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DBMS > Datomic vs. Microsoft Azure Table Storage vs. mSQL vs. Sphinx vs. Splice Machine

System Properties Comparison Datomic vs. Microsoft Azure Table Storage vs. mSQL vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonmSQL infoMini SQL  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA Wide Column Store for rapid development using massive semi-structured datasetsmSQL (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 DBMSWide column storeRelational DBMSSearch engineRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Score1.27
Rank#167  Overall
#77  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.datomic.comazure.microsoft.com/­en-us/­services/­storage/­tableshughestech.com.au/­products/­msqlsphinxsearch.comsplicemachine.com
Technical documentationdocs.datomic.comsphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperCognitectMicrosoftHughes TechnologiesSphinx Technologies Inc.Splice Machine
Initial release20122012199420012014
Current release1.0.6735, June 20234.4, October 20213.5.1, February 20233.1, March 2021
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialcommercial 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 servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureCC++Java
Server operating systemsAll OS with a Java VMhostedAIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnoyes
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.nonono
Secondary indexesyesnoyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnonoA subset of ANSI SQL is implemented infono subqueries, aggregate functions, views, foreign keys, triggersSQL-like query language (SphinxQL)yes
APIs and other access methodsRESTful HTTP APIRESTful HTTP APIJDBC
ODBC
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesClojure
Java
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
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 proceduresyes infoTransaction Functionsnononoyes infoJava
TriggersBy using transaction functionsnononoyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersSharding infoImplicit feature of the cloud servicenoneSharding 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 nodesnone infoBut extensive use of caching in the application peersyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.nonenoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencynoneImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDoptimistic lockingnonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesnoyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes 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.yes inforecommended only for testing and developmentnonoyes
User concepts infoAccess controlnoAccess rights based on private key authentication or shared access signaturesnonoAccess rights for users, groups and roles according to SQL-standard

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More resources
DatomicMicrosoft Azure Table StoragemSQL infoMini SQLSphinxSplice Machine
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