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DBMS > Apache IoTDB vs. DataFS vs. GridDB vs. MarkLogic vs. Microsoft Azure Data Explorer

System Properties Comparison Apache IoTDB vs. DataFS vs. GridDB vs. MarkLogic vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDataFS  Xexclude from comparisonGridDB  Xexclude from comparisonMarkLogic  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 FlinkAll data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.Scalable in-memory time series database optimized for IoT and Big DataOperational and transactional Enterprise NoSQL databaseFully managed big data interactive analytics platform
Primary database modelTime Series DBMSObject oriented DBMSTime Series DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS infocolumn oriented
Secondary database modelsGraph DBMSKey-value store
Relational DBMS
Document 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#164  Overall
#14  Time Series DBMS
Score0.09
Rank#360  Overall
#17  Object oriented DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websiteiotdb.apache.orgnewdatabase.comgriddb.netwww.marklogic.comazure.microsoft.com/­services/­data-explorer
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldev.mobiland.com/­Overview.xspdocs.griddb.netdocs.marklogic.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationMobiland AGToshiba CorporationMarkLogic Corp.Microsoft
Initial release20182018201320012019
Current release1.1.0, April 20231.1.263, October 20225.1, August 202211.0, December 2022cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial inforestricted free version is availablecommercial
Cloud-based only infoOnly available as a cloud servicenonononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C++
Server operating systemsAll OS with a Java VM (>= 1.8)WindowsLinuxLinux
OS X
Windows
hosted
Data schemeyesClasses, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)yesschema-free infoSchema can be enforcedFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes 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.nononoyesyes
Secondary indexesyesnoyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like query languagenoSQL92, SQL-like TQL (Toshiba Query Language)yes infoSQL92Kusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
Native API
.NET Client API
Proprietary client DLL
WinRT client
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C#
C++
VB.Net
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyesnoyes infovia XQuery or JavaScriptYes, possible languages: KQL, Python, R
Triggersyesno, except callback-events from server when changes happenedyesyesyes 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)Proprietary Sharding systemShardingShardingSharding 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 replicasSource-replica replicationyesyes 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 SparknoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsSpark 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 ConsistencyImmediate consistency within container, eventual consistency across containersImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at container levelACID infocan act as a resource manager in an XA/JTA transactionno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyes, with Range Indexesno
User concepts infoAccess controlyesWindows-ProfileAccess rights for users can be defined per databaseRole-based access control at the document and subdocument levelsAzure Active Directory Authentication
More information provided by the system vendor
Apache IoTDBDataFSGridDBMarkLogicMicrosoft Azure Data Explorer
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
Apache IoTDBDataFSGridDBMarkLogicMicrosoft Azure Data Explorer
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