herodb/benches

HeroDB Benchmarks

This directory contains comprehensive performance benchmarks for HeroDB's storage backends (redb and sled).

Quick Start

# Run all benchmarks
cargo bench

# Run specific suite
cargo bench --bench single_ops

# Quick run (fewer samples)
cargo bench -- --quick

Benchmark Suites

1. Single Operations (single_ops.rs)

Measures individual operation latency:

• String operations: SET, GET, DEL, EXISTS
• Hash operations: HSET, HGET, HGETALL, HDEL, HEXISTS
• List operations: LPUSH, RPUSH, LPOP, RPOP, LRANGE

2. Bulk Operations (bulk_ops.rs)

Tests throughput with varying batch sizes:

• Bulk insert (100, 1K, 10K records)
• Bulk read (sequential and random)
• Bulk update and delete
• Mixed workload (70% reads, 30% writes)

3. Scan Operations (scan_ops.rs)

Evaluates iteration and filtering:

• SCAN with pattern matching
• HSCAN for hash fields
• KEYS operation
• DBSIZE, HKEYS, HVALS

4. Concurrent Operations (concurrent_ops.rs)

Simulates multi-client scenarios:

• Concurrent writes (10, 50 clients)
• Concurrent reads (10, 50 clients)
• Mixed concurrent workload
• Concurrent hash and list operations

5. Memory Profiling (memory_profile.rs)

Tracks memory usage patterns:

• Per-operation memory allocation
• Peak memory usage
• Memory efficiency (bytes per record)
• Allocation count tracking

Common Infrastructure

The common/ directory provides shared utilities:

• data_generator.rs: Deterministic test data generation
• backends.rs: Backend setup and management
• metrics.rs: Custom metrics collection and export

Results Analysis

Parse Results

python3 scripts/parse_results.py target/criterion --csv results.csv --json results.json

Compare Backends

python3 scripts/compare_backends.py results.csv --export comparison.csv

View HTML Reports

Open target/criterion/report/index.html in a browser for interactive charts.

Documentation

• Running Benchmarks - Quick start guide
• Benchmarking Guide - Complete user guide
• Architecture - System design
• Implementation Plan - Development details
• Sample Results - Example analysis

Key Features

✅ Statistical Rigor: Uses Criterion for statistically sound measurements ✅ Fair Comparison: Identical test datasets across all backends ✅ Reproducibility: Fixed random seeds for deterministic results ✅ Comprehensive Coverage: Single ops, bulk ops, scans, concurrency ✅ Memory Profiling: Custom allocator tracking ✅ Multiple Formats: Terminal, CSV, JSON, HTML outputs

Performance Tips

For Accurate Results

1. System Preparation

   • Close unnecessary applications
   • Disable CPU frequency scaling
   • Ensure stable power supply

2. Benchmark Configuration

   • Use sufficient sample size (100+)
   • Allow proper warm-up time
   • Run multiple iterations

3. Environment Isolation

   • Use temporary directories
   • Clean state between benchmarks
   • Avoid shared resources

For Faster Iteration

# Quick mode (fewer samples)
cargo bench -- --quick

# Specific operation only
cargo bench -- single_ops/strings/set

# Specific backend only
cargo bench -- redb

Troubleshooting

High Variance

• Close background applications
• Disable CPU frequency scaling
• Increase sample size

Out of Memory

• Run suites separately
• Reduce dataset sizes
• Increase system swap

Slow Benchmarks

• Use --quick flag
• Run specific benchmarks
• Reduce measurement time

See Running Benchmarks for detailed troubleshooting.

Contributing

When adding new benchmarks:

1. Follow existing patterns in benchmark files
2. Use common infrastructure (data_generator, backends)
3. Ensure fair comparison between backends
4. Add documentation for new metrics
5. Test with both --quick and full runs

Example Output

single_ops/strings/set/redb/100bytes
                        time:   [1.234 µs 1.245 µs 1.256 µs]
                        thrpt:  [802.5K ops/s 810.2K ops/s 818.1K ops/s]

single_ops/strings/set/sled/100bytes
                        time:   [1.567 µs 1.578 µs 1.589 µs]
                        thrpt:  [629.5K ops/s 633.7K ops/s 638.1K ops/s]

License

Same as HeroDB project.