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DBMS > Datomic vs. HBase vs. Hive vs. Milvus

System Properties Comparison Datomic vs. HBase vs. Hive vs. Milvus

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonHBase  Xexclude from comparisonHive  Xexclude from comparisonMilvus  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityWide-column store based on Apache Hadoop and on concepts of BigTabledata warehouse software for querying and managing large distributed datasets, built on HadoopA DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelRelational DBMSWide column storeRelational DBMSVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Websitewww.datomic.comhbase.apache.orghive.apache.orgmilvus.io
Technical documentationdocs.datomic.comhbase.apache.org/­book.htmlcwiki.apache.org/­confluence/­display/­Hive/­Homemilvus.io/­docs/­overview.md
DeveloperCognitectApache Software Foundation infoApache top-level project, originally developed by PowersetApache Software Foundation infoinitially developed by Facebook
Initial release2012200820122019
Current release1.0.6735, June 20232.3.4, January 20213.1.3, April 20222.3.4, January 2024
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoApache version 2Open Source infoApache Version 2Open Source infoApache Version 2.0
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 languageJava, ClojureJavaJavaC++, Go
Server operating systemsAll OS with a Java VMLinux
Unix
Windows infousing Cygwin
All OS with a Java VMLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Data schemeyesschema-free, schema definition possibleyes
Typing infopredefined data types such as float or dateyesoptions to bring your own types, AVROyesVector, Numeric and String
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 indexesyesnoyesno
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsno
APIs and other access methodsRESTful HTTP APIJava API
RESTful HTTP API
Thrift
JDBC
ODBC
Thrift
RESTful HTTP API
Supported programming languagesClojure
Java
C
C#
C++
Groovy
Java
PHP
Python
Scala
C++
Java
PHP
Python
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyes infoTransaction Functionsyes infoCoprocessors in Javayes infouser defined functions and integration of map-reduceno
TriggersBy using transaction functionsyesnono
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersMulti-source replication
Source-replica replication
selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual ConsistencyEventual ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDSingle row ACID (across millions of columns)nono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentyesyes
User concepts infoAccess controlnoAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACAccess rights for users, groups and rolesRole based access control and fine grained access rights
More information provided by the system vendor
DatomicHBaseHiveMilvus
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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