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DBMS > BigObject vs. GigaSpaces vs. Kinetica vs. TimescaleDB

System Properties Comparison BigObject vs. GigaSpaces vs. Kinetica vs. TimescaleDB

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Editorial information provided by DB-Engines
NameBigObject  Xexclude from comparisonGigaSpaces  Xexclude from comparisonKinetica  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionAnalytic DBMS for real-time computations and queriesHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsFully vectorized database across both GPUs and CPUsA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMS infoa hierachical model (tree) can be imposedDocument store
Object oriented DBMS infoValues are user defined objects
Relational DBMSTime Series DBMS
Secondary database modelsGraph DBMS
Search engine
Spatial DBMS
Time Series DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.19
Rank#329  Overall
#146  Relational DBMS
Score1.03
Rank#188  Overall
#32  Document stores
#6  Object oriented DBMS
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websitebigobject.iowww.gigaspaces.comwww.kinetica.comwww.timescale.com
Technical documentationdocs.bigobject.iodocs.gigaspaces.com/­latest/­landing.htmldocs.kinetica.comdocs.timescale.com
DeveloperBigObject, Inc.Gigaspaces TechnologiesKineticaTimescale
Initial release2015200020122017
Current release15.5, September 20207.1, August 20212.15.0, May 2024
License infoCommercial or Open Sourcecommercial infofree community edition availableOpen Source infoApache Version 2; Commercial licenses availablecommercialOpen Source infoApache 2.0
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.
Implementation languageJava, C++, .NetC, C++C
Server operating systemsLinux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
Linux
macOS
Solaris
Windows
LinuxLinux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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 infoXML can be used for describing objects metadatanoyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-99 for query and DML statementsSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
APIs and other access methodsfluentd
ODBC
RESTful HTTP API
GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languages.Net
C++
Java
Python
Scala
C++
Java
JavaScript (Node.js)
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresLuayesuser defined functionsuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyes, event driven architectureyes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
Source-replica replicationSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoMap-Reduce pattern can be built with XAP task executorsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyes infoautomatically between fact table and dimension tablesnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayes infoRead/write lock on objects (tables, trees)yesyesyes
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.yesyesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlnoRole-based access controlAccess rights for users and roles on table levelfine grained access rights according to SQL-standard

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More resources
BigObjectGigaSpacesKineticaTimescaleDB
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