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DBMS > CrateDB vs. Hazelcast vs. IBM Db2 warehouse vs. Tkrzw

System Properties Comparison CrateDB vs. Hazelcast vs. IBM Db2 warehouse vs. Tkrzw

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonHazelcast  Xexclude from comparisonIBM Db2 warehouse infoformerly named IBM dashDB  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionDistributed Database based on LuceneA widely adopted in-memory data gridCloud-based data warehousing serviceA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Key-value storeRelational DBMSKey-value store
Secondary database modelsRelational DBMSDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score1.37
Rank#160  Overall
#74  Relational DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitecratedb.comhazelcast.comwww.ibm.com/­products/­db2/­warehousedbmx.net/­tkrzw
Technical documentationcratedb.com/­docshazelcast.org/­imdg/­docs
DeveloperCrateHazelcastIBMMikio Hirabayashi
Initial release2013200820142020
Current release5.3.6, November 20230.9.3, August 2020
License infoCommercial or Open SourceOpen SourceOpen Source infoApache Version 2; commercial licenses availablecommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesno
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 languageJavaJavaC++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VMhostedLinux
macOS
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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.noyes infothe object must implement a serialization strategyno infoImport/export of XML data possibleno
Secondary indexesyesyesyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitySQL-like query languageyesno
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JCache
JPA
Memcached protocol
RESTful HTTP API
.NET Client API
JDBC
ODBC
OLE DB
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
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions (Javascript)yes infoEvent Listeners, Executor ServicesPL/SQL, SQL PLno
Triggersnoyes infoEventsyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyes infoReplicated Mapyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesno
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 strategyone or two-phase-commit; repeatable reads; read commitedACID
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.noyesyesyes infousing specific database classes
User concepts infoAccess controlrights management via user accountsRole-based access controlfine grained access rights according to SQL-standardno
More information provided by the system vendor
CrateDBHazelcastIBM Db2 warehouse infoformerly named IBM dashDBTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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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CrateDBHazelcastIBM Db2 warehouse infoformerly named IBM dashDBTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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