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DBMS > Apache IoTDB vs. EsgynDB vs. GigaSpaces vs. Milvus

System Properties Comparison Apache IoTDB vs. EsgynDB vs. GigaSpaces vs. Milvus

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonGigaSpaces  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-class SQL-on-Hadoop solution, powered by Apache TrafodionHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsA DBMS designed for efficient storage of vector data and vector similarity searches
Primary database modelTime Series DBMSRelational DBMSDocument store
Object oriented DBMS infoValues are user defined objects
Vector DBMS
Secondary database modelsGraph DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score0.97
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Websiteiotdb.apache.orgwww.esgyn.cnwww.gigaspaces.commilvus.io
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.gigaspaces.com/­latest/­landing.htmlmilvus.io/­docs/­overview.md
DeveloperApache Software FoundationEsgynGigaspaces Technologies
Initial release2018201520002019
Current release1.1.0, April 202315.5, September 20202.3.4, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2; Commercial licenses availableOpen 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 languageJavaC++, JavaJava, C++, .NetC++, Go
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxLinux
macOS
Solaris
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 infoXML can be used for describing objects metadatano
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languageyesSQL-99 for query and DML statementsno
APIs and other access methodsJDBC
Native API
ADO.NET
JDBC
ODBC
GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
All languages supporting JDBC/ODBC/ADO.Net.Net
C++
Java
Python
Scala
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesJava Stored Proceduresyesno
Triggersyesnoyes, event driven architectureno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication between multi datacentersMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesyes infoMap-Reduce pattern can be built with XAP task executorsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYBounded 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 datanoACIDACIDno
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.yesnoyesyes
User concepts infoAccess controlyesfine grained access rights according to SQL-standardRole-based access controlRole based access control and fine grained access rights
More information provided by the system vendor
Apache IoTDBEsgynDBGigaSpacesMilvus
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 IoTDBEsgynDBGigaSpacesMilvus
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