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DBMS > CrateDB vs. Microsoft Azure Data Explorer vs. openGauss vs. Sphinx vs. Splice Machine

System Properties Comparison CrateDB vs. Microsoft Azure Data Explorer vs. openGauss vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonopenGauss  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionDistributed Database based on LuceneFully managed big data interactive analytics platformAn enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by HuaweiOpen source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMS infocolumn orientedRelational DBMSSearch engineRelational DBMS
Secondary database modelsRelational 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
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score1.06
Rank#184  Overall
#84  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitecratedb.comazure.microsoft.com/­services/­data-explorergitee.com/­opengauss
opengauss.org
sphinxsearch.comsplicemachine.com
Technical documentationcratedb.com/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.opengauss.org/­en
gitee.com/­opengauss/­docs
sphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperCrateMicrosoftHuawei and openGauss communitySphinx Technologies Inc.Splice Machine
Initial release20132019201920012014
Current releasecloud service with continuous releases3.0, March 20223.5.1, February 20233.1, March 2021
License infoCommercial or Open SourceOpen SourcecommercialOpen SourceOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageJavaC, C++, JavaC++Java
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedLinuxFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)Fixed schema with schema-less datatypes (dynamic)yesyesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesnoyes
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.noyesno
Secondary indexesyesall fields are automatically indexedyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilityKusto Query Language (KQL), SQL subsetANSI SQL 2011SQL-like query language (SphinxQL)yes
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
ODBC
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languages.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Java
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions (Javascript)Yes, possible languages: KQL, Python, Ryesnoyes infoJava
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud servicehorizontal partitioning (by range, list and hash)Sharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Eventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategynoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes
User concepts infoAccess controlrights management via user accountsAzure Active Directory AuthenticationAccess rights for users, groups and roles according to SQL-standardnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
CrateDBMicrosoft Azure Data ExploreropenGaussSphinxSplice Machine
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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CrateDBMicrosoft Azure Data ExploreropenGaussSphinxSplice Machine
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