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Compressing for Performance over Cost in Opensearch
- Opensearch allows users to configure data storage at the index level, enabling optimization for efficiency and speed based on specific use cases.
- Recommended compression algorithms vary based on the frequency of writes and reads to an index.
- Codecs like zstd_no_dict, zstd, and best_compression offer different trade-offs between compression ratio, speed, CPU, and memory usage.
- Factors such as node size, CPU usage, and real-time requirements should influence codec selection.
- LZ4 prioritizes speed with low CPU and memory usage, while zstd offers a balance between compression ratio and speed.
- Zstd_no_dict provides faster write operations with reduced disk footprint compared to other codecs.
- LZ4 excels in compression and decompression speeds, making it ideal for scenarios where speed is crucial.
- Tips for optimizing performance and storage costs in Opensearch clusters include sharding, adjusting compression levels, and utilizing hardware acceleration.
- Understanding developer usage patterns and access needs is key to selecting the appropriate codec for a cluster.
- Balancing trade-offs and considering long-term cluster management are essential in codec selection decisions.
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