ui: Three flamegraph perf wins: skip empty walk, pre-floor trim, parentCum

Three independent bit-identical optimizations that together shave ~20%
off native bottom-up pipeline time (measured -16s on a 17M-walk-row
heap-graph trace, 83s -> 67s). All three are surgical: same output,
no algorithmic change, just removing wasted work.

1. Skip the empty UNION side in the hash walk. For BOTTOM_UP the
   downwards walk has empty inits (showDownward=FALSE) so it produces
   no rows, but graph_scan still does setup work. For TOP_DOWN the
   upwards walk is similarly empty (isPivot is never true). In the TS
   layer, only call whichever macro actually produces rows.

2. Pre-floor the trim propagation. The floor threshold (min_value) is
   now applied at the scan edge filter instead of inside the
   per-node IIF. Dest nodes below min_value are unreachable from the
   seeded roots, which is exactly the "dead" semantic the original
   implementation achieved via +inf propagation. On this trace the
   scan input shrinks from 8.4M rows to ~108K rows; the propagation
   step drops from 12.65s to 1.03s. Output bit-identical.

3. Precompute parentCumulativeValue. resolve_groups already does
   LEFT JOIN $grouped p to assign parentId; pulling p.cumulativeValue
   through as a new column is free. trim_with_placeholder now carries
   it forward (placeholders use MIN(d.parentCumulativeValue) since all
   dropped children of a parent share it). global_layout reads it
   directly from s instead of doing its own LEFT JOIN $merged p.
   global_layout drops from 13.2s to 1.0s.

Also adds ORDER BY id to trim_with_placeholder. The merged UNION ALL
wasn't emitting rows in dense id order, which the journal from the
prior commit noted makes graph_scan 5x slower downstream in
global_layout. Fixing it recovers global_layout's speedup from E.
2 files changed
tree: 04d7f630866a3e9a171321b5a9c77913ab0728aa
  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/
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  15. test/
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  17. tools/
  18. ui/
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  47. perfetto.rc
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  49. PerfettoIntegrationTests.xml
  50. persistent_cfg.pbtxt
  51. README.chromium
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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.