opentracing vs dd-trace vs prom-client vs jaeger-client vs zipkin vs tracer
Distributed Tracing Libraries Comparison
1 Year
opentracingdd-traceprom-clientjaeger-clientzipkintracerSimilar Packages:
What's Distributed Tracing Libraries?

Distributed tracing libraries are essential tools for monitoring and troubleshooting microservices architectures. They help developers understand the flow of requests through various services, identify bottlenecks, and optimize performance. These libraries provide a way to collect and visualize trace data, enabling teams to pinpoint issues and improve overall system reliability. Each library has its unique features and integrations, catering to different needs in the observability landscape.

Package Weekly Downloads Trend
Github Stars Ranking
Stat Detail
Package
Downloads
Stars
Size
Issues
Publish
License
opentracing4,233,5961,091195 kB35-Apache-2.0
dd-trace3,633,5026782.46 MB2942 days ago(Apache-2.0 OR BSD-3-Clause)
prom-client3,072,2633,211126 kB1188 months agoApache-2.0
jaeger-client460,514553-03 years agoApache-2.0
zipkin49,956567-765 years agoApache-2.0
tracer34,0791,15337 kB7a year agoMIT
Feature Comparison: opentracing vs dd-trace vs prom-client vs jaeger-client vs zipkin vs tracer

Integration

  • opentracing:

    opentracing is designed to be vendor-agnostic, allowing you to integrate with any tracing backend that supports the OpenTracing standard, giving you flexibility in choosing your observability tools.

  • dd-trace:

    dd-trace integrates seamlessly with Datadog, providing out-of-the-box support for various frameworks and languages, making it easy to set up and start monitoring your applications immediately.

  • prom-client:

    prom-client integrates well with Prometheus, enabling you to collect and expose metrics from your application for monitoring, but it does not provide tracing capabilities.

  • jaeger-client:

    jaeger-client supports multiple languages and integrates with various systems, allowing you to trace requests across different services and visualize them in the Jaeger UI.

  • zipkin:

    zipkin provides easy integration with various libraries and frameworks, allowing you to collect trace data from multiple sources and visualize it in the Zipkin UI.

  • tracer:

    tracer is lightweight and easy to integrate into existing applications, making it suitable for quick implementations without complex setups.

Data Collection

  • opentracing:

    opentracing provides a flexible API for manual instrumentation, allowing you to define spans and context propagation as needed, giving you control over what data is collected.

  • dd-trace:

    dd-trace automatically collects trace data, including spans and context propagation, without requiring extensive manual instrumentation, simplifying the monitoring process.

  • prom-client:

    prom-client focuses on metrics collection rather than tracing, allowing you to gather performance metrics like request counts and latencies, which can complement trace data.

  • jaeger-client:

    jaeger-client allows for detailed data collection, including custom tags and logs, giving you deep insights into the performance of your microservices.

  • zipkin:

    zipkin collects trace data in a structured format, allowing you to visualize the flow of requests and identify latency issues across your services.

  • tracer:

    tracer provides basic data collection capabilities, allowing you to instrument your code with minimal effort, making it suitable for simple use cases.

Performance Impact

  • opentracing:

    opentracing itself does not impose performance overhead, as it is an API specification. The performance impact depends on the underlying implementation you choose.

  • dd-trace:

    dd-trace is designed to minimize performance overhead, using sampling and asynchronous data transmission to ensure that monitoring does not significantly impact application performance.

  • prom-client:

    prom-client has a low performance impact when collecting metrics, but it is not designed for tracing, so it should be used in conjunction with a tracing library for complete observability.

  • jaeger-client:

    jaeger-client is optimized for high throughput and low latency, making it suitable for production environments where performance is critical.

  • zipkin:

    zipkin is designed to handle large volumes of trace data efficiently, but the performance impact can vary based on how it is implemented and the volume of data being collected.

  • tracer:

    tracer is lightweight and has minimal performance impact, making it suitable for applications where overhead needs to be kept to a minimum.

Visualization

  • opentracing:

    opentracing does not provide visualization itself, as it is an API specification. Visualization depends on the tracing backend you choose to implement.

  • dd-trace:

    dd-trace provides rich visualizations in the Datadog dashboard, allowing you to see traces, performance metrics, and error rates in a unified view, making it easy to identify issues.

  • prom-client:

    prom-client does not provide visualization for traces but can be used with Grafana to visualize metrics, offering insights into application performance alongside trace data.

  • jaeger-client:

    jaeger-client offers a powerful UI for visualizing traces, allowing you to see the entire request flow and identify bottlenecks in your microservices architecture.

  • zipkin:

    zipkin provides a user-friendly interface for visualizing trace data, allowing you to see the relationships between services and identify latency issues quickly.

  • tracer:

    tracer does not include built-in visualization tools, but you can export trace data to other systems for visualization, depending on your needs.

Community and Support

  • opentracing:

    opentracing has a large community of contributors and users, providing a wealth of resources and documentation to help developers implement tracing in their applications.

  • dd-trace:

    dd-trace benefits from strong community support and extensive documentation, backed by Datadog's resources, making it easy to find help and examples.

  • prom-client:

    prom-client has a solid community around Prometheus, with many resources available for users looking to implement metrics collection in their applications.

  • jaeger-client:

    jaeger-client has a vibrant open-source community, with ample documentation and support available for users, making it easier to troubleshoot and implement.

  • zipkin:

    zipkin has a strong open-source community, with good documentation and support available, making it easier for developers to implement and troubleshoot.

  • tracer:

    tracer has a smaller community compared to others, but it is straightforward to use, and documentation is available for common use cases.

How to Choose: opentracing vs dd-trace vs prom-client vs jaeger-client vs zipkin vs tracer
  • opentracing:

    Opt for opentracing if you want a vendor-neutral API for distributed tracing. It allows you to switch between different tracing implementations without changing your code, providing flexibility in your observability strategy.

  • dd-trace:

    Choose dd-trace if you are using Datadog as your monitoring solution. It provides seamless integration with Datadog's APM, allowing for detailed performance monitoring and error tracking across your services.

  • prom-client:

    Use prom-client if you are focused on metrics collection and monitoring with Prometheus. It is specifically designed for exporting metrics and is not a tracing library, but it complements tracing by providing performance metrics alongside traces.

  • jaeger-client:

    Select jaeger-client if you need a robust, open-source solution for distributed tracing. Jaeger is designed for high scalability and performance, making it suitable for large-scale applications that require detailed insights into request flows.

  • zipkin:

    Select zipkin if you prefer a distributed tracing system that is easy to set up and use. Zipkin provides a user-friendly interface for visualizing trace data and is well-suited for applications that need quick insights into request latency.

  • tracer:

    Choose tracer if you are looking for a simple and lightweight tracing library. It is easy to set up and can be a good choice for smaller applications or when you want to add basic tracing capabilities without much overhead.

README for opentracing

Build Status Coverage Status NPM Published Version Node Version Join the chat at https://gitter.im/opentracing/opentracing-javascript

OpenTracing API for JavaScript

This library is a JavaScript implementation of Open Tracing API. It is intended for use both on the server and in the browser.

Required Reading

To fully understand this platform API, it's helpful to be familiar with the OpenTracing project and terminology more specifically.

Quick Start

Install the package using npm:

npm install --save opentracing

Example

The package contains an example using a naive MockTracer implementation. To run the example:

npm run example

The output should look something like:

Spans:
    parent_span - 1521ms
        tag 'custom':'tag value'
        tag 'alpha':'1000'
    child_span - 503ms
        tag 'alpha':'200'
        tag 'beta':'50'

Code snippet

In your JavaScript code, add instrumentation to the operations to be tracked. This is composed primarily of using "spans" around operations of interest and adding log statements to capture useful data relevant to those operations.

const http = require('http');
const opentracing = require('opentracing');

// NOTE: the default OpenTracing tracer does not record any tracing information.
// Replace this line with the tracer implementation of your choice.
const tracer = new opentracing.Tracer();

const span = tracer.startSpan('http_request');
const opts = {
    host : 'example.com',
    method: 'GET',
    port : '80',
    path: '/',
};
http.request(opts, res => {
    res.setEncoding('utf8');
    res.on('error', err => {
        // assuming no retries, mark the span as failed
        span.setTag(opentracing.Tags.ERROR, true);
        span.log({'event': 'error', 'error.object': err, 'message': err.message, 'stack': err.stack});
        span.finish();
    });
    res.on('data', chunk => {
        span.log({'event': 'data_received', 'chunk_length': chunk.length});
    });
    res.on('end', () => {
        span.log({'event': 'request_end'});
        span.finish();
    });
}).end();

As noted in the source snippet, the default behavior of the opentracing package is to act as a "no op" implementation. To capture and make the tracing data actionable, the tracer object should be initialized with the OpenTracing implementation of your choice as in the pseudo-code below:

const CustomTracer = require('tracing-implementation-of-your-choice');
const tracer = new CustomTracer();

Usage in the browser

The package contains two bundles built with webpack that can be included using a standard <script> tag. The library will be exposed under the global opentracing namespace:

  • dist/opentracing-browser.min.js - minified, no runtime checks
  • dist/opentracing-browser.js - debug version with runtime checks

Usage with TypeScript

Since the source is written in TypeScript, if you are using TypeScript, you can just npm install the package and it will work out of the box. This is especially useful for implementors who want to type check their implementation with the base interface.

Global tracer

The library also provides a global singleton tracer for convenience. This can be set and accessed via the following:

opentracing.initGlobalTracer(new CustomTracer());

const tracer = opentracing.globalTracer();

Note: globalTracer() returns a wrapper on the actual tracer object. This is done for the convenience of use as it ensures that the function will always return a non-null object. This can be helpful in cases where it is difficult or impossible to know precisely when initGlobalTracer is called (for example, when writing a utility library that does not control the initialization process). For more precise control, individual Tracer objects can be used instead of the global tracer.

API Documentation

There is a hosted copy of the current generated ESDoc API Documentation here.

Contributing & developer information

See the OpenTracing website for general information on contributing to OpenTracing.

The project is written in TypeScript and built using a npm scripts. Run:

  • npm run build creates the compiled, distributable JavaScript code in ./lib
  • npm run watch incrementally compiles on file changes
  • npm run webpack creates the bundles for the browser in ./dist
  • npm test runs the tests
  • npm run typedoc generates the documentation in ./typedoc

Note: The minimum supported Node version for development is >=6. Tests can however be run on any version that this project supports (>=0.10).

OpenTracing tracer implementations

This section is intended for developers wishing to implement their own tracers. Developers who simply wish to use OpenTracing can safely ignore this information.

Custom tracer implementation

Implementations can subclass opentracing.Tracer, opentracing.Span, and the other API classes to build an OpenTracing tracer and implement the underscore prefixed methods such as _addTag to pick up a bit of common code implemented in the base classes.

API compatibility testing

If mocha is being used for unit testing, test/api_compatibility can be used to test the custom tracer. The file exports a single function that expects as an argument a function that will return a new instance of the tracer.

const apiCompatibilityChecks = require('opentracing/lib/test/api_compatibility.js').default;
apiCompatibilityChecks(() => new CustomTracer());

LICENSE

Apache License 2.0

MockTracer

A minimal example tracer is provided in the src/mock_tracer directory of the source code.