opentracing vs prom-client vs datadog-metrics vs zipkin vs sentry vs elastic-apm-node vs newrelic
Application Performance Monitoring (APM) and Observability
opentracingprom-clientdatadog-metricszipkinsentryelastic-apm-nodenewrelicSimilar Packages:

Application Performance Monitoring (APM) and Observability

Application Performance Monitoring (APM) and Observability tools are essential for modern web applications to ensure optimal performance, reliability, and user experience. These tools help developers and operations teams monitor, trace, and analyze the behavior of applications in real-time. They provide insights into application performance, detect anomalies, and identify bottlenecks, enabling faster troubleshooting and optimization. APM tools typically offer features like distributed tracing, error tracking, and metrics collection, which are crucial for maintaining the health of applications, especially in complex, microservices-based architectures. By leveraging these tools, organizations can proactively address performance issues, improve application stability, and deliver a seamless experience to their users.

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opentracing5,178,0921,089195 kB35-Apache-2.0
prom-client5,175,5913,430126 kB1342 years agoApache-2.0
datadog-metrics3,916,78414593.9 kB8a year agoMIT
zipkin64,773571-766 years agoApache-2.0
sentry11,936396.56 MB245 days agoFSL-1.1-Apache-2.0
elastic-apm-node0595906 kB3675 months agoBSD-2-Clause
newrelic09972.78 MB762 hours agoApache-2.0

Feature Comparison: opentracing vs prom-client vs datadog-metrics vs zipkin vs sentry vs elastic-apm-node vs newrelic

Metrics Collection

  • opentracing:

    opentracing focuses on collecting tracing data rather than metrics. It provides a standardized API for instrumenting applications and services to capture distributed traces, which can be sent to various tracing backends for analysis.

  • prom-client:

    prom-client is designed for exposing custom metrics in a format compatible with Prometheus. It supports various metric types, including counters, gauges, histograms, and summaries, allowing for flexible and detailed monitoring.

  • datadog-metrics:

    datadog-metrics provides comprehensive metrics collection with support for custom metrics, tags, and integrations with various services. It allows for real-time monitoring and alerting, making it easy to track application performance and resource usage.

  • zipkin:

    zipkin collects tracing data to visualize the flow of requests across distributed systems. It captures timing information for each segment of a trace, helping identify latency bottlenecks and performance issues in microservices architectures.

  • sentry:

    sentry primarily focuses on error and performance metrics. It collects data on application errors, crashes, and slow transactions, providing insights into the root causes of issues and their impact on users.

  • elastic-apm-node:

    elastic-apm-node collects detailed performance metrics, including transaction durations, database queries, and external HTTP calls. It integrates seamlessly with the Elastic Stack, allowing for advanced analysis and visualization of APM data.

  • newrelic:

    newrelic offers extensive metrics collection across applications, infrastructure, and third-party services. It provides real-time analytics, anomaly detection, and customizable dashboards for comprehensive performance monitoring.

Distributed Tracing

  • opentracing:

    opentracing is a vendor-neutral API for distributed tracing, allowing developers to instrument their applications and services consistently. It provides a standardized way to create and propagate traces across service boundaries, enabling interoperability between different tracing systems.

  • prom-client:

    prom-client does not provide distributed tracing capabilities, as it is focused on metrics collection. However, it can be used alongside tracing tools to provide complementary performance data.

  • datadog-metrics:

    datadog-metrics integrates with Datadog's distributed tracing capabilities, allowing for end-to-end visibility of requests across services. It provides detailed trace data, including spans, tags, and error information, which can be correlated with metrics and logs for comprehensive analysis.

  • zipkin:

    zipkin is a dedicated distributed tracing system that provides detailed visualization of request flows across microservices. It helps identify latency issues, trace errors, and understand the interactions between services in a distributed architecture.

  • sentry:

    sentry includes performance monitoring features that support distributed tracing. It allows for the visualization of traces across services, helping identify slow transactions and performance bottlenecks.

  • elastic-apm-node:

    elastic-apm-node supports distributed tracing out of the box, capturing traces across multiple services and providing a unified view of application performance. It integrates with the Elastic Stack, enabling advanced visualization and analysis of trace data.

  • newrelic:

    newrelic offers robust distributed tracing features, allowing for deep visibility into the flow of requests across services. It provides detailed trace data, including spans, attributes, and error information, which can be analyzed to identify bottlenecks and optimize performance.

Error Tracking

  • opentracing:

    opentracing does not provide error tracking functionality directly, as it is focused on tracing. However, errors can be annotated within traces, allowing for better visibility when integrated with tracing backends.

  • prom-client:

    prom-client does not include error tracking features, as it is designed for metrics collection. Error tracking can be implemented separately and correlated with metrics for a more comprehensive monitoring solution.

  • datadog-metrics:

    datadog-metrics integrates with Datadog's error tracking capabilities, allowing for the correlation of errors with metrics and traces. This integration helps identify the root causes of issues and their impact on application performance.

  • zipkin:

    zipkin focuses on tracing and does not provide dedicated error tracking features. However, errors can be annotated within traces, allowing for some visibility when analyzing trace data.

  • sentry:

    sentry is a leading error tracking platform that provides real-time insights into application errors, crashes, and performance issues. It offers detailed reports, stack traces, and context to help developers identify and fix bugs quickly.

  • elastic-apm-node:

    elastic-apm-node includes error tracking features that capture exceptions, stack traces, and contextual information. Errors are linked to transactions and traces, providing a comprehensive view of application issues within the Elastic Stack.

  • newrelic:

    newrelic offers advanced error tracking and analytics, including automatic detection of errors, detailed stack traces, and integration with performance data. It provides tools for diagnosing and resolving issues quickly.

Integration

  • opentracing:

    opentracing is designed to be vendor-agnostic, allowing for integration with multiple tracing systems. It provides a standardized API that can be implemented by various tracing backends, enabling interoperability and flexibility in choosing tracing solutions.

  • prom-client:

    prom-client integrates with Prometheus for metrics scraping and alerting. It can be used alongside other monitoring tools and dashboards, such as Grafana, to visualize and analyze metrics data.

  • datadog-metrics:

    datadog-metrics integrates seamlessly with Datadog's ecosystem, including infrastructure monitoring, log management, and security tools. It supports integrations with cloud providers, container platforms, and third-party services, making it a versatile choice for comprehensive observability.

  • zipkin:

    zipkin integrates with various instrumentation libraries and frameworks to collect tracing data. It can be used alongside other observability tools and supports integration with visualization platforms for analyzing trace data.

  • sentry:

    sentry integrates with various development tools, CI/CD pipelines, and third-party services. It supports integrations with platforms like GitHub, Slack, and Jira, facilitating collaboration and streamlined issue resolution.

  • elastic-apm-node:

    elastic-apm-node integrates tightly with the Elastic Stack, allowing for seamless ingestion of APM data into Elasticsearch. It also supports integrations with various frameworks, databases, and third-party services, enabling end-to-end observability.

  • newrelic:

    newrelic offers extensive integrations across applications, infrastructure, and third-party services. It supports a wide range of technologies, including cloud platforms, databases, and messaging systems, providing a unified view of performance across the entire stack.

Ease of Use: Code Examples

  • opentracing:

    opentracing provides a standardized API for implementing distributed tracing. While it requires more effort to instrument applications manually, it offers flexibility and interoperability across different tracing systems.

  • prom-client:

    prom-client is easy to use for exposing custom metrics in a Prometheus-compatible format. It provides a simple API for creating and updating metrics, making it quick to integrate into existing applications.

  • datadog-metrics:

    datadog-metrics provides a simple API for sending custom metrics to Datadog. It is easy to set up and use, especially for applications already integrated with the Datadog platform. The library supports tagging and batching of metrics, which helps optimize performance and reduce network overhead.

  • zipkin:

    zipkin requires instrumentation of applications to collect tracing data. While it may require more setup compared to some other tools, it provides valuable insights into distributed systems once integrated.

  • sentry:

    sentry offers a simple SDK for error tracking and performance monitoring. It is easy to integrate with various frameworks and platforms, providing real-time insights with minimal configuration.

  • elastic-apm-node:

    elastic-apm-node is straightforward to integrate with Node.js applications. It requires minimal configuration and provides automatic instrumentation for popular frameworks and libraries. The integration with the Elastic Stack allows for powerful visualization and analysis of APM data.

  • newrelic:

    newrelic offers a user-friendly interface and comprehensive documentation. The New Relic Node.js agent is easy to install and configure, providing automatic instrumentation and detailed performance insights with minimal setup.

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

  • opentracing:

    Choose opentracing if you want a vendor-neutral, open standard for distributed tracing. It is best suited for applications that need to implement tracing across multiple services and want the flexibility to switch between different tracing backends.

  • prom-client:

    Choose prom-client if you need a lightweight, open-source solution for exposing application metrics in a format compatible with Prometheus. It is ideal for applications that require custom metrics and want to leverage Prometheus for monitoring and alerting.

  • datadog-metrics:

    Choose datadog-metrics if you need a comprehensive monitoring solution that integrates seamlessly with Datadog's platform. It is ideal for applications that require detailed metrics, dashboards, and alerting capabilities across various services and infrastructure.

  • zipkin:

    Choose zipkin if you need a distributed tracing system that helps visualize and analyze the flow of requests across microservices. It is suitable for applications that require detailed tracing to identify latency issues and optimize service-to-service communication.

  • sentry:

    Choose sentry if your primary focus is on error tracking and performance monitoring. Sentry provides detailed insights into application errors, crashes, and performance issues, making it ideal for development teams that want to improve code quality and user experience.

  • elastic-apm-node:

    Choose elastic-apm-node if you are already using the Elastic Stack (Elasticsearch, Logstash, Kibana) and want to integrate APM data into your existing observability setup. It is suitable for applications that need deep performance insights and error tracking within the Elastic ecosystem.

  • newrelic:

    Choose newrelic if you need a full-featured APM solution with advanced analytics, real-time monitoring, and support for a wide range of technologies. New Relic is ideal for enterprises that require comprehensive performance management and scalability.

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.