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DBMS > GridGain vs. Hypertable vs. Kinetica vs. SingleStore

System Properties Comparison GridGain vs. Hypertable vs. Kinetica vs. SingleStore

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Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonHypertable  Xexclude from comparisonKinetica  Xexclude from comparisonSingleStore infoformer name was MemSQL  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteAn open source BigTable implementation based on distributed file systems such as HadoopFully vectorized database across both GPUs and CPUsMySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type
Primary database modelKey-value store
Relational DBMS
Wide column storeRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Spatial DBMS
Time Series DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score5.60
Rank#62  Overall
#35  Relational DBMS
Websitewww.gridgain.comwww.kinetica.comwww.singlestore.com
Technical documentationwww.gridgain.com/­docs/­index.htmldocs.kinetica.comdocs.singlestore.com
DeveloperGridGain Systems, Inc.Hypertable Inc.KineticaSingleStore Inc.
Initial release2007200920122013
Current releaseGridGain 8.5.10.9.8.11, March 20167.1, August 20218.5, January 2024
License infoCommercial or Open SourcecommercialOpen Source infoGNU version 3. Commercial license availablecommercialcommercial infofree developer edition available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities
Implementation languageJava, C++, .NetC++C, C++C++, Go
Server operating systemsLinux
OS X
Solaris
Windows
Linux
OS X
Windows infoan inofficial Windows port is available
LinuxLinux info64 bit version required
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesnoyesyes
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.yesnono
Secondary indexesyesrestricted infoonly exact value or prefix value scansyesyes
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLnoSQL-like DML and DDL statementsyes infobut no triggers and foreign keys
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
C++ API
Thrift
JDBC
ODBC
RESTful HTTP API
Cluster Management API infoas HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
Perl
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
Bash
C
C#
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)nouser defined functionsyes
Triggersyes (cache interceptors and events)noyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infohash partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)selectable replication factor on file system levelSource-replica replicationSource-replica replication infostores two copies of each physical data partition on two separate nodes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)yesnono infocan define user-defined aggregate functions for map-reduce-style calculations
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoAll updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlSecurity Hooks for custom implementationsnoAccess rights for users and roles on table levelFine grained access control via users, groups and roles
More information provided by the system vendor
GridGainHypertableKineticaSingleStore infoformer name was MemSQL
Specific characteristicsSingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more
Competitive advantagesSingleStore’s competitive advantages include: Easy and Simplified Architecture with...
» more
Typical application scenariosDriving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more
Key customersIEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more
Market metricsCustomers in various industries worldwide including US and International Industry...
» more
Licensing and pricing modelsF ree Tier and Enterprise Edition
» more

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More resources
GridGainHypertableKineticaSingleStore infoformer name was MemSQL
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SingleStore: The Increasing Momentum of Multi-Model Database Systems
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