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DBMS > Apache Phoenix vs. Google Cloud Datastore vs. HBase vs. Kinetica vs. searchxml

System Properties Comparison Apache Phoenix vs. Google Cloud Datastore vs. HBase vs. Kinetica vs. searchxml

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHBase  Xexclude from comparisonKinetica  Xexclude from comparisonsearchxml  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformWide-column store based on Apache Hadoop and on concepts of BigTableFully vectorized database across both GPUs and CPUsDBMS for structured and unstructured content wrapped with an application server
Primary database modelRelational DBMSDocument storeWide column storeRelational DBMSNative XML DBMS
Search engine
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.00
Rank#383  Overall
#7  Native XML DBMS
#25  Search engines
Websitephoenix.apache.orgcloud.google.com/­datastorehbase.apache.orgwww.kinetica.comwww.searchxml.net/­category/­products
Technical documentationphoenix.apache.orgcloud.google.com/­datastore/­docshbase.apache.org/­book.htmldocs.kinetica.comwww.searchxml.net/­support/­handouts
DeveloperApache Software FoundationGoogleApache Software Foundation infoApache top-level project, originally developed by PowersetKineticainformationpartners gmbh
Initial release20142008200820122015
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20192.3.4, January 20217.1, August 20211.0
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache version 2commercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC, C++C++
Server operating systemsLinux
Unix
Windows
hostedLinux
Unix
Windows infousing Cygwin
LinuxWindows
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-free, schema definition possibleyesschema-free
Typing infopredefined data types such as float or dateyesyes, details hereoptions to bring your own types, AVROyesyes
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.nonononoyes
Secondary indexesyesyesnoyesyes
SQL infoSupport of SQLyesSQL-like query language (GQL)noSQL-like DML and DDL statementsno
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
JDBC
ODBC
RESTful HTTP API
RESTful HTTP API
WebDAV
XQuery
XSLT
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
C++
Java
JavaScript (Node.js)
Python
C++ infomost other programming languages supported via APIs
Server-side scripts infoStored proceduresuser defined functionsusing Google App Engineyes infoCoprocessors in Javauser defined functionsyes infoon the application server
TriggersnoCallbacks using the Google Apps Engineyesyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using PaxosMulti-source replication
Source-replica replication
Source-replica replicationyes infosychronisation to multiple collections
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyes infousing Google Cloud Dataflowyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual 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.Immediate Consistency or Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsSingle row ACID (across millions of columns)nomultiple readers, single writer
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.yesnoyesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAccess rights for users and roles on table levelDomain, group and role-based access control at the document level and for application services

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More resources
Apache PhoenixGoogle Cloud DatastoreHBaseKineticasearchxml
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