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DBMS > Apache IoTDB vs. Cubrid vs. Hazelcast vs. IBM Db2 Event Store vs. VictoriaMetrics

System Properties Comparison Apache IoTDB vs. Cubrid vs. Hazelcast vs. IBM Db2 Event Store vs. VictoriaMetrics

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonCubrid  Xexclude from comparisonHazelcast  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonVictoriaMetrics  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 FlinkCUBRID is an open-source SQL-based relational database management system with object extensions for OLTPA widely adopted in-memory data gridDistributed Event Store optimized for Internet of Things use casesA fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelTime Series DBMSRelational DBMSKey-value storeEvent Store
Time Series DBMS
Time Series DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.19
Rank#176  Overall
#15  Time Series DBMS
Score1.31
Rank#169  Overall
#78  Relational DBMS
Score6.87
Rank#55  Overall
#6  Key-value stores
Score0.23
Rank#316  Overall
#2  Event Stores
#28  Time Series DBMS
Score1.42
Rank#161  Overall
#14  Time Series DBMS
Websiteiotdb.apache.orgcubrid.com (korean)
cubrid.org (english)
hazelcast.comwww.ibm.com/­products/­db2-event-storevictoriametrics.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcubrid.org/­manualshazelcast.org/­imdg/­docswww.ibm.com/­docs/­en/­db2-event-storedocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperApache Software FoundationCUBRID Corporation, CUBRID FoundationHazelcastIBMVictoriaMetrics
Initial release20182008200820172018
Current release1.1.0, April 202311.0, January 20215.3.6, November 20232.0v1.91, May 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoApache Version 2; commercial licenses availablecommercial infofree developer edition availableOpen Source infoApache Version 2.0
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++, JavaJavaC and C++Go
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
Windows
All OS with a Java VMLinux infoLinux, macOS, Windows for the developer additionFreeBSD
Linux
macOS
OpenBSD
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyes
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 infothe object must implement a serialization strategynono
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languageyesSQL-like query languageyes infothrough the embedded Spark runtimeno
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
ODBC
OLE DB
JCache
JPA
Memcached protocol
RESTful HTTP API
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
Server-side scripts infoStored proceduresyesJava Stored Proceduresyes infoEvent Listeners, Executor Servicesyesno
Triggersyesyesyes infoEventsnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replicationyes infoReplicated MapActive-active shard replicationSynchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknoyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDone or two-phase-commit; repeatable reads; read commitednono
Concurrency infoSupport for concurrent manipulation of datayesyesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyesno
User concepts infoAccess controlyesfine grained access rights according to SQL-standardRole-based access controlfine grained access rights according to SQL-standard

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More resources
Apache IoTDBCubridHazelcastIBM Db2 Event StoreVictoriaMetrics
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