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DBMS > Apache IoTDB vs. H2 vs. HBase vs. Spark SQL vs. STSdb

System Properties Comparison Apache IoTDB vs. H2 vs. HBase vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonH2  Xexclude from comparisonHBase  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  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 FlinkFull-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.Wide-column store based on Apache Hadoop and on concepts of BigTableSpark SQL is a component on top of 'Spark Core' for structured data processingKey-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelTime Series DBMSRelational DBMSWide column storeRelational DBMSKey-value store
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score8.33
Rank#46  Overall
#30  Relational DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.10
Rank#357  Overall
#51  Key-value stores
Websiteiotdb.apache.orgwww.h2database.comhbase.apache.orgspark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.h2database.com/­html/­main.htmlhbase.apache.org/­book.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationThomas MuellerApache Software Foundation infoApache top-level project, originally developed by PowersetApache Software FoundationSTS Soft SC
Initial release20182005200820142011
Current release1.1.0, April 20232.2.220, July 20232.3.4, January 20213.5.0 ( 2.13), September 20234.0.8, September 2015
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infodual-licence (Mozilla public license, Eclipse public license)Open Source infoApache version 2Open Source infoApache 2.0Open Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJavaScalaC#
Server operating systemsAll OS with a Java VM (>= 1.8)All OS with a Java VMLinux
Unix
Windows infousing Cygwin
Linux
OS X
Windows
Windows
Data schemeyesyesschema-free, schema definition possibleyesyes
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVROyesyes infoprimitive types and user defined types (classes)
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 indexesyesyesnonono
SQL infoSupport of SQLSQL-like query languageyesnoSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
Native API
JDBC
ODBC
Java API
RESTful HTTP API
Thrift
JDBC
ODBC
.NET Client API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
JavaC
C#
C++
Groovy
Java
PHP
Python
Scala
Java
Python
R
Scala
C#
Java
Server-side scripts infoStored proceduresyesJava Stored Procedures and User-Defined Functionsyes infoCoprocessors in Javanono
Triggersyesyesyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasWith clustering: 2 database servers on different computers operate on identical copies of a databaseMulti-source replication
Source-replica replication
nonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency or Eventual Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDSingle row ACID (across millions of columns)nono
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyesyes
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.yesyesyesno
User concepts infoAccess controlyesfine grained access rights according to SQL-standardAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACnono

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More resources
Apache IoTDBH2HBaseSpark SQLSTSdb
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