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DBMS > Apache IoTDB vs. Apache Phoenix vs. BigObject vs. Citus vs. Ingres

System Properties Comparison Apache IoTDB vs. Apache Phoenix vs. BigObject vs. Citus vs. Ingres

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonApache Phoenix  Xexclude from comparisonBigObject  Xexclude from comparisonCitus  Xexclude from comparisonIngres  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA scale-out RDBMS with evolutionary schema built on Apache HBaseAnalytic DBMS for real-time computations and queriesScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLWell established RDBMS
Primary database modelTime Series DBMSRelational DBMSRelational DBMS infoa hierachical model (tree) can be imposedRelational DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score0.19
Rank#329  Overall
#146  Relational DBMS
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score3.80
Rank#82  Overall
#44  Relational DBMS
Websiteiotdb.apache.orgphoenix.apache.orgbigobject.iowww.citusdata.comwww.actian.com/­databases/­ingres
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlphoenix.apache.orgdocs.bigobject.iodocs.citusdata.comdocs.actian.com/­ingres
DeveloperApache Software FoundationApache Software FoundationBigObject, Inc.Actian Corporation
Initial release20182014201520101974 infooriginally developed at University Berkely in early 1970s
Current release1.1.0, April 20235.0-HBase2, July 2018 and 4.15-HBase1, December 20198.1, December 201811.2, May 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0commercial infofree community edition availableOpen Source infoAGPL, commercial license also availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaCC
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
Unix
Windows
Linux infodistributed as a docker-image
OS X infodistributed as a docker-image (boot2docker)
Windows infodistributed as a docker-image (boot2docker)
LinuxAIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
Data schemeyesyes infolate-bound, schema-on-read capabilitiesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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 infospecific XML type available, but no XML query functionalityno infobut tools for importing/exporting data from/to XML-files available
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageyesSQL-like DML and DDL statementsyes infostandard, with numerous extensionsyes
APIs and other access methodsJDBC
Native API
JDBCfluentd
ODBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresyesuser defined functionsLuauser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.yes
Triggersyesnonoyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingnoneShardinghorizontal partitioning infoIngres Star to access multiple databases simultaneously
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
noneSource-replica replication infoother methods possible by using 3rd party extensionsIngres Replicator
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkHadoop integrationnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual ConsistencynoneImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes infoautomatically between fact table and dimension tablesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoRead/write lock on objects (tables, trees)yesyes infoMVCC
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.yesyesyesnono
User concepts infoAccess controlyesAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancynofine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache IoTDBApache PhoenixBigObjectCitusIngres
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