DBMS > Ehcache vs. Google BigQuery vs. Milvus vs. Quasardb vs. Valentina Server

System Properties Comparison Ehcache vs. Google BigQuery vs. Milvus vs. Quasardb vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

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

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

Score	5.23
Rank	#68	Overall
	#8	Key-value stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	1.81
Rank	#144	Overall
	#5	Vector DBMS

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Score	0.18
Rank	#329	Overall
	#144	Relational DBMS

Website

www.ehcache.org

cloud.google.com/bigquery

milvus.io

quasar.ai

www.valentina-db.net

Technical documentation

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

milvus.io/docs/overview.md

doc.quasar.ai/master

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

Developer

Terracotta Inc, owned by Software AG

Google

quasardb

Paradigma Software

Initial release

2009

2010

2019

2009

1999

Current release

3.10.0, March 2022

2.3.4, January 2024

3.14.1, January 2024

5.7.5

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

commercial

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

Linux
OS X
Windows

Data scheme

schema-free

yes

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

yes

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

JCache

RESTful HTTP/JSON API

RESTful HTTP API

HTTP API

ODBC

Supported programming languages

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

user defined functions

in JavaScript

yes

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

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

yes

supports JTA and can work as an XA resource

Since BigQuery is designed for querying data

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

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 privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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

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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