54.5 Streaming Compression: Memory-Efficient Patterns
In scenarios involving massive datasets or continuous data streams, traditional compression methods that require the entire dataset to be loaded into memory are infeasible. Streaming compression addresses this by processing data in a sequential, chunk-by-chart manner, maintaining a constant, low memory footprint regardless of the total data size. The core principle involves a continuous cycle: read a manageable chunk of data from the source (e.g., a file, network socket, or sensor), compress that chunk, write the compressed output, and then repeat. This pattern is fundamental to tools like gzip and tar when used in pipes (cat bigfile.log | gzip > archive.gz) and is essential for applications like log rotation, real-time data processing, and serving compressed content on web servers.