zarr-chunk-optimization
By uw-ssec
Benchmark and optimize Zarr chunking strategies for multi-dimensional scientific datasets on S3/GCS: analyze access patterns from xarray workflows, generate synthetic data, run comprehensive benchmarks across spatial/temporal/spectral patterns, produce ranked recommendations with trade-off reports, and safely rechunk datasets.
Component Overview
Install
npx claudepluginhub uw-ssec/rse-plugins --plugin zarr-chunk-optimizationComponent Details
Commands (5)
Analyze benchmark results and generate a performance report with ranked recommendations and trade-off explanations
Run comprehensive chunking benchmarks on Zarr dataset and generate performance report with recommendations
Generate a synthetic Zarr dataset for controlled chunking benchmarks with configurable dimensions and compression
Apply a specific chunking configuration to a Zarr dataset with validation and progress reporting
Explore chunking trade-offs from existing benchmark results for different access pattern scenarios
Agents (2)
Zarr chunking optimization expert that benchmarks multi-dimensional array storage for cloud object stores (S3, GCS) and generates recommendations based on Nguyen et al. (2023) methodology.
Expert in interpreting Zarr chunking benchmark results, analyzing performance trade-offs across access patterns, and generating actionable recommendations with research-backed context from Nguyen et al. (2023).
Skills (5)
Identify, formalize, and prioritize data access patterns for multi-dimensional Zarr datasets. Translates user workflow descriptions into benchmark-ready pattern definitions with xarray operation mappings.
Benchmark and optimize Zarr chunking strategies for multi-dimensional scientific datasets. Measures wall-clock time, peak memory, and I/O metrics across spatial, time-series, and spectral access patterns following Nguyen et al. (2023) methodology.
Generate structured benchmark reports with configuration comparisons, performance bias analysis, ranked recommendations, and trade-off explanations from Zarr chunking benchmark results.
Safely apply chunking configurations to Zarr datasets with validation, progress reporting, memory-bounded execution, and rollback safety. Supports local and cloud storage backends.
Generate synthetic Zarr datasets with configurable dimensions, shapes, data types, and compression for controlled chunking benchmarks. Supports local and cloud storage backends (S3, GCS).
README
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