DBMS > Manticore Search vs. Milvus vs. Riak TS vs. SurrealDB vs. SWC-DB

System Properties Comparison Manticore Search vs. Milvus vs. Riak TS vs. SurrealDB vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Multi-storage database for search, including full-text search.

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

A fully ACID transactional, developer-friendly, multi-model DBMS

A high performance, scalable Wide Column DBMS

Primary database model

Search engine

Vector DBMS

Time Series DBMS

Document store
Graph DBMS

Wide column store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Time Series DBMS

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

Score	0.23
Rank	#301	Overall
	#21	Search engines

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Score	1.11
Rank	#174	Overall
	#30	Document stores
	#17	Graph DBMS

Score	0.01
Rank	#378	Overall
	#13	Wide column stores

Website

manticoresearch.com

milvus.io

surrealdb.com

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

Technical documentation

manual.manticoresearch.com

milvus.io/docs/overview.md

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

surrealdb.com/docs

Developer

Manticore Software

Open Source, formerly Basho Technologies

SurrealDB Ltd

Alex Kashirin

Initial release

2017

2019

2015

2022

2020

Current release

6.0, February 2023

2.4.4, May 2024

3.0.0, September 2022

v1.5.0, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

GPL version 2

Open Source

Apache Version 2.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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++

C++, Go

Erlang

Rust

C++

Server operating systems

FreeBSD
Linux
macOS
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X

Linux
macOS
Windows

Linux

Data scheme

Fixed schema

schema-free

Typing

predefined data types such as float or date

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

Vector, Numeric and String

yes

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

restricted

SQL

Support of SQL

SQL-like query language

yes, limited

SQL-like query language

APIs and other access methods

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

RESTful HTTP API

HTTP API
Native Erlang Interface

GraphQL
RESTful HTTP API
WebSocket

Proprietary protocol
Thrift

Supported programming languages

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

Deno
Go
JavaScript (Node.js)
Rust

C++

Server-side scripts

Stored procedures

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication based on Galera library

selectable replication factor

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

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

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

yes

User concepts

Access control

Role based access control and fine grained access rights

yes, based on authentication and database rules

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus 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

DB-Engines blog posts

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