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DBMS > Apache IoTDB vs. MonetDB vs. Postgres-XL vs. Splice Machine vs. WakandaDB

System Properties Comparison Apache IoTDB vs. MonetDB vs. Postgres-XL vs. Splice Machine vs. WakandaDB

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMonetDB  Xexclude from comparisonPostgres-XL  Xexclude from comparisonSplice Machine  Xexclude from comparisonWakandaDB  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA relational database management system that stores data in columnsBased on PostgreSQL enhanced with MPP and write-scale-out cluster featuresOpen-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 modelTime Series DBMSRelational DBMSRelational DBMSRelational DBMSObject oriented DBMS
Secondary database modelsDocument store
Spatial DBMS
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score1.72
Rank#141  Overall
#64  Relational DBMS
Score0.53
Rank#254  Overall
#117  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score0.10
Rank#356  Overall
#16  Object oriented DBMS
Websiteiotdb.apache.orgwww.monetdb.orgwww.postgres-xl.orgsplicemachine.comwakanda.github.io
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.monetdb.org/­Documentationwww.postgres-xl.org/­documentationsplicemachine.com/­how-it-workswakanda.github.io/­doc
DeveloperApache Software FoundationMonetDB BVSplice MachineWakanda SAS
Initial release201820042014 infosince 2012, originally named StormDB20142012
Current release1.1.0, April 2023Dec2023 (11.49), December 202310 R1, October 20183.1, March 20212.7.0 (AprilĀ 29, 2019), April 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoMozilla Public License 2.0Open Source infoMozilla public licenseOpen Source infoAGPL 3.0, commercial license availableOpen Source infoAGPLv3, extended commercial license available
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 languageJavaCCJavaC++, JavaScript
Server operating systemsAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
Solaris
Windows
Linux
macOS
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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.noyes infoXML type, but no XML query functionalityno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageyes infoSQL 2003 with some extensionsyes infodistributed, parallel query executionyesno
APIs and other access methodsJDBC
Native API
JDBC
native C library infoMAPI library (MonetDB application programming interface)
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
Native Spark Datasource
ODBC
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
JavaScript
Server-side scripts infoStored proceduresyesyes, in SQL, C, Ruser defined functionsyes infoJavayes
Triggersyesyesyesyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding via remote tableshorizontal partitioningShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnone infoSource-replica replication available in experimental statusMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknonoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoMVCCACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlyesfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standardyes

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More resources
Apache IoTDBMonetDBPostgres-XLSplice MachineWakandaDB
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