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DBMS > Google Cloud Datastore vs. Solr vs. Transbase vs. Vitess

System Properties Comparison Google Cloud Datastore vs. Solr vs. Transbase vs. Vitess

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonSolr  Xexclude from comparisonTransbase  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA widely used distributed, scalable search engine based on Apache LuceneA resource-optimized, high-performance, universally applicable RDBMSScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelDocument storeSearch engineRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score41.02
Rank#24  Overall
#3  Search engines
Score0.17
Rank#334  Overall
#148  Relational DBMS
Score0.88
Rank#203  Overall
#95  Relational DBMS
Websitecloud.google.com/­datastoresolr.apache.orgwww.transaction.de/­en/­products/­transbase.htmlvitess.io
Technical documentationcloud.google.com/­datastore/­docssolr.apache.org/­resources.htmlwww.transaction.de/­en/­products/­transbase/­features.htmlvitess.io/­docs
DeveloperGoogleApache Software FoundationTransaction Software GmbHThe Linux Foundation, PlanetScale
Initial release2008200619872013
Current release9.6.1, May 2024Transbase 8.3, 202215.0.2, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercial infofree development licenseOpen Source infoApache Version 2.0, commercial licenses available
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 languageJavaC and C++Go
Server operating systemshostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)FreeBSD
Linux
macOS
Solaris
Windows
Docker
Linux
macOS
Data schemeschema-freeyes infoDynamic Fields enables on-the-fly addition of new fieldsyesyes
Typing infopredefined data types such as float or dateyes, details hereyes infosupports customizable data types and automatic typingyesyes
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.noyesno
Secondary indexesyesyes infoAll search fields are automatically indexedyesyes
SQL infoSupport of SQLSQL-like query language (GQL)Solr Parallel SQL Interfaceyesyes infowith proprietary extensions
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
Proprietary native API
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresusing Google App EngineJava pluginsyesyes infoproprietary syntax
TriggersCallbacks using the Google Apps Engineyes infoUser configurable commands triggered on index changesyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosyesSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reducenono
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 ConsistencyImmediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyesyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsoptimistic lockingyesACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infotable locks or row locks depending on storage engine
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.noyesnoyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yesfine grained access rights according to SQL-standardUsers with fine-grained authorization concept infono user groups or roles

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More resources
Google Cloud DatastoreSolrTransbaseVitess
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