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DBMS > Citus vs. Dgraph vs. EsgynDB vs. GeoSpock vs. Google Cloud Bigtable

System Properties Comparison Citus vs. Dgraph vs. EsgynDB vs. GeoSpock vs. Google Cloud Bigtable

Editorial information provided by DB-Engines
NameCitus  Xexclude from comparisonDgraph  Xexclude from comparisonEsgynDB  Xexclude from comparisonGeoSpock  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLDistributed and scalable native Graph DBMSEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionSpatial and temporal data processing engine for extreme data scaleGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.
Primary database modelRelational DBMSGraph DBMSRelational DBMSRelational DBMSKey-value store
Wide column store
Secondary database modelsDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score1.45
Rank#156  Overall
#15  Graph DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Websitewww.citusdata.comdgraph.iowww.esgyn.cngeospock.comcloud.google.com/­bigtable
Technical documentationdocs.citusdata.comdgraph.io/­docscloud.google.com/­bigtable/­docs
DeveloperDgraph Labs, Inc.EsgynGeoSpockGoogle
Initial release2010201620152015
Current release8.1, December 20182.0, September 2019
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availableOpen Source infoApache 2.0commercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCGoC++, JavaJava, Javascript
Server operating systemsLinuxLinux
OS X
Windows
Linuxhostedhosted
Data schemeyesschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesyesno
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 functionalitynononono
Secondary indexesyesyesyestemporal, categoricalno
SQL infoSupport of SQLyes infostandard, with numerous extensionsnoyesANSI SQL for query only (using Presto)no
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
GraphQL query language
gRPC (using protocol buffers) API
HTTP API
ADO.NET
JDBC
ODBC
JDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.noJava Stored Proceduresnono
Triggersyesnononono
Partitioning methods infoMethods for storing different data on different nodesShardingyesShardingAutomatic shardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsSynchronous replication via RaftMulti-source replication between multi datacentersInternal replication in Colossus, and regional replication between two clusters in different zones
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesnoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)
Foreign keys infoReferential integrityyesnoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDnoAtomic single-row operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
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-standardno infoPlanned for future releasesfine grained access rights according to SQL-standardAccess rights for users can be defined per tableAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
CitusDgraphEsgynDBGeoSpockGoogle Cloud Bigtable
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