DBMS > Cassandra vs. Milvus vs. Quasardb vs. Valentina Server

System Properties Comparison Cassandra vs. Milvus vs. Quasardb vs. Valentina Server

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

Distributed, high-performance timeseries database

Object-relational database and reports server

Primary database model

Secondary database models

Vector DBMS

starting with release V5

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

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

Technical documentation

cassandra.apache.org/doc/latest

milvus.io/docs/overview.md

doc.quasar.ai/master

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

quasardb

Paradigma Software

Initial release

2008

2019

2009

1999

Current release

4.1.3, July 2023

2.3.4, January 2024

3.14.1, January 2024

5.7.5

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

Java

C++, Go

C++

Server operating systems

BSD
Linux
OS X
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes

integer and binary

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.

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

with tags

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like query language

yes

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

RESTful HTTP API

HTTP API

ODBC

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Access rights for users can be defined per object

Role based access control and fine grained access rights

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

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

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
» more

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

Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
» more

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

Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
» more

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

Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

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

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
» more

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.