Benchmarks assess database compression techniques by measuring three main factors: compression efficiency, performance impact, and resource usage. First, compression efficiency is evaluated through the compression ratio, which compares the original data size to the compressed size. For example, a technique that reduces a 100 GB dataset to 25 GB achieves a 4:1 ratio. However, higher ratios aren’t always better if they sacrifice data integrity or query speed. Benchmarks also test how compression handles diverse data types—like text, numbers, or JSON—since some algorithms perform better on specific formats (e.g., dictionary encoding for repetitive text).
Next, benchmarks analyze the performance impact of compression on database operations. This includes measuring the time and CPU/memory overhead required to compress data during writes and decompress it during reads. For instance, a technique like LZ4 might offer fast compression and decompression with moderate ratios, while Zlib provides higher ratios at the cost of slower speeds. Benchmarks simulate real-world scenarios, such as running OLTP workloads (many small transactions) or OLAP queries (large scans), to see how compression affects latency and throughput. For example, a columnar storage format using run-length encoding might speed up aggregate queries in OLAP but slow down row-level updates in OLTP.
Finally, benchmarks use standardized datasets and tools to ensure fair comparisons. Popular benchmarks like TPC-H (analytical workloads) or YCSB (NoSQL key-value workloads) are adapted to test compression. They report metrics such as query execution time, storage saved, and CPU utilization. For example, a benchmark might show that Snappy compression reduces storage by 50% but increases CPU usage by 20% during bulk loads. These results help developers choose the right balance between space savings, speed, and hardware costs based on their specific use case.