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DBMS > CouchDB vs. Google Cloud Datastore vs. Sphinx vs. Splice Machine

System Properties Comparison CouchDB vs. Google Cloud Datastore vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines
NameCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionA native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOpen source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument storeDocument storeSearch engineRelational DBMS
Secondary database modelsSpatial DBMS infousing the Geocouch extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score9.30
Rank#45  Overall
#7  Document stores
Score4.47
Rank#76  Overall
#12  Document stores
Score5.98
Rank#56  Overall
#5  Search engines
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitecouchdb.apache.orgcloud.google.com/­datastoresphinxsearch.comsplicemachine.com
Technical documentationdocs.couchdb.org/­en/­stablecloud.google.com/­datastore/­docssphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperApache Software Foundation infoApache top-level project, originally developed by Damien Katz, a former Lotus Notes developerGoogleSphinx Technologies Inc.Splice Machine
Initial release2005200820012014
Current release3.3.3, December 20233.5.1, February 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache version 2commercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageErlangC++Java
Server operating systemsAndroid
BSD
Linux
OS X
Solaris
Windows
hostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or datenoyes, details herenoyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesyes infovia viewsyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoSQL-like query language (GQL)SQL-like query language (SphinxQL)yes
APIs and other access methodsRESTful HTTP/JSON APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresView functions in JavaScriptusing Google App Enginenoyes infoJava
TriggersyesCallbacks using the Google Apps Enginenoyes
Partitioning methods infoMethods for storing different data on different nodesSharding infoimproved architecture with release 2.0ShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using PaxosnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infousing Google Cloud DataflownoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoatomic operations within a single document possibleACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID
Concurrency infoSupport for concurrent manipulation of datayes infostrategy: optimistic lockingyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlAccess rights for users can be defined per databaseAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noAccess rights for users, groups and roles according to SQL-standard

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More resources
CouchDB infostands for "Cluster Of Unreliable Commodity Hardware"Google Cloud DatastoreSphinxSplice Machine
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