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

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

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparisonPostgres-XL  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA Graph DBMS optimized for distributed clusters infoIt was forked from the latest code base of Titan in January 2017Based on PostgreSQL enhanced with MPP and write-scale-out cluster featuresA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument storeGraph 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
Score1.94
Rank#129  Overall
#12  Graph DBMS
Score0.49
Rank#256  Overall
#117  Relational DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitecloud.google.com/­datastorejanusgraph.orgwww.postgres-xl.orgsolr.apache.org
Technical documentationcloud.google.com/­datastore/­docsdocs.janusgraph.orgwww.postgres-xl.org/­documentationsolr.apache.org/­resources.html
DeveloperGoogleLinux Foundation; originally developed as Titan by AureliusApache Software Foundation
Initial release200820172014 infosince 2012, originally named StormDB2006
Current release0.6.3, February 202310 R1, October 20189.6.0, April 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open 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 languageJavaCJava
Server operating systemshostedLinux
OS X
Unix
Windows
Linux
macOS
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeyesyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyes, details hereyesyesyes 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.nonoyes 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
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
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
Clojure
Java
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 nodesShardingyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)horizontal 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 Dataflowyes infovia Faunus, a graph analytics enginenospark-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.Eventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyes infoRelationships in graphsyesno
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 persistentyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcastyesyes
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)User authentification and security via Rexster Graph Serverfine grained access rights according to SQL-standardyes

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More resources
Google Cloud DatastoreJanusGraph infosuccessor of TitanPostgres-XLSolr
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