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DBMS > GridGain vs. Spark SQL vs. TerarkDB vs. TigerGraph

System Properties Comparison GridGain vs. Spark SQL vs. TerarkDB vs. TigerGraph

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Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonSpark SQL  Xexclude from comparisonTerarkDB  Xexclude from comparisonTigerGraph  Xexclude from comparison
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteSpark SQL is a component on top of 'Spark Core' for structured data processingA key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDBA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time
Primary database modelKey-value store
Relational DBMS
Relational DBMSKey-value storeGraph 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
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Score1.83
Rank#139  Overall
#13  Graph DBMS
Websitewww.gridgain.comspark.apache.org/­sqlgithub.com/­bytedance/­terarkdbwww.tigergraph.com
Technical documentationwww.gridgain.com/­docs/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKcdocs.tigergraph.com
DeveloperGridGain Systems, Inc.Apache Software FoundationByteDance, originally Terark
Initial release2007201420162017
Current releaseGridGain 8.5.13.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercial inforestricted open source version availablecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetScalaC++C++
Server operating systemsLinux
OS X
Solaris
Windows
Linux
OS X
Windows
Linux
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesnoyes
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.yesnonono
Secondary indexesyesnono
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsnoSQL-like query language (GSQL)
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
C++ API
Java API
GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
Java
Python
R
Scala
C++
Java
C++
Java
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)nonoyes
Triggersyes (cache interceptors and events)nonono
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)nonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)noyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynononoyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlSecurity Hooks for custom implementationsnonoRole-based access control

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More resources
GridGainSpark SQLTerarkDBTigerGraph
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