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DBMS > Apache IoTDB vs. GridDB vs. MarkLogic vs. VelocityDB

System Properties Comparison Apache IoTDB vs. GridDB vs. MarkLogic vs. VelocityDB

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGridDB  Xexclude from comparisonMarkLogic  Xexclude from comparisonVelocityDB  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 FlinkScalable in-memory time series database optimized for IoT and Big DataOperational and transactional Enterprise NoSQL databaseA .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)
Primary database modelTime Series DBMSTime Series DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Graph DBMS
Object oriented DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score5.92
Rank#58  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#6  Search engines
Score0.05
Rank#358  Overall
#36  Graph DBMS
#16  Object oriented DBMS
Websiteiotdb.apache.orggriddb.netwww.marklogic.comvelocitydb.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.griddb.netdocs.marklogic.comvelocitydb.com/­UserGuide
DeveloperApache Software FoundationToshiba CorporationMarkLogic Corp.VelocityDB Inc
Initial release2018201320012011
Current release1.1.0, April 20235.1, August 202211.0, December 20227.x
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial inforestricted free version is availablecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++C#
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxLinux
OS X
Windows
Any that supports .NET
Data schemeyesyesschema-free infoSchema can be enforcedyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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.nonoyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageSQL92, SQL-like TQL (Toshiba Query Language)yes infoSQL92no
APIs and other access methodsJDBC
Native API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
.Net
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
Server-side scripts infoStored proceduresyesnoyes infovia XQuery or JavaScriptno
TriggersyesyesyesCallbacks are triggered when data changes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate consistency within container, eventual consistency across containersImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at container levelACID infocan act as a resource manager in an XA/JTA transactionACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.yesyesyes, with Range Indexesyes
User concepts infoAccess controlyesAccess rights for users can be defined per databaseRole-based access control at the document and subdocument levelsBased on Windows Authentication
More information provided by the system vendor
Apache IoTDBGridDBMarkLogicVelocityDB
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Apache IoTDBGridDBMarkLogicVelocityDB
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