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DBMS > Apache IoTDB vs. Fujitsu Enterprise Postgres vs. Heroic vs. Milvus

System Properties Comparison Apache IoTDB vs. Fujitsu Enterprise Postgres vs. Heroic vs. Milvus

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonHeroic  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 FlinkEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchA DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelTime Series DBMSRelational DBMSTime Series DBMSVector DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score0.46
Rank#265  Overall
#22  Time Series DBMS
Score2.78
Rank#103  Overall
#3  Vector DBMS
Websiteiotdb.apache.orgwww.postgresql.fastware.comgithub.com/­spotify/­heroicmilvus.io
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.postgresql.fastware.com/­product-manualsspotify.github.io/­heroicmilvus.io/­docs/­overview.md
DeveloperApache Software FoundationPostgreSQL Global Development Group, Fujitsu Australia Software TechnologySpotify
Initial release201820142019
Current release1.1.0, April 2023Fujitsu Enterprise Postgres 14, January 20222.3.4, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Zilliz Cloud – Cloud-native service for Milvus
Implementation languageJavaCJavaC++, Go
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
Windows
Linux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Data schemeyesyesschema-free
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.nonono
Secondary indexesyesyesyes infovia Elasticsearchno
SQL infoSupport of SQLSQL-like query languageyesnono
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
HQL (Heroic Query Language, a JSON-based language)
HTTP API
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesuser defined functionsnono
Triggersyesyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)partitioning by range, list and by hashShardingSharding
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 replicationyes
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
Immediate ConsistencyEventual Consistency
Immediate Consistency
Bounded 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 datanoACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yesyes
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.yesnoyes
User concepts infoAccess controlyesfine grained access rights according to SQL-standardRole based access control and fine grained access rights
More information provided by the system vendor
Apache IoTDBFujitsu Enterprise PostgresHeroicMilvus
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 IoTDBFujitsu Enterprise PostgresHeroicMilvus
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