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DBMS > EJDB vs. HEAVY.AI vs. Hypertable vs. Kinetica vs. Splice Machine

System Properties Comparison EJDB vs. HEAVY.AI vs. Hypertable vs. Kinetica vs. Splice Machine

Editorial information provided by DB-Engines
NameEJDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonHypertable  Xexclude from comparisonKinetica  Xexclude from comparisonSplice Machine  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionEmbeddable document-store database library with JSON representation of queries (in MongoDB style)A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareAn open source BigTable implementation based on distributed file systems such as HadoopFully vectorized database across both GPUs and CPUsOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument storeRelational DBMSWide column storeRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.30
Rank#302  Overall
#44  Document stores
Score2.30
Rank#127  Overall
#60  Relational DBMS
Score0.74
Rank#229  Overall
#105  Relational DBMS
Score0.54
Rank#257  Overall
#118  Relational DBMS
Websitegithub.com/­Softmotions/­ejdbgithub.com/­heavyai/­heavydb
www.heavy.ai
www.kinetica.comsplicemachine.com
Technical documentationgithub.com/­Softmotions/­ejdb/­blob/­master/­README.mddocs.heavy.aidocs.kinetica.comsplicemachine.com/­how-it-works
DeveloperSoftmotionsHEAVY.AI, Inc.Hypertable Inc.KineticaSplice Machine
Initial release20122016200920122014
Current release5.10, January 20220.9.8.11, March 20167.1, August 20213.1, March 2021
License infoCommercial or Open SourceOpen Source infoGPLv2Open Source infoApache Version 2; enterprise edition availableOpen Source infoGNU version 3. Commercial license availablecommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC++ and CUDAC++C, C++Java
Server operating systemsserver-lessLinuxLinux
OS X
Windows infoan inofficial Windows port is available
LinuxLinux
OS X
Solaris
Windows
Data schemeschema-freeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyes infostring, integer, double, bool, date, object_idyesnoyesyes
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 indexesnonorestricted infoonly exact value or prefix value scansyesyes
SQL infoSupport of SQLnoyesnoSQL-like DML and DDL statementsyes
APIs and other access methodsin-process shared libraryJDBC
ODBC
Thrift
Vega
C++ API
Thrift
JDBC
ODBC
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
Supported programming languagesActionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby
All languages supporting JDBC/ODBC/Thrift
Python
C++
Java
Perl
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnononouser defined functionsyes infoJava
Triggersnononoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoRound robinShardingShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replicationselectable replication factor on file system levelSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesnoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityno infotypically not needed, however similar functionality with collection joins possiblenonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonononoACID
Concurrency infoSupport for concurrent manipulation of datayes infoRead/Write Lockingyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyesyes
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 controlnofine grained access rights according to SQL-standardnoAccess rights for users and roles on table levelAccess rights for users, groups and roles according to SQL-standard

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More resources
EJDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022HypertableKineticaSplice Machine
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