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DBMS > Apache Impala vs. Apache IoTDB vs. Cubrid vs. IBM Db2 Event Store

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

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache IoTDB  Xexclude from comparisonCubrid  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopAn 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 OLTPDistributed Event Store optimized for Internet of Things use cases
Primary database modelRelational DBMSTime Series DBMSRelational DBMSEvent Store
Time Series DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.20
Rank#169  Overall
#78  Relational DBMS
Score0.19
Rank#323  Overall
#2  Event Stores
#28  Time Series DBMS
Websiteimpala.apache.orgiotdb.apache.orgcubrid.com (korean)
cubrid.org (english)
www.ibm.com/­products/­db2-event-store
Technical documentationimpala.apache.org/­impala-docs.htmliotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcubrid.org/­manualswww.ibm.com/­docs/­en/­db2-event-store
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationCUBRID Corporation, CUBRID FoundationIBM
Initial release2013201820082017
Current release4.1.0, June 20221.1.0, April 202311.0, January 20212.0
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoApache Version 2.0commercial infofree developer edition available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC, C++, JavaC and C++
Server operating systemsLinuxAll OS with a Java VM (>= 1.8)Linux
Windows
Linux infoLinux, macOS, Windows for the developer addition
Data schemeyesyesyesyes
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.nononono
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageyesyes infothrough the embedded Spark runtime
APIs and other access methodsJDBC
ODBC
JDBC
Native API
ADO.NET
JDBC
ODBC
OLE DB
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesJava Stored Proceduresyes
Triggersnoyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replicationActive-active shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceIntegration with Hadoop and Sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesNo - written data is immutable
Durability infoSupport for making data persistentyesyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosyesfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache ImpalaApache IoTDBCubridIBM Db2 Event Store
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