opentracing vs prom-client vs datadog-metrics vs newrelic vs elastic-apm-node vs zipkin vs sentry
Monitoring and Observability Libraries Comparison
1 Year
opentracingprom-clientdatadog-metricsnewrelicelastic-apm-nodezipkinsentrySimilar Packages:
What's Monitoring and Observability Libraries?

These libraries are designed to help developers monitor, trace, and analyze the performance of their applications. They provide insights into application behavior, error tracking, and performance metrics, enabling developers to identify bottlenecks and improve overall system reliability. By integrating these tools, developers can gain visibility into their applications, understand user interactions, and optimize resource usage, ultimately enhancing user experience and operational efficiency.

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opentracing4,130,8851,090195 kB35-Apache-2.0
prom-client2,801,6373,241126 kB1209 months agoApache-2.0
datadog-metrics1,565,66814393.9 kB84 months agoMIT
newrelic925,6589772.28 MB639 days agoApache-2.0
elastic-apm-node447,675588930 kB34224 days agoBSD-2-Clause
zipkin42,615570-765 years agoApache-2.0
sentry9,573---14 years ago-
Feature Comparison: opentracing vs prom-client vs datadog-metrics vs newrelic vs elastic-apm-node vs zipkin vs sentry

Integration

  • opentracing:

    OpenTracing is designed to be vendor-agnostic, allowing developers to integrate with multiple tracing systems without being locked into a single vendor's solution.

  • prom-client:

    Prom-client is designed to work seamlessly with Prometheus, making it easy to expose application metrics for monitoring and alerting in cloud-native environments.

  • datadog-metrics:

    Datadog Metrics offers extensive integrations with cloud providers, databases, and other services, allowing for seamless data collection and visualization across your entire stack.

  • newrelic:

    New Relic provides a wide range of integrations with popular frameworks, libraries, and cloud services, enabling comprehensive monitoring across diverse environments.

  • elastic-apm-node:

    Elastic APM Node integrates well with the Elastic Stack, making it easy to correlate application performance data with logs and metrics stored in Elasticsearch.

  • zipkin:

    Zipkin can be integrated with various instrumentation libraries and frameworks, allowing for easy tracing of requests across microservices.

  • sentry:

    Sentry integrates with various programming languages and frameworks, providing SDKs that facilitate easy error tracking and performance monitoring across your applications.

Error Tracking

  • opentracing:

    OpenTracing does not provide built-in error tracking; it focuses on distributed tracing and requires integration with other tools for error monitoring.

  • prom-client:

    Prom-client does not include error tracking features; it is primarily focused on metrics collection for Prometheus.

  • datadog-metrics:

    Datadog Metrics provides basic error tracking capabilities, but its primary focus is on performance metrics rather than detailed error analysis.

  • newrelic:

    New Relic excels in error tracking, providing real-time alerts and detailed insights into application errors, including affected users and performance impact.

  • elastic-apm-node:

    Elastic APM Node offers comprehensive error tracking features, including detailed stack traces and contextual information to help diagnose issues quickly.

  • zipkin:

    Zipkin does not focus on error tracking; it specializes in tracing requests and analyzing latency across services.

  • sentry:

    Sentry is a powerful error tracking tool that captures exceptions in real-time, providing detailed context and insights to help developers resolve issues efficiently.

Performance Monitoring

  • opentracing:

    OpenTracing does not provide performance monitoring features; it is focused on distributed tracing and requires integration with other tools for performance metrics.

  • prom-client:

    Prom-client is designed for performance monitoring by exposing application metrics for Prometheus, allowing for efficient monitoring and alerting.

  • datadog-metrics:

    Datadog Metrics provides robust performance monitoring capabilities, including real-time dashboards and alerts for various application metrics.

  • newrelic:

    New Relic provides comprehensive performance monitoring features, including application performance metrics, transaction traces, and user interaction tracking.

  • elastic-apm-node:

    Elastic APM Node offers in-depth performance monitoring, including transaction tracing and detailed metrics for application performance analysis.

  • zipkin:

    Zipkin specializes in performance monitoring through distributed tracing, allowing developers to analyze request latency and service dependencies.

  • sentry:

    Sentry provides some performance monitoring features, but its primary focus is on error tracking rather than comprehensive performance metrics.

Usability

  • opentracing:

    OpenTracing requires a deeper understanding of distributed tracing concepts, which may present a steeper learning curve for new users.

  • prom-client:

    Prom-client is straightforward to use for developers familiar with Prometheus, but may require additional setup for those new to the ecosystem.

  • datadog-metrics:

    Datadog Metrics offers a user-friendly interface with customizable dashboards and easy navigation, making it accessible for teams of all skill levels.

  • newrelic:

    New Relic is known for its intuitive user interface and ease of use, making it simple for developers to monitor application performance without extensive training.

  • elastic-apm-node:

    Elastic APM Node is relatively easy to set up, especially for teams already using the Elastic Stack, but may require some learning for new users.

  • zipkin:

    Zipkin has a more technical interface that may require some familiarity with tracing concepts, but it provides powerful insights into request flows.

  • sentry:

    Sentry provides a clean and user-friendly interface, making it easy for developers to track errors and performance issues without extensive configuration.

Community and Support

  • opentracing:

    OpenTracing has a growing community, but as a specification, it relies on third-party implementations for support and resources.

  • prom-client:

    Prom-client has a dedicated community within the Prometheus ecosystem, with good documentation and community support available.

  • datadog-metrics:

    Datadog Metrics has a strong community and extensive documentation, along with professional support options for enterprise users.

  • newrelic:

    New Relic has a robust support system and a large community, providing users with access to resources, forums, and professional support services.

  • elastic-apm-node:

    Elastic APM Node benefits from the large Elastic community and comprehensive documentation, making it easy to find resources and support.

  • zipkin:

    Zipkin has a supportive community and good documentation, but may have less extensive resources compared to larger commercial solutions.

  • sentry:

    Sentry has an active community and provides extensive documentation, along with professional support options for users needing assistance.

How to Choose: opentracing vs prom-client vs datadog-metrics vs newrelic vs elastic-apm-node vs zipkin vs sentry
  • opentracing:

    Use OpenTracing if you want a vendor-neutral API for distributed tracing that allows you to switch between different tracing implementations without changing your code. It is best for applications that require flexibility in choosing tracing backends.

  • prom-client:

    Choose Prom-client if you are looking for a lightweight solution to expose application metrics in a format compatible with Prometheus. It is ideal for applications that need simple metric collection and monitoring without the overhead of a full-fledged APM solution.

  • datadog-metrics:

    Choose Datadog Metrics if you need a comprehensive monitoring solution that integrates seamlessly with cloud services and provides advanced analytics features. It is ideal for teams looking for a robust dashboarding and alerting system with extensive integrations.

  • newrelic:

    Opt for New Relic if you seek a user-friendly interface and strong support for a wide range of programming languages and frameworks. It is particularly beneficial for teams that prioritize real-time performance monitoring and detailed transaction tracing.

  • elastic-apm-node:

    Select Elastic APM Node if you are already using the Elastic Stack (Elasticsearch, Logstash, Kibana) and want to leverage its powerful search and analytics capabilities. It is suitable for applications that require detailed performance monitoring and error tracking with a focus on distributed tracing.

  • zipkin:

    Use Zipkin if you are focused on distributed tracing and need a dedicated solution for tracking requests across microservices. It is suitable for applications that require in-depth analysis of service dependencies and latency.

  • sentry:

    Select Sentry if you need a robust error tracking solution that provides real-time alerts and detailed stack traces. It is particularly useful for applications that require immediate feedback on errors and performance issues, helping developers quickly identify and resolve problems.

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.