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DBMS > Apache IoTDB vs. BaseX vs. Graph Engine vs. GridGain vs. Microsoft Azure Data Explorer

System Properties Comparison Apache IoTDB vs. BaseX vs. Graph Engine vs. GridGain vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonBaseX  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonGridGain  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 FlinkLight-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineGridGain is an in-memory computing platform, built on Apache IgniteFully managed big data interactive analytics platform
Primary database modelTime Series DBMSNative XML DBMSGraph DBMS
Key-value store
Key-value store
Relational DBMS
Relational DBMS infocolumn oriented
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
Score1.73
Rank#142  Overall
#4  Native XML DBMS
Score0.61
Rank#240  Overall
#21  Graph DBMS
#35  Key-value stores
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websiteiotdb.apache.orgbasex.orgwww.graphengine.iowww.gridgain.comazure.microsoft.com/­services/­data-explorer
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.basex.orgwww.graphengine.io/­docs/­manualwww.gridgain.com/­docs/­index.htmldocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationBaseX GmbHMicrosoftGridGain Systems, Inc.Microsoft
Initial release20182007201020072019
Current release1.1.0, April 202310.7, August 2023GridGain 8.5.1cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoBSD licenseOpen Source infoMIT Licensecommercialcommercial
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 languageJavaJava.NET and CJava, C++, .Net
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Windows
.NETLinux
OS X
Solaris
Windows
hosted
Data schemeyesschema-freeyesyesFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesno infoXQuery supports typesyesyesyes 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.nonoyesyes
Secondary indexesyesyesyesall fields are automatically indexed
SQL infoSupport of SQLSQL-like query languagenonoANSI-99 for query and DML statements, subset of DDLKusto Query Language (KQL), SQL subset
APIs and other access methodsJDBC
Native API
Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ
RESTful HTTP APIHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Actionscript
C
C#
Haskell
Java
JavaScript infoNode.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic
C#
C++
F#
Visual Basic
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyesyesyesyes (compute grid and cache interceptors can be used instead)Yes, possible languages: KQL, Python, R
Triggersyesyes infovia eventsnoyes (cache interceptors and events)yes 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)nonehorizontal partitioningShardingSharding 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 replicasnoneyes (replicated cache)yes 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 Sparknoyes (compute grid and hadoop accelerator)Spark 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 integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanomultiple readers, single writernoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyesno
User concepts infoAccess controlyesUsers with fine-grained authorization concept on 4 levelsSecurity Hooks for custom implementationsAzure Active Directory Authentication

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More resources
Apache IoTDBBaseXGraph Engine infoformer name: TrinityGridGainMicrosoft Azure Data Explorer
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