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DBMS > Ingres vs. Milvus vs. Solr vs. Trafodion vs. Ultipa

System Properties Comparison Ingres vs. Milvus vs. Solr vs. Trafodion vs. Ultipa

Editorial information provided by DB-Engines
NameIngres  Xexclude from comparisonMilvus  Xexclude from comparisonSolr  Xexclude from comparisonTrafodion  Xexclude from comparisonUltipa  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionWell established RDBMSA DBMS designed for efficient storage of vector data and vector similarity searchesA widely used distributed, scalable search engine based on Apache LuceneTransactional SQL-on-Hadoop DBMSHigh performance Graph DBMS supporting HTAP high availability cluster deployment
Primary database modelRelational DBMSVector DBMSSearch engineRelational DBMSGraph DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.11
Rank#81  Overall
#44  Relational DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Score0.13
Rank#335  Overall
#31  Graph DBMS
Websitewww.actian.com/­databases/­ingresmilvus.iosolr.apache.orgtrafodion.apache.orgwww.ultipa.com
Technical documentationdocs.actian.com/­ingresmilvus.io/­docs/­overview.mdsolr.apache.org/­resources.htmltrafodion.apache.org/­documentation.htmlwww.ultipa.com/­document
DeveloperActian CorporationApache Software FoundationApache Software Foundation, originally developed by HPUltipa
Initial release1974 infooriginally developed at University Berkely in early 1970s2019200620142019
Current release11.2, May 20222.3.4, January 20249.6.0, April 20242.3.0, February 2019
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache Version 2Open Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonononono
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 languageCC++, GoJavaC++, Java
Server operating systemsAIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
Data schemeyesyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyesVector, Numeric and Stringyes infosupports customizable data types and automatic typingyes
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.no infobut tools for importing/exporting data from/to XML-files availablenoyesno
Secondary indexesyesnoyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLyesnoSolr Parallel SQL Interfaceyes
APIs and other access methods.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
RESTful HTTP APIJava API
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC++
Go
Java
JavaScript (Node.js)
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
All languages supporting JDBC/ODBC/ADO.NetC++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesnoJava pluginsJava Stored Procedures
Triggersyesnoyes infoUser configurable commands triggered on index changesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning infoIngres Star to access multiple databases simultaneouslyShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesIngres Replicatoryesyes, via HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceyes infovia user defined functions and HBase
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnooptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayes infoMVCCyesyesyes
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.noyesyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardRole based access control and fine grained access rightsyesfine grained access rights according to SQL-standard
More information provided by the system vendor
IngresMilvusSolrTrafodionUltipa
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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More resources
IngresMilvusSolrTrafodionUltipa
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