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DBMS > Apache Druid vs. GridGain vs. Milvus vs. Solr

System Properties Comparison Apache Druid vs. GridGain vs. Milvus vs. Solr

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGridGain  Xexclude from comparisonMilvus  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataGridGain is an in-memory computing platform, built on Apache IgniteA DBMS designed for efficient storage of vector data and vector similarity searchesA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMS
Time Series DBMS
Key-value store
Relational DBMS
Vector DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score1.55
Rank#150  Overall
#26  Key-value stores
#70  Relational DBMS
Score2.78
Rank#103  Overall
#3  Vector DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websitedruid.apache.orgwww.gridgain.commilvus.iosolr.apache.org
Technical documentationdruid.apache.org/­docs/­latest/­designwww.gridgain.com/­docs/­index.htmlmilvus.io/­docs/­overview.mdsolr.apache.org/­resources.html
DeveloperApache Software Foundation and contributorsGridGain Systems, Inc.Apache Software Foundation
Initial release2012200720192006
Current release29.0.1, April 2024GridGain 8.5.12.4.4, May 20249.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoApache Version 2.0Open 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 languageJavaJava, C++, .NetC++, GoJava
Server operating systemsLinux
OS X
Unix
Linux
OS X
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)
Data schemeyes infoschema-less columns are supportedyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyesVector, 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.noyesnoyes
Secondary indexesyesyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL for queryingANSI-99 for query and DML statements, subset of DDLnoSolr Parallel SQL Interface
APIs and other access methodsJDBC
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
RESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
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 proceduresnoyes (compute grid and cache interceptors can be used instead)noJava plugins
Triggersnoyes (cache interceptors and events)noyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesyes (replicated cache)yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)nospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyBounded 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 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.noyesyesyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemSecurity Hooks for custom implementationsRole based access control and fine grained access rightsyes
More information provided by the system vendor
Apache DruidGridGainMilvusSolr
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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Apache DruidGridGainMilvusSolr
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