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DBMS > Apache IoTDB vs. Drizzle vs. GridDB vs. Microsoft Azure Data Explorer vs. Sphinx

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

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDrizzle  Xexclude from comparisonGridDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSphinx  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
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 FlinkMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Scalable in-memory time series database optimized for IoT and Big DataFully managed big data interactive analytics platformOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMSTime Series DBMSRelational DBMS infocolumn orientedSearch engine
Secondary database modelsKey-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
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Websiteiotdb.apache.orggriddb.netazure.microsoft.com/­services/­data-explorersphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.griddb.netdocs.microsoft.com/­en-us/­azure/­data-explorersphinxsearch.com/­docs
DeveloperApache Software FoundationDrizzle project, originally started by Brian AkerToshiba CorporationMicrosoftSphinx Technologies Inc.
Initial release20182008201320192001
Current release1.1.0, April 20237.2.4, September 20125.1, August 2022cloud service with continuous releases3.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoGNU GPLOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercialOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C++C++
Server operating systemsAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
LinuxhostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesyesFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesno
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 indexesyesyesyesall fields are automatically indexedyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageyes infowith proprietary extensionsSQL92, SQL-like TQL (Toshiba Query Language)Kusto Query Language (KQL), SQL subsetSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
JDBCJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Java
PHP
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.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 proceduresyesnonoYes, possible languages: KQL, Python, Rno
Triggersyesno infohooks for callbacks inside the server can be used.yesyes 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)ShardingShardingSharding infoImplicit feature of the cloud serviceSharding 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 replicasMulti-source replication
Source-replica replication
Source-replica replicationyes 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 SparknoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate consistency within container, eventual consistency across containersEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at container levelnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes 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.yesyesno
User concepts infoAccess controlyesPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users can be defined per databaseAzure Active Directory Authenticationno
More information provided by the system vendor
Apache IoTDBDrizzleGridDBMicrosoft Azure Data ExplorerSphinx
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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Apache IoTDBDrizzleGridDBMicrosoft Azure Data ExplorerSphinx
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