A NOSQL STUDY: APACHE CASSANDRA

Shujaat Hussain

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 vs MySQL 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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Terabytes of data, ~1,000,000 ops/s

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