DataDog/dd-trace-java

Datadog APM client for Java

Owned by Datadog

View on GitHub ↗Jump to charts ↓Open shareable report →

DataDog/dd-trace-java has 700+ stars and 350+ forks on GitHub, is written in Java, and was last pushed on Oct 18, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 39 minutes ago
Added to GitGenius on October 11th, 2026
Created on April 24th, 2017
Open Issues & Pull Requests: 489 (+2)
GitHub issues: Enabled
Number of forks: 364
Total Stargazers: 738 (+0)
Total Subscribers: 626 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 7.4 hours
Mean response time: 42.7 days
90th percentile: 106.0 days
Tracked items: 249

How this project is maintained

Roughly one issue in five opened in the past year never receives a reply. 98% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "comp: profiling" is answered fastest, typically in under an hour, while "type: feature request" waits about 3 days. 63% of tracked open issues have had no activity in three months, so the open count overstates what is actively being worked. Only 43% of issues opened in the past year have been closed.

Charts & Analytics

Fetching additional details & charts...

Issue Activity (beta)

Open issues: 112
New in 7 days: 2
Closed in 7 days: 0
Avg open age: 653 days
Stale 30+ days: 95
Stale 90+ days: 84

Recent activity

Opened in 7 days: 2
Closed in 7 days: 0
Comments in 7 days: 5
Events in 7 days: 12

Top labels

  • type: bug report (169)
  • type: feature request (36)
  • comp: profiling (16)
  • comp: ci visibility (13)
  • comp: native-image (9)
  • comp: context propagation (8)
  • comp: tooling (7)
  • comp: asm waf (5)

Most active issues this week

Sign in to see which issues are moving.
Sign in

Detailed Description

dd-trace-java is an APM client library for Java that automatically and manually instruments applications to collect distributed traces and continuous profiling data.

The library solves the problem of observing Java application behavior in production by using bytecode instrumentation via ByteBuddy to automatically capture traces from common frameworks and libraries without requiring code changes. It also supports manual instrumentation through APIs for custom tracing logic. The collected trace data powers distributed tracing visualization, continuous profiling, error tracking, continuous integration visibility, deployment tracking, and code hotspot analysis within the Datadog platform.

Teams running Java applications should adopt this tool if they use Datadog for observability and want automatic instrumentation of their services with minimal configuration overhead. It suits monolithic and microservices architectures where understanding request flows across service boundaries and identifying performance bottlenecks are priorities. The library handles a broad range of integrations across popular frameworks, reducing the need for manual instrumentation in most cases, though custom instrumentation APIs are available for application-specific logic.

The project maintains a predictable release cadence with new minor versions released during the first full week of each month. Development is guided by structured contribution processes documented in building, testing, and instrumentation guides that help contributors understand how the bytecode instrumentation system works and how to add support for new libraries. The repository provides clear documentation for setting up development environments and creating new instrumentations, indicating an organized approach to managing contributions at scale.