Choosing the Right Codec: Trade offs and Decision Framework

The Three Way Trade: Every compression decision involves trading space, CPU, and latency. A fourth dimension emerges in distributed systems: splittability, which determines whether large files can be processed in parallel. Speed First Codecs (LZ4, Snappy): These prioritize low CPU and low latency, achieving 1.5x to 2.5x ratios but compressing at hundreds of MB/s per core. Engineers pick these for online services, message queues, and intermediate shuffle data because any extra 5 to 10 milliseconds on the hot path can violate a p99 Service Level Agreement (SLA). However, you pay with higher storage and network costs. The decision point: If your service has a 50 millisecond p99 SLA and currently sits at 40 milliseconds, adding even 10 milliseconds of compression overhead pushes you into violation territory under load spikes. Ratio First Codecs (XZ, bzip2): These reach 5x to 10x on text or logs, ideal for cold backups and compliance archives read rarely. The cost is high CPU and compression latencies sometimes reaching seconds for gigabyte scale blobs. This is acceptable when data is written once and read infrequently, but not in interactive analytics where analysts expect sub second or single digit second response times. Balanced Codecs (zlib, Zstd): Classic zlib gives about 3x ratio with moderate CPU. Zstd operates across a wide spectrum of levels: at the same ratio as zlib, it runs 3 to 5 times faster; at the same speed, it shrinks data by an extra 10 to 15 percent. Some teams prefer zlib for ecosystem maturity. Others adopt Zstd when CPU cost becomes a bottleneck or when they want dictionary based gains on small objects. The Splittability Problem: File level codecs like gzip are not splittable. A 1 GB gzip file must be processed by a single worker, which creates stragglers in distributed systems. If an analytics job scans 10 TB as large gzip files, a few workers spend minutes decompressing while others sit idle, dictating overall job completion time. Block aware formats that compress independent blocks solve this but incur slightly worse ratios and more metadata overhead. The trade is worth it for batch processing at scale. Decision Framework: For online services with p99 latency SLAs under 100 milliseconds, use Snappy or LZ4. For intermediate data with balanced read and write patterns, use Zstd at low to mid levels. For cold archives accessed infrequently, use Zstd at high levels or XZ. For distributed batch processing, ensure your format uses independent compressed blocks to enable parallelism.