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DBMS > Apache Phoenix vs. Google Cloud Datastore vs. MarkLogic vs. Tkrzw

System Properties Comparison Apache Phoenix vs. Google Cloud Datastore vs. MarkLogic vs. Tkrzw

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMarkLogic  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  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 PlatformOperational and transactional Enterprise NoSQL databaseA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMSDocument storeDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Key-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.06
Rank#123  Overall
#58  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitephoenix.apache.orgcloud.google.com/­datastorewww.progress.com/­marklogicdbmx.net/­tkrzw
Technical documentationphoenix.apache.orgcloud.google.com/­datastore/­docswww.progress.com/­marklogic/­documentation
DeveloperApache Software FoundationGoogleMarkLogic Corp.Mikio Hirabayashi
Initial release2014200820012020
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 201911.0, December 20220.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial inforestricted free version is availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++
Server operating systemsLinux
Unix
Windows
hostedLinux
OS X
Windows
Linux
macOS
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-free infoSchema can be enforcedschema-free
Typing infopredefined data types such as float or dateyesyes, details hereyesno
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.nonoyesno
Secondary indexesyesyesyes
SQL infoSupport of SQLyesSQL-like query language (GQL)yes infoSQL92no
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
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++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functionsusing Google App Engineyes infovia XQuery or JavaScriptno
TriggersnoCallbacks using the Google Apps Engineyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Multi-source replication using Paxosyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyes infousing Google Cloud Dataflowyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsno
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 ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID infocan act as a resource manager in an XA/JTA transaction
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.yesnoyes, with Range Indexesyes infousing specific database classes
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)Role-based access control at the document and subdocument levelsno

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Apache PhoenixGoogle Cloud DatastoreMarkLogicTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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