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DBMS > Apache IoTDB vs. H2 vs. Microsoft Azure Data Explorer

System Properties Comparison Apache IoTDB vs. H2 vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonH2  Xexclude from comparisonMicrosoft Azure Data Explorer  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.Fully managed big data interactive analytics platform
Primary database modelTime Series DBMSRelational DBMSRelational DBMS infocolumn oriented
Secondary database modelsSpatial DBMSDocument 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
Score8.13
Rank#49  Overall
#31  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websiteiotdb.apache.orgwww.h2database.comazure.microsoft.com/­services/­data-explorer
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.h2database.com/­html/­main.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationThomas MuellerMicrosoft
Initial release201820052019
Current release1.1.0, April 20232.2.220, July 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infodual-licence (Mozilla public license, Eclipse public license)commercial
Cloud-based only infoOnly available as a cloud servicenonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava
Server operating systemsAll OS with a Java VM (>= 1.8)All OS with a Java VMhosted
Data schemeyesyesFixed schema with schema-less datatypes (dynamic)
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-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.nonoyes
Secondary indexesyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageyesKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
Native API
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Java.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyesJava Stored Procedures and User-Defined FunctionsYes, possible languages: KQL, Python, R
Triggersyesyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneSharding infoImplicit feature of the cloud service
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 databaseyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlyesfine grained access rights according to SQL-standardAzure Active Directory Authentication

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More resources
Apache IoTDBH2Microsoft Azure Data Explorer
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