Chunking Strategy

Chunking Strategy Benchmarking

Benchmark and optimize Zarr chunking strategies for multi-dimensional datasets stored in cloud object stores (S3, GCS) or local filesystems. This skill helps you determine the optimal chunk configuration for your specific access patterns before committing to a rechunking operation.

Research basis: Nguyen et al. (2023), "Impact of Chunk Size on Read Performance of Zarr Data in Cloud-based Object Stores" (DOI: 10.1002/essoar.10511054.2)

Inputs This Skill Expects

When the user invokes this skill, collect the following information:

Required Inputs

1. Dataset location:

   • Local path: /data/mydata.zarr
   • S3: s3://bucket/path/to/data.zarr
   • GCS: gs://bucket/path/to/data.zarr

2. Dimension names: E.g., ['time', 'frequency', 'baseline']

3. Current chunk shape: E.g., (1, 2048, 2048) (query with ds.chunks)

4. Access pattern priorities: Which patterns matter most?

   • "Primarily spatial access" → optimize for spatial
   • "Mixed workload" → find balanced strategy
   • "Don't know" → test all three and report trade-offs

Optional Inputs

5. Memory budget: E.g., "8 GB" (typical laptop), "64 GB" (HPC node)

   • Used to filter out configurations that exceed available RAM

6. Sample size: How much data to benchmark?

   • Default: Subset that requires 10-20 chunks per access pattern
   • User can override: "Use first 500 time steps"

7. Candidate configurations: User can suggest specific chunk shapes to test

   • Default: Generate grid by varying one dimension at a time

8. Number of runs: Minimum 5 (default), can increase for high-variance networks

Outputs This Skill Produces

1. Structured Markdown Report

Sections:

• Dataset Summary: Shape, dtype, current chunking, total size
• Benchmark Environment: Python version, instance type, network conditions
• Results Tables: One per access pattern with mean/std/min/max for each config
• Performance Bias Analysis: Which configs are balanced vs specialized
• Memory Analysis: Which configs fit within specified memory budget
• Recommendation: 1-3 suggested chunk configurations with reasoning

Example recommendation:

## Recommendation

**For mixed workloads (spatial + time-series access):**

Use chunk shape **(50, 512, 512)**:
- Spatial access: 8.3 s ± 0.7 s (23% slower than optimal)
- Time-series access: 11.2 s ± 0.9 s (18% slower than optimal)
- Performance bias: 1.35 (well-balanced)
- Peak memory: 3.8 GB (fits in 8 GB budget)

**For spatial-only workloads:**

Use chunk shape **(10, 1024, 1024)** if memory permits:
- Spatial access: 6.1 s ± 0.5 s (optimal for this pattern)
- Peak memory: 10.2 GB (requires 16 GB system)

2. Environment Metadata (JSON)

Saved alongside the report for reproducibility:

{
  "date": "2024-03-08",
  "python_version": "3.11.7",
  "xarray_version": "2024.1.0",
  "zarr_version": "2.17.0",
  "instance_type": "t2.xlarge",
  "storage_backend": "AWS S3 us-east-1"
}

3. (Optional) Dask Performance Report

If using Dask, generate dask-report.html showing task graphs and memory usage over time.

Implementation Notes for the Agent

Benchmarking Script Structure

The agent should use the helper scripts in scripts/:

• benchmark_runner.py: Core benchmarking loop (5+ runs, timing, memory measurement)
• rechunk.py: Rechunking utilities for generating test configurations
• synthetic_data.py: Generate synthetic Zarr data if no real dataset is available

Cache Clearing

Critical: Clear caches between every run to measure cold-cache performance.

macOS:

sudo purge

Linux:

sync
sudo sh -c 'echo 3 > /proc/sys/vm/drop_caches'

fsspec:

# Disable or clear between runs
fsspec.config.conf['cache_storage'] = None

See references/benchmarking-methodology.md for complete methodology.

Statistical Validity

• Minimum 5 runs per configuration per access pattern
• Report mean ± std and [min, max] for each metric
• If std/mean > 0.3, increase run count or investigate variance source

Memory Measurement

Use tracemalloc (built-in) or memory_profiler (more accurate):

import tracemalloc

tracemalloc.start()
result = ds.sel(time=42).compute()
current, peak = tracemalloc.get_traced_memory()
tracemalloc.stop()

Always report peak memory, not mean.

Timing

Use time.perf_counter() (not time.time()):

import time

start = time.perf_counter()
result = ds.sel(time=42).compute()
wall_time = time.perf_counter() - start

Progressive Disclosure References

The references/ folder contains detailed documentation the agent should load on-demand:

• README.md: Table of contents describing when to load each reference
• nguyen-2023.md: Research paper summary and findings
• access-patterns.md: Detailed explanation of the three access patterns
• memory-constraints.md: All-or-nothing constraint and memory measurement
• benchmarking-methodology.md: Best practices, pitfalls, and reproducibility
• cloud-storage-patterns.md: S3/GCS semantics, latency, caching, and cost optimization
• performance-interpretation.md: Metrics, performance bias, reading reports, and translating results

Load references only when needed to avoid context bloat. The README.md serves as a table of contents for progressive disclosure.

Domain-Agnostic Design

Designed to work with any multi-dimensional Zarr dataset:

• Climate data: time × latitude × longitude
• Medical imaging: patient × slice × x × y
• Hyperspectral: x × y × wavelength

The user defines their dimension names and which dimensions are sliced vs loaded for each access pattern.

Limitations

• Read performance only: This skill does not benchmark write operations or rechunking performance
• Cloud storage focus: Optimized for S3/GCS access patterns; local disk I/O may have different characteristics
• Sample-based: Benchmarks use data samples for speed; full dataset performance may differ slightly
• No compression benchmarking: Assumes compression codec is already chosen (handled by separate plugin)

Resources and References

Research Papers

• Nguyen et al. (2023): DOI 10.1002/essoar.10511054.2
• Lee et al. (2025): DOI 10.48550/arXiv.2503.18037

Tools

• xarray: https://docs.xarray.dev/
• Zarr: https://zarr.readthedocs.io/
• rechunker: https://rechunker.readthedocs.io/

Related Work

• OVRO-LWA Portal: https://github.com/uw-ssec/ovro-lwa-portal
• AgentSkills.io: https://agentskills.io