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DBMS > CrateDB vs. GeoSpock vs. HugeGraph vs. RDF4J

System Properties Comparison CrateDB vs. GeoSpock vs. HugeGraph vs. RDF4J

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonGeoSpock  Xexclude from comparisonHugeGraph  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionDistributed Database based on LuceneSpatial and temporal data processing engine for extreme data scaleA fast-speed and highly-scalable Graph DBMSRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSGraph DBMSRDF store
Secondary database modelsRelational DBMSTime Series 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
Score0.17
Rank#335  Overall
#31  Graph DBMS
Score0.74
Rank#222  Overall
#9  RDF stores
Websitecratedb.comgeospock.comgithub.com/­hugegraph
hugegraph.apache.org
rdf4j.org
Technical documentationcratedb.com/­docshugegraph.apache.org/­docsrdf4j.org/­documentation
DeveloperCrateGeoSpockBaiduSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release201320182004
Current release2.0, September 20190.9
License infoCommercial or Open SourceOpen SourcecommercialOpen Source infoApache Version 2.0Open Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud servicenoyesnono
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, JavascriptJavaJava
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedLinux
macOS
Unix
Linux
OS X
Unix
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)yesyesyes infoRDF Schemas
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.nonono
Secondary indexesyestemporal, categoricalyes infoalso supports composite index and range indexyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilityANSI SQL for query only (using Presto)nono
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBCJava API
RESTful HTTP API
TinkerPop Gremlin
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
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
Groovy
Java
Python
Java
PHP
Python
Server-side scripts infoStored proceduresuser defined functions (Javascript)noasynchronous Gremlin script jobsyes
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic shardingyes infodepending on used storage backend, e.g. Cassandra and HBasenone
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelyes infodepending on used storage backend, e.g. Cassandra and HBasenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonovia hugegraph-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyes infoedges in graph
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 strategynoACIDACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlrights management via user accountsAccess rights for users can be defined per tableUsers, roles and permissionsno
More information provided by the system vendor
CrateDBGeoSpockHugeGraphRDF4J infoformerly known as Sesame
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
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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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CrateDBGeoSpockHugeGraphRDF4J infoformerly known as Sesame
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