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DBMS > Firebase Realtime Database vs. Kdb vs. Milvus vs. Spark SQL

System Properties Comparison Firebase Realtime Database vs. Kdb vs. Milvus vs. Spark SQL

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Editorial information provided by DB-Engines
NameFirebase Realtime Database  Xexclude from comparisonKdb  Xexclude from comparisonMilvus  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.High performance Time Series DBMSA DBMS designed for efficient storage of vector data and vector similarity searchesSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument storeTime Series DBMS
Vector DBMS
Vector DBMSRelational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.29
Rank#39  Overall
#6  Document stores
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitefirebase.google.com/­products/­realtime-databasekx.commilvus.iospark.apache.org/­sql
Technical documentationfirebase.google.com/­docs/­databasecode.kx.commilvus.io/­docs/­overview.mdspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperGoogle infoacquired by Google 2014Kx Systems, a division of First Derivatives plcApache Software Foundation
Initial release20122000 infokdb was released 2000, kdb+ in 200320192014
Current release3.6, May 20182.3.4, January 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercialcommercial infofree 32-bit versionOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
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 languageqC++, GoScala
Server operating systemshostedLinux
OS X
Solaris
Windows
Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Linux
OS X
Windows
Data schemeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesVector, 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.noyesnono
Secondary indexesyesyes infotable attribute 'grouped'nono
SQL infoSupport of SQLnoSQL-like query language (q)noSQL-like DML and DDL statements
APIs and other access methodsAndroid
iOS
JavaScript API
RESTful HTTP API
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
RESTful HTTP APIJDBC
ODBC
Supported programming languagesJava
JavaScript
Objective-C
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
C++
Go
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored procedureslimited functionality with using 'rules'user defined functionsnono
TriggersCallbacks are triggered when data changesyes infowith viewsnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono infosimilar paradigm used for internal processingno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate ConsistencyBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnonono
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.yesyesno
User concepts infoAccess controlyes, based on authentication and database rulesrights management via user accountsRole based access control and fine grained access rightsno
More information provided by the system vendor
Firebase Realtime DatabaseKdbMilvusSpark SQL
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
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RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
» more
As of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsupon request
» more
Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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Firebase Realtime DatabaseKdbMilvusSpark SQL
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