Cassandra Core Concepts
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The document discusses different data storage options for small, medium, and large datasets. It argues that relational databases do not scale well for large datasets due to limitations with replication, normalization, sharding, and high availability. The document then introduces Apache Cassandra as a fast, distributed, highly available, and linearly scalable database that addresses these limitations through its use of a hash ring architecture and tunable consistency levels. It describes Cassandra's key features including replication, compaction, and multi-datacenter support.
Cassandra Core Concepts
- 1. ©2013 DataStax Confidential. Do not distribute without consent. Jon Haddad, Luke Tillman Technical Evangelists, DataStax @rustyrazorblade, @LukeTillman Cassandra Core Concepts 1
- 2. Small Data • 100's of MB to low GB, single user • sed, awk, grep are great • sqlite • Limitations: • bad for multiple concurrent users (file sharing!)
- 3. Medium Data • Fits on 1 machine • RDBMS is fine • postgres • mysql • Supports hundreds of concurrent users • ACID makes us feel good • Scales vertically
- 4. Can RDBMS work for big data?
- 5. Replication: ACID is a lie Client Master Slave replication lag Consistent results? Nope!
- 6. Third Normal Form Doesn't Scale • Queries are unpredictable • Users are impatient • Data must be denormalized • If data > memory, you = history • Disk seeks are the worst (SELECT CONCAT(city_name,', ',region) value, latitude, longitude, id, population, ( 3959 * acos( cos( radians($latitude) ) * cos( radians( latitude ) ) * cos( radians( longitude ) - radians($longitude) ) + sin( radians($latitude) ) * sin( radians( latitude ) ) ) ) AS distance, CASE region WHEN '$region' THEN 1 ELSE 0 END AS region_match FROM `cities` $where and foo_count > 5 ORDER BY region_match desc, foo_count desc limit 0, 11) UNION (SELECT CONCAT(city_name,', ',region) value, latitude, longitude, id, population, ( 3959 * acos( cos( radians($latitude) ) * cos( radians( latitude ) ) * cos( radians( longitude ) - radians($longitude) ) + sin( radians($latitude) ) * sin( radians( latitude ) ) ) )
- 7. Sharding is a Nightmare • Data is all over the place • No more joins • No more aggregations • Denormalize all the things • Querying secondary indexes requires hitting every shard • Adding shards requires manually moving data • Schema changes
- 8. High Availability.. not really • Master failover… who's responsible? • Another moving part… • Bolted on hack • Multi-DC is a mess • Downtime is frequent • Change database settings (innodb buffer pool, etc) • Drive, power supply failures • OS updates
- 9. Summary of Failure • Scaling is a pain • ACID is naive at best • You aren't consistent • Re-sharding is a manual process • We're going to denormalize for performance • High availability is complicated, requires additional operational overhead
- 10. Lessons Learned • Consistency is not practical • So we give it up • Manual sharding & rebalancing is hard • So let's build in • Every moving part makes systems more complex • So let's simplify our architecture - no more master / slave • Scaling up is expensive • We want commodity hardware • Scatter / gather no good • We denormalize for real time query performance • Goal is to always hit 1 machine
- 11. What is Apache Cassandra? • Fast Distributed Database • High Availability • Linear Scalability • Predictable Performance • No SPOF • Multi-DC • Commodity Hardware • Easy to manage operationally • Not a drop in replacement for RDBMS
- 12. Hash Ring • No master / slave / replica sets • No config servers, zookeeper • Data is partitioned around the ring • Data is replicated to RF=N servers • All nodes hold data and can answer queries (both reads & writes) • Location of data on ring is determined by partition key
- 13. CAP Tradeoffs • Impossible to be both consistent and highly available during a network partition • Latency between data centers also makes consistency impractical • Cassandra chooses Availability & Partition Tolerance over Consistency
- 14. Replication • Data is replicated automatically • You pick number of servers • Called “replication factor” or RF • Data is ALWAYS replicated to each replica • If a machine is down, missing data is replayed via hinted handoff
- 15. Consistency Levels • Per query consistency • ALL, QUORUM, ONE • How many replicas for query to respond OK
- 16. Multi DC • Typical usage: clients write to local DC, replicates async to other DCs • Replication factor per keyspace per datacenter • Datacenters can be physical or logical
- 17. Reads & Writes
- 18. The Write Path • Writes are written to any node in the cluster (coordinator) • Writes are written to commit log, then to memtable • Every write includes a timestamp • Memtable flushed to disk periodically (sstable) • New memtable is created in memory • Deletes are a special write case, called a “tombstone”
- 19. Compaction • sstables are immutable • updates are written to new sstables • eventually we have too many files on disk • Merged through compaction, only latest data is kept based on timestamp sstable sstable sstable sstable
- 20. The Read Path • Any server may be queried, it acts as the coordinator • Contacts nodes with the requested key • On each node, data is pulled from SSTables and merged • Consistency< ALL performs read repair in background (read_repair_chance)
- 21. What are your options?
- 22. Open Source • Latest, bleeding edge features • Monthly releases (tick tock) • File JIRAs • Support via mailing list & IRC • Fix bugs • cassandra.apache.org • Perfect for hacking
- 23. DataStax Enterprise • Integrated Multi-DC Search • Integrated Spark for Analytics • Free Startup Program • <3MM rev & <$30M funding • Extended support • Additional QA • Focused on stable releases for enterprise
- 25. ©2013 DataStax Confidential. Do not distribute without consent. 25