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DBMS > Apache IoTDB vs. Drizzle vs. Riak TS vs. Solr

System Properties Comparison Apache IoTDB vs. Drizzle vs. Riak TS vs. Solr

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDrizzle  Xexclude from comparisonRiak TS  Xexclude from comparisonSolr  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
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 FlinkMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KVA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelTime Series DBMSRelational DBMSTime Series DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websiteiotdb.apache.orgsolr.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.tiot.jp/­riak-docs/­riak/­ts/­latestsolr.apache.org/­resources.html
DeveloperApache Software FoundationDrizzle project, originally started by Brian AkerOpen Source, formerly Basho TechnologiesApache Software Foundation
Initial release2018200820152006
Current release1.1.0, April 20237.2.4, September 20123.0.0, September 20229.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoGNU GPLOpen SourceOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++ErlangJava
Server operating systemsAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
Linux
OS X
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesnoyes infosupports customizable data types and automatic typing
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.nonoyes
Secondary indexesyesyesrestrictedyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageyes infowith proprietary extensionsyes, limitedSolr Parallel SQL Interface
APIs and other access methodsJDBC
Native API
JDBCHTTP API
Native Erlang Interface
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Java
PHP
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
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyesnoErlangJava plugins
Triggersyesno infohooks for callbacks inside the server can be used.yes infopre-commit hooks and post-commit hooksyes infoUser configurable commands triggered on index 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 replicasMulti-source replication
Source-replica replication
selectable replication factoryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknoyesspark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesno infolinks between datasets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnooptimistic locking
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.yesyes
User concepts infoAccess controlyesPluggable authentication mechanisms infoe.g. LDAP, HTTPnoyes

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More resources
Apache IoTDBDrizzleRiak TSSolr
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