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 \
  --timeout <ms> \
  --cache-size <pages> \
  --format human|json|csv

Every run produces a dhat-heap.json in the current directory. This file contains per-allocation-site data for the entire run.

Built-in Workload Profiles

Profile	Description	Setup
insert-heavy	100% INSERT statements	Creates table
read-heavy	90% SELECT by id / 10% INSERT	Seeds 10k rows
mixed	50% SELECT / 50% INSERT	Seeds 10k rows
scan-heavy	Full table scans with LIKE	Seeds 10k rows
series-blob	INSERT INTO bench(data) SELECT zeroblob(2048) FROM generate_series(1, ?)	Creates bench; batch-size is the series length

Profiles implement the Profile trait in perf/memory/src/profile/. To add a new workload, create a new file implementing the trait and wire it into the WorkloadProfile enum in main.rs.

Understanding the Output

The benchmark reports three categories of metrics:

RSS (process-level)

Measured via memory-stats crate. Includes everything: heap, mmap'd files (WAL, DB pages pulled into OS page cache), tokio runtime, etc. Snapshots are taken at phase transitions (setup -> run) and after each batch.

• Baseline: RSS before any DB work (runtime overhead)
• Peak: Highest RSS observed during the run
• Net growth: Final RSS minus baseline — the memory attributable to the workload

Heap (dhat)

Precise allocation tracking via the dhat global allocator. Only counts explicit heap allocations (malloc/alloc), not mmap.

• Current: Bytes still allocated at measurement time
• Peak: Highest simultaneous live allocation during the entire run
• Total allocs: Number of individual allocation calls
• Total bytes: Cumulative bytes allocated (includes freed memory) — measures allocation pressure

Disk

File sizes after the benchmark completes:

• DB file: The .db file
• WAL file: The .db-wal file (WAL mode only)
• Log file: The .db-log file (MVCC logical log only)

Analyzing dhat Output

After running a benchmark, use the analysis script to produce a readable report from dhat-heap.json:

# Overview: top allocation sites by bytes live at global peak
python3 perf/memory/analyze-dhat.py dhat-heap.json --top 15 --modules

# Focus on a specific subsystem
python3 perf/memory/analyze-dhat.py dhat-heap.json --filter mvcc --stacks
python3 perf/memory/analyze-dhat.py dhat-heap.json --filter btree --stacks
python3 perf/memory/analyze-dhat.py dhat-heap.json --filter page_cache --stacks

# Sort by different metrics
python3 perf/memory/analyze-dhat.py dhat-heap.json --sort-by eb  # bytes at exit (leaks)
python3 perf/memory/analyze-dhat.py dhat-heap.json --sort-by tb  # total bytes (pressure)
python3 perf/memory/analyze-dhat.py dhat-heap.json --sort-by mb  # max live bytes per site

# JSON output for programmatic use
python3 perf/memory/analyze-dhat.py dhat-heap.json --json

Sort Metrics

Flag	Metric	Use when
gb	Bytes live at global peak (default)	Finding what dominates memory at the high-water mark
eb	Bytes live at exit	Finding memory leaks or things that never get freed
tb	Total bytes allocated	Finding allocation pressure hotspots (GC churn)
mb	Max bytes live per site	Finding per-site high-water marks
tbk	Total allocation count	Finding chatty allocators (many small allocs)

Analysis Flags

• --top N — Show top N sites (default 15)
• --filter PATTERN — Filter to sites/stacks containing substring (e.g. mvcc, btree, wal, pager)
• --stacks — Show full callstacks for top allocation sites
• --modules — Aggregate by crate/module for a high-level breakdown
• --json — Machine-readable aggregated output

Typical Workflow

When investigating memory usage or a suspected regression:

1. Run the benchmark with parameters matching the scenario:

   cargo run -p memory-benchmark -- --mode mvcc --workload mixed -i 500 -b 100 --connections 4
   

2. Get the high-level picture — which modules use the most memory:

   python3 perf/memory/analyze-dhat.py dhat-heap.json --modules --top 20
   

3. Drill into the hot module — e.g. if turso_core dominates:

   python3 perf/memory/analyze-dhat.py dhat-heap.json --filter turso_core --stacks --top 10
   

4. Check for leaks — anything still alive at exit that shouldn't be:

   python3 perf/memory/analyze-dhat.py dhat-heap.json --sort-by eb --top 10
   

5. Compare modes — run the same workload under WAL and MVCC and compare the reports to see the memory cost of MVCC versioning.

Concurrency Details

When --connections > 1:

• Setup phase (schema creation, seeding) always runs on a single connection sequentially
• Run phase spawns one tokio task per connection, each executing its batch concurrently
• --checkpoint adds a final single-connection PRAGMA wal_checkpoint(TRUNCATE) phase after the run phase
• Each connection gets busy_timeout set (default 30s, configurable via --timeout)
• WAL mode uses BEGIN, MVCC uses BEGIN CONCURRENT
• The Profile trait's next_batch(connections) returns one batch per connection with non-overlapping row IDs

Adding a New Profile

1. Create perf/memory/src/profile/your_profile.rs implementing the Profile trait
2. Add pub mod your_profile; to perf/memory/src/profile/mod.rs
3. Add a variant to WorkloadProfile enum in main.rs
4. Wire it into create_profile() in main.rs

The Profile trait:

pub trait Profile {
    fn name(&self) -> &str;
    fn next_batch(&mut self, connections: usize) -> (Phase, Vec<Vec<WorkItem>>);
}

Return Phase::Setup for schema/seeding (single batch), Phase::Run for measured work (one batch per connection), Phase::Done when finished.

Keeping This Skill Up to Date

This skill document is the source of truth for how agents use the memory benchmark tooling. If you modify the perf/memory crate — adding profiles, changing CLI flags, altering output format, updating the analysis script, changing the Profile trait, etc. — update this SKILL.md to match. Specifically:

• New CLI flags: add to the "Running Benchmarks" section
• New profiles: add to the "Built-in Workload Profiles" table
• Changed output metrics: update the "Understanding the Output" section
• New analyze-dhat.py flags or sort metrics: update the "Analyzing dhat Output" section
• Changed Profile trait signature: update "Adding a New Profile"

Future agents rely on this document being accurate. Stale instructions cause wasted work.