DBMS > Milvus vs. Riak TS vs. Snowflake vs. SWC-DB vs. Tarantool

System Properties Comparison Milvus vs. Riak TS vs. Snowflake vs. SWC-DB vs. Tarantool

Editorial information provided by DB-Engines

Name

Description

A DBMS designed for efficient storage of vector data and vector similarity searches

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Cloud-based data warehousing service for structured and semi-structured data

A high performance, scalable Wide Column DBMS

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Document store
Key-value store
Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

with Tarantool/GIS extension

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	2.78
Rank	#103	Overall
	#4	Vector DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Website

milvus.io

www.snowflake.com

github.com/kashirin-alex/swc-db
www.swcdb.org

www.tarantool.io

Technical documentation

milvus.io/docs/overview.md

www.tiot.jp/riak-docs/riak/ts/latest

docs.snowflake.net/manuals/index.html

www.tarantool.io/en/doc

Developer

Open Source, formerly Basho Technologies

Snowflake Computing Inc.

Alex Kashirin

Initial release

2019

2015

2014

2020

2008

Current release

2.4.4, May 2024

3.0.0, September 2022

0.5, April 2021

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

Open Source

GPL V3

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++, Go

Erlang

C++

C and C++

Server operating systems

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X

hosted

Linux

BSD
Linux
macOS

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

Vector, Numeric and String

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

restricted

yes

SQL

Support of SQL

yes, limited

yes

SQL-like query language

Full-featured ANSI SQL support

APIs and other access methods

RESTful HTTP API

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

Proprietary protocol
Thrift

Open binary protocol

Supported programming languages

C++
Go
Java
JavaScript (Node.js)
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

C++

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

Erlang

user defined functions

Lua, C and SQL stored procedures

Triggers

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

links between datasets can be stored

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

Role based access control and fine grained access rights

Users with fine-grained authorization concept, user roles and pluggable authentication

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
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Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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