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DBMS > Google Cloud Datastore vs. OpenQM vs. Postgres-XL vs. Solr

System Properties Comparison Google Cloud Datastore vs. OpenQM vs. Postgres-XL vs. Solr

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonOpenQM infoalso called QM  Xexclude from comparisonPostgres-XL  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformQpenQM is a high-performance, self-tuning, multi-value DBMSBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument storeMultivalue DBMSRelational DBMSSearch engine
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.27
Rank#298  Overall
#10  Multivalue DBMS
Score0.49
Rank#256  Overall
#117  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitecloud.google.com/­datastorewww.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-open-qmwww.postgres-xl.orgsolr.apache.org
Technical documentationcloud.google.com/­datastore/­docswww.postgres-xl.org/­documentationsolr.apache.org/­resources.html
DeveloperGoogleRocket Software, originally Martin PhillipsApache Software Foundation
Initial release200819932014 infosince 2012, originally named StormDB2006
Current release3.4-1210 R1, October 20189.6.0, April 2024
License infoCommercial or Open SourcecommercialOpen Source infoGPLv2, extended commercial license availableOpen Source infoMozilla public licenseOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCJava
Server operating systemshostedAIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows
Linux
macOS
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyes infowith some exceptionsyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyes, details hereyesyes infosupports customizable data types and automatic typing
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.noyesyes infoXML type, but no XML query functionalityyes
Secondary indexesyesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query language (GQL)noyes infodistributed, parallel query executionSolr Parallel SQL Interface
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Java API
RESTful HTTP/JSON API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Basic
C
Java
Objective C
PHP
Python
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresusing Google App Engineyesuser defined functionsJava plugins
TriggersCallbacks using the Google Apps Engineyesyesyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingyeshorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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 ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID infoMVCCoptimistic locking
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.nonoyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights can be defined down to the item levelfine grained access rights according to SQL-standardyes

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More resources
Google Cloud DatastoreOpenQM infoalso called QMPostgres-XLSolr
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