A NOSQL Study: Apache Cassandra

A NOSQL Study: Apache Cassandra ShujaatHussain

Data Model A single column

Data Model A single row

Data Model

CAP Theorem • Consistency –the system is in a consistent state after an operation • Availability –the system is “always on”, no downtime • Partition tolerance–the system continues to function even when split into disconnected subsets (by a network disruption)

Performance vsMySQL w/ 50GB • MySQL • 300ms write • 350ms read • Cassandra • 0.12ms write • 15ms read

Querying: Overview • You need a key or keys: • Single: key=‘a’ • Range: key=‘a’ through ’f’ • And columns to retrieve: • Slice: cols={bar through kite} • By name: key=‘b’ cols={bar, cat, llama} • Nothing like SQL “WHERE col=‘faz’”

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Problems • Terabytes of data; high transaction rate (reads dominated) • Multiple clusters • Management nightmare (high effort, error prone) • Unsatisfied availability requirements (geographic isolation) • Solution • Cassandra as primary data store • Datacenter and rack-aware replication

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Inbox Search • 100 TB • 160 nodes • 1/2 billion writes per day (2yr old number?)

Pros • Advantages • Massive scalability • High availability • Lower cost (than competitive solutions at that scale) • (usually) predictable elasticity • Schema flexibility, sparse & semi-structured data

Cons • Disadvantages • Limited query capabilities (so far) • Eventual consistency is not intuitive to program for • Makes client applications more complicated • No standardizatrion • Portability might be an issue • Insufficient access control

A NOSQL Study: Apache Cassandra