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DBMS > Badger vs. Google Cloud Datastore vs. Milvus vs. Solr

System Properties Comparison Badger vs. Google Cloud Datastore vs. Milvus vs. Solr

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMilvus  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA 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 storeDocument storeVector DBMSSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#320  Overall
#47  Key-value stores
Score4.36
Rank#72  Overall
#12  Document stores
Score2.78
Rank#103  Overall
#4  Vector DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websitegithub.com/­dgraph-io/­badgercloud.google.com/­datastoremilvus.iosolr.apache.org
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgercloud.google.com/­datastore/­docsmilvus.io/­docs/­overview.mdsolr.apache.org/­resources.html
DeveloperDGraph LabsGoogleApache Software Foundation
Initial release2017200820192006
Current release2.4.4, May 20249.6.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache Version 2.0Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
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 languageGoC++, GoJava
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedLinux
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-freeschema-freeyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or datenoyes, details hereVector, 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 indexesnoyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoSQL-like query language (GQL)noSolr Parallel SQL Interface
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP APIJava API
RESTful HTTP/JSON API
Supported programming languagesGo.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
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 proceduresnousing Google App EnginenoJava plugins
TriggersnoCallbacks using the Google Apps Enginenoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication using Paxosyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnooptimistic 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.nonoyesyes
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role based access control and fine grained access rightsyes
More information provided by the system vendor
BadgerGoogle Cloud DatastoreMilvusSolr
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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BadgerGoogle Cloud DatastoreMilvusSolr
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