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DBMS > Citus vs. Drizzle vs. GridDB vs. Microsoft Azure Data Explorer vs. Sphinx

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

Editorial information provided by DB-Engines
NameCitus  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.
DescriptionScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLMySQL 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 modelRelational DBMSRelational DBMSTime Series DBMSRelational DBMS infocolumn orientedSearch engine
Secondary database modelsDocument storeKey-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
Score2.21
Rank#118  Overall
#56  Relational 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
Websitewww.citusdata.comgriddb.netazure.microsoft.com/­services/­data-explorersphinxsearch.com
Technical documentationdocs.citusdata.comdocs.griddb.netdocs.microsoft.com/­en-us/­azure/­data-explorersphinxsearch.com/­docs
DeveloperDrizzle project, originally started by Brian AkerToshiba CorporationMicrosoftSphinx Technologies Inc.
Initial release20102008201320192001
Current release8.1, December 20187.2.4, September 20125.1, August 2022cloud service with continuous releases3.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availableOpen 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 languageCC++C++C++
Server operating systemsLinuxFreeBSD
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.yes infospecific XML type available, but no XML query functionalitynoyes
Secondary indexesyesyesyesall fields are automatically indexedyes infofull-text index on all search fields
SQL infoSupport of SQLyes infostandard, with numerous extensionsyes infowith proprietary extensionsSQL92, SQL-like TQL (Toshiba Query Language)Kusto Query Language (KQL), SQL subsetSQL-like query language (SphinxQL)
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBCJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocol
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
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 proceduresuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.nonoYes, 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 nodesShardingShardingShardingSharding 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 nodesSource-replica replication infoother methods possible by using 3rd party extensionsMulti-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 methodsnonoConnector 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 systemImmediate ConsistencyImmediate consistency within container, eventual consistency across containersEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyesyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID 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.noyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users can be defined per databaseAzure Active Directory Authenticationno
More information provided by the system vendor
CitusDrizzleGridDBMicrosoft 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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CitusDrizzleGridDBMicrosoft Azure Data ExplorerSphinx
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