind some middleware

Then you should call `statsviz.NewServer()` (with or without options depending on your use case) in order to access the `Index()` and `Ws()` methods.

```go
srv, err := statsviz.NewServer(); // Create server or handle error
if err != nil { /* handle error */ }

// Do something with the handlers.
srv.Index() // UI (dashboard) handler func
srv.Ws() // Websocket handler func
```

Examples for the following cases, and more, are found in the [\_example](./_example/README.md) directory:

- use of `http.DefaultServeMux` or your own `http.ServeMux`
- wrap HTTP handler behind a middleware
- register the web page at `/foo/bar` instead of `/debug/statsviz`
- use `https://` rather than `http://`
- register Statsviz handlers with various Go HTTP libraries/frameworks:
 - [echo](https://github.com/labstack/echo/)
 - [fasthttp](https://github.com/valyala/fasthttp)
 - [fiber](https://github.com/gofiber/fiber/)
 - [gin](https://github.com/gin-gonic/gin)
 - and many others thanks to many contributors!


## How Does That Work?

Statsviz is made of two parts:

- The `Ws` serves a Websocket endpoint. When a client connects, your program's [runtime/metrics](https://pkg.go.dev/runtime/metrics) are sent to the browser, once per second, via the websocket connection.

- the `Index` http handler serves Statsviz user interface at `/debug/statsviz` at the address served by your program. When served, the UI connects to the Websocket endpoint and starts receiving data points.


## Documentation


### Go API

Check out the API reference on [pkg.go.dev](https://pkg.go.dev/github.com/arl/statsviz#section-documentation).

### Web User Interface


#### Top Bar

<img alt="webui-annotated" src="https://github.com/arl/statsviz/raw/readme-docs/webui-annotated.png">

##### Category Selector

<img alt="menu-categories" src="https://github.com/arl/statsviz/raw/readme-docs/menu-categories.png">

Each plot belongs to one or more categories. The category selector allows you to filter the visible plots by categories.

##### Visible Time Range

<img alt="menu-timerange" src="https://github.com/arl/statsviz/raw/readme-docs/menu-timerange.png">

Use the time range selector to define the visualized time span.

##### Show/Hide GC events

<img alt="menu-gc-events" src="https://github.com/arl/statsviz/raw/readme-docs/menu-gc-events.png">

Show or hide the vertical lines representing garbage collection events.

##### Pause updates

<img alt="menu-play" src="https://github.com/arl/statsviz/raw/readme-docs/menu-play.png">

Pause or resume the plot updates.


#### Plot Controls

<img alt="webui-annotated" src="https://github.com/arl/statsviz/raw/readme-docs/plot-controls-annotated.png">


### Plots

Which plots are visible depends on:
 - your Go version,since some plots are only available in newer versions.
 - what plot categories are currently selected. By default all plots are shown.

#### Allocation and Free Rate

<img width="50%" alt="alloc-free-rate" src="https://github.com/arl/statsviz/raw/readme-docs/plots/alloc-free-rate.png">

#### CGO Calls

<img width="50%" alt="cgo" src="https://github.com/arl/statsviz/raw/readme-docs/plots/cgo.png">

#### CPU (GC)

<img width="50%" alt="cpu-gc" src="https://github.com/arl/statsviz/raw/readme-docs/plots/cpu-gc.png">

#### CPU (Overall)

<img width="50%" alt="cpu-overall" src="https://github.com/arl/statsviz/raw/readme-docs/plots/cpu-overall.png">

#### CPU (Scavenger)

<img width="50%" alt="cpu-scavenger" src="https://github.com/arl/statsviz/raw/readme-docs/plots/cpu-scavenger.png">

#### Garbage Collection

<img width="50%" alt="garbage-collection" src="https://github.com/arl/statsviz/raw/readme-docs/plots/garbage-collection.png">

#### GC Cycles

<img width="50%" alt="gc-cycles" src="https://github.com/arl/statsviz/raw/readme-docs/plots/gc-cycles.png">

#### GC Pauses

<img width="50%" alt="gc-pauses" src="https://github.com/arl/statsviz/raw/readme-docs/plots/gc-pauses.png">

#### GC Scan

<img width="50%" alt="gc-scan" src="https://github.com/arl/statsviz/raw/readme-docs/plots/gc-scan.png">

#### GC Stack Size

<img width="50%" alt="gc-stack-size" src="https://github.com/arl/statsviz/raw/readme-docs/plots/gc-stack-size.png">

#### Goroutines

<img width="50%" alt="goroutines" src="https://github.com/arl/statsviz/raw/readme-docs/plots/goroutines.png">

#### Heap (Details)

<img width="50%" alt="heap-details" src="https://github.com/arl/statsviz/raw/readme-docs/plots/heap-details.png">

#### Live Bytes

<img width="50%" alt="live-bytes" src="https://github.com/arl/statsviz/raw/readme-docs/plots/live-bytes.png">

#### Live Objects

<img width="50%" alt="live-objects" src="https://github.com/arl/statsviz/raw/readme-docs/plots/live-objects.png">

#### Memory Classes

<img width="50%" alt="memory-classes" src="https://github.com/arl/statsviz/raw/readme-docs/plots/memory-classes.png">

#### MSpan/MCache

<img width="50%" alt="mspan-mcache" src="https://github.com/arl/statsviz/raw/readme-docs/plots/mspan-mcache.png">

#### Mutex Wait

<img width="50%" alt="mutex-wait" src="https://github.com/arl/statsviz/raw/readme-docs/plots/mutex-wait.png">

#### Runnable Time

<img width="50%" alt="runnable-time" src="https://github.com/arl/statsviz/raw/readme-docs/plots/runnable-time.png">

#### Scheduling Events

<img width="50%" alt="sched-events" src="https://github.com/arl/statsviz/raw/readme-docs/plots/sched-events.png">

#### Size Classes

<img width="50%" alt="size-classes" src="https://github.com/arl/statsviz/raw/readme-docs/plots/size-classes.png">

#### GC Pauses

<img width="50%" alt="gc-pauses" src="https://github.com/arl/statsviz/raw/readme-docs/plots/gc-pauses.png">


### User Plots

Since `v0.6` you can add your own plots to Statsviz dashboard, in order to easily
visualize your application metrics next to runtime metrics.

Please see the [userplots example](_example/userplots/main.go).


## Questions / Troubleshooting

Either use GitHub's [discussions](https://github.com/arl/statsviz/discussions) or come to say hi and ask a live question on [#statsviz channel on Gopher's slack](https://gophers.slack.com/archives/C043DU4NZ9D).

## Contributing

Please use [issues](https://github.com/arl/statsviz/issues/new/choose) for bugs and feature requests. 
Pull-requests are always welcome! 
More details in [CONTRIBUTING.md](CONTRIBUTING.md).

## Changelog

See [CHANGELOG.md](./CHANGELOG.md).

## License: MIT

See [LICENSE](LICENSE)

statsviz

profile
=======

Simple profiling support package for Go

[![Build Status](https://travis-ci.org/pkg/profile.svg?branch=master)](https://travis-ci.org/pkg/profile) [![GoDoc](http://godoc.org/github.com/pkg/profile?status.svg)](http://godoc.org/github.com/pkg/profile)


installation
------------

    go get github.com/pkg/profile

usage
-----

Enabling profiling in your application is as simple as one line at the top of your main function

```go
import "github.com/pkg/profile"

func main() {
    defer profile.Start().Stop()
    ...
}
```

options
-------

What to profile is controlled by config value passed to profile.Start.
By default CPU profiling is enabled.

```go
import "github.com/pkg/profile"

func main() {
    // p.Stop() must be called before the program exits to
    // ensure profiling information is written to disk.
    p := profile.Start(profile.MemProfile, profile.ProfilePath("."), profile.NoShutdownHook)
    ...
    // You can enable different kinds of memory profiling, either Heap or Allocs where Heap
    // profiling is the default with profile.MemProfile.
    p := profile.Start(profile.MemProfileAllocs, profile.ProfilePath("."), profile.NoShutdownHook)
}
```

Several convenience package level values are provided for cpu, memory, and block (contention) profiling.

For more complex options, consult the [documentation](http://godoc.org/github.com/pkg/profile).

contributing
------------

We welcome pull requests, bug fixes and issue reports.

Before proposing a change, please discuss it first by raising an issue.


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