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DBMS > GridDB vs. Hive vs. Sphinx vs. Splice Machine

System Properties Comparison GridDB vs. Hive vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonHive  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionScalable in-memory time series database optimized for IoT and Big Datadata warehouse software for querying and managing large distributed datasets, built on HadoopOpen 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 modelTime Series DBMSRelational DBMSSearch engineRelational DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitegriddb.nethive.apache.orgsphinxsearch.comsplicemachine.com
Technical documentationdocs.griddb.netcwiki.apache.org/­confluence/­display/­Hive/­Homesphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperToshiba CorporationApache Software Foundation infoinitially developed by FacebookSphinx Technologies Inc.Splice Machine
Initial release2013201220012014
Current release5.1, August 20223.1.3, April 20223.5.1, February 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC++Java
Server operating systemsLinuxAll OS with a Java VMFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampyesnoyes
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.no
Secondary indexesyesyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)SQL-like DML and DDL statementsSQL-like query language (SphinxQL)yes
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Java
PHP
Python
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 proceduresnoyes infouser defined functions and integration of map-reducenoyes infoJava
Triggersyesnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding 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 nodesSource-replica replicationselectable replication factornoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes infoquery execution via MapReducenoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, 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.yesyes
User concepts infoAccess controlAccess rights for users can be defined per databaseAccess rights for users, groups and rolesnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
GridDBHiveSphinxSplice Machine
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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GridDBHiveSphinxSplice Machine
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