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DBMS > CrateDB vs. Cubrid vs. Hazelcast vs. Splunk

System Properties Comparison CrateDB vs. Cubrid vs. Hazelcast vs. Splunk

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonCubrid  Xexclude from comparisonHazelcast  Xexclude from comparisonSplunk  Xexclude from comparison
DescriptionDistributed Database based on LuceneCUBRID is an open-source SQL-based relational database management system with object extensions for OLTPA widely adopted in-memory data gridAnalytics Platform for Big Data
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSKey-value storeSearch engine
Secondary database modelsRelational DBMSDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score1.20
Rank#169  Overall
#78  Relational DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score86.45
Rank#14  Overall
#2  Search engines
Websitecratedb.comcubrid.com (korean)
cubrid.org (english)
hazelcast.comwww.splunk.com
Technical documentationcratedb.com/­docscubrid.org/­manualshazelcast.org/­imdg/­docsdocs.splunk.com/­Documentation/­Splunk
DeveloperCrateCUBRID Corporation, CUBRID FoundationHazelcastSplunk Inc.
Initial release2013200820082003
Current release11.0, January 20215.3.6, November 2023
License infoCommercial or Open SourceOpen SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2; commercial licenses availablecommercial infoLimited free edition and free developer edition available
Cloud-based only infoOnly available as a cloud servicenononono
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++, JavaJava
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
Windows
All OS with a Java VMLinux
OS X
Solaris
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesyes
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 infothe object must implement a serialization strategyyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilityyesSQL-like query languageno infoSplunk Search Processing Language for search commands
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
ADO.NET
JDBC
ODBC
OLE DB
JCache
JPA
Memcached protocol
RESTful HTTP API
HTTP REST
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
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C#
Java
JavaScript
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions (Javascript)Java Stored Proceduresyes infoEvent Listeners, Executor Servicesyes
Triggersnoyesyes infoEventsyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelSource-replica replicationyes infoReplicated MapMulti-source replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Foreign keys infoReferential integritynoyesnono
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 strategyACIDone or two-phase-commit; repeatable reads; read commitedno infoA 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlrights management via user accountsfine grained access rights according to SQL-standardRole-based access controlAccess rights for users and roles
More information provided by the system vendor
CrateDBCubridHazelcastSplunk
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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More resources
CrateDBCubridHazelcastSplunk
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