memory-benchmark

name

memory-benchmark

description

How to benchmark and analyze memory usage in Turso using the memory-benchmark crate and dhat heap profiler. Use this skill whenever the user mentions memory usage, memory profiling, allocation tracking, heap analysis, memory regression, memory benchmarking, dhat, or wants to understand where memory is being allocated during SQL workloads. Also use when investigating memory growth in WAL or MVCC mode. IMPORTANT - If you modify the perf/memory crate (add profiles, change CLI flags, change output format, etc.), update this skill document to reflect those changes so it stays accurate for future agents.

Memory Benchmarking & Analysis

The perf/memory crate benchmarks memory usage of SQL workloads under WAL and MVCC journal modes. It uses dhat as the global allocator to track every heap allocation, and memory-stats for process-level RSS snapshots.

Location

• Benchmark crate: perf/memory/
• Analysis script: perf/memory/analyze-dhat.py
• dhat output: dhat-heap.json (written to CWD after each run)

Running Benchmarks

Always run in release mode — debug builds have wildly different allocation patterns and the results are not representative of real-world usage.

# Basic: single connection, WAL mode, insert-heavy workload
cargo run --release -p memory-benchmark -- --mode wal --workload insert-heavy -i 100 -b 100

# MVCC with concurrent connections
cargo run --release -p memory-benchmark -- --mode mvcc --workload mixed -i 100 -b 100 --connections 4

# Run a final checkpoint after the workload
cargo run --release -p memory-benchmark -- --mode wal --workload read-heavy --checkpoint

# All CLI options
cargo run --release -p memory-benchmark -- \
  --mode wal|mvcc \
  --workload insert-heavy|read-heavy|mixed|scan-heavy|series-blob \
  -i <iterations> \
  -b <batch-size> \
  --connections <N> \
  --checkpoint \
  -- <ms> \
  --cache-size <pages> \
  --format human|json|csv