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DBMS > Apache IoTDB vs. Milvus vs. Oracle Berkeley DB vs. Solr

System Properties Comparison Apache IoTDB vs. Milvus vs. Oracle Berkeley DB vs. Solr

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMilvus  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSolr  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 FlinkA DBMS designed for efficient storage of vector data and vector similarity searchesWidely used in-process key-value storeA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelTime Series DBMSVector DBMSKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Search 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
Score2.78
Rank#103  Overall
#4  Vector DBMS
Score2.01
Rank#126  Overall
#21  Key-value stores
#3  Native XML DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websiteiotdb.apache.orgmilvus.iowww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlsolr.apache.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlmilvus.io/­docs/­overview.mddocs.oracle.com/­cd/­E17076_05/­html/­index.htmlsolr.apache.org/­resources.html
DeveloperApache Software FoundationOracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software Foundation
Initial release2018201919942006
Current release1.1.0, April 20232.4.4, May 202418.1.40, May 20209.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infocommercial license availableOpen Source infoApache Version 2
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.
Zilliz Cloud – Cloud-native service for Milvus
Implementation languageJavaC++, GoC, Java, C++ (depending on the Berkeley DB edition)Java
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesVector, Numeric and Stringnoyes 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 infoonly with the Berkeley DB XML editionyes
Secondary indexesyesnoyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query languagenoyes infoSQL interfaced based on SQLite is availableSolr Parallel SQL Interface
APIs and other access methodsJDBC
Native API
RESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C++
Go
Java
JavaScript (Node.js)
Python
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresyesnonoJava plugins
Triggersyesnoyes infoonly for the SQL APIyes 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)ShardingnoneSharding
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 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
Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.yesyesyesyes
User concepts infoAccess controlyesRole based access control and fine grained access rightsnoyes
More information provided by the system vendor
Apache IoTDBMilvusOracle Berkeley DBSolr
Specific characteristicsMilvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantagesHighly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosRAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Key customersMilvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricsAs of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsMilvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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