Benchmark Docs

Benchmark Documentation Skill

This skill ensures proper handling of benchmark documentation across multiple platforms (ARM/Apple Silicon and x86_64/Intel/AMD).

For comprehensive benchmarking instructions, see docs/guides/benchmarking.md.

This skill focuses on documentation-specific rules and multi-platform considerations.

Critical Rules

NEVER replace platform-specific benchmarks with each other.

When adding new benchmark data:

1. Keep both ARM and x86_64 benchmarks - Never delete one platform's data when adding another
2. Add new platform data alongside existing data - Create separate sections or files per platform
3. Name files with platform suffix - e.g., jq-comparison-m1.jsonl, jq-comparison-zen4.jsonl
4. Update docs to show both - README and docs should reference benchmarks from all platforms

Benchmark File Locations

For complete inventory, see docs/benchmarks/inventory.md.

Key locations:

• data/bench/generated/ - Input files (git-ignored)
• data/bench/results/ - Output (git-ignored)
• docs/benchmarks/*.md - Documentation (tracked)
• README.md - Summary (tracked)

Updating Benchmark Documentation

For complete documentation update workflow, see docs/guides/benchmarking.md.

Quick Workflow

1. Build the benchmark runner: cargo build --release --features bench-runner
2. Run benchmarks: ./target/release/succinctly bench run jq_bench
3. Review output: data/bench/results/<timestamp>/jq-bench.md
4. Update documentation:
   • docs/benchmarks/jq.md - Full results with all patterns/sizes
   • README.md - Summary highlights only
5. Follow platform parity rules (see below)

README Table Requirements

Platform Parity

Every benchmark table in README.md must have data for BOTH platforms:

• x86_64 (AMD Ryzen 9 7950X or equivalent)
• ARM (Apple M1 Max or equivalent)

If a table only shows one platform, add the missing platform's data from docs/benchmarks/jq.md.

Pattern Names

Never abbreviate pattern names in tables. Use full names:

• pathological not patholog.
• comprehensive not compreh.

Bold Formatting in Tables

When using bold for values in tables, ensure spaces are OUTSIDE the ** markers:

<!-- CORRECT -->
|  **59.6ms** |

<!-- WRONG (won't render as bold) -->
| ** 59.6ms** |

See the markdown-tables skill for complete table formatting rules.

Benchmark Patterns

Before Running Benchmarks

CRITICAL: Always compile before benchmarking:

cargo build --release --features bench-runner

See docs/guides/benchmarking.md for environment setup and troubleshooting.

Quick checks:

• Build is up to date: cargo build --release --features bench-runner
• CPU load is low: uptime
• Test data exists: ls data/bench/generated/

Benchmark Commands Reference

For complete command reference, see docs/guides/benchmarking.md.

IMPORTANT: Always use the unified benchmark runner (succinctly bench), not dev bench.

Running Benchmarks

Always build first, then run benchmarks:

# Step 1: Build the benchmark runner (required before running benchmarks)
cargo build --release --features bench-runner

# Step 2: Generate test data (if not already present)
./target/release/succinctly json generate-suite
./target/release/succinctly yaml generate-suite

# Step 3: Run benchmarks using the unified runner
./target/release/succinctly bench run jq_bench      # JSON vs jq
./target/release/succinctly bench run yq_bench      # YAML vs yq
./target/release/succinctly bench run jq_comparison # Criterion JSON benchmarks
./target/release/succinctly bench run yq_comparison # Criterion YAML benchmarks

Listing Available Benchmarks

./target/release/succinctly bench list

Running Multiple Benchmarks

# Run all JSON benchmarks
./target/release/succinctly bench run jq_bench jq_comparison

# Run all YAML benchmarks
./target/release/succinctly bench run yq_bench yq_comparison yaml_bench

Memory Collection

Memory is collected by default for CLI benchmarks. Use --no-memory to skip.

Benchmark Types and Memory Support

Memory Flag Usage

# Memory collected by default
./target/release/succinctly bench run yq_bench

# Skip memory collection (faster)
./target/release/succinctly bench run yq_bench --no-memory

# All CLI benchmarks support --no-memory
./target/release/succinctly bench run jq_bench --no-memory
./target/release/succinctly bench run dsv_cli --no-memory

Unified Runner Output

When running via succinctly bench run, CLI benchmark results are saved to the output directory:

data/bench/results/<timestamp>/
  metadata.json      # System info
  summary.json       # Run summary
  jq_bench.jsonl     # Raw results with peak_memory_bytes
  jq_bench.md        # Markdown with memory columns
  yq_bench.jsonl
  yq_bench.md
  stdout/
    jq_bench.txt     # Console output
    yq_bench.txt

yq Benchmark Query Types

The yq benchmark supports multiple query types to exercise different execution paths:

Running yq Benchmarks

Always build first: cargo build --release --features bench-runner

# Run yq CLI benchmark (memory is collected by default)
./target/release/succinctly bench run yq_bench

# Run specific query types
./target/release/succinctly bench run yq_bench --queries identity
./target/release/succinctly bench run yq_bench --queries identity,first_element

# Focus on M2 streaming with the navigation pattern
./target/release/succinctly bench run yq_bench --patterns navigation --sizes 10mb,100mb

# Skip memory collection (faster, but no memory comparison)
./target/release/succinctly bench run yq_bench --no-memory

Query Type Aliases

Multiple aliases are accepted for each query type:

Running bench-compare Benchmarks

For complete instructions, see docs/guides/benchmarking.md.

Quick steps:

1. Generate data: cargo run --release --features cli -- json generate-suite
2. Run: cd bench-compare && cargo bench --bench json_parsers
3. Update documentation (see "Updating Documentation" section below)

Rust JSON Parser Comparison

The bench-compare/ subproject benchmarks succinctly against other Rust JSON parsers.

When to Use Each Parser

Key Learnings

1. sonic-rs is fastest for full document access - ~670 MiB/s parse, ~420 MiB/s parse+traverse on ARM
2. succinctly trades speed for memory - 46% of input size vs 8-21x for DOM parsers
3. succinctly wins on selective queries - jq-style queries are 1.2-6.3x faster than jq because unused data isn't parsed
4. simd-json has highest memory overhead - 20x input size due to tape-based representation

Performance Characteristics (ARM, Apple M1 Max)

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