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DBMS > Apache IoTDB vs. HBase vs. SingleStore

System Properties Comparison Apache IoTDB vs. HBase vs. SingleStore

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonHBase  Xexclude from comparisonSingleStore infoformer name was MemSQL  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 FlinkWide-column store based on Apache Hadoop and on concepts of BigTableMySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type
Primary database modelTime Series DBMSWide column storeRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Time Series DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.19
Rank#176  Overall
#15  Time Series DBMS
Score31.25
Rank#26  Overall
#2  Wide column stores
Score5.91
Rank#61  Overall
#34  Relational DBMS
Websiteiotdb.apache.orghbase.apache.orgwww.singlestore.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlhbase.apache.org/­book.htmldocs.singlestore.com
DeveloperApache Software FoundationApache Software Foundation infoApache top-level project, originally developed by PowersetSingleStore Inc.
Initial release201820082013
Current release1.1.0, April 20232.3.4, January 20218.5, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache version 2commercial infofree developer edition available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities
Implementation languageJavaJavaC++, Go
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
Unix
Windows infousing Cygwin
Linux info64 bit version required
Data schemeyesschema-free, schema definition possibleyes
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROyes
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.nonono
Secondary indexesyesnoyes
SQL infoSupport of SQLSQL-like query languagenoyes infobut no triggers and foreign keys
APIs and other access methodsJDBC
Native API
Java API
RESTful HTTP API
Thrift
Cluster Management API infoas HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
Bash
C
C#
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesyes infoCoprocessors in Javayes
Triggersyesyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding infohash partitioning
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
Source-replica replication infostores two copies of each physical data partition on two separate nodes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesno infocan define user-defined aggregate functions for map-reduce-style calculations
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoSingle row ACID (across millions of columns)ACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoAll updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes
User concepts infoAccess controlyesAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACFine grained access control via users, groups and roles
More information provided by the system vendor
Apache IoTDBHBaseSingleStore infoformer name was MemSQL
Specific characteristicsSingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more
Competitive advantagesSingleStore’s competitive advantages include: Easy and Simplified Architecture with...
» more
Typical application scenariosDriving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more
Key customersIEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more
Market metricsCustomers in various industries worldwide including US and International Industry...
» more
Licensing and pricing modelsF ree Tier and Enterprise Edition
» more

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Apache IoTDBHBaseSingleStore infoformer name was MemSQL
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