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DBMS > Datomic vs. Ignite vs. Spark SQL vs. Splice Machine

System Properties Comparison Datomic vs. Ignite vs. Spark SQL vs. Splice Machine

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonIgnite  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.Spark SQL is a component on top of 'Spark Core' for structured data processingOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSKey-value store
Relational DBMS
Relational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.76
Rank#145  Overall
#66  Relational DBMS
Score3.64
Rank#89  Overall
#13  Key-value stores
#48  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#255  Overall
#116  Relational DBMS
Websitewww.datomic.comignite.apache.orgspark.apache.org/­sqlsplicemachine.com
Technical documentationdocs.datomic.comapacheignite.readme.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-works
DeveloperCognitectApache Software FoundationApache Software FoundationSplice Machine
Initial release2012201520142014
Current release1.0.6735, June 2023Apache Ignite 2.63.5.0 ( 2.13), September 20233.1, March 2021
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureC++, Java, .NetScalaJava
Server operating systemsAll OS with a Java VMLinux
OS X
Solaris
Windows
Linux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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 indexesyesyesnoyes
SQL infoSupport of SQLnoANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsyes
APIs and other access methodsRESTful HTTP APIHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesClojure
Java
C#
C++
Java
PHP
Python
Ruby
Scala
Java
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infoTransaction Functionsyes (compute grid and cache interceptors can be used instead)noyes infoJava
TriggersBy using transaction functionsyes (cache interceptors and events)noyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingyes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersyes (replicated cache)noneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)Yes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes
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 developmentyesnoyes
User concepts infoAccess controlnoSecurity Hooks for custom implementationsnoAccess rights for users, groups and roles according to SQL-standard

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DatomicIgniteSpark SQLSplice Machine
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