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DBMS > EventStoreDB vs. Spark SQL vs. SwayDB

System Properties Comparison EventStoreDB vs. Spark SQL vs. SwayDB

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Editorial information provided by DB-Engines
NameEventStoreDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonSwayDB  Xexclude from comparison
DescriptionIndustrial-strength, open-source database solution built from the ground up for event sourcing.Spark SQL is a component on top of 'Spark Core' for structured data processingAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
Primary database modelEvent StoreRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.10
Rank#179  Overall
#1  Event Stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.00
Rank#382  Overall
#59  Key-value stores
Websitewww.eventstore.comspark.apache.org/­sqlswaydb.simer.au
Technical documentationdevelopers.eventstore.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperEvent Store LimitedApache Software FoundationSimer Plaha
Initial release201220142018
Current release21.2, February 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen SourceOpen Source infoApache 2.0Open Source infoGNU Affero GPL V3.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaScala
Server operating systemsLinux
Windows
Linux
OS X
Windows
Data schemeyesschema-free
Typing infopredefined data types such as float or dateyesno
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
Secondary indexesnono
SQL infoSupport of SQLSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Supported programming languagesJava
Python
R
Scala
Java
Kotlin
Scala
Server-side scripts infoStored proceduresnono
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoAtomic execution of operations
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlnono

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More resources
EventStoreDBSpark SQLSwayDB
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