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

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

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonPlanetScale  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 FlinkScalable, distributed, serverless MySQL database platform built on top of VitessDistributed, fault tolerant key-value storeOpen source search engine for searching in data from different sources, e.g. relational databases
Primary database modelTime Series DBMSRelational DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexesSearch engine
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score1.09
Rank#178  Overall
#82  Relational DBMS
Score3.84
Rank#76  Overall
#9  Key-value stores
Score5.97
Rank#56  Overall
#5  Search engines
Websiteiotdb.apache.orgplanetscale.comsphinxsearch.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlplanetscale.com/­docswww.tiot.jp/­riak-docs/­riak/­kv/­latestsphinxsearch.com/­docs
DeveloperApache Software FoundationPlanetScaleOpenSource, formerly Basho TechnologiesSphinx Technologies Inc.
Initial release2018202020092001
Current release1.1.0, April 20233.2.0, December 20223.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache version 2, commercial enterprise editionOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaGoErlangC++
Server operating systemsAll OS with a Java VM (>= 1.8)Docker
Linux
macOS
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.nono
Secondary indexesyesyesrestrictedyes infofull-text index on all search fields
SQL infoSupport of SQLSQL-like query languageyes infowith proprietary extensionsnoSQL-like query language (SphinxQL)
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
MySQL protocol
ODBC
HTTP API
Native Erlang Interface
Proprietary protocol
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
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 proceduresyesyes infoproprietary syntaxErlangno
Triggersyesyesyes 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)ShardingSharding 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 replicasMulti-source replication
Source-replica replication
selectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual Consistency across shards
Immediate Consistency within a shard
Eventual Consistency
Foreign keys infoReferential integritynoyes infonot for MyISAM storage engineno infolinks between data sets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at shard levelnono
Concurrency infoSupport for concurrent manipulation of datayesyes infotable locks or row locks depending on storage engineyesyes
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 controlyesUsers with fine-grained authorization concept infono user groups or rolesyes, using Riak Securityno

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More resources
Apache IoTDBPlanetScaleRiak KVSphinx
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