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DBMS > CrateDB vs. Datomic vs. GeoSpock vs. Hypertable

System Properties Comparison CrateDB vs. Datomic vs. GeoSpock vs. Hypertable

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonDatomic  Xexclude from comparisonGeoSpock  Xexclude from comparisonHypertable  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionDistributed Database based on LuceneDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilitySpatial and temporal data processing engine for extreme data scaleAn open source BigTable implementation based on distributed file systems such as Hadoop
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSRelational DBMSWide column store
Secondary database modelsRelational DBMSTime Series DBMS
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
Score1.59
Rank#150  Overall
#69  Relational DBMS
Websitecratedb.comwww.datomic.comgeospock.com
Technical documentationcratedb.com/­docsdocs.datomic.com
DeveloperCrateCognitectGeoSpockHypertable Inc.
Initial release201320122009
Current release1.0.6735, June 20232.0, September 20190.9.8.11, March 2016
License infoCommercial or Open SourceOpen Sourcecommercial infolimited edition freecommercialOpen Source infoGNU version 3. Commercial license available
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 languageJavaJava, ClojureJava, JavascriptC++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAll OS with a Java VMhostedLinux
OS X
Windows infoan inofficial Windows port is available
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesschema-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.nonono
Secondary indexesyesyestemporal, categoricalrestricted infoonly exact value or prefix value scans
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitynoANSI SQL for query only (using Presto)no
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
RESTful HTTP APIJDBCC++ API
Thrift
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
Clojure
Java
C++
Java
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions (Javascript)yes infoTransaction Functionsnono
TriggersnoBy using transaction functionsnono
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersAutomatic shardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelnone infoBut extensive use of caching in the application peersselectable replication factor on file system level
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
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 strategyACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and developmentno
User concepts infoAccess controlrights management via user accountsnoAccess rights for users can be defined per tableno
More information provided by the system vendor
CrateDBDatomicGeoSpockHypertable
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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CrateDBDatomicGeoSpockHypertable
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