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DBMS > Apache IoTDB vs. Firebase Realtime Database vs. Kinetica vs. Milvus

System Properties Comparison Apache IoTDB vs. Firebase Realtime Database vs. Kinetica vs. Milvus

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonKinetica  Xexclude from comparisonMilvus  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Fully vectorized database across both GPUs and CPUsA DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelTime Series DBMSDocument storeRelational DBMSVector DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score13.60
Rank#39  Overall
#6  Document stores
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score3.01
Rank#89  Overall
#4  Vector DBMS
Websiteiotdb.apache.orgfirebase.google.com/­products/­realtime-databasewww.kinetica.commilvus.io
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlfirebase.google.com/­docs/­databasedocs.kinetica.commilvus.io/­docs/­overview.md
DeveloperApache Software FoundationGoogle infoacquired by Google 2014Kinetica
Initial release2018201220122019
Current release1.1.0, April 20237.1, August 20212.4.4, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache Version 2.0
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 languageJavaC, C++C++, Go
Server operating systemsAll OS with a Java VM (>= 1.8)hostedLinuxLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyesVector, 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 indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languagenoSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
Native API
Android
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Java
JavaScript
Objective-C
C++
Java
JavaScript (Node.js)
Python
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyeslimited functionality with using 'rules'user defined functionsno
TriggersyesCallbacks are triggered when data changesyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate Consistency or Eventual Consistency depending on configurationBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesnono
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.yesyes infoGPU vRAM or System RAMyes
User concepts infoAccess controlyesyes, based on authentication and database rulesAccess rights for users and roles on table levelRole based access control and fine grained access rights
More information provided by the system vendor
Apache IoTDBFirebase Realtime DatabaseKineticaMilvus
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 IoTDBFirebase Realtime DatabaseKineticaMilvus
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