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DBMS > Apache IoTDB vs. Kdb vs. Riak KV vs. Sphinx

System Properties Comparison Apache IoTDB vs. Kdb vs. Riak KV vs. Sphinx

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonKdb  Xexclude from comparisonRiak KV  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 FlinkHigh performance Time Series DBMSDistributed, fault tolerant key-value storeOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSTime Series DBMS
Vector DBMS
Key-value store infowith links between data sets and object tags for the creation of secondary indexesSearch engine
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score7.73
Rank#45  Overall
#2  Time Series DBMS
#1  Vector DBMS
Score3.84
Rank#76  Overall
#9  Key-value stores
Score5.97
Rank#56  Overall
#5  Search engines
Websiteiotdb.apache.orgkx.comsphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcode.kx.comwww.tiot.jp/­riak-docs/­riak/­kv/­latestsphinxsearch.com/­docs
DeveloperApache Software FoundationKx Systems, a division of First Derivatives plcOpenSource, formerly Basho TechnologiesSphinx Technologies Inc.
Initial release20182000 infokdb was released 2000, kdb+ in 200320092001
Current release1.1.0, April 20233.6, May 20183.2.0, December 20223.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infofree 32-bit versionOpen Source infoApache version 2, commercial enterprise editionOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaqErlangC++
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Solaris
Windows
Linux
OS X
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesnono
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
Secondary indexesyesyes infotable attribute 'grouped'restrictedyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageSQL-like query language (q)noSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
HTTP API
Native Erlang Interface
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesuser defined functionsErlangno
Triggersyesyes infowith viewsyes infopre-commit hooks and post-commit hooksno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)horizontal partitioningSharding infono "single point of failure"Sharding 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 replicasSource-replica replicationselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkno infosimilar paradigm used for internal processingyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesno infolinks between data sets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
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
User concepts infoAccess controlyesrights management via user accountsyes, using Riak Securityno
More information provided by the system vendor
Apache IoTDBKdbRiak KVSphinx
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsupon request
» more

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Apache IoTDBKdbRiak KVSphinx
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