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DBMS > Bangdb vs. BigchainDB vs. Kinetica vs. Splice Machine vs. TerarkDB

System Properties Comparison Bangdb vs. BigchainDB vs. Kinetica vs. Splice Machine vs. TerarkDB

Editorial information provided by DB-Engines
NameBangdb  Xexclude from comparisonBigchainDB  Xexclude from comparisonKinetica  Xexclude from comparisonSplice Machine  Xexclude from comparisonTerarkDB  Xexclude from comparison
DescriptionConverged and high performance database for device data, events, time series, document and graphBigchainDB is scalable blockchain database offering decentralization, immutability and native assetsFully vectorized database across both GPUs and CPUsOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA 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 modelDocument store
Graph DBMS
Time Series DBMS
Document storeRelational DBMSRelational DBMSKey-value store
Secondary database modelsSpatial DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.08
Rank#347  Overall
#47  Document stores
#34  Graph DBMS
#31  Time Series DBMS
Score0.79
Rank#212  Overall
#36  Document stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitebangdb.comwww.bigchaindb.comwww.kinetica.comsplicemachine.comgithub.com/­bytedance/­terarkdb
Technical documentationdocs.bangdb.combigchaindb.readthedocs.io/­en/­latestdocs.kinetica.comsplicemachine.com/­how-it-worksbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperSachin Sinha, BangDBKineticaSplice MachineByteDance, originally Terark
Initial release20122016201220142016
Current releaseBangDB 2.0, October 20217.1, August 20213.1, March 2021
License infoCommercial or Open SourceOpen Source infoBSD 3Open Source infoAGPL v3commercialOpen Source infoAGPL 3.0, commercial license availablecommercial inforestricted open source version available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++PythonC, C++JavaC++
Server operating systemsLinuxLinuxLinuxLinux
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyes: string, long, double, int, geospatial, stream, eventsnoyesyesno
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.nononono
Secondary indexesyes infosecondary, composite, nested, reverse, geospatialyesyesno
SQL infoSupport of SQLSQL like support with command line toolnoSQL-like DML and DDL statementsyesno
APIs and other access methodsProprietary protocol
RESTful HTTP API
CLI Client
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
C++ API
Java API
Supported programming languagesC
C#
C++
Java
Python
Go
Haskell
Java
JavaScript
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
Server-side scripts infoStored proceduresnouser defined functionsyes infoJavano
Triggersyes, Notifications (with Streaming only)yes infotriggers when inserted values for one or more columns fall within a specified rangeyesno
Partitioning methods infoMethods for storing different data on different nodesSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmShardingShardingShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor, Knob for CAP (enterprise version only)selectable replication factorSource-replica replicationMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable consistency, set CAP knob accordinglyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDno
Concurrency infoSupport for concurrent manipulation of datayes, optimistic concurrency controlyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyes, implements WAL (Write ahead log) as wellyes,with MongoDB ord RethinkDByesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes, run db with in-memory only modeyes infoGPU vRAM or System RAMyesyes
User concepts infoAccess controlyes (enterprise version only)yesAccess rights for users and roles on table levelAccess rights for users, groups and roles according to SQL-standardno

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More resources
BangdbBigchainDBKineticaSplice MachineTerarkDB
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