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DBMS > Apache IoTDB vs. MarkLogic vs. Prometheus vs. Sphinx

System Properties Comparison Apache IoTDB vs. MarkLogic vs. Prometheus vs. Sphinx

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMarkLogic  Xexclude from comparisonPrometheus  Xexclude from comparisonSphinx  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 FlinkOperational and transactional Enterprise NoSQL databaseOpen-source Time Series DBMS and monitoring systemOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Time Series DBMSSearch engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score7.69
Rank#50  Overall
#3  Time Series DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Websiteiotdb.apache.orgwww.progress.com/­marklogicprometheus.iosphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.progress.com/­marklogic/­documentationprometheus.io/­docssphinxsearch.com/­docs
DeveloperApache Software FoundationMarkLogic Corp.Sphinx Technologies Inc.
Initial release2018200120152001
Current release1.1.0, April 202311.0, December 20223.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial inforestricted free version is availableOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence 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 languageJavaC++GoC++
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Windows
Linux
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesschema-free infoSchema can be enforcedyesyes
Typing infopredefined data types such as float or dateyesyesNumeric data onlyno
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.noyesno infoImport of XML data possible
Secondary indexesyesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageyes infoSQL92noSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native 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
RESTful HTTP/JSON APIProprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesyes infovia XQuery or JavaScriptnono
Triggersyesyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyesyes infoby Federationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistencynone
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infocan act as a resource manager in an XA/JTA transactionnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes, with Range Indexesno
User concepts infoAccess controlyesRole-based access control at the document and subdocument levelsnono

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Apache IoTDBMarkLogicPrometheusSphinx
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