Apache Cassandra Opinion and Fact
- 1. Oh why is my cluster’s read performance terrible?
- 2. What it is. • In one sentence: – A data store that resembles: • A Hash table (k/v) that’s evenly distributed across a cluster of servers. Practically speaking, not n level k/v. • Or, An excel spread sheet that is chopped up and housed on different servers. • Basic Nomenclature..
- 3. Where did it come from? • Legacy: – Google Big Table and Amazon Dynamo. – Open sourced by Facebook in 2009. – They had designed it for ‘Inbox search’
- 4. What it is not. • Not a general purpose data store. – Highly specialized use cases. • The Business use case must align with Cassandra architecture. – No transactions – No joins. (make multiple roundtrips). De-normalize data. – No stored procedures. – No range queries across keys (default) – No Referential Integrity (pk constraint, foreign keys). – No locking. – Uses timestamp to upsert data. – Charlie must be aghast.
- 5. Who uses it? • Web scale companies. – Netflix, Twitter. • Capture clickstream data. • User activity/gaming. • Backing store for search tools (lucene) – Structured/Unstructured data. • Trend: Web scale companies moving from distributed Mysql to Cassandra.
- 6. Interest over time/google trend…
- 8. Where do people use it? • Mostly in analytic/reporting ‘stacks’. – Fire hose (value proposition 1) in vast amounts of ‘log like’ data. – Hopefully your data model ensures that your data is physically clustered (value proposition 2) on read. - Data that is physically clustered is conducive to reporting. - Can be used ‘real time’ but not its strength.
- 9. Important to know right up front. • Designed for High Write rates (all activity is sequential io). – If improperly used, read performance will suffer. – Always strive to minimize disk seek on read. • Millions of Inserts should result in 10’s of thousands of Row Keys. (not millions of keys) • Main usage pattern: High Write / Low Read (rates). See Netflix slide. • Anti-pattern: (oltp like) Millions of inserts / Millions of reads. (for main data tables) • If your Cassandra use is kosher, then you will find that IO is the bottleneck. Need better performance? Simply add more boxes (more io bandwidth to your cluster) • It’s all about physically clustering your data for efficient reads. • You have a query in mind, well, design a Cassandra table that satisfies your query. (lots of data duplication all over the place). Make sure that your query is satisfied by navigating to a single row key. • Favors throughput over latency.
- 10. • With analytics/reporting in mind: – Let’s explore RDMS storage inefficiency (For large query) and Cassandra’s value proposition # 2.
- 11. Data in an RDBMS (physical) Block size 8k Symbol Price Time Select * … db block 1 MSFT 28.01 t1 1 k row size Where … Symbol=MSFT … MSFT 28.03 t5 Minimum IO = 24K (8k x blocks visited) 3 seeks. db block 20 … Slow. … MSFT 28.03 t7 … db block 1000 … … MSFT 28.01 t22 …
- 12. Data in a Cassandra (physical) Select * Where KEY Col/Value Symbol=MSFT MSFT t1 => 28.03 t5 => 28.03 t7=>28.03 t22=>28.01 Minimum IO = 8K (8k x 1) - 1 seek to KEY (+ overhead), then sequentially read the data. - You want to make sure that you are getting a lot of value per seek! Cassandra likes “Wide Rows”. - Your data is physically clustered and sorted (t1,t5…). - Millions of inserts have resulted in thousands of keys. (high write/ low read) - Fast
- 13. What it is, in depth: • Log Structured Data store. (all activity is sequentially written) • Favors Availability and Partition tolerance over Consistency. – Consistency is tunable, but if you tune for high consistency you trade off performance. – Cassandra consistency is not the same as database consistency. It is ReadYourWrites consistency. • Column oriented. • TTL (time to live / expire data ) • Compaction (coalesce data in files)
- 14. System Properties • Distributed / elastic scalability. (value proposition 3) • Fault Tolerant – Rack aware, Inter/intra datacenter data replication. (value proposition 4) • Peer to peer, no single point of failure. (value proposition 5) (write/read from any node, it will act as the proxy to the cluster). No master node. • Durable.
- 15. Evenly distributes data (default) • Consistent hashing. • Token Range: 0 – 2^127-1 • Your ‘key’ gets Assigned a token. Eg. Key = smith = token 15, place it on the Eastern Node.
- 16. Replication Factor = 3 • Consistency Level • Hinted Handoff
- 18. DataTypes.
- 19. ACID? • A/I/D ( in bits and bobs) • BASE. Basically Available Soft-state Eventual consistency
- 20. Cassandra/Future • Will slowly take on more rdbms like features. – Cassandra 1.1 has row level isolation. Previously you could read some one else’s inflight data.
- 21. Reference: CAP Theorem. • Consistency (all nodes see the same data at the same time) • Availability (a guarantee that every request receives a response about whether it was successful or failed) • Partition tolerance (the system continues to operate despite arbitrary message loss or failure of part of the system)