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DBMS > Datomic vs. Sphinx vs. Splice Machine

System Properties Comparison Datomic vs. Sphinx vs. Splice Machine

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Editorial information provided by DB-Engines
NameDatomic  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 durabilityOpen 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 DBMSSearch engineRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.13
Rank#100  Overall
#50  Relational DBMS
Score6.34
Rank#60  Overall
#7  Search engines
Score0.79
Rank#190  Overall
#93  Relational DBMS
Websitewww.datomic.comsphinxsearch.comsplicemachine.com
Technical documentationdocs.datomic.comsphinxsearch.com/­docsdoc.splicemachine.com
DeveloperCognitectSphinx Technologies Inc.Splice Machine
Initial release201220012014
Current release0.9.5951, July 20193.2.1, January 20202.0
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureC++Java
Server operating systemsAll OS with a Java VMFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesnoyes
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.no
Secondary indexesyesyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoSQL-like query language (SphinxQL)yes
APIs and other access methodsRESTful HTTP APIProprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesClojure
Java
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 Functionsnoyes infoJava
TriggersBy using transaction functionsnoyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersSharding 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 peersnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yes 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 developmentyes
User concepts infoAccess controlnonoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
DatomicSphinxSplice Machine
Specific characteristicsTHE ONLY SCALE-OUT SQL RDBMS WITH BUILT-IN MACHINE LEARNING For data-rich, real-time...
» more
Competitive advantagesOperational Workloads: Full ACID support with high performance at scale Multi-row...
» more
Typical application scenariosNew AI/ML and IoT Applications Data Warehouse Offloads Application Modernization...
» more
Licensing and pricing modelsKubernetes Community Edition: https://hub.helm.sh/charts/splicemachine/splice-helm...
» more
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