DBMS > Blueflood vs. H2 vs. HyperSQL vs. Milvus vs. YottaDB

System Properties Comparison Blueflood vs. H2 vs. HyperSQL vs. Milvus vs. YottaDB

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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

A fast and solid embedded Key-value store

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

using the Octo plugin

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.20
Rank	#317	Overall
	#47	Key-value stores

Website

Technical documentation

github.com/rax-maas/blueflood/wiki

www.h2database.com/html/main.html

hsqldb.org/web/hsqlDocsFrame.html

milvus.io/docs/overview.md

yottadb.com/resources/documentation

Developer

Rackspace

Thomas Mueller

YottaDB, LLC

Initial release

2013

2005

2001

2019

2001

Current release

2.2.220, July 2023

2.7.2, June 2023

2.3.4, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

based on BSD license

Open Source

Apache Version 2.0

Open Source

AGPL 3.0

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

Java

C++, Go

Server operating systems

Linux
OS X

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Docker
Linux

Data scheme

predefined scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

by using the Octo plugin

APIs and other access methods

HTTP REST

JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

RESTful HTTP API

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

Java

All languages supporting JDBC/ODBC
Java

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

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

With clustering: 2 database servers on different computers operate on identical copies of a database

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

Users and groups based on OS-security mechanisms

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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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