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flutter/third_party/perfetto/refs/heads/dev/lalitm/flamegraph-trim-placeholder
commit
e9b25b2548b2ec9b702f3d75767cf08c9aefa546
[log]
author
Lalit Maganti <lalitm@google.com>
Fri Apr 24 00:26:29 2026 +0000
committer
Lalit Maganti <lalitm@google.com>
Fri Apr 24 00:26:29 2026 +0000
tree
87d03d1d0efac4db2bd6631a7e169c92eed654c8
parent
d64923f67b3ba5a2fb8aa42f4595404af0f9e543 [diff]
ui: Fold flamegraph trim alive-set computation into propagation scan

The previous trim pipeline ran a separate alive_set step that joined the
full merged table against propagated to derive a per-node alive flag.
On WASM-sized inputs (millions of merged rows) this was the single
slowest step in the trim pipeline (~58s vs ~2s native).

Have the propagation scan compute the alive flag inline as part of the
same expression that already evaluates the per-parent ratio + absolute
floor checks. The alive_set macro now becomes a one-line filter
(SELECT id WHERE alive) over the propagated table, with no JOIN. The
propagated index is no longer needed and is removed.

Net effect on native: alive step drops from 2.1s -> 0.06s on a 2.5M-row
bottom-up flamegraph. WASM should see the same kind of improvement
since the elimination is structural rather than micro-optimization.
2 files changed
tree: 87d03d1d0efac4db2bd6631a7e169c92eed654c8
  1. .github/
  2. bazel/
  3. build_overrides/
  4. buildtools/
  5. contrib/
  6. docs/
  7. examples/
  8. gn/
  9. include/
  10. infra/
  11. protos/
  12. python/
  13. sdk/
  14. src/
  15. test/
  16. third_party/
  17. tools/
  18. ui/
  19. CLAUDE.md ⇨ docs/AGENTS.md
  20. GEMINI.md ⇨ docs/AGENTS.md
  21. .bazelignore
  22. .bazelrc
  23. .bazelversion
  24. .clang-format
  25. .clang-tidy
  26. .git-blame-ignore-revs
  27. .gitallowed
  28. .gitattributes
  29. .gitignore
  30. .gn
  31. .style.yapf
  32. Android.bp
  33. Android.bp.extras
  34. BUILD
  35. BUILD.extras
  36. BUILD.gn
  37. CHANGELOG
  38. CONTRIBUTORS.txt
  39. DIR_METADATA
  40. heapprofd.rc
  41. LICENSE
  42. meson.build
  43. METADATA
  44. MODULE.bazel
  45. MODULE.bazel.lock
  46. MODULE_LICENSE_APACHE2
  47. OWNERS
  48. OWNERS.github
  49. perfetto.rc
  50. perfetto_flags.aconfig
  51. PerfettoIntegrationTests.xml
  52. persistent_cfg.pbtxt
  53. README.chromium
  54. README.md
  55. TEST_MAPPING
  56. traced_perf.rc
  57. WORKSPACE
README.md

Perfetto - System profiling, app tracing and trace analysis

Perfetto is an open-source suite of SDKs, daemons and tools which use tracing to help developers understand the behaviour of complex systems and root-cause functional and performance issues on client and embedded systems.

It is a production-grade tool that is the default tracing system for the Android operating system and the Chromium browser.

Core Components

Perfetto is not a single tool, but a collection of components that work together:

  • High-performance tracing daemons: For capturing tracing information from many processes on a single machine into a unified trace file.
  • Low-overhead tracing SDK: A C++17 library for direct userspace-to-userspace tracing of timings and state changes in your application.
  • Extensive OS-level probes: For capturing system-wide context on Android and Linux (e.g. scheduling states, CPU frequencies, memory profiling, callstack sampling).
  • Browser-based UI: A powerful, fully local UI for visualizing and exploring large, multi-GB traces on a timeline. It works in all major browsers, requires no installation, and can open traces from other tools.
  • SQL-based analysis library: A powerful engine that allows you to programmatically query traces using SQL to automate analysis and extract custom metrics.

Why Use Perfetto?

Perfetto was designed to be a versatile and powerful tracing system for a wide range of use cases.

  • For Android App & Platform Developers: Debug and root-cause functional and performance issues like slow startups, dropped frames (jank), animation glitches, low memory kills, and ANRs. Profile both Java/Kotlin and native C++ memory usage with heap dumps and profiles.
  • For C/C++ Developers (Linux, macOS, Windows): Use the Tracing SDK to instrument your application with custom trace points to understand its execution flow, find performance bottlenecks, and debug complex behavior. On Linux, you can also perform detailed CPU and native heap profiling.
  • For Linux Kernel & System Developers: Get deep insights into kernel behavior. Perfetto acts as an efficient userspace daemon for ftrace, allowing you to visualize scheduling, syscalls, interrupts, and custom kernel tracepoints on a timeline.
  • For Chromium Developers: Perfetto is the tracing backend for chrome://tracing. Use it to debug and root-cause issues in the browser, V8, and Blink.
  • For Performance Engineers & SREs: Analyze and visualize a wide range of profiling and tracing formats, not just Perfetto's. Use the powerful SQL interface to programmatically analyze traces from tools like Linux perf, macOS Instruments, Chrome JSON traces, and more.

Getting Started

We‘ve designed our documentation to guide you to the right information as quickly as possible, whether you’re a newcomer to performance analysis or an experienced developer.

  1. New to tracing? If you're unfamiliar with concepts like tracing and profiling, start here:

  2. Ready to dive in? Our “Getting Started” guide is the main entry point for all users. It will help you find the right tutorials and documentation for your specific needs:

  3. Want the full overview? For a comprehensive look at what Perfetto is, why it's useful, and who uses it, see our main documentation page:

Debian Distribution

For users interested in the Debian distribution of Perfetto, the official source of truth and packaging efforts are maintained at Debian Perfetto Salsa Repository

Community & Support

Have questions? Need help?

We follow Google's Open Source Community Guidelines.