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DBMS > Apache IoTDB vs. Microsoft Azure Table Storage vs. OrigoDB vs. TimescaleDB

System Properties Comparison Apache IoTDB vs. Microsoft Azure Table Storage vs. OrigoDB vs. TimescaleDB

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonOrigoDB  Xexclude from comparisonTimescaleDB  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 Wide Column Store for rapid development using massive semi-structured datasetsA fully ACID in-memory object graph databaseA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelTime Series DBMSWide column storeDocument store
Object oriented DBMS
Time Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Score0.06
Rank#380  Overall
#50  Document stores
#18  Object oriented DBMS
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websiteiotdb.apache.orgazure.microsoft.com/­en-us/­services/­storage/­tablesorigodb.comwww.timescale.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlorigodb.com/­docsdocs.timescale.com
DeveloperApache Software FoundationMicrosoftRobert Friberg et alTimescale
Initial release201820122009 infounder the name LiveDB2017
Current release1.1.0, April 20232.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC#C
Server operating systemsAll OS with a Java VM (>= 1.8)hostedLinux
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesUser defined using .NET types and collectionsnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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 infocan be achieved using .NETyes
Secondary indexesyesnoyesyes
SQL infoSupport of SQLSQL-like query languagenonoyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
Native API
RESTful HTTP API.NET Client API
HTTP API
LINQ
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesnoyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyesnoyes infoDomain Eventsyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoImplicit feature of the cloud servicehorizontal partitioning infoclient side managed; servers are not synchronizedyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonodepending on modelyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanooptimistic lockingACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoWrite ahead logyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlyesAccess rights based on private key authentication or shared access signaturesRole based authorizationfine grained access rights according to SQL-standard

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More resources
Apache IoTDBMicrosoft Azure Table StorageOrigoDBTimescaleDB
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