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DBMS > Google Cloud Spanner vs. mSQL vs. Sphinx vs. Splice Machine

System Properties Comparison Google Cloud Spanner vs. mSQL vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines
NameGoogle Cloud Spanner  Xexclude from comparisonmSQL infoMini SQL  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionA 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.mSQL (Mini SQL) is a simple and lightweight RDBMSOpen 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 Spark
Primary database modelRelational DBMSRelational DBMSSearch engineRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.89
Rank#105  Overall
#53  Relational DBMS
Score1.67
Rank#151  Overall
#70  Relational DBMS
Score6.03
Rank#60  Overall
#6  Search engines
Score0.54
Rank#255  Overall
#116  Relational DBMS
Websitecloud.google.com/­spannerhughestech.com.au/­products/­msqlsphinxsearch.comsplicemachine.com
Technical documentationcloud.google.com/­spanner/­docssphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperGoogleHughes TechnologiesSphinx Technologies Inc.Splice Machine
Initial release2017199420012014
Current release4.4, October 20213.5.1, February 20233.1, March 2021
License infoCommercial or Open Sourcecommercialcommercial infofree licenses can be providedOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC++Java
Server operating systemshostedAIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesnoyes
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
Secondary indexesyesyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLyes infoQuery statements complying to ANSI 2011A subset of ANSI SQL is implemented infono subqueries, aggregate functions, views, foreign keys, triggersSQL-like query language (SphinxQL)yes
APIs and other access methodsgRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
JDBC
ODBC
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
C
C++
Delphi
Java
Perl
PHP
Tcl
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnononoyes infoJava
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding 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 with 3 replicas for regional instances.nonenoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud DataflownonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencynoneImmediate Consistency
Foreign keys infoReferential integrityyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoStrict serializable isolationnonoACID
Concurrency infoSupport for concurrent manipulation of datayesnoyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
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)nonoAccess rights for users, groups and roles according to SQL-standard

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More resources
Google Cloud SpannermSQL infoMini SQLSphinxSplice Machine
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