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DBMS > Apache Phoenix vs. GridDB vs. HEAVY.AI vs. TimescaleDB

System Properties Comparison Apache Phoenix vs. GridDB vs. HEAVY.AI vs. TimescaleDB

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGridDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseScalable in-memory time series database optimized for IoT and Big DataA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSTime Series DBMSRelational DBMSTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
Spatial DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score1.64
Rank#145  Overall
#67  Relational DBMS
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websitephoenix.apache.orggriddb.netgithub.com/­heavyai/­heavydb
www.heavy.ai
www.timescale.com
Technical documentationphoenix.apache.orgdocs.griddb.netdocs.heavy.aidocs.timescale.com
DeveloperApache Software FoundationToshiba CorporationHEAVY.AI, Inc.Timescale
Initial release2014201320162017
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20195.1, August 20225.10, January 20222.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2; enterprise edition availableOpen 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 languageJavaC++C++ and CUDAC
Server operating systemsLinux
Unix
Windows
LinuxLinuxLinux
OS X
Windows
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyesyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesnumerics, 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.nononoyes
Secondary indexesyesyesnoyes
SQL infoSupport of SQLyesSQL92, SQL-like TQL (Toshiba Query Language)yesyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBCJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
Thrift
Vega
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
All languages supporting JDBC/ODBC/Thrift
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresuser defined functionsnonouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoRound robinyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replicationMulti-source replicationSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate consistency within container, eventual consistency across containersImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID at container levelnoACID
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.yesyesyesno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users can be defined per databasefine grained access rights according to SQL-standardfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache PhoenixGridDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022TimescaleDB
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Apache PhoenixGridDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022TimescaleDB
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