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DBMS > Cachelot.io vs. EsgynDB vs. Hive vs. Milvus vs. Solr

System Properties Comparison Cachelot.io vs. EsgynDB vs. Hive vs. Milvus vs. Solr

Editorial information provided by DB-Engines
NameCachelot.io  Xexclude from comparisonEsgynDB  Xexclude from comparisonHive  Xexclude from comparisonMilvus  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionIn-memory caching systemEnterprise-class SQL-on-Hadoop solution, powered by Apache Trafodiondata warehouse software for querying and managing large distributed datasets, built on HadoopA DBMS designed for efficient storage of vector data and vector similarity searchesA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelKey-value storeRelational DBMSRelational DBMSVector DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.00
Rank#383  Overall
#60  Key-value stores
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Score42.91
Rank#24  Overall
#3  Search engines
Websitecachelot.iowww.esgyn.cnhive.apache.orgmilvus.iosolr.apache.org
Technical documentationcwiki.apache.org/­confluence/­display/­Hive/­Homemilvus.io/­docs/­overview.mdsolr.apache.org/­resources.html
DeveloperEsgynApache Software Foundation infoinitially developed by FacebookApache Software Foundation
Initial release20152015201220192006
Current release3.1.3, April 20222.3.4, January 20249.6.0, April 2024
License infoCommercial or Open SourceOpen Source infoSimplified BSD LicensecommercialOpen Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoApache Version 2
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 languageC++C++, JavaJavaC++, GoJava
Server operating systemsFreeBSD
Linux
OS X
LinuxAll OS with a Java VMLinux
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)
Data schemeschema-freeyesyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or datenoyesyesVector, Numeric and Stringyes 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.nononoyes
Secondary indexesnoyesyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoyesSQL-like DML and DDL statementsnoSolr Parallel SQL Interface
APIs and other access methodsMemcached protocolADO.NET
JDBC
ODBC
JDBC
ODBC
Thrift
RESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languages.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby
All languages supporting JDBC/ODBC/ADO.NetC++
Java
PHP
Python
C++
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
Server-side scripts infoStored proceduresnoJava Stored Proceduresyes infouser defined functions and integration of map-reducenoJava plugins
Triggersnonononoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication between multi datacentersselectable replication factoryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infoquery execution via MapReducenospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyEventual ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnonooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentnoyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardAccess rights for users, groups and rolesRole based access control and fine grained access rightsyes
More information provided by the system vendor
Cachelot.ioEsgynDBHiveMilvusSolr
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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Cachelot.ioEsgynDBHiveMilvusSolr
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