ArangoDB employs several techniques to handle data compression, aiming to optimize storage efficiency and reduce disk space usage while maintaining query performance and data accessibility.
The main methods ArangoDB uses to handle data compression :Dictionary Compression :
* ArangoDB utilizes dictionary compression techniques to compress string values, particularly in indexes and text-based attributes.
* Dictionary compression involves building a dictionary of unique string values encountered in the data and encoding each string with a shorter code or reference to the dictionary entry.
* By replacing repeated string values with shorter codes or references, dictionary compression reduces storage overhead and improves compression ratios for string data.
Block-level Compression :
* ArangoDB supports block-level compression for data stored on disk, where data blocks are compressed individually before being written to disk.
* Block-level compression algorithms, such as LZ4, Snappy, or Zstandard, are used to compress data blocks, reducing storage space requirements and improving disk I/O performance.
* Compressed data blocks are decompressed on-the-fly when read from disk, ensuring transparent access to compressed data without impacting query performance.
Page-level Compression :
* ArangoDB supports page-level compression for indexes and collections, where data pages are compressed as a whole before being written to disk.
* Page-level compression algorithms, such as LZ4, Snappy, or Zstandard, are applied to entire data pages, including index entries, documents, and metadata.
* Page-level compression reduces disk space consumption and minimizes I/O overhead by compressing entire data pages at once, rather than compressing individual records or attributes.
Adaptive Compression Policies :
* ArangoDB provides configurable compression policies and settings that allow administrators to adjust compression levels and algorithms based on data characteristics and workload requirements.
* Administrators can specify compression settings at the collection, index, or database level, choosing the most suitable compression algorithm and compression level for different types of data and access patterns.
Transparent Compression and Decompression :
* ArangoDB handles compression and decompression transparently, ensuring that compressed data is automatically compressed and decompressed as needed during read and write operations.
* Application developers and users interact with ArangoDB using standard database interfaces and APIs, without needing to be aware of the underlying compression mechanisms.
Storage Engine Integration :
* ArangoDB's storage engines, such as RocksDB and MMFiles, integrate with compression libraries and codecs to implement efficient data compression and decompression algorithms.
* Storage engine-specific optimizations and configurations ensure that data compression is seamlessly integrated into the storage layer, providing consistent compression benefits across different storage engines.
