|Original author(s)||Avinash Lakshman, Prashant Malik|
|Developer(s)||Apache Software Foundation|
|Stable release||2.1.2 / November 10, 2014|
|License||Apache License 2.0|
Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters, with asynchronous masterless replication allowing low latency operations for all clients.
Cassandra also places a high value on performance. In 2012, University of Toronto researchers studying NoSQL systems concluded that "In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes in all experiments" although "this comes at the price of high write and read latencies."
Cassandra's data model is a partitioned row store with tunable consistency. Rows are organized into tables; the first component of a table's primary key is the partition key; within a partition, rows are clustered by the remaining columns of the key. Other columns may be indexed separately from the primary key.
Tables may be created, dropped, and altered at runtime without blocking updates and queries.
Apache Cassandra was initially developed at Facebook to power their Inbox Search feature by Avinash Lakshman (one of the authors of Amazon's Dynamo) and Prashant Malik. It was released as an open source project on Google code in July 2008. In March 2009, it became an Apache Incubator project. On February 17, 2010 it graduated to a top-level project.
Releases after graduation include
- 0.6, released Apr 12 2010, added support for integrated caching, and Apache Hadoop MapReduce
- 0.7, released Jan 08 2011, added secondary indexes and online schema changes
- 0.8, released Jun 2 2011, added the Cassandra Query Language (CQL), self-tuning memtables, and support for zero-downtime upgrades
- 1.0, released Oct 17 2011, added integrated compression, leveled compaction, and improved read performance
- 1.1, released Apr 23 2012, added self-tuning caches, row-level isolation, and support for mixed ssd/spinning disk deployments
- 1.2, released Jan 2 2013, added clustering across virtual nodes, inter-node communication, atomic batches, and request tracing
- 2.0, released Sep 4 2013, added lightweight transactions (based on the Paxos consensus protocol), triggers, improved compactions
- 2.0.4, released Dec 30 2013, added allowing specifying datacenters to participate in a repair, client encryption support to sstableloader, allow removing snapshots of no-longer-existing CFs
- 2.1.0 released Sep 10 2014 
Licensing and support
Apache Cassandra is an Apache Software Foundation project, so it has an Apache License (version 2.0).
- Every node in the cluster has the same role. There is no single point of failure. Data is distributed across the cluster (so each node contains different data), but there is no master as every node can service any request.
- Supports replication and multi data center replication
- Replication strategies are configurable. Cassandra is designed as a distributed system, for deployment of large numbers of nodes across multiple data centers. Key features of Cassandra’s distributed architecture are specifically tailored for multiple-data center deployment, for redundancy, for failover and disaster recovery.
- Read and write throughput both increase linearly as new machines are added, with no downtime or interruption to applications.
- Data is automatically replicated to multiple nodes for fault-tolerance. Replication across multiple data centers is supported. Failed nodes can be replaced with no downtime.
- Tunable consistency
- Writes and reads offer a tunable level of consistency, all the way from "writes never fail" to "block for all replicas to be readable", with the quorum level in the middle.
- MapReduce support
- Cassandra has Hadoop integration, with MapReduce support. There is support also for Apache Pig and Apache Hive.
- Query language
- Cassandra introduces CQL (Cassandra Query Language), a SQL-like alternative to the traditional RPC interface. Language drivers are available for Java (JDBC), Python (DBAPI2), Node.JS (Helenus), Go (gocql) and C++.
|This section requires expansion with: informational details and clarification. (September 2012)|
Cassandra is essentially a hybrid between a key-value and a column-oriented (or tabular) database.
- A column family (called "table" since CQL 3) resembles a table in an RDBMS. Column families contain rows and columns. Each row is uniquely identified by a row key. Each row has multiple columns, each of which has a name, value, and a timestamp. Unlike a table in an RDBMS, different rows in the same column family do not have to share the same set of columns, and a column may be added to one or multiple rows at any time.
Each key in Cassandra corresponds to a value which is an object. Each key has values as columns, and columns are grouped together into sets called column families.
Thus, each key identifies a row of a variable number of elements. These column families could be considered then as tables. A table in Cassandra is a distributed multi dimensional map indexed by a key.
Furthermore, applications can specify the sort order of columns within a Super Column or Simple Column family.
When the cluster for Apache Cassandra is designed, an important point is to select the right partitioner. Two partitioners exist:
- RandomPartitioner (RP): This partitioner randomly distributes the key-value pairs over the network, resulting in a good load balancing. Compared to OPP, more nodes have to be accessed to get a number of keys.
- OrderPreservingPartitioner (OPP): This partitioner distributes the key-value pairs in a natural way so that similar keys are not far away. The advantage is that fewer nodes have to be accessed. The drawback is the uneven distribution of the key-value pairs.
||This section contains weasel words: vague phrasing that often accompanies biased or unverifiable information. (October 2013)|
- @WalmartLabs (previously Kosmix) uses Cassandra with SSD
- Apple uses 75,000 Cassandra nodes, as revealed at Cassandra Summit San Francisco 2014, although it has not elaborated for which products, services or features.
- AppScale uses Cassandra as a back-end for Google App Engine applications
- CERN uses Cassandra for its ATLAS experiment to archive the online DAQ system's monitoring information
- Cisco's WebEx uses Cassandra to store user feed and activity in near real time.
- Cloudkick uses Cassandra to store the server metrics of their users.
- Constant Contact uses Cassandra in their email and social media marketing applications. Over 200 nodes are deployed.
- Digg, a large social news website, announced on Sep 9th, 2009 that it is rolling out its use of Cassandra and confirmed this on March 8, 2010. TechCrunch has since linked Cassandra to Digg v4 reliability criticisms and recent company struggles. Lead engineers at Digg later rebuked these criticisms as red herring and blamed a lack of load testing.
- Facebook used Cassandra to power Inbox Search, with over 200 nodes deployed. This was abandoned in late 2010 when they built Facebook Messaging platform on HBase as they "found Cassandra's eventual consistency model to be a difficult pattern".
- Formspring uses Cassandra to count responses, as well as store Social Graph data (followers, following, blockers, blocking) for 26 Million accounts with 10 million responses a day
- IBM has done research in building a scalable email system based on Cassandra.
- Mahalo.com uses Cassandra to record user activity logs and topics for their Q&A website
- Netflix uses Cassandra as their back-end database for their streaming services
- Ooyala built a scalable, flexible, real-time analytics engine using Cassandra
- Openwave uses Cassandra as a distributed database and as a distributed storage mechanism for their next generation messaging platform
- OpenX is running over 130 nodes on Cassandra for their OpenX Enterprise product to store and replicate advertisements and targeting data for ad delivery
- Plaxo has "reviewed 3 billion contacts in [their] database, compared them with publicly available data sources, and identified approximately 600 million unique people with contact info."
- PostRank used Cassandra as their backend database
- Rackspace is known to use Cassandra internally.
- Reddit switched to Cassandra from memcacheDB on March 12, 2010 and experienced some problems in May due to insufficient nodes in their cluster.
- RockYou uses Cassandra to record every single click for 50 million Monthly Active Users in real-time for their online games
- SoundCloud uses Cassandra to store the dashboard of their users
- Talentica Software uses Cassandra as a back-end for Analytics Application with Cassandra cluster of 30 nodes and inserting around 200GB data on daily basis.[not in citation given]
- Twitter announced it was planning to move entirely from MySQL to Cassandra, though soon after retracted this, keeping Tweets in MySQL while using Cassandra for analytics.
- Urban Airship uses Cassandra with the mobile service hosting for over 160 million application installs across 80 million unique devices
- Zoho uses Cassandra for generating the inbox preview in their Zoho#Zoho Mail service
Facebook moved off its pre-Apache Cassandra deployment in late 2010 when they replaced Inbox Search with the Facebook Messaging platform. In 2012, Facebook began using Apache Cassandra in its Instagram unit.
- BigTable - Original distributed database by Google
- Distributed database
- Distributed hash table (DHT)
- Dynamo (storage system) - Cassandra borrows many elements from Dynamo
- Apache Accumulo - Secure Apache Hadoop based distributed database.
- Berkeley DB
- Druid (open-source data store)
- Hypertable - Apache Hadoop based distributed database. Very similar to BigTable
- Casares, Joaquin (2012-11-05). "Multi-datacenter Replication in Cassandra". DataStax. Retrieved 2013-07-25.
Cassandra’s innate datacenter concepts are important as they allow multiple workloads to be run across multiple datacenters…
- Rabl, Tilmann; Sadoghi, Mohammad; Jacobsen, Hans-Arno; Villamor, Sergio Gomez-; Mulero -, Victor Muntes; Mankovskii, Serge (2012-08-27). "Solving Big Data Challenges for Enterprise Application Performance Management". VLDB. Retrieved 2013-07-25.
In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes in all experiments... this comes at the price of high write and read latencies
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- The Apache Software Foundation Announces Apache Cassandra Release 0.6 : The Apache Software Foundation Blog
- The Apache Software Foundation Announces Apache Cassandra 0.7 : The Apache Software Foundation Blog
- [Cassandra-user] [RELEASE] 0.8.0 - Grokbase
- Cassandra 1.0.0. Is Ready for the Enterprise
- The Apache Software Foundation Announces Apache Cassandra™ v1.1 : The Apache Software Foundation Blog
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When building a Cassandra cluster, the “key” question (sorry, that’s weak) is whether to use the RandomPartitioner (RP), or the OrderPreservingPartitioner (OPP). These control how your data is distributed over your nodes. Once you have chosen your partitioner, you cannot change without wiping your data, so think carefully! The problem with OPP: If the distribution of keys used by individual column families is different, their sets of keys will not fall evenly across the ranges assigned to nodes. Thus nodes will end up storing preponderances of keys (and the associated data) corresponding to one column family or another. If as is likely column families store differing quantities of data with their keys, or store data accessed according to differing usage patterns, then some nodes will end up with disproportionately more data than others, or serving more “hot” data than others.
- "@WalmartLabs". walmartlabs.com. Retrieved 11 December 2014.
- Lev Walker: A definition of "Massive scale" from Apple. #CassandraSummit on Twitter
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- 4 Months with Cassandra, a love story |Cloudkick, manage servers better
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- Watch Cassandra at Mahalo.com |DataStax Episodes |Blip
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- Ad Serving Technology - Advanced Optimization, Forecasting, & Targeting |OpenX
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||This article's use of external links may not follow Wikipedia's policies or guidelines. (January 2012)|
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