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The "ArchitectureOverview" page has been changed by tuxracer69. http://wiki.apache.org/cassandra/ArchitectureOverview -------------------------------------------------- New page: (WORK IN PROGESS!) This is an overview of Cassandra architecture aimed at Cassandra users. Developers should probably look at the Developers links on the wiki's [[../|front page]] Information is mainly based on [[http://assets.en.oreilly.com/1/event/27/Cassandra_%20Open%20Source%20Bigtable%20+%20Dynamo%20Presentation.pdf|J Ellis OSCON 09 presentation ]] == Motivation == Scaling reads to a relational database is hard Scaling writes to a relational database is virtually impossible ... and when you do, it usually isn't relational anymore * The new face of data Scale out, not up Online load balancing, cluster growth Flexible schema Key-oriented queries CAP-aware * CAP theorem Pick two of Consistency, Availability, Partition tolerance Two famous papers * Bigtable: A distributed storage system for structured data, 2006 * Dynamo: amazon's highly available keyvalue store, 2007 Two approaches * Bigtable: "How can we build a distributed db on top of GFS?" * Dynamo: "How can we build a distributed hash table appropriate for the data center?" 10,000 ft summary * Dynamo partitioning and replication * Log-structured ColumnFamily data model similar to Bigtable's Cassandra highlights * High availability * Incremental scalability * Eventually consistent * Tunable tradeoffs between consistency and latency * Minimal administration * No SPF (Single Point of Failure) Dynamo architecture & Lookup Architecture details O(1) node lookup Explicit replication Eventually consistent Architecture layers Messaging service Gossip Failure detection Cluster state Partitioner Replication Commit log Memtable SSTable Indexes Compaction Tombstones Hinted handoff Read repair Bootstrap Monitoring Admin tools Writes Any node Partitioner Commitlog, memtable SSTable Compaction Wait for W responses Memtable / SSTable Disk Commit log SSTable format Key / data SSTable Indexes Bloom filter Key Column (Similar to Hadoop MapFile / Tfile) Compaction Merge keys Combine columns Discard tombstones Remove Deletion marker (tombstone) necessary to suppress data in older SSTables, until compaction Read repair complicates things a little Eventually consistent complicates things more Solution: configurable delay before tombstone GC, after which tombstones are not repaired Cassandra write properties No reads No seeks Fast Atomic within ColumnFamily Always writable Read path Any node Partitioner Wait for R responses Wait for N R responses in the background and perform read repair Cassandra read properties Read multiple SSTables Slower than writes (but still fast) Seeks can be mitigated with more RAM Scales to billions of rows Consistency in a BASE world If W + R > N, you will have consistency W=1, R=N W=N, R=1 W=Q, R=Q where Q = N / 2 + 1 vs MySQL with 50GB of data MySQL ~300ms write ~350ms read ~0.12ms write ~15ms read
