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DBMS > Milvus vs. Tarantool vs. TimescaleDB

System Properties Comparison Milvus vs. Tarantool vs. TimescaleDB

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Editorial information provided by DB-Engines
NameMilvus  Xexclude from comparisonTarantool  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionA DBMS designed for efficient storage of vector data and vector similarity searchesIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelVector DBMSDocument store
Key-value store
Relational DBMS
Time Series DBMS
Secondary database modelsSpatial DBMS infowith Tarantool/GIS extensionRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.81
Rank#144  Overall
#5  Vector DBMS
Score1.67
Rank#150  Overall
#25  Document stores
#25  Key-value stores
#69  Relational DBMS
Score4.87
Rank#74  Overall
#4  Time Series DBMS
Websitemilvus.iowww.tarantool.iowww.timescale.com
Technical documentationmilvus.io/­docs/­overview.mdwww.tarantool.io/­en/­docdocs.timescale.com
DeveloperVKTimescale
Initial release201920082017
Current release2.3.4, January 20242.10.0, May 20222.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool EnterpriseOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
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++, GoC and C++C
Server operating systemsLinux
macOS info10.14 or later
Windows infowith WSL 2 enabled
BSD
Linux
macOS
Linux
OS X
Windows
Data schemeFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
Typing infopredefined data types such as float or dateVector, Numeric and Stringstring, double, decimal, uuid, integer, blob, boolean, datetimenumerics, 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 indexesnoyesyes
SQL infoSupport of SQLnoFull-featured ANSI SQL supportyes infofull PostgreSQL SQL syntax
APIs and other access methodsRESTful HTTP APIOpen binary protocolADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC++
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnoLua, C and SQL stored proceduresuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyes, before/after data modification events, on replication events, client session eventsyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.yes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
Source-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemBounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency
Casual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Immediate Consistency
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactionsACID
Concurrency infoSupport for concurrent manipulation of datayesyes, cooperative multitaskingyes
Durability infoSupport for making data persistentyesyes, write ahead loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes, full featured in-memory storage engine with persistenceno
User concepts infoAccess controlRole based access control and fine grained access rightsAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
fine grained access rights according to SQL-standard
More information provided by the system vendor
MilvusTarantoolTimescaleDB
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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
MilvusTarantoolTimescaleDB
DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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Recent citations in the news

Zilliz Unveils Game-Changing Features for Vector Search
22 March 2024, Datanami

What Is Milvus Vector Database?
6 October 2023, The New Stack

AI-Powered Search Engine With Milvus Vector Database on Vultr
31 January 2024, SitePoint

Milvus 2.4 Unveils Game-Changing Features for Enhanced Vector Search
20 March 2024, GlobeNewswire

Zilliz Cloud boosts vector database performance
31 January 2024, InfoWorld

provided by Google News

In-Memory Showdown: Redis vs. Tarantool
1 September 2021, Хабр

Deploying Tarantool Cartridge applications with zero effort (Part 1)
16 December 2019, Хабр

Deploying Tarantool Cartridge applications with zero effort (Part 2)
13 April 2020, Хабр

Тarantool Cartridge: Sharding Lua Backend in Three Lines
9 October 2019, Хабр

RAMAX Group получила партнерский статус уровня Gold по продукту Tarantool
16 April 2024, spbit.ru

provided by Google News

TimescaleDB Is a Vector Database Now, Too
25 September 2023, Datanami

Timescale Acquires PopSQL to Bring a Modern, Collaborative SQL GUI to PostgreSQL Developers
4 April 2024, PR Newswire

Visualizing IoT Data at Scale With Hopara and TimescaleDB
16 May 2023, Embedded Computing Design

Power IoT and time-series workloads with TimescaleDB for Azure Database for PostgreSQL
18 March 2019, azure.microsoft.com

Timescale Valuation Rockets to Over $1B with $110M Round, Marking the Explosive Rise of Time-Series Data
22 February 2022, Business Wire

provided by Google News



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