Implementation:Lance format Lance MemWalReadBench

Knowledge Sources	Lance
Domains	Benchmarking, Performance
Last Updated	2026-02-08 19:33 GMT

Overview

Description

MemWalReadBench is a Criterion-based benchmark that measures LSM (Log-Structured Merge) scanner read performance in the Lance MemWAL subsystem. It compares scanning performance between a single base Lance table (baseline) and an LSM scan that combines a base table with flushed MemTables and an active MemTable.

The benchmark sets up a BenchContext containing:

A base dataset with configurable row count
Two flushed MemTable generations
One active MemTable

Four benchmark groups exercise different read patterns:

LSM Scan — Full table scan with and without memtables, measuring total row throughput.
LSM Scan Projected — Scan with column projection (selecting a subset of columns).
LSM Point Lookup — Primary key-based point lookups using the LsmPointLookupPlanner.
LSM Vector Search — KNN vector similarity search across LSM levels using LsmVectorSearchPlanner.

Usage

This benchmark is used to evaluate the read overhead introduced by the MemWAL layer compared to direct table access. It helps developers understand the performance cost of layered LSM scanning and to optimize the LsmScanner, LsmPointLookupPlanner, and LsmVectorSearchPlanner components.

Code Reference

Source Location

rust/lance/benches/mem_wal_read.rs (1059 lines)

Signature

The benchmark defines a single orchestrator function that delegates to four sub-benchmarks:

fn all_benchmarks(c: &mut Criterion) {
    bench_scan(c);
    bench_scan_with_projection(c);
    bench_point_lookup(c);
    bench_vector_search(c);
}

Import

use lance::dataset::mem_wal::scanner::{
    ActiveMemTableRef, LsmDataSourceCollector, LsmPointLookupPlanner,
    LsmScanner, LsmVectorSearchPlanner, RegionSnapshot,
};
use lance::dataset::mem_wal::{DatasetMemWalExt, MemWalConfig, RegionWriterConfig};
use lance::dataset::{Dataset, WriteParams};
use lance_linalg::distance::DistanceType;
use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};

I/O Contract

Inputs

Parameter	Type	Default	Description
`DATASET_PREFIX`	Environment variable	Temp directory	Base URI for datasets (supports S3, GCS, Azure, or local path)
`BASE_ROWS`	Environment variable	10000	Number of rows in the base table
`MEMTABLE_ROWS`	Environment variable	1000	Number of rows per MemTable generation
`BATCH_SIZE`	Environment variable	100	Rows per write batch
`SAMPLE_SIZE`	Environment variable	100	Number of benchmark iterations (minimum 10)
`VECTOR_DIM`	Environment variable	128	Vector dimension for the vector search benchmark

Outputs

Output	Type	Description
Criterion report	HTML/JSON	Throughput statistics (rows/sec) for each benchmark group, with optional flamegraph profiling on Linux
Comparison data	Criterion stats	Compares "base_only" scan vs "lsm" scan with memtable layers

Usage Examples

Run the full benchmark suite against a local temporary directory:

cargo bench -p lance --bench mem_wal_read

Run against S3 storage:

export AWS_DEFAULT_REGION=us-east-1
export DATASET_PREFIX=s3://your-bucket/bench/mem_wal_read
cargo bench -p lance --bench mem_wal_read

Run against a specific local directory with custom parameters:

export DATASET_PREFIX=/tmp/bench/mem_wal_read
export BASE_ROWS=50000
export MEMTABLE_ROWS=5000
cargo bench -p lance --bench mem_wal_read

Run only the vector search sub-benchmark:

cargo bench -p lance --bench mem_wal_read -- "vector"

Related Pages

Implementation:Lance_format_Lance_MemWalWriteBench — Benchmark for MemWAL write throughput
Implementation:Lance_format_Lance_MemtableReadBench — Benchmark comparing MemTable vs in-memory Lance table read performance

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Principle

Implementation

Heuristic

Environment