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jaegertracing/jaeger

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22,890 stars·2,957 forks·Go·Apache-2.0·44 viewswww.jaegertracing.io↗

Jaeger

Jaeger is a distributed tracing platform used for collecting, storing, and visualizing request flows across microservices. It identifies performance bottlenecks and errors by tracking requests as they move through multiple service boundaries. The system includes telemetry collectors, a multi-tenant backend, and a trace visualizer.

The platform provides a multi-tenant tracing infrastructure that isolates data and queries by tenant to support shared environments. It supports standardized telemetry ingestion via the OpenTelemetry Protocol over gRPC and HTTP. To manage storage costs and overhead, it employs adaptive trace sampling to dynamically adjust the volume of captured request data based on traffic patterns.

The system handles distributed trace storage through pluggable database backends and manages the data lifecycle via automated index rollover and cleanup. Its analysis capabilities include tag-based searches, transaction timeline visualization, service dependency graphs, and side-by-side trace execution comparison. Security is addressed through TLS communication encryption and trace data anonymization.

The project supports custom distribution building and cross-platform binary compilation to create tailored executables based on selected extensions and processors.

Features

  • Distributed Tracing Backends - Provides specialized storage and analysis engines for capturing and visualizing request flows across microservices.
  • Distributed Tracing - Implements a complete platform for collecting, storing, and analyzing telemetry data across distributed service architectures.
  • Distributed Storage - Persists trace data to distributed database backends for scalable long-term storage and retrieval.
  • Multi-Tenant Data Management - Provides a storage system that logically isolates trace data and queries by tenant to support shared environments.
  • Multi-Tenant Infrastructure - Isolates trace data and queries for different teams or environments within a shared distributed tracing platform.
  • Multi-Tenant Isolation Layers - Segregates trace records and queries by tenant using metadata headers to support shared environments.
  • Multi-tenancy Isolation - Implements architectural isolation of trace data and queries via HTTP headers to support multi-tenant environments.
  • Distributed Tracing and Execution Analysis - Analyzes request flows across service boundaries using timelines and graphs to locate performance bottlenecks.
  • Distributed Tracing - Tracks requests across multiple services using unique identifiers to identify performance bottlenecks.
  • Telemetry Collectors - Ships a telemetry collector that ingests and processes data using the OpenTelemetry Protocol via gRPC and HTTP.
  • Performance Monitoring - Visualizes transaction flows and latency across service boundaries to identify system delays.
  • Telemetry Ingestion - Implements connectors and protocols for streaming application telemetry into the observability backend.
  • Data Lifecycle Management - Automates the rollover and deletion of old trace indices to ensure storage efficiency.
  • Pluggable Storage Drivers - Provides a provider interface that allows different database engines to store and retrieve trace records.
  • Trace Comparison - Enables side-by-side comparison of two different traces to identify performance regressions or behavioral changes.
  • Tag-Based Search - Provides a search interface to locate specific transactions using metadata tags and regular expressions.
  • Trace Sampling - Dynamically adjusts the volume of captured traces based on traffic patterns to balance visibility and storage costs.
  • Adaptive Sampling - Dynamically adjusts the volume of captured request data based on traffic patterns to manage storage costs.
  • Trace Visualization - Provides a web interface for rendering and navigating large-scale distributed traces through timelines and dependency graphs.
  • Observability Backends - Distributed tracing platform for monitoring microservices.
  • Monitoring and Alerting - Distributed tracing system.
  • Monitoring & APM - Distributed tracing system for microservices.
  • Observability and Monitoring - Distributed tracing platform for microservices.
  • Performance Monitoring - Distributed tracing system.
  • Tracing And Profiling - Platform for distributed tracing and monitoring.
  • Microservice Reference Implementations - Distributed tracing system for microservice monitoring.
  • Performance Analysis - Distributed tracing system for monitoring microservices.

Star history

Star history chart for jaegertracing/jaegerStar history chart for jaegertracing/jaeger

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Jaeger

These projects share indexed features with Jaeger. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • victoriametrics/victoriametricsVictoriaMetrics avatar

    VictoriaMetrics/VictoriaMetrics

    16,343View on GitHub↗

    VictoriaMetrics is a high-performance, scalable time series database and observability platform designed for long-term storage and analysis of metric, log, and trace data. It functions as a unified backend for monitoring ecosystems, offering full compatibility with industry-standard protocols and query languages. The system is built to handle massive data volumes through a distributed architecture that supports horizontal scaling and efficient data lifecycle management. The platform distinguishes itself through a storage engine that utilizes consistent hashing for data sharding and log-struct

    Godatabasegrafanagraphite
    View on GitHub↗16,343
  • uptrace/uptraceuptrace avatar

    uptrace/uptrace

    4,098View on GitHub↗

    Uptrace is an OpenTelemetry-based observability platform designed to collect, store, and analyze distributed traces, metrics, and logs. It functions as a centralized logging backend, a distributed tracing system, and a metrics engine to monitor application performance and system health. The platform is distinguished by AI-powered operational capabilities, allowing users to query telemetry data and manage monitoring dashboards using natural language. It specifically includes specialized monitoring for generative AI pipelines, tracking token usage and response quality for LLM interactions and r

    Goapmapplication-monitoringclickhouse
    View on GitHub↗4,098
  • grafana/tempografana avatar

    grafana/tempo

    5,079View on GitHub↗

    Grafana Tempo is a high-scale distributed tracing backend and columnar trace database. It serves as an observability data store that persists and queries spans and traces using OpenTelemetry standards, allowing for the analysis of request flows across microservices. The system distinguishes itself by using an object-store based backend with columnar Parquet storage. This architecture enables efficient attribute searching and large-scale data retrieval through dedicated attribute columnization and block-based data partitioning. It includes a specialized TraceQL query engine for filtering trace

    Godistributed-tracinggrafana
    View on GitHub↗5,079
  • openzipkin/zipkinopenzipkin avatar

    openzipkin/zipkin

    17,431View on GitHub↗

    Zipkin is an open-source distributed tracing system designed to collect, store, and visualize timing data across complex service architectures. It provides a platform for monitoring request lifecycles, enabling developers to identify latency bottlenecks and performance issues by tracking operations as they move through heterogeneous service environments. The system distinguishes itself through a standardized data model and a pluggable storage architecture that supports various backend databases. It utilizes sampling strategies to manage telemetry volume and employs asynchronous collection met

    Javadistributed-tracingobservabilityopenzipkin
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Frequently asked questions

What does jaegertracing/jaeger do?

Jaeger is a distributed tracing platform used for collecting, storing, and visualizing request flows across microservices. It identifies performance bottlenecks and errors by tracking requests as they move through multiple service boundaries. The system includes telemetry collectors, a multi-tenant backend, and a trace visualizer.

What are the main features of jaegertracing/jaeger?

The main features of jaegertracing/jaeger are: Distributed Tracing Backends, Distributed Tracing, Distributed Storage, Multi-Tenant Data Management, Multi-Tenant Infrastructure, Multi-Tenant Isolation Layers, Multi-tenancy Isolation, Distributed Tracing and Execution Analysis.

Which projects share features with jaegertracing/jaeger?

Projects with overlapping indexed features include: victoriametrics/victoriametrics — VictoriaMetrics is a high-performance, scalable time series database and observability platform designed for long-term… uptrace/uptrace — Uptrace is an OpenTelemetry-based observability platform designed to collect, store, and analyze distributed traces,… grafana/tempo — Grafana Tempo is a high-scale distributed tracing backend and columnar trace database. It serves as an observability… openzipkin/zipkin — Zipkin is an open-source distributed tracing system designed to collect, store, and visualize timing data across… signoz/signoz — SigNoz is a full-stack observability platform designed to collect, store, and visualize metrics, logs, and distributed… langchain-ai/deepagents — Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing…