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DBMS > Citus vs. CrateDB vs. EsgynDB vs. GridGain

System Properties Comparison Citus vs. CrateDB vs. EsgynDB vs. GridGain

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Editorial information provided by DB-Engines
NameCitus  Xexclude from comparisonCrateDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonGridGain  Xexclude from comparison
DescriptionScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLDistributed Database based on LuceneEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGridGain is an in-memory computing platform, built on Apache Ignite
Primary database modelRelational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSColumnar
Key-value store
Object oriented DBMS
Relational DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.13
Rank#117  Overall
#56  Relational DBMS
Score0.69
Rank#226  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#11  Vector DBMS
Score0.15
Rank#325  Overall
#144  Relational DBMS
Score1.48
Rank#150  Overall
#1  Columnar
#26  Key-value stores
#2  Object oriented DBMS
#69  Relational DBMS
Websitewww.citusdata.comcratedb.comwww.esgyn.cnwww.gridgain.com
Technical documentationdocs.citusdata.comcratedb.com/­docswww.gridgain.com/­docs/­index.html
DeveloperCrateEsgynGridGain Systems, Inc.
Initial release2010201320152007
Current release8.1, December 20185.8.1, August 2024GridGain 8.5.1
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availableOpen Sourcecommercialcommercial, open source
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCJavaC++, JavaJava, C++, .Net, Python, REST, SQL
Server operating systemsLinuxAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinuxLinux
OS X
Solaris
Windows
z/OS
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesyes
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.yes infospecific XML type available, but no XML query functionalitynonoyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyes infostandard, with numerous extensionsyes, but no triggers and constraints, and PostgreSQL compatibilityyesANSI-99 for query and DML statements, subset of DDL
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
ADO.NET
JDBC
ODBC
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.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
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Java
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions (Javascript)Java Stored Proceduresyes (compute grid and cache interceptors can be used instead)
Triggersyesnonoyes (cache interceptors and events)
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsConfigurable replication on table/partition-levelMulti-source replication between multi datacentersyes (replicated cache)
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesyes (compute grid and hadoop accelerator)
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infounique row identifiers can be used for implementing an optimistic concurrency control strategyACIDACID
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.nononoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardrights management via user accountsfine grained access rights according to SQL-standardRole-based access control
Security Hooks for custom implementations

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More resources
CitusCrateDBEsgynDBGridGain
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