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

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

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

Primary database model

Secondary database models

Spatial DBMS

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

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

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

Website

blueflood.io

github.com/dragonflydb/dragonfly
www.dragonflydb.io

www.h2database.com

hsqldb.org

milvus.io

Technical documentation

github.com/rax-maas/blueflood/wiki

www.dragonflydb.io/docs

www.h2database.com/html/main.html

hsqldb.org/web/hsqlDocsFrame.html

milvus.io/docs/overview.md

Developer

Rackspace

DragonflyDB team and community contributors

Thomas Mueller

Initial release

2013

2023

2005

2001

2019

Current release

1.0, March 2023

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

BSL 1.1

Open Source

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

Open Source

based on BSD license

Open Source

Apache Version 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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

Java

C++

Java

C++, Go

Server operating systems

Linux
OS X

Linux

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

Data scheme

predefined scheme

scheme-free

yes

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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

APIs and other access methods

HTTP REST

Proprietary protocol

RESP - REdis Serialization Protocol

JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

RESTful HTTP API

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

Java

All languages supporting JDBC/ODBC
Java

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

Server-side scripts

Stored procedures

Lua

Java Stored Procedures and User-Defined Functions

Java, SQL

Triggers

publish/subscribe channels provide some trigger functionality

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

Source-replica replication

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

none

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

Eventual Consistency

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

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

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

Password-based authentication

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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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