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DBMS > Badger vs. Spark SQL vs. Splice Machine vs. WakandaDB

System Properties Comparison Badger vs. Spark SQL vs. Splice Machine vs. WakandaDB

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  Xexclude from comparisonWakandaDB  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.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 SparkWakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelKey-value storeRelational DBMSRelational DBMSObject oriented DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score0.03
Rank#364  Overall
#17  Object oriented DBMS
Websitegithub.com/­dgraph-io/­badgerspark.apache.org/­sqlsplicemachine.comwakanda.github.io
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-workswakanda.github.io/­doc
DeveloperDGraph LabsApache Software FoundationSplice MachineWakanda SAS
Initial release2017201420142012
Current release3.5.0 ( 2.13), September 20233.1, March 20212.7.0 (AprilĀ 29, 2019), April 2019
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoAGPL 3.0, commercial license availableOpen Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoScalaJavaC++, JavaScript
Server operating systemsBSD
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or datenoyesyesyes
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
Secondary indexesnonoyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsyesno
APIs and other access methodsJDBC
ODBC
JDBC
Native Spark Datasource
ODBC
RESTful HTTP API
Supported programming languagesGoJava
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
JavaScript
Server-side scripts infoStored proceduresnonoyes infoJavayes
Triggersnonoyesyes
Partitioning methods infoMethods for storing different data on different nodesnoneyes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesnonenoneMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlnonoAccess rights for users, groups and roles according to SQL-standardyes

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More resources
BadgerSpark SQLSplice MachineWakandaDB
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