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DBMS > Google Cloud Datastore vs. Prometheus vs. Sphinx vs. Splice Machine vs. Valentina Server

System Properties Comparison Google Cloud Datastore vs. Prometheus vs. Sphinx vs. Splice Machine vs. Valentina Server

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonPrometheus  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOpen-source Time Series DBMS and monitoring systemOpen source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkObject-relational database and reports server
Primary database modelDocument storeTime Series DBMSSearch engineRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score7.69
Rank#50  Overall
#3  Time Series DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score0.21
Rank#325  Overall
#144  Relational DBMS
Websitecloud.google.com/­datastoreprometheus.iosphinxsearch.comsplicemachine.comwww.valentina-db.net
Technical documentationcloud.google.com/­datastore/­docsprometheus.io/­docssphinxsearch.com/­docssplicemachine.com/­how-it-worksvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperGoogleSphinx Technologies Inc.Splice MachineParadigma Software
Initial release20082015200120141999
Current release3.5.1, February 20233.1, March 20215.7.5
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license availablecommercial
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC++Java
Server operating systemshostedLinux
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeschema-freeyesyesyesyes
Typing infopredefined data types such as float or dateyes, details hereNumeric data onlynoyesyes
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.nono infoImport of XML data possible
Secondary indexesyesnoyes infofull-text index on all search fieldsyesyes
SQL infoSupport of SQLSQL-like query language (GQL)noSQL-like query language (SphinxQL)yesyes
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP/JSON APIProprietary protocolJDBC
Native Spark Datasource
ODBC
ODBC
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresusing Google App Enginenonoyes infoJavayes
TriggersCallbacks using the Google Apps Enginenonoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyes infoby FederationnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud DataflownonoYes, via Full Spark Integrationno
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.noneImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nonoAccess rights for users, groups and roles according to SQL-standardfine grained access rights according to SQL-standard

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Google Cloud DatastorePrometheusSphinxSplice MachineValentina Server
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