DBMS > Blueflood vs. CouchDB vs. Milvus vs. Sadas Engine vs. ToroDB

System Properties Comparison Blueflood vs. CouchDB vs. Milvus vs. Sadas Engine vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Scalable TimeSeries DBMS based on Cassandra

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	2.78
Rank	#103	Overall
	#3	Vector DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

github.com/torodb/server

Technical documentation

github.com/rax-maas/blueflood/wiki

docs.couchdb.org/en/stable

milvus.io/docs/overview.md

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Rackspace

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

SADAS s.r.l.

8Kdata

Initial release

2013

2005

2019

2006

2016

Current release

3.3.3, December 2023

2.3.4, January 2024

8.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

Open Source

Apache Version 2.0

commercial

free trial version available

Open Source

AGPL-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

Java

Erlang

C++, Go

C++

Java

Server operating systems

Linux
OS X

Android
BSD
Linux
OS X
Solaris
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

AIX
Linux
Windows

All OS with a Java 7 VM

Data scheme

predefined scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes

string, integer, double, boolean, date, object_id

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.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP REST

RESTful HTTP/JSON API

RESTful HTTP API

JDBC
ODBC
Proprietary protocol

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

improved architecture with release 2.0

Sharding

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single document possible

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users can be defined per database

Role based access control and fine grained access rights

Access rights for users, groups and roles according to SQL-standard

Access rights for 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...
» 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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