How Compression Algorithms Work: Building Blocks and Common Codecs

The Two Core Techniques: Most compression algorithms combine two fundamental approaches. First, dictionary or LZ style compression finds repeated sequences in data and encodes them as references to earlier occurrences. When you see "the quick brown fox" multiple times, instead of storing it repeatedly, you store it once and use pointers for subsequent instances. Second, entropy coding assigns shorter codes to frequently appearing symbols and longer codes to rare ones. Methods like Huffman coding, arithmetic coding, or Asymmetric Numeral Systems (ANS) implement this principle. If the letter "e" appears 100 times and "z" appears twice, "e" gets a short code. The Codec Spectrum: Different algorithms make different trade offs across this foundation. Understanding where each codec sits on the speed versus ratio spectrum helps you choose correctly. Why Zstandard Stands Out: Zstd represents modern codec design by combining wide windows (many megabytes versus zlib's 32 KB), branchless decoding that reduces CPU branch mispredictions, and ANS based entropy coding. The result: at the same ratio as zlib, Zstd compresses 3 to 5 times faster and decompresses roughly 2 times faster. Practical Example: A logging pipeline ingesting 5 GB/s might use Snappy at the producer level, which can compress at 400 MB/s per core and adds under 1 millisecond to p99 latency. This halves network traffic from producers to brokers. For long term storage, the same data gets recompressed with Zstd at a higher level, achieving 4x ratio and cutting storage costs substantially. The data is written once with high compression, then read many times where fast decompression matters.