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DBMS > Apache Phoenix vs. Google Cloud Spanner vs. MarkLogic vs. TimesTen

System Properties Comparison Apache Phoenix vs. Google Cloud Spanner vs. MarkLogic vs. TimesTen

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonMarkLogic  Xexclude from comparisonTimesTen  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseA horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.Operational and transactional Enterprise NoSQL databaseAn in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.
Primary database modelRelational DBMSRelational DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.90
Rank#125  Overall
#59  Relational DBMS
Score2.69
Rank#101  Overall
#51  Relational DBMS
Score4.15
Rank#70  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#8  Search engines
Score1.26
Rank#164  Overall
#75  Relational DBMS
Websitephoenix.apache.orgcloud.google.com/­spannerwww.progress.com/­marklogicwww.oracle.com/­database/­technologies/­related/­timesten.html
Technical documentationphoenix.apache.orgcloud.google.com/­spanner/­docswww.progress.com/­marklogic/­documentationdocs.oracle.com/­en/­database/­other-databases/­timesten/­index.html
DeveloperApache Software FoundationGoogleMarkLogic Corp.Oracle infooriginally founded in HP Labs it was acquired by Oracle in 2005
Initial release2014201720011998
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 201911.0, December 2022Release 22.1
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial inforestricted free version is availablecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++
Server operating systemsLinux
Unix
Windows
hostedLinux
OS X
Windows
IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64
Data schemeyes infolate-bound, schema-on-read capabilitiesyesschema-free infoSchema can be enforcedyes
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.nonoyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesyes infoQuery statements complying to ANSI 2011yes infoSQL92yes
APIs and other access methodsJDBCgRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP 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
ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C++
Java
Node.js
PL/SQL
Python
Server-side scripts infoStored proceduresuser defined functionsnoyes infovia XQuery or JavaScriptPL/SQL
Triggersnonoyesno
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 with 3 replicas for regional instances.yesMulti-source replication
Source-replica replication
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 ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoStrict serializable isolationACID infocan act as a resource manager in an XA/JTA transactionACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoby means of logfiles and checkpoints
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes, with Range Indexesyes
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 levelsfine grained access rights according to SQL-standard

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More resources
Apache PhoenixGoogle Cloud SpannerMarkLogicTimesTen
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Progress MarkLogic World
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