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DBMS > Datomic vs. FatDB vs. Prometheus vs. Spark SQL vs. TimescaleDB

System Properties Comparison Datomic vs. FatDB vs. Prometheus vs. Spark SQL vs. TimescaleDB

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonFatDB  Xexclude from comparisonPrometheus  Xexclude from comparisonSpark SQL  Xexclude from comparisonTimescaleDB  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA .NET NoSQL DBMS that can integrate with and extend SQL Server.Open-source Time Series DBMS and monitoring systemSpark SQL is a component on top of 'Spark Core' for structured data processingA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSDocument store
Key-value store
Time Series DBMSRelational DBMSTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score8.42
Rank#47  Overall
#2  Time Series DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitewww.datomic.comprometheus.iospark.apache.org/­sqlwww.timescale.com
Technical documentationdocs.datomic.comprometheus.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.timescale.com
DeveloperCognitectFatCloudApache Software FoundationTimescale
Initial release20122012201520142017
Current release1.0.6735, June 20233.5.0 ( 2.13), September 20232.15.0, May 2024
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureC#GoScalaC
Server operating systemsAll OS with a Java VMWindowsLinux
Windows
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesNumeric data onlyyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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 infoImport of XML data possiblenoyes
Secondary indexesyesyesnonoyes
SQL infoSupport of SQLnono infoVia inetgration in SQL ServernoSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
APIs and other access methodsRESTful HTTP API.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
RESTful HTTP/JSON APIJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesClojure
Java
C#.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
Java
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infoTransaction Functionsyes infovia applicationsnonouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
TriggersBy using transaction functionsyes infovia applicationsnonoyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingyes, utilizing Spark Coreyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersselectable replication factoryes infoby FederationnoneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
noneImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyesyes
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 developmentnonono
User concepts infoAccess controlnono infoCan implement custom security layer via applicationsnonofine grained access rights according to SQL-standard

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More resources
DatomicFatDBPrometheusSpark SQLTimescaleDB
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