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DBMS > Datomic vs. Graph Engine vs. Prometheus vs. Spark SQL

System Properties Comparison Datomic vs. Graph Engine vs. Prometheus vs. Spark SQL

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonPrometheus  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineOpen-source Time Series DBMS and monitoring systemSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSGraph DBMS
Key-value store
Time Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score0.67
Rank#232  Overall
#21  Graph DBMS
#34  Key-value stores
Score7.69
Rank#50  Overall
#3  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.datomic.comwww.graphengine.ioprometheus.iospark.apache.org/­sql
Technical documentationdocs.datomic.comwww.graphengine.io/­docs/­manualprometheus.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperCognitectMicrosoftApache Software Foundation
Initial release2012201020152014
Current release1.0.6735, June 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoMIT LicenseOpen Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, Clojure.NET and CGoScala
Server operating systemsAll OS with a Java VM.NETLinux
Windows
Linux
OS X
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesNumeric data onlyyes
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 infoImport of XML data possibleno
Secondary indexesyesnono
SQL infoSupport of SQLnononoSQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP APIRESTful HTTP APIRESTful HTTP/JSON APIJDBC
ODBC
Supported programming languagesClojure
Java
C#
C++
F#
Visual Basic
.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infoTransaction Functionsyesnono
TriggersBy using transaction functionsnonono
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peershorizontal partitioningShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersyes infoby Federationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistencynone
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyesyes
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 developmentyesnono
User concepts infoAccess controlnonono

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DatomicGraph Engine infoformer name: TrinityPrometheusSpark SQL
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