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DBMS > Apache Impala vs. Datomic vs. HBase vs. Milvus

System Properties Comparison Apache Impala vs. Datomic vs. HBase vs. Milvus

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatomic  Xexclude from comparisonHBase  Xexclude from comparisonMilvus  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDatomic 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 BigTableA DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelRelational DBMSRelational DBMSWide column storeVector DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score27.97
Rank#26  Overall
#2  Wide column stores
Score2.78
Rank#103  Overall
#4  Vector DBMS
Websiteimpala.apache.orgwww.datomic.comhbase.apache.orgmilvus.io
Technical documentationimpala.apache.org/­impala-docs.htmldocs.datomic.comhbase.apache.org/­book.htmlmilvus.io/­docs/­overview.md
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaCognitectApache Software Foundation infoApache top-level project, originally developed by Powerset
Initial release2013201220082019
Current release4.1.0, June 20221.0.7075, December 20232.3.4, January 20212.4.4, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infolimited edition freeOpen 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 languageC++Java, ClojureJavaC++, Go
Server operating systemsLinuxAll OS with a Java VMLinux
Unix
Windows infousing Cygwin
Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Data schemeyesyesschema-free, schema definition possible
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVROVector, 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.nononono
Secondary indexesyesyesnono
SQL infoSupport of SQLSQL-like DML and DDL statementsnonono
APIs and other access methodsJDBC
ODBC
RESTful HTTP APIJava API
RESTful HTTP API
Thrift
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCClojure
Java
C
C#
C++
Groovy
Java
PHP
Python
Scala
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infoTransaction Functionsyes infoCoprocessors in Javano
TriggersnoBy using transaction functionsyesno
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornone infoBut extensive use of caching in the application peersMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual 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 datanoACIDSingle row ACID (across millions of columns)no
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and developmentyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosnoAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACRole based access control and fine grained access rights
More information provided by the system vendor
Apache ImpalaDatomicHBaseMilvus
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 ImpalaDatomicHBaseMilvus
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