DBMS > Ehcache vs. Microsoft Azure Cosmos DB vs. Milvus vs. Quasardb

System Properties Comparison Ehcache vs. Microsoft Azure Cosmos DB vs. Milvus vs. Quasardb

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Globally distributed, horizontally scalable, multi-model database service

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

Distributed, high-performance timeseries database

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

www.ehcache.org

azure.microsoft.com/services/cosmos-db

milvus.io

quasar.ai

Technical documentation

www.ehcache.org/documentation

learn.microsoft.com/azure/cosmos-db

milvus.io/docs/overview.md

doc.quasar.ai/master

Developer

Terracotta Inc, owned by Software AG

Microsoft

quasardb

Initial release

2009

2014

2019

2009

Current release

3.10.0, March 2022

2.3.4, January 2024

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Apache Version 2.0

commercial

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

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

Java

C++, Go

C++

Server operating systems

All OS with a Java VM

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

BSD
Linux
OS X
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

Vector, Numeric and String

yes

integer and binary

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

All properties auto-indexed by default

yes

with tags

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JCache

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

RESTful HTTP API

HTTP API

Supported programming languages

Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

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

Server-side scripts

Stored procedures

JavaScript

Triggers

yes

Cache Event Listeners

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

supports JTA and can work as an XA resource

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access rights can be defined down to the item level

Role based access control and fine grained access rights

Cryptographically strong user authentication and audit trail

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

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.