DBMS > Apache Cassandra vs. Apache Druid vs. Milvus vs. Prometheus

System Properties Comparison Apache Cassandra vs. Apache Druid vs. Milvus vs. Prometheus

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Open-source Time Series DBMS and monitoring system

Primary database model

Wide column store

Relational DBMS
Time Series DBMS

Vector DBMS

Time Series DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	98.94
Rank	#11	Overall
	#1	Wide column stores

Score	2.85
Rank	#96	Overall
	#50	Relational DBMS
	#6	Time Series DBMS

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	7.56
Rank	#49	Overall
	#3	Time Series DBMS

Website

Technical documentation

cassandra.apache.org/doc/latest

druid.apache.org/docs/latest/design

milvus.io/docs/overview.md

prometheus.io/docs

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Apache Software Foundation and contributors

Initial release

2008

2012

2019

2015

Current release

5.0-rc1, July 2024

30.0.0, June 2024

2.4.4, May 2024

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache license v2

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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

Server operating systems

BSD
Linux
OS X
Windows

Linux
OS X
Unix

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
Windows

Data scheme

schema-free

yes

schema-less columns are supported

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

Numeric data only

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.

Import of XML data possible

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

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

SQL for querying

APIs and other access methods

Proprietary protocol

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

JDBC
RESTful HTTP/JSON API

RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

manual/auto, time-based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

yes, via HDFS, S3 or other storage engines

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

none

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

Access rights for users can be defined per object

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Role based access control and fine grained access rights

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

Apache 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

News

How to Create Vector Embeddings in Node.js
25 September 2024

Glean and DataStax Partner to Help Developers Harness Powerful Enterprise Search
23 September 2024

DataStax Named a Leader in The Forrester Wave™: Vector Databases, Q3 2024
23 September 2024

How to Build a Notion Clone with Astra DB and Mongoose
20 September 2024

How to Monitor DataStax-Powered RAG Applications with Fiddler
19 September 2024

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.