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DBMS > AgensGraph vs. Citus vs. CrateDB vs. Spark SQL

System Properties Comparison AgensGraph vs. Citus vs. CrateDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonCitus  Xexclude from comparisonCrateDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLDistributed Database based on LuceneSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelGraph DBMS
Relational DBMS
Relational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.23
Rank#315  Overall
#26  Graph DBMS
#140  Relational DBMS
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#10  Vector DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitebitnine.net/­agensgraphwww.citusdata.comcratedb.comspark.apache.org/­sql
Technical documentationbitnine.net/­documentationdocs.citusdata.comcratedb.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperBitnine Global Inc.CrateApache Software Foundation
Initial release2016201020132014
Current release2.1, December 20188.1, December 20183.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache License 2.0Open Source infoAGPL, commercial license also availableOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCCJavaScala
Server operating systemsLinux
OS X
Windows
LinuxAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
OS X
Windows
Data schemedepending on used data modelyesFlexible Schema (defined schema, partial schema, schema free)yes
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.noyes infospecific XML type available, but no XML query functionalitynono
Secondary indexesyesyesyesno
SQL infoSupport of SQLyesyes infostandard, with numerous extensionsyes, but no triggers and constraints, and PostgreSQL compatibilitySQL-like DML and DDL statements
APIs and other access methodsCypher Query Language
JDBC
ADO.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
JDBC
ODBC
Supported programming languagesC
Java
JavaScript
Python
.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
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions (Javascript)no
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodesno, but can be realized using table inheritanceShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replication infoother methods possible by using 3rd party extensionsConfigurable replication on table/partition-levelnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Foreign keys infoReferential integrityyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno infounique row identifiers can be used for implementing an optimistic concurrency control strategyno
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.nononono
User concepts infoAccess controlfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardrights management via user accountsno

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More resources
AgensGraphCitusCrateDBSpark SQL
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