DBMS > Ehcache vs. GeoMesa vs. Milvus vs. VelocityDB vs. Vitess

System Properties Comparison Ehcache vs. GeoMesa vs. Milvus vs. VelocityDB vs. Vitess

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Key-value store

Spatial DBMS

Vector DBMS

Graph DBMS
Object oriented DBMS

Relational DBMS

Secondary database models

Document store
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	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

Technical documentation

www.ehcache.org/documentation

www.geomesa.org/documentation/stable/user/index.html

milvus.io/docs/overview.md

velocitydb.com/UserGuide

vitess.io/docs

Developer

Terracotta Inc, owned by Software AG

CCRi and others

VelocityDB Inc

The Linux Foundation, PlanetScale

Initial release

2009

2014

2019

2011

2013

Current release

3.10.0, March 2022

4.0.5, February 2024

2.3.4, January 2024

7.x

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache License 2.0

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0, commercial licenses available

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

Scala

C++, Go

Server operating systems

All OS with a Java VM

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Any that supports .NET

Docker
Linux
macOS

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

APIs and other access methods

JCache

RESTful HTTP API

.Net

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Java

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

.Net

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Cache Event Listeners

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

depending on storage layer

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

depending on storage layer

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

depending on storage layer

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

supports JTA and can work as an XA resource

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

In-memory capabilities

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

yes

depending on storage layer

yes

User concepts

Access control

yes

depending on the DBMS used for storage

Role based access control and fine grained access rights

Based on Windows Authentication

Users with fine-grained authorization concept

no user groups or 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

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.

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More resources

DB-Engines blog posts

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2 June 2023, Matthias Gelbmann

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