the cassandra distributed database
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The Cassandra Distributed Database
Eric Evanseevans@rackspace.com
@jericevans
FOSDEMFebruary 7, 2010
A prophetess in Troy during the Trojan War. Her predictions werealways true, but never believed.
A massively scalable, decentralized, structured data store (akadatabase).
Outline
1 Project History
2 Description
3 Case Studies
4 Roadmap
• 7 new committers added
• Dozens of contributors
• 100+ people on IRC
• Hundreds of closed issues (bugs, features, etc)
• 3 major releases, 2 point releases
• Graduation to TLP?
Outline
1 Project History
2 Description
3 Case Studies
4 Roadmap
Cassandra is...
• O(1) DHT
• Eventual consistency
• Tunable trade-offs, consistency vs. latency
But...
• Values are structured, indexed
• Columns / column families
• Slicing w/ predicates (queries)
Column families
Supercolumn families
Querying
• get(): retrieve by column name
• multiget(): by column name for a set of keys
• get slice(): by column name, or a range of names• returning columns• returning super columns
• multiget slice(): a subset of columns for a set of keys
• get count: number of columns or sub-columns
• get range slice(): subset of columns for a range of keys
Column comparators
• TimeUUID
• LexicalUUID
• UTF8
• Long
• Bytes
• ...
Updating
• insert(): add/update column (by key)
• batch insert(): add/update multiple columns (by key)
• remove(): remove a column
• batch mutate(): like batch insert() but can also delete(new for 0.6, deprecates batch insert())
• Remove key range RSN
Consistency
CAP Theorem: choose any two of Consistency, Availability, orPartition tolerance.
• Zero
• One
• Quorum ((N / 2) + 1)
• All
Client API
• Thrift (12 different languages!)
• Ruby• http://github.com/fauna/cassandra/tree/master• http://github.com/NZKoz/cassandra object/tree/master
• Python• http://github.com/digg/lazyboy/tree/master• http://github.com/driftx/Telephus/tree/master (Twisted)
• Scala• http://github.com/viktorklang/Cassidy/tree/master• http://github.com/nodeta/scalandra/tree/master
Performance vs MySQL w/ 50GB
• MySQL• 300ms write• 350ms read
• Cassandra• 0.12ms write• 15ms read
Writes
About writes...
• No reads
• No seeks
• Sequential disk access
• Atomic within a column family
• Fast
• Any node
• Always writeable (hinted hand-off)
Reads
About reads...
• Any node
• Read repair
• Usual caching conventions apply
Outline
1 Project History
2 Description
3 Case Studies
4 Roadmap
Case 1: Digg
Digg is a social news site that allows people to discover and sharecontent from anywhere on the Internet by submitting stories andlinks, and voting and commenting on submitted stories and links.
Ranked 98th by Alexa.com.
Digg
Problem
• Terabytes of data; high transaction rate (reads dominated)
• Multiple clusters; heavily sharded
• Management nightmare (high effort, error prone)
• Unsatisfied availability requirements (geographic isolation)
Solution
• Currently production on ”Green Badges”
• Cassandra as primary data store RSN
• Datacenter and rack-aware replication
Case 2: Twitter
Twitter is a social networking and microblogging service thatenables its users to send and read tweets, text-based posts of up to140 characters.
Ranked 12th by Alexa.com.
MySQL
• Terabytes of data, ˜1,000,000 ops/s
• Calls for heavy sharding, light replication
• Schema changes are very difficult, (if possible at all)
• Manual sharding is very high effort
• Automated sharding and replication is Hard
Case 3: Facebook
Facebook is a social networking site where users can create aprofile, add friends, and send them messages. Users can also joingroups organized by location or other points of common interest.
Ranked #2 by Alexa.com.
Inbox Search
• 100 TB
• 160 nodes
• 1/2 billion writes per day (2yr old number?)
Case 4: Mahalo
Mahalo.com is a web directory and knowledge exchange. Itdifferentiates itself by tracking and building hand-crafted resultsets for many of the popular search terms.
(it also means ”thank you” in Hawaiian)
MySQL
• Partial deployment; 16 million video records (and growing)
• Writes (and storage) rapidly exceeding single box limitations
• Managability suffering (clustering is painful)
• Concerns over availability
Outline
1 Project History
2 Description
3 Case Studies
4 Roadmap
0.6
• batch mutate command
• authentication (basic)
• new consistency level, ANY
• fat client
• mmapped i/o reads (default on 64bit jvm)
• improved write concurrency (HH)
• networking optimizations
• row caching
• improved management tools
• per-keyspace replication factor
0.7
• more efficient compactions (row sizes bigger than memory)
• easier (dynamic?) column family changes
• SSTable versioning
• SSTable compression
• support for column family truncation
• improved configuration handling
• remove key range command
• even more improved management tools
• vector clocks w/ server-side conflict resolution
THE END
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