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DBMS > Apache Phoenix vs. Citus vs. GeoSpock vs. Google Cloud Datastore

System Properties Comparison Apache Phoenix vs. Citus vs. GeoSpock vs. Google Cloud Datastore

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonCitus  Xexclude from comparisonGeoSpock  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLSpatial and temporal data processing engine for extreme data scaleAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
Primary database modelRelational DBMSRelational DBMSRelational DBMSDocument store
Secondary database modelsDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Websitephoenix.apache.orgwww.citusdata.comgeospock.comcloud.google.com/­datastore
Technical documentationphoenix.apache.orgdocs.citusdata.comcloud.google.com/­datastore/­docs
DeveloperApache Software FoundationGeoSpockGoogle
Initial release201420102008
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20198.1, December 20182.0, September 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL, commercial license also availablecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaCJava, Javascript
Server operating systemsLinux
Unix
Windows
Linuxhostedhosted
Data schemeyes infolate-bound, schema-on-read capabilitiesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesyes, details here
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 indexesyesyestemporal, categoricalyes
SQL infoSupport of SQLyesyes infostandard, with numerous extensionsANSI SQL for query only (using Presto)SQL-like query language (GQL)
APIs and other access methodsJDBCADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresuser defined functionsuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.nousing Google App Engine
TriggersnoyesnoCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodesShardingShardingAutomatic shardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Source-replica replication infoother methods possible by using 3rd party extensionsMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnonoyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.
Foreign keys infoReferential integritynoyesnoyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
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.yesnonono
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyfine 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
Apache PhoenixCitusGeoSpockGoogle Cloud Datastore
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