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DBMS > Apache IoTDB vs. Drizzle vs. openGauss vs. Solr

System Properties Comparison Apache IoTDB vs. Drizzle vs. openGauss vs. Solr

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDrizzle  Xexclude from comparisonopenGauss  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.An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by HuaweiA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelTime Series DBMSRelational DBMSRelational DBMSSearch engine
Secondary database modelsDocument store
Spatial DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score1.06
Rank#184  Overall
#84  Relational DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websiteiotdb.apache.orggitee.com/­opengauss
opengauss.org
solr.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.opengauss.org/­en
gitee.com/­opengauss/­docs
solr.apache.org/­resources.html
DeveloperApache Software FoundationDrizzle project, originally started by Brian AkerHuawei and openGauss communityApache Software Foundation
Initial release2018200820192006
Current release1.1.0, April 20237.2.4, September 20123.0, March 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++C, C++, JavaJava
Server operating systemsAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
LinuxAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesyesyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesyesyes 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 indexesyesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query languageyes infowith proprietary extensionsANSI SQL 2011Solr Parallel SQL Interface
APIs and other access methodsJDBC
Native API
JDBCJDBC
ODBC
Java API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Java
PHP
C
C++
Java
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyesnoyesJava plugins
Triggersyesno infohooks for callbacks inside the server can be used.yesyes 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)Shardinghorizontal partitioning (by range, list and hash)Sharding
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
Source-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonospark-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
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDoptimistic 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.yesnoyes
User concepts infoAccess controlyesPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles according to SQL-standardyes

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More resources
Apache IoTDBDrizzleopenGaussSolr
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