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DBMS > Apache IoTDB vs. GeoMesa vs. Microsoft Azure Data Explorer vs. mSQL

System Properties Comparison Apache IoTDB vs. GeoMesa vs. Microsoft Azure Data Explorer vs. mSQL

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGeoMesa  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonmSQL infoMini SQL  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 FlinkGeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.Fully managed big data interactive analytics platformmSQL (Mini SQL) is a simple and lightweight RDBMS
Primary database modelTime Series DBMSSpatial DBMSRelational DBMS infocolumn orientedRelational DBMS
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
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.78
Rank#213  Overall
#4  Spatial DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.27
Rank#167  Overall
#77  Relational DBMS
Websiteiotdb.apache.orgwww.geomesa.orgazure.microsoft.com/­services/­data-explorerhughestech.com.au/­products/­msql
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.geomesa.org/­documentation/­stable/­user/­index.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationCCRi and othersMicrosoftHughes Technologies
Initial release2018201420191994
Current release1.1.0, April 20235.0.0, May 2024cloud service with continuous releases4.4, October 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache License 2.0commercialcommercial infofree licenses can be provided
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScalaC
Server operating systemsAll OS with a Java VM (>= 1.8)hostedAIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
Data schemeyesyesFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.nonoyesno
Secondary indexesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query languagenoKusto Query Language (KQL), SQL subsetA subset of ANSI SQL is implemented infono subqueries, aggregate functions, views, foreign keys, triggers
APIs and other access methodsJDBC
Native API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Delphi
Java
Perl
PHP
Tcl
Server-side scripts infoStored proceduresyesnoYes, possible languages: KQL, Python, Rno
Triggersyesnoyes 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)depending on storage layerSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasdepending on storage layeryes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
depending on storage layerEventual Consistency
Immediate Consistency
none
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
Concurrency infoSupport for concurrent manipulation of datayesyesyesno
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesdepending on storage layernono
User concepts infoAccess controlyesyes infodepending on the DBMS used for storageAzure Active Directory Authenticationno

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More resources
Apache IoTDBGeoMesaMicrosoft Azure Data ExplorermSQL infoMini SQL
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