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DBMS > GridDB vs. Milvus vs. Netezza vs. TimescaleDB

System Properties Comparison GridDB vs. Milvus vs. Netezza vs. TimescaleDB

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Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonMilvus  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionScalable in-memory time series database optimized for IoT and Big DataA DBMS designed for efficient storage of vector data and vector similarity searchesData warehouse and analytics appliance part of IBM PureSystemsA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelTime Series DBMSVector DBMSRelational DBMSTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score2.31
Rank#113  Overall
#3  Vector DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitegriddb.netmilvus.iowww.ibm.com/­products/­netezzawww.timescale.com
Technical documentationdocs.griddb.netmilvus.io/­docs/­overview.mddocs.timescale.com
DeveloperToshiba CorporationIBMTimescale
Initial release2013201920002017
Current release5.1, August 20222.3.4, January 20242.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2.0commercialOpen Source infoApache 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 languageC++C++, GoC
Server operating systemsLinuxLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
Linux infoincluded in applianceLinux
OS X
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampVector, Numeric and Stringyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nonoyes
Secondary indexesyesnoyesyes
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)noyesyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
RESTful HTTP APIJDBC
ODBC
OLE DB
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Go
Java
JavaScript (Node.js)
Python
C
C++
Fortran
Java
Lua
Perl
Python
R
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnonoyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyesnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelnoACIDACID
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 controlAccess rights for users can be defined per databaseRole based access control and fine grained access rightsUsers with fine-grained authorization conceptfine grained access rights according to SQL-standard
More information provided by the system vendor
GridDBMilvusNetezza infoAlso called PureData System for Analytics by IBMTimescaleDB
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Milvus is an open-source and cloud-native vector database built for production-ready...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Highly available, versatile, and robust with millisecond latency. Supports batch...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more
Market metricsGitHub trending repository
» more
As of January 2024, 25k+ GitHub stars 10M+ downloads and installations​ ​ 3k+ enterprise...
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more
Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
» more

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More resources
GridDBMilvusNetezza infoAlso called PureData System for Analytics by IBMTimescaleDB
DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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