Alternatives to Jaeger logo

Alternatives to Jaeger

Zipkin, AppDynamics, Prometheus, OpenTracing, and Datadog are the most popular alternatives and competitors to Jaeger.
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What is Jaeger and what are its top alternatives?

Jaeger is an open-source distributed tracing system that helps users monitor and troubleshoot microservices-based distributed systems. Its key features include end-to-end tracing, high scalability, support for multiple programming languages, and integration with various cloud platforms. However, some limitations of Jaeger include the complexity of setting up and maintaining the system, as well as potential performance overhead in highly dynamic environments.

  1. Zipkin: Zipkin is another popular open-source distributed tracing system that offers features similar to Jaeger. It supports multiple backends, scalable architecture, and compatibility with various programming languages. However, Zipkin may lack some advanced features found in Jaeger.
  2. OpenTelemetry: OpenTelemetry is a set of APIs, libraries, agents, and instrumentation to provide observability for distributed systems. It offers vendor-agnostic instrumentation and interoperability, as well as active community support. However, OpenTelemetry may require more configuration compared to Jaeger.
  3. SkyWalking: Apache SkyWalking is an APM tool with distributed tracing capabilities that supports various backend systems and programming languages. It offers advanced features like service mesh integration and anomaly detection. However, SkyWalking may have a steeper learning curve than Jaeger.
  4. Instana: Instana is a commercial APM solution that includes distributed tracing as one of its features. It provides automatic instrumentation, AI-powered analysis, and deep visibility into microservices architectures. However, Instana comes with a cost compared to the open-source Jaeger.
  5. Datadog APM: Datadog APM is a cloud-based application performance monitoring solution that offers distributed tracing capabilities. It provides end-to-end visibility, smart alerting, and deep integrations with various technologies. However, Datadog APM is a paid service and may not be as customizable as Jaeger.
  6. New Relic: New Relic is an APM tool that includes distributed tracing features for monitoring complex application environments. It offers real-time insights, detailed performance metrics, and seamless integration with various platforms. However, New Relic may come with a higher cost compared to Jaeger.
  7. LightStep: LightStep is a distributed tracing solution designed for monitoring and debugging microservices architectures. It provides advanced analytics, real-time exploration, and distributed context propagation. However, LightStep may have a more limited free tier compared to Jaeger.
  8. Honeycomb: Honeycomb is a platform for observability that includes distributed tracing capabilities for diagnosing performance issues in complex systems. It offers high granularity data, adaptive sampling, and collaborative debugging features. However, Honeycomb may have a higher price point compared to Jaeger.
  9. Wavefront: Wavefront is a monitoring and analytics platform that includes distributed tracing functionality for tracking requests across microservices. It offers real-time analytics, machine learning-driven insights, and scalable architecture. However, Wavefront may be more focused on metrics monitoring compared to Jaeger.
  10. AppDynamics: AppDynamics is an APM platform that provides distributed tracing capabilities for monitoring application performance in real-time. It offers visibility into user transactions, code-level diagnostics, and integration with various technologies. However, AppDynamics may have a higher cost compared to Jaeger.

Top Alternatives to Jaeger

  • Zipkin
    Zipkin

    It helps gather timing data needed to troubleshoot latency problems in service architectures. Features include both the collection and lookup of this data. ...

  • AppDynamics
    AppDynamics

    AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics. ...

  • Prometheus
    Prometheus

    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. ...

  • OpenTracing
    OpenTracing

    Consistent, expressive, vendor-neutral APIs for distributed tracing and context propagation. ...

  • Datadog
    Datadog

    Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog! ...

  • Splunk
    Splunk

    It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data. ...

  • Titan
    Titan

    Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time. ...

  • JavaScript
    JavaScript

    JavaScript is most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic,and supports object-oriented, imperative, and functional programming styles. ...

Jaeger alternatives & related posts

Zipkin logo

Zipkin

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    AppDynamics logo

    AppDynamics

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    Farzeem Diamond Jiwani
    Software Engineer at IVP · | 8 upvotes · 1.4M views

    Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

    Current Environment: .NET Core Web app hosted on Microsoft IIS

    Future Environment: Web app will be hosted on Microsoft Azure

    Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

    Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

    Please advise on the above. Thanks!

    See more

    We are evaluating an APM tool and would like to select between AppDynamics or Datadog. Our applications are largely hosted on Microsoft Azure but we would keep the option to move to AWS or Google Cloud Platform in the future.

    In addition to core Azure services, we will be hosting other components - including MongoDB, Keycloak, PagerDuty, etc. Our applications are largely C# and React-based using frontend for Backend patterns and Azure API gateway. In addition, there are close to 50+ external services integrated using both REST and SOAP.

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    Prometheus logo

    Prometheus

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    Matt Menzenski
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    Conor Myhrvold
    Tech Brand Mgr, Office of CTO at Uber · | 15 upvotes · 5M views

    Why we spent several years building an open source, large-scale metrics alerting system, M3, built for Prometheus:

    By late 2014, all services, infrastructure, and servers at Uber emitted metrics to a Graphite stack that stored them using the Whisper file format in a sharded Carbon cluster. We used Grafana for dashboarding and Nagios for alerting, issuing Graphite threshold checks via source-controlled scripts. While this worked for a while, expanding the Carbon cluster required a manual resharding process and, due to lack of replication, any single node’s disk failure caused permanent loss of its associated metrics. In short, this solution was not able to meet our needs as the company continued to grow.

    To ensure the scalability of Uber’s metrics backend, we decided to build out a system that provided fault tolerant metrics ingestion, storage, and querying as a managed platform...

    https://eng.uber.com/m3/

    (GitHub : https://github.com/m3db/m3)

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    OpenTracing logo

    OpenTracing

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    Consistent, expressive, vendor-neutral APIs for distributed tracing and context propagation.
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        Noah Zoschke
        Engineering Manager at Segment · | 30 upvotes · 298.8K views

        We just launched the Segment Config API (try it out for yourself here) — a set of public REST APIs that enable you to manage your Segment configuration. Behind the scenes the Config API is built with Go , GRPC and Envoy.

        At Segment, we build new services in Go by default. The language is simple so new team members quickly ramp up on a codebase. The tool chain is fast so developers get immediate feedback when they break code, tests or integrations with other systems. The runtime is fast so it performs great at scale.

        For the newest round of APIs we adopted the GRPC service #framework.

        The Protocol Buffer service definition language makes it easy to design type-safe and consistent APIs, thanks to ecosystem tools like the Google API Design Guide for API standards, uber/prototool for formatting and linting .protos and lyft/protoc-gen-validate for defining field validations, and grpc-gateway for defining REST mapping.

        With a well designed .proto, its easy to generate a Go server interface and a TypeScript client, providing type-safe RPC between languages.

        For the API gateway and RPC we adopted the Envoy service proxy.

        The internet-facing segmentapis.com endpoint is an Envoy front proxy that rate-limits and authenticates every request. It then transcodes a #REST / #JSON request to an upstream GRPC request. The upstream GRPC servers are running an Envoy sidecar configured for Datadog stats.

        The result is API #security , #reliability and consistent #observability through Envoy configuration, not code.

        We experimented with Swagger service definitions, but the spec is sprawling and the generated clients and server stubs leave a lot to be desired. GRPC and .proto and the Go implementation feels better designed and implemented. Thanks to the GRPC tooling and ecosystem you can generate Swagger from .protos, but it’s effectively impossible to go the other way.

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        Robert Zuber

        Our primary source of monitoring and alerting is Datadog. We’ve got prebuilt dashboards for every scenario and integration with PagerDuty to manage routing any alerts. We’ve definitely scaled past the point where managing dashboards is easy, but we haven’t had time to invest in using features like Anomaly Detection. We’ve started using Honeycomb for some targeted debugging of complex production issues and we are liking what we’ve seen. We capture any unhandled exceptions with Rollbar and, if we realize one will keep happening, we quickly convert the metrics to point back to Datadog, to keep Rollbar as clean as possible.

        We use Segment to consolidate all of our trackers, the most important of which goes to Amplitude to analyze user patterns. However, if we need a more consolidated view, we push all of our data to our own data warehouse running PostgreSQL; this is available for analytics and dashboard creation through Looker.

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        Splunk logo

        Splunk

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        Titan logo

        Titan

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              Relatively fast to the end user
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              Functional programming
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              Its everywhere
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              Because I love functions
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              JavaScript is the New PHP
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              Expansive community
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              Can be used in backend, frontend and DB
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              Easy
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            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber · | 44 upvotes · 12.5M views

            How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

            Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

            Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

            https://eng.uber.com/distributed-tracing/

            (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

            Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

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