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DBMS > Badger vs. Coveo vs. Milvus vs. Splice Machine

System Properties Comparison Badger vs. Coveo vs. Milvus vs. Splice Machine

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonCoveo  Xexclude from comparisonMilvus  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code developmentA DBMS designed for efficient storage of vector data and vector similarity searchesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelKey-value storeSearch engineVector DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#328  Overall
#48  Key-value stores
Score2.14
Rank#116  Overall
#11  Search engines
Score3.01
Rank#89  Overall
#4  Vector DBMS
Score0.54
Rank#244  Overall
#114  Relational DBMS
Websitegithub.com/­dgraph-io/­badgerwww.coveo.commilvus.iosplicemachine.com
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.coveo.commilvus.io/­docs/­overview.mdsplicemachine.com/­how-it-works
DeveloperDGraph LabsCoveoSplice Machine
Initial release2017201220192014
Current release2.4.4, May 20243.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache Version 2.0Open Source infoAGPL 3.0, commercial license available
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
Linux
OS X
Solaris
Windows
Data schemeschema-freehybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configurationyes
Typing infopredefined data types such as float or datenoyesVector, Numeric and Stringyes
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.nonono
Secondary indexesnoyesnoyes
SQL infoSupport of SQLnononoyes
APIs and other access methodsRESTful HTTP APIRESTful HTTP APIJDBC
Native Spark Datasource
ODBC
Supported programming languagesGoC#
Java
JavaScript
Python
C++
Go
Java
JavaScript (Node.js)
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnononoyes infoJava
Triggersnoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesnoneyesShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
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.noyesyes
User concepts infoAccess controlnogranular access controls, API key management, content filtersRole based access control and fine grained access rightsAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
BadgerCoveoMilvusSplice Machine
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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BadgerCoveoMilvusSplice Machine
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