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DBMS > CrateDB vs. PouchDB vs. RDF4J vs. TinkerGraph

System Properties Comparison CrateDB vs. PouchDB vs. RDF4J vs. TinkerGraph

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonPouchDB  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonTinkerGraph  Xexclude from comparison
DescriptionDistributed Database based on LuceneJavaScript DBMS with an API inspired by CouchDBRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document storeRDF storeGraph DBMS
Secondary database modelsRelational 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
Score2.34
Rank#112  Overall
#21  Document stores
Score0.74
Rank#222  Overall
#9  RDF stores
Score0.13
Rank#345  Overall
#35  Graph DBMS
Websitecratedb.compouchdb.comrdf4j.orgtinkerpop.apache.org/­docs/­current/­reference/­#tinkergraph-gremlin
Technical documentationcratedb.com/­docspouchdb.com/­guidesrdf4j.org/­documentation
DeveloperCrateApache Software FoundationSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release2013201220042009
Current release7.1.1, June 2019
License infoCommercial or Open SourceOpen SourceOpen SourceOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache 2.0
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 languageJavaJavaScriptJavaJava
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportserver-less, requires a JavaScript environment (browser, Node.js)Linux
OS X
Unix
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyes infoRDF Schemasschema-free
Typing infopredefined data types such as float or dateyesnoyesyes
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 indexesyesyes infovia viewsyesno
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitynonono
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
HTTP REST infoonly for PouchDB Server
JavaScript API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
TinkerPop 3
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
JavaScriptJava
PHP
Python
Groovy
Java
Server-side scripts infoStored proceduresuser defined functions (Javascript)View functions in JavaScriptyesno
Triggersnoyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infowith a proxy-based framework, named couchdb-loungenonenone
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
nonenone
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
Eventual Consistencynone
Foreign keys infoReferential integritynonoyes infoRelationships in graphs
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 strategynoACID infoIsolation support depends on the API usedno
Concurrency infoSupport for concurrent manipulation of datayesyesno
Durability infoSupport for making data persistentyesyes infoby using IndexedDB, WebSQL or LevelDB as backendyes infoin-memory storage is supported as welloptional
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlrights management via user accountsnonono
More information provided by the system vendor
CrateDBPouchDBRDF4J infoformerly known as SesameTinkerGraph
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
CrateDBPouchDBRDF4J infoformerly known as SesameTinkerGraph
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