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[1] https://blogs.oracle.com/dbstorage/compressing-your-indexes:-index-key-compression-part-1
[2] https://blogs.oracle.com/dbstorage/compressing-your-indexes:-advanced-index-compression-part-2
[3] https://docs.mongodb.com/manual/core/wiredtiger/#storage-wiredtiger-compression

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Compression

The whole pages could be compressed. This gives 2x-4x reduciton in size on average. Two different approaches are used in practice - without in-memory compression, with in-memory compression

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• Lower compression rates
• More complex algorithms

Examples:

1. Oracle Advanced Compression [1], [2]
2. MS SQL Page compression [3] 

[1] https://blogs.oracle.com/dbstorage/updates-in-row-compressed-tables
[2] https://blogs.oracle.com/dbstorage/advanced-row-compression-improvements-with-oracle-database-12c-release-2
[3] https://docs.microsoft.com/en-us/sql/relational-databases/data-compression/page-compression-implementation?view=sql-server-2017

WAL Compression

Write-ahead log writes all changes to data to journal. Data is read back only during crash recovery. Data chunks being written to journal may be compressed. It reduces number of disk writes and saves WAL space increasing likelihood of delta-update in case of temporal node shutdown. Compression could be applied to specific records - the larger the record, the more savings are expected. Compression typically takes more time than decompression, so operation latency may increase. However less number of IO calls typically increases overall system throughput because disk resources are typically more deficient than CPU.

Examples:

1. PostgreSQL
2. MariaDB (MySQL)
3. MongoDB

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