Cassandra under the hood - What happens when you write and read
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Cassandra uses commit logs, memtables, and SSTables to handle writes efficiently. Commit logs store writes sequentially, memtables buffer in-memory, and SSTables store sorted, compressed data files on disk. For reads, Cassandra uses bloom filters and indexes to locate keys in memtables and SSTables, then retrieves the data. Compaction merges SSTables to improve performance and remove obsolete data. Snapshots and repair use Merkle trees to backup consistent data sets and repair differences between nodes.
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Cassandra under the hood - What happens when you write and read
- 1. Cassandra under the hood Richard Low rlow@acunu.com
- 2. Outline • What happens when you write? • Commit logs • Memtables “richard”:{ “email”:”rlow@acunu.com” • SSTables } ? • What happens when you read? • Point queries • Range queries • Repair and snapshots
- 3. Why should we care? • Help understand performance • Understand performance implications of data model • Helps to fix it if something goes wrong • Interesting!
- 4. Writes
- 5. Writes (2) Insert Commit log Memtable SSTable { Bloom filter, Index, Data }
- 6. Commit log Insert Commit log Memtable SSTable { Bloom filter, Index, Data }
- 7. Commit log • Each insert written to commit log first • Stored in insertion order • Inserts not acknowledged until written to commit log • Batch vs periodic • In case of crash, can replay
- 8. Memtable Memtable
- 9. Memtable • In memory store of insertions • ConcurrentSkipListMap • When too large, flushed to disk • Ensures all writes to disk are sequential
- 10. SSTable Insert Commit log Memtable SSTable { Bloom filter, Index, Data }
- 11. SSTables • Stores actual data, sorted by key • Contains a Bloom filter and index to help find keys • Read only
- 12. Bloom filters • Probabilistic data structure • Answers membership queries: • ‘Does the set contain x?’ • Can give false positives, never false negatives • Space efficient • Typical size: 1 byte per key
- 13. How it works together Bloom filter Index Data 011010111010010 k_0 -> 0 k_0.................................................... k_128 -> 4582 .....k_1............................................... k_256 -> 9242 .........k_2...........k_3..........................
- 14. How it works together Bloom filter Index Data 011010111010010 k_0 -> 0 k_0.................................................... k_128 -> 4582 .....k_1............................................... k_256 -> 9242 .........k_2...........k_3.......................... Contains x? Where is x? Retrieve x
- 15. How it works together Memory Disk Bloom filter Index Data 011010111010010 k_0 -> 0 k_0.................................................... k_128 -> 4582 .....k_1............................................... k_256 -> 9242 .........k_2...........k_3.......................... Contains x? Where is x? Retrieve x
- 16. Point queries Memtables k_0 k_1 -> -> ......... ......... k_0 k_1 -> -> ......... ......... k_0 k_1 -> -> ......... ......... k_2 -> ......... k_2 -> ......... k_2 -> ......... SSTables k_0.................................................... .....k_1............................................... k_0.................................................... .....k_1............................................... .........k_2...........k_3.......................... .........k_2...........k_3.......................... k_0 -> 0 k_0 -> 0 k_128 -> 4582 k_128 -> 4582 k_256 -> 9242 k_256 -> 9242 k_0.................................................... k_0.................................................... .....k_1............................................... .....k_1............................................... .........k_2...........k_3.......................... .........k_2...........k_3.......................... k_0 -> 0 k_0 -> 0 k_128 -> 4582 k_128 -> 4582 k_256 -> 9242 k_256 -> 9242
- 17. Point queries Memtables k_0 k_1 -> -> ......... ......... k_0 k_1 -> -> ......... ......... k_0 k_1 -> -> ......... ......... k_2 -> ......... k_2 -> ......... k_2 -> ......... SSTables k_0.................................................... .....k_1............................................... k_0.................................................... .....k_1............................................... .........k_2...........k_3.......................... .........k_2...........k_3.......................... k_0 -> 0 k_0 -> 0 k_128 -> 4582 k_128 -> 4582 k_256 -> 9242 k_256 -> 9242 1. Query filter k_0.................................................... k_0.................................................... .....k_1............................................... .....k_1............................................... .........k_2...........k_3.......................... .........k_2...........k_3.......................... k_0 -> 0 k_0 -> 0 k_128 -> 4582 k_128 -> 4582 k_256 -> 9242 k_256 -> 9242
- 18. Point queries Memtables k_0 k_1 -> -> ......... ......... k_0 k_1 -> -> ......... ......... k_0 k_1 -> -> ......... ......... k_2 -> ......... k_2 -> ......... k_2 -> ......... SSTables k_0.................................................... .....k_1............................................... k_0.................................................... .....k_1............................................... .........k_2...........k_3.......................... .........k_2...........k_3.......................... k_0 -> 0 k_0 -> 0 k_128 -> 4582 k_128 -> 4582 k_256 -> 9242 k_256 -> 9242 1. Query filter 2. Find location k_0.................................................... k_0.................................................... .....k_1............................................... .....k_1............................................... .........k_2...........k_3.......................... .........k_2...........k_3.......................... k_0 -> 0 k_0 -> 0 k_128 -> 4582 k_128 -> 4582 k_256 -> 9242 k_256 -> 9242
- 19. Point queries Memtables k_0 k_1 -> -> ......... ......... k_0 k_1 -> -> ......... ......... k_0 k_1 -> -> ......... ......... k_2 -> ......... k_2 -> ......... k_2 -> ......... SSTables k_0.................................................... .....k_1............................................... k_0.................................................... .....k_1............................................... .........k_2...........k_3.......................... .........k_2...........k_3.......................... k_0 -> 0 k_0 -> 0 k_128 -> 4582 k_128 -> 4582 k_256 -> 9242 k_256 -> 9242 1. Query filter 2. Find location 3. Read data k_0.................................................... k_0.................................................... .....k_1............................................... .....k_1............................................... .........k_2...........k_3.......................... .........k_2...........k_3.......................... k_0 -> 0 k_0 -> 0 k_128 -> 4582 k_128 -> 4582 k_256 -> 9242 k_256 -> 9242
- 20. Range queries • Bloom filters useless • Use index to locate portion of SSTable • Read data, merge results • Necessary to lookup in every SSTable data file • Disk I/O proportional to #SSTables
- 21. Compaction • Merges SSTables • Removes overwrites and obsolete tombstones • Improves range query performance • Major compaction creates one SSTable
- 22. Write optimised • All writes are sequential on disk • Each write is written multiple times during compactions • Bloom filters mean approx. one I/O per read • Avoid a read-modify-write data model
- 23. Scaling • In memory: • Buffers • Memtables • Bloom filters • Index • If not enough memory, significant performance impact
- 24. Repair: Merkle Trees • Repair builds a Merkle tree • Compared with replicas • Efficient • If differences are found, portions of SSTables are streamed • Requires full disk scan to build
- 25. Snapshot • For backup, want consistent set of SSTables • nodetool snapshot does this • Creates hard links to existing SSTables • Implies data will be copied after a few compactions
- 26. Summary • How writes end up on disk • How point queries and range queries find the data • Implications • Repair • Snapshot
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