Architecture

Benchmarking Methodology

The discipline of designing, executing, and reporting reproducible performance tests for S3-based data systems, covering throughput, latency, concurrency, cost efficiency, and scalability across storage, query, and ingestion layers.

6 connections 3 resources

Summary

What it is

The discipline of designing, executing, and reporting reproducible performance tests for S3-based data systems, covering throughput, latency, concurrency, cost efficiency, and scalability across storage, query, and ingestion layers.

Where it fits

Benchmarking methodology provides the measurement framework for evaluating design decisions across the S3 ecosystem — comparing table formats, query engines, file sizes, storage tiers, and compaction strategies with controlled, reproducible experiments.

Misconceptions / Traps
  • S3 performance is not deterministic. Request latency varies by prefix partition, time of day, and region load. Benchmarks must account for variance with multiple runs and percentile reporting (p50, p99), not just averages.
  • Comparing table formats on the same benchmark (e.g., TPC-DS) does not capture real-world differences in maintenance cost, metadata overhead, or concurrent writer performance.
  • Benchmark results on one S3-compatible platform (AWS S3) do not transfer to another (MinIO, R2). S3 compatibility does not imply performance equivalence.
Key Connections
  • scoped_to S3, Lakehouse — performance measurement for S3-based systems
  • constrains Performance-per-Dollar — benchmarks quantify the cost/performance tradeoff
  • relates_to Capacity Planning — benchmark results feed capacity planning models
  • relates_to Cold Scan Latency — benchmarks measure scan latency under different configurations

Definition

What it is

A standardized approach to measuring and comparing performance of S3-compatible storage systems, query engines, and table formats — covering throughput, latency, cost-per-query, and scalability under controlled conditions.

Why it exists

Vendor benchmarks are often misleading due to cherry-picked configurations and non-representative workloads. A rigorous benchmarking methodology enables apples-to-apples comparison of S3-based systems, informing architecture decisions with reproducible evidence.

Primary use cases

Storage system selection, query engine comparison, validating performance claims, capacity planning validation.

Connections 6

Outbound 5
Inbound 1

Resources 3