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DBMS > Apache IoTDB vs. Microsoft Azure Data Explorer vs. SiteWhere vs. Sphinx

System Properties Comparison Apache IoTDB vs. Microsoft Azure Data Explorer vs. SiteWhere vs. Sphinx

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSiteWhere  Xexclude from comparisonSphinx  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 FlinkFully managed big data interactive analytics platformM2M integration platform for persisting/querying time series dataOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMS infocolumn orientedTime Series DBMSSearch engine
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score3.28
Rank#83  Overall
#45  Relational DBMS
Score0.07
Rank#347  Overall
#33  Time Series DBMS
Score5.97
Rank#56  Overall
#5  Search engines
Websiteiotdb.apache.orgazure.microsoft.com/­services/­data-explorergithub.com/­sitewhere/­sitewheresphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.microsoft.com/­en-us/­azure/­data-explorersitewhere1.sitewhere.io/­index.htmlsphinxsearch.com/­docs
DeveloperApache Software FoundationMicrosoftSiteWhereSphinx Technologies Inc.
Initial release2018201920102001
Current release1.1.0, April 2023cloud service with continuous releases3.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoCommon Public Attribution License Version 1.0Open Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++
Server operating systemsAll OS with a Java VM (>= 1.8)hostedLinux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)predefined schemeyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesno
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.noyesno
Secondary indexesyesall fields are automatically indexednoyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageKusto Query Language (KQL), SQL subsetnoSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP RESTProprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesYes, possible languages: KQL, Python, Rno
Triggersyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoImplicit feature of the cloud serviceSharding infobased on HBaseSharding infoPartitioning is done manually, search queries against distributed index is supported
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.selectable replication factor infobased on HBasenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlyesAzure Active Directory AuthenticationUsers with fine-grained authorization conceptno

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Apache IoTDBMicrosoft Azure Data ExplorerSiteWhereSphinx
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