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DBMS > Apache Impala vs. Milvus vs. PouchDB vs. Splice Machine

System Properties Comparison Apache Impala vs. Milvus vs. PouchDB vs. Splice Machine

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonMilvus  Xexclude from comparisonPouchDB  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopA DBMS designed for efficient storage of vector data and vector similarity searchesJavaScript DBMS with an API inspired by CouchDBOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSVector DBMSDocument storeRelational 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
Score2.78
Rank#103  Overall
#3  Vector DBMS
Score2.34
Rank#112  Overall
#21  Document stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteimpala.apache.orgmilvus.iopouchdb.comsplicemachine.com
Technical documentationimpala.apache.org/­impala-docs.htmlmilvus.io/­docs/­overview.mdpouchdb.com/­guidessplicemachine.com/­how-it-works
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationSplice Machine
Initial release2013201920122014
Current release4.1.0, June 20222.3.4, January 20247.1.1, June 20193.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0Open SourceOpen Source infoAGPL 3.0, commercial license available
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++C++, GoJavaScriptJava
Server operating systemsLinuxLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
server-less, requires a JavaScript environment (browser, Node.js)Linux
OS X
Solaris
Windows
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesVector, Numeric and Stringnoyes
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 indexesyesnoyes infovia viewsyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnonoyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP APIHTTP REST infoonly for PouchDB Server
JavaScript API
JDBC
Native Spark Datasource
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC++
Go
Java
JavaScript (Node.js)
Python
JavaScriptC#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoView functions in JavaScriptyes infoJava
Triggersnonoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infowith a proxy-based framework, named couchdb-loungeShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyesYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes infoby using IndexedDB, WebSQL or LevelDB as backendyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosRole based access control and fine grained access rightsnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Apache ImpalaMilvusPouchDBSplice 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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More resources
Apache ImpalaMilvusPouchDBSplice Machine
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