Developer(s) Couchbase, Inc. Development status active Operating system | Initial release August 2010 (2010-08) Written in C++, Erlang, C, Go | |
 | ||
Stable release 4.5 / June 22, 2016 (2016-06-22) | ||
Couchbase Server, originally known as Membase, is an open-source, distributed (shared-nothing architecture) multi-model NoSQL document-oriented database software package that is optimized for interactive applications. These applications may serve many concurrent users by creating, storing, retrieving, aggregating, manipulating and presenting data. In support of these kinds of application needs, Couchbase Server is designed to provide easy-to-scale key-value or JSON document access with low latency and high sustained throughput. It is designed to be clustered from a single machine to very large-scale deployments spanning many machines. A version originally called Couchbase Lite was later marketed as Couchbase Mobile combined with other software.
Contents
- Product history
- Architecture
- Cluster manager
- Replication and fail over
- Data manager
- Data format
- Object managed cache
- Storage engine
- Performance
- Licensing and support
- N1QL
- Example
- References
Couchbase Server provided client protocol compatibility with memcached, but added disk persistence, data replication, live cluster reconfiguration, rebalancing and multitenancy with data partitioning.
Product history
Membase was developed by several leaders of the memcached project, who had founded a company, NorthScale, to develop a key-value store with the simplicity, speed, and scalability of memcached, but also the storage, persistence and querying capabilities of a database. The original membase source code was contributed by NorthScale, and project co-sponsors Zynga and Naver Corporation (then known as NHN) to a new project on membase.org in June 2010.
On February 8, 2011, the Membase project founders and Membase, Inc. announced a merger with CouchOne (a company with many of the principal players behind CouchDB) with an associated project merger. The merged company was called Couchbase, Inc. In January 2012, Couchbase released Couchbase Server 1.8. In September, 2012, Orbitz said it had changed some of its systems to use Couchbase. On December 2012, Couchbase Server 2.0 (announced in July 2011) was released and included a new JSON document store, indexing and querying, incremental MapReduce and replication across data centers.
Architecture
Every Couchbase node consists of a data service, index service, query service, and cluster manager component. Starting with the 4.0 release, the three services can be distributed to run on separate nodes of the cluster if needed. In the parlance of Eric Brewer’s CAP theorem, Couchbase is normally a CP type system meaning it provides consistency and partition tolerance, or it can be set up as an AP system with multiple clusters.
Cluster manager
The cluster manager supervises the configuration and behavior of all the servers in a Couchbase cluster. It configures and supervises inter-node behavior like managing replication streams and re-balancing operations. It also provides metric aggregation and consensus functions for the cluster, and a RESTful cluster management interface. The cluster manager uses the Erlang programming language and the Open Telecom Platform.
Replication and fail-over
Data replication within the nodes of a cluster can be controlled with several parameters. In December 2012, replication was also supported between different data centers.
Data manager
The data manager stores and retries documents in response to data operations from applications. It asynchronously writes data to disk after acknowledging to the client. In version 1.7 and later, applications can optionally ensure data is written to more than one server or to disk before acknowledging a write to the client. Parameters define item ages that affect when data is persisted, and how max memory and migration from main-memory to disk is handled. It supports working sets greater than a memory quota per "node" or "bucket". External systems can subscribe to filtered data streams, supporting, for example, full text search indexing, data analytics or archiving.
Data format
A document is the most basic unit of data manipulation in Couchbase Server. Documents are stored in JSON document format with no predefined schemas.
Object-managed cache
Couchbase Server includes a built-in multi-threaded object-managed cache that implements memcached compatible APIs such as get, set, delete, append, prepend etc.
Storage engine
Couchbase Server has a tail-append storage design that is immune to data corruption, OOM killers or sudden loss of power. Data is written to the data file in an append-only manner, which enables Couchbase to do mostly sequential writes for update, and provide an optimized access patterns for disk I/O.
Performance
A performance benchmark done by Altoros in 2012, compared Couchbase Server with other technologies. Cisco Systems published a benchmark that measured the latency and throughput of Couchbase Server with a mixed workload in 2012.
Licensing and support
Couchbase Server is a packaged version of Couchbase's open source software technology and is available in a community edition without recent bug fixes with Apache 2.0 license. and an edition for commercial use. Couchbase Server builds are available for Ubuntu, Debian, Red Hat, SUSE, Oracle Linux, Microsoft Windows and Mac OS X operating systems.
Couchbase has supported software developers' kits for the programming languages .Net, PHP, Ruby, Python, C, Node.js, Java, and Go.
N1QL
A query language called the non-first normal form query language, N1QL (pronounced nickel), is used for manipulating the JSON data in Couchbase, just like SQL manipulates data in RDBMS. It has SELECT, INSERT, UPDATE, DELETE, MERGE statements to operate on JSON data. It was announced in March 2015 as "SQL for documents".
The N1QL data model is non-first normal form (N1NF) with support for nested attributes and domain-oriented normalization. The N1QL data model is also a proper superset and generalization of the relational model.
Example
SELECT * FROM `bucket` WHERE LIKE "%@gmail.com";
SELECT * FROM `bucket` WHERE ANY x IN friends SATISFIES x.name = "cate" END;