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DBMS > CrateDB vs. GBase vs. Microsoft Azure Data Explorer vs. Percona Server for MongoDB vs. VictoriaMetrics

System Properties Comparison CrateDB vs. GBase vs. Microsoft Azure Data Explorer vs. Percona Server for MongoDB vs. VictoriaMetrics

Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonGBase  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPercona Server for MongoDB  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
DescriptionDistributed Database based on LuceneWidely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.Fully managed big data interactive analytics platformA drop-in replacement for MongoDB Community Edition with enterprise-grade features.A fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSRelational DBMS infocolumn orientedDocument storeTime Series 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
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
Score1.05
Rank#186  Overall
#86  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.60
Rank#246  Overall
#39  Document stores
Score1.23
Rank#172  Overall
#15  Time Series DBMS
Websitecratedb.comwww.gbase.cnazure.microsoft.com/­services/­data-explorerwww.percona.com/­mongodb/­software/­percona-server-for-mongodbvictoriametrics.com
Technical documentationcratedb.com/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.percona.com/­percona-distribution-for-mongodbdocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperCrateGeneral Data Technology Co., Ltd.MicrosoftPerconaVictoriaMetrics
Initial release20132004201920152018
Current releaseGBase 8a, GBase 8s, GBase 8ccloud service with continuous releases3.4.10-2.10, November 2017v1.91, May 2023
License infoCommercial or Open SourceOpen SourcecommercialcommercialOpen Source infoGPL Version 2Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesnono
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, Java, PythonC++Go
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinuxhostedLinuxFreeBSD
Linux
macOS
OpenBSD
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.noyesyesnono
Secondary indexesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilityStandard with numerous extensionsKusto Query Language (KQL), SQL subsetnono
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
ADO.NET
C API
JDBC
ODBC
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
proprietary protocol using JSONGraphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
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
C#.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk
Server-side scripts infoStored proceduresuser defined functions (Javascript)user defined functionsYes, possible languages: KQL, Python, RJavaScriptno
Triggersnoyesyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynono
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by range, list and hash) and vertical partitioningSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replicationSynchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyEventual Consistency
Immediate Consistency
Eventual Consistency
Immediate Consistency
Eventual Consistency
Foreign keys infoReferential integritynoyesnonono
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 strategyACIDnonono
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.nonoyes infovia In-Memory Engineno
User concepts infoAccess controlrights management via user accountsyesAzure Active Directory AuthenticationAccess rights for users and roles
More information provided by the system vendor
CrateDBGBaseMicrosoft Azure Data ExplorerPercona Server for MongoDBVictoriaMetrics
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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CrateDBGBaseMicrosoft Azure Data ExplorerPercona Server for MongoDBVictoriaMetrics
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