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DBMS > HugeGraph vs. Kinetica vs. Oracle Berkeley DB vs. Spark SQL vs. TerarkDB

System Properties Comparison HugeGraph vs. Kinetica vs. Oracle Berkeley DB vs. Spark SQL vs. TerarkDB

Editorial information provided by DB-Engines
NameHugeGraph  Xexclude from comparisonKinetica  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTerarkDB  Xexclude from comparison
DescriptionA fast-speed and highly-scalable Graph DBMSFully vectorized database across both GPUs and CPUsWidely used in-process key-value storeSpark 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 MongoDB
Primary database modelGraph DBMSRelational DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMSKey-value store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.08
Rank#367  Overall
#56  Key-value stores
Websitegithub.com/­hugegraph
hugegraph.apache.org
www.kinetica.comwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sqlgithub.com/­bytedance/­terarkdb
Technical documentationhugegraph.apache.org/­docsdocs.kinetica.comdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperBaiduKineticaOracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software FoundationByteDance, originally Terark
Initial release20182012199420142016
Current release0.97.1, August 202118.1.40, May 20203.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infocommercial license availableOpen Source infoApache 2.0commercial inforestricted open source version available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC, C++C, Java, C++ (depending on the Berkeley DB edition)ScalaC++
Server operating systemsLinux
macOS
Unix
LinuxAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Windows
Data schemeyesyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesnoyesno
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.nonoyes infoonly with the Berkeley DB XML editionnono
Secondary indexesyes infoalso supports composite index and range indexyesyesnono
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyes infoSQL interfaced based on SQLite is availableSQL-like DML and DDL statementsno
APIs and other access methodsJava API
RESTful HTTP API
TinkerPop Gremlin
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
C++ API
Java API
Supported programming languagesGroovy
Java
Python
C++
Java
JavaScript (Node.js)
Python
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Java
Python
R
Scala
C++
Java
Server-side scripts infoStored proceduresasynchronous Gremlin script jobsuser defined functionsnonono
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes infoonly for the SQL APInono
Partitioning methods infoMethods for storing different data on different nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseShardingnoneyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infodepending on used storage backend, e.g. Cassandra and HBaseSource-replica replicationSource-replica replicationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsvia hugegraph-sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infoedges in graphyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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 RAMyesnoyes
User concepts infoAccess controlUsers, roles and permissionsAccess rights for users and roles on table levelnonono

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More resources
HugeGraphKineticaOracle Berkeley DBSpark SQLTerarkDB
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