analyzing-sql-traces

name

analyzing-sql-traces

description

Extracts raw trace data from Perfetto traces, runs arbitrary SQL queries for custom follow-up analysis, and applies expert cognitive principles (Tiered Flow Analysis, Semantic Mismatch, Redundancy) to identify performance bottlenecks, structural redundancies, and tracer gaps. Use when you need to analyze a trace under a specific focus/entrypoint slice, identify uninstrumented 'black boxes', or execute arbitrary SQL queries on trace databases directly using SQLite/Perfetto SQL syntax, and generate a precise instrumentation breakdown plan or refactoring instructions for the Codebase Agent. Don't use for capture or compilation tasks.

Analyzing SQL Traces

A specialized skill for analyzing Perfetto browser traces (individually or comparatively) to detect performance bottlenecks and generate codebase refactoring or instrumentation recommendations.

1. Prerequisites & Context

• Treatment Traces (one or more .pb files).
• Control Traces (optional, one or more .pb files for comparison).
• Target Slice or Metric Window (e.g., Startup.FirstWebContents.FirstContentfulPaint, OmniboxEditModel::OpenMatch).
• Analysis Mode Input: Select either descendants (to analyze child slices of a specific target) or window (to analyze all slices overlapping a metric window).

⚠️ Safety & Sandbox Compliance (Zero-Grant Rule)

To prevent triggering unnecessary user permission/access grant prompts:

• ALWAYS write all intermediate and final outputs to the parent E2E session's unified analysis directory inside the workspace: out/e2e_nla_run_{parent_session_id}/analysis/ (where {parent_session_id} is passed by the Orchestrator).
  • Raw data / comparison reports: out/e2e_nla_run_{parent_session_id}/analysis/raw_trace_data.txt (Mode A, Text flamegraph) out/e2e_nla_run_{parent_session_id}/analysis/raw_trace_report.md (Mode A, Markdown report) out/e2e_nla_run_{parent_session_id}/analysis/comparison_report.md (Mode B, Markdown report) out/e2e_nla_run_{parent_session_id}/analysis/comparison_flamegraph.txt (Mode B, Text flamegraph)
  • Structured JSON: out/e2e_nla_run_{parent_session_id}/analysis/trace_analysis_results.json
  • Markdown Dispatch Report: out/e2e_nla_run_{parent_session_id}/analysis/trace_analysis_dispatch_report.md
• NEVER execute shell utilities like mkdir, ls, touch, or rm to manage these files.
• ALWAYS rely on the internal Python APIs inside trace_analyzer.py or trace_comparator.py to programmatically create directories and manage files silently.
• ALWAYS run the scripts with vpython3 agents/skills/analyzing-sql-traces/scripts/trace_analyzer.py or vpython3 agents/skills/analyzing-sql-traces/scripts/trace_comparator.py to avoid extra permmision grant prompts.

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2. Core Workflow

Step 1: Determine the Analysis Mode & Run Extraction

Mode A: Single-Group Analysis (Only Treatment Traces Provided)

First, run the trace analyzer to produce an aggregated text flamegraph:

vpython3 agents/skills/analyzing-sql-traces/scripts/trace_analyzer.py \
  --traces {path/to/treatment_trace_*.pb} \
  --target "{focus_slice_or_metric}" \
  --mode {descendants|window} \
  --format text \
  --output out/e2e_nla_run_{parent_session_id}/analysis/raw_trace_data.txt

Second, run the trace analyzer to produce a markdown report with cumulative redundancy analysis:

vpython3 agents/skills/analyzing-sql-traces/scripts/trace_analyzer.py \
  --traces {path/to/treatment_trace_*.pb} \
  --target "{focus_slice_or_metric}" \
  --mode {descendants|window} \
  --format markdown \
  --output out/e2e_nla_run_{parent_session_id}/analysis/raw_trace_report.md

Read the generated out/e2e_nla_run_{parent_session_id}/analysis/raw_trace_data.txt and out/e2e_nla_run_{parent_session_id}/analysis/raw_trace_report.md using view_file.

Mode B: Comparative Analysis (Both Control and Treatment Traces Provided)

First, run the trace comparator to generate the tabular comparative report:

vpython3 agents/skills/analyzing-sql-traces/scripts/trace_comparator.py \
  --control {path/to/control_trace_*.pb} \
  --experiment {path/to/treatment_trace_*.pb} \
  --target "{focus_slice_or_metric}" \
  --mode {descendants|window} \
  --format markdown \
  --output out/e2e_nla_run_{parent_session_id}/analysis/comparison_report.md

Second, run the trace comparator to generate the high-level comparative text flamegraph (use --min-dur to filter out minor slices, e.g., $\ge 5.0\text{ ms}$):

vpython3 agents/skills/analyzing-sql-traces/scripts/trace_comparator.py \
  --control {path/to/control_trace_*.pb} \
  --experiment {path/to/treatment_trace_*.pb} \
  --target "{focus_slice_or_metric}" \
  --mode {descendants|window} \
  --format text \
  --min-dur 5.0 \
  --output out/e2e_nla_run_{parent_session_id}/analysis/comparison_flamegraph.txt

Read the generated out/e2e_nla_run_{parent_session_id}/analysis/comparison_report.md and out/e2e_nla_run_{parent_session_id}/analysis/comparison_flamegraph.txt using view_file.

Advanced Filtering & Aggregation Options (Optional)

Both scripts (trace_analyzer.py and trace_comparator.py) support optional flags to refine slice selection when multiple events share the same name:

• Aggregation Mode (--aggregate): If the target slice can be called multiple times, use this flag to aggregate all occurrences (cumulative durations and self-times) into a single merged call tree.
• Slice Argument Filtering (--arg-key <key> and --arg-value <value>): To analyze only a specific call out of multiple occurrences, filter by its arguments (e.g. --arg-key "task.posted_from.file_name" --arg-value "content/browser/browser_main_loop.cc"). Note: The --arg-value parameter supports SQL LIKE operator syntax (e.g. %google.com/search% to perform prefix or wildcard substring matches).
• Parent Bounding Target (--boundary-target <name>): Restricts the target slice search to only those occurrences that fall chronologically within the execution time windows of a specified parent/boundary event (descendants mode only). Use with --boundary-arg-key <key> and --boundary-arg-value <value> to target specific parent navigation/workflow windows.

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Step 2: Apply Cognitive Principles

Open and read the mandatory reasoning guide to evaluate the results, focusing on browser logic and filtering out infrastructure noise: file:///.agents/skills/analyzing-sql-traces/references/cognitive_principles.md

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Step 3: Run Arbitrary SQL Queries (Follow-up Analysis)

If you need custom details or want to perform follow-up analysis not covered by the default trace analyzer/comparator (e.g. searching for specific args, getting stats on specific threads, custom joins), ALWAYS run the arbitrary query script query_trace.py rather than creating a custom script yourself.

Usage Guideline

Run the query_trace.py helper script using vpython3:

vpython3 agents/skills/analyzing-sql-traces/scripts/query_trace.py \
  --trace {path/to/trace.pb} \
  --query "{sql_query}"

Example:

vpython3 agents/skills/analyzing-sql-traces/scripts/query_trace.py \
  --trace out/Default/trace.pb \
  --query "SELECT name, sum(dur)/1e6 AS total_dur_ms FROM slice GROUP BY name ORDER BY total_dur_ms DESC LIMIT 10;"

Common Perfetto Tables & Schemas

Here are common SQLite tables available in Perfetto trace databases:

slice Table

Contains individual track event slices (slices represent synchronous work on a thread).

• id (INT): Unique ID for the slice
• name (STRING): Name of the slice / event
• ts (INT): Start timestamp in nanoseconds
• dur (INT): Duration in nanoseconds
• track_id (INT): Track ID on which the slice executed
• parent_id (INT): Parent slice ID (if nested)
• arg_set_id (INT): ID referencing key-value arguments associated with this slice

process Table

• upid (INT): Unique process ID
• name (STRING): Name of the process (e.g. Browser, Renderer, GPU Process)
• pid (INT): OS process ID

thread Table

• utid (INT): Unique thread ID
• name (STRING): Name of the thread (e.g. CrBrowserMain, Compositor)
• upid (INT): Parent process ID
• tid (INT): OS thread ID

thread_track Table

• id (INT): Track ID
• utid (INT): Thread ID associated with this track

args Table

Contains key-value arguments associated with slices.

• arg_set_id (INT): Reference ID matching slice's arg_set_id
• key (STRING): Hierarchical argument key (e.g. task.posted_from.file_name)
• string_value / int_value / real_value (STRING / INT / REAL): Argument value

Reference Queries

Get Top 10 Longest Slices

SELECT s.name, s.dur / 1e6 AS dur_ms, t.name AS thread_name, p.name AS process_name
FROM slice s
JOIN thread_track tt ON s.track_id = tt.id
JOIN thread t USING(utid)
JOIN process p USING(upid)
ORDER BY s.dur DESC
LIMIT 10;

List All Processes and Threads in a Trace

SELECT p.name AS process_name, p.upid, t.name AS thread_name, t.utid
FROM process p
JOIN thread t USING(upid)
ORDER BY process_name, thread_name;

Find Slices by Name containing a substring

SELECT name, dur/1e6 AS dur_ms, ts
FROM slice
WHERE name LIKE '%FirstContentfulPaint%'
ORDER BY ts ASC;

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3. Output Artifact Contracts

You must generate two separate outputs to complete this task:

Output A: Structured Dispatch JSON

This payload is designed for direct parsing by the Orchestrator to feed to the Codebase & Instrumentation Agent. Save it to out/e2e_nla_run_{parent_session_id}/analysis/trace_analysis_results.json.

{
  "status": "SUCCESS",
  "analysis": {
    "target_slice": "FocusSliceName",
    "total_duration_ms": 260.6,
    "bottlenecks": [
      {
        "method_name": "CulpritMethodName",
        "severity_score": 8.5,
        "vectors": {
          "critical_path": true,
          "relative_overhead": 0.22,
          "semantic_simplicity": "HIGH" | "MEDIUM" | "LOW",
          "cumulative_redundancy": true
        },
        "breakdown_strategy": {
          "type"  |  |  | 
           
            |  | 
            
           
           
        
      
    
  

Output B: Markdown Dispatch Report (For Orchestrator Review)

Save a beautifully formatted report to out/e2e_nla_run_{parent_session_id}/analysis/trace_analysis_dispatch_report.md.

• Format: Use GitHub-style alerts (> [!IMPORTANT]) for the Codebase Agent Dispatch Instructions to make them stand out.
• Structure:
  1. Executive Summary: Overall metrics (total time, depth, count of bottlenecks).
  2. Flow-Aware Inefficiencies: Detailed analysis of slow flows (include simple Mermaid diagrams of the redundancy path if applicable).
  3. Prioritized Bottlenecks: Ranked list with direct codebase instructions.
  4. Redundancy Summary Table: Top 10 repeated operations.