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From Apache Wiki <>
Subject [Cassandra Wiki] Update of "ArchitectureOverview" by tuxracer69
Date Tue, 17 Nov 2009 17:17:18 GMT
Dear Wiki user,

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The "ArchitectureOverview" page has been changed by tuxracer69.


  === CAP theorem ===
- The '''CAP''' theorem (Brewer00) states that you have to pick two of '''Consistency''',
'''Availability''', '''Partition tolerance'''. (You can't have the three at the same time).
+ The '''CAP''' theorem ([[|Brewer]])
states that you have to pick two of '''Consistency''', '''Availability''', '''Partition tolerance''':
You can't have the three at the same time and get an acceptable latency.
  Cassandra values Availability and Partitioning tolerance ('''AP'''). Tradeoffs between consistency
and latency are tunable in Cassandra.  You can get strong consistency with Cassandra (with
an increased latency). But, you can't get row locking: that is a definite win for HBase.
@@ -191, +191 @@

  == Consistency ==
  See also the [[API|API]] documentation.
- If
+ Consistency describes how and whether a system is left in a consistent state after an operation.
In distributed data systems like Cassandra, this usually means that once a writer has written,
all readers will see that write.
+ On the contrary to the strong consistency used in most relational databases ('''ACID'''
for ''Atomicity Consistency Isolation Durability'') Cassandra is at the other end of the spectrum
('''BASE''' for ''Basically Available Soft-state Eventual consistency'').
+ Cassandra weak consistency comes in the form of eventual consistency which means the database
eventually reaches a consistent state.  As the data is replicated, the latest version of something
is sitting on some node in the cluster, but older versions are still out there on other nodes,
but eventually all nodes will see the latest version.
+ More specifically:
  R=read replica count
  W=write replica count
  N=replication factor

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