DBMS > Hazelcast vs. Heroic vs. Milvus vs. TypeDB

System Properties Comparison Hazelcast vs. Heroic vs. Milvus vs. TypeDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

TypeDB provides developers with an expressive, customizable type system to manage their data using an award-winning query language, TypeQL, while building on a high-performance, distributed architecture.

Primary database model

The type-theoretic data model of TypeDB subsumes the graph database model.
Object oriented DBMS

The data model of TypeDB comprises object-oriented features such as class inheritance and interfaces.
Relational DBMS

The type-theoretic data model of TypeDB subsumes the relational database model.

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	0.13
Rank	#335	Overall
	#29	Time Series DBMS

Score	3.01
Rank	#89	Overall
	#4	Vector DBMS

Score	0.65
Rank	#230	Overall
	#20	Graph DBMS
	#9	Object oriented DBMS
	#107	Relational DBMS

Website

hazelcast.com

github.com/spotify/heroic

milvus.io

typedb.com

Technical documentation

hazelcast.org/imdg/docs

spotify.github.io/heroic

milvus.io/docs/overview.md

typedb.com/docs

Developer

Hazelcast

Spotify

Vaticle

Initial release

2008

2014

2019

2016

Current release

5.3.6, November 2023

2.4.4, May 2024

2.28.3, June 2024

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

GPL Version 3, 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

C++, Go

Java

Server operating systems

All OS with a Java VM

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X
Windows

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.

yes

the object must implement a serialization strategy

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

RESTful HTTP API

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (IDE)

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

All JVM based languages
C
C++
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

yes

Synchronous replication via raft

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency
Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

substituted by the relationship feature

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

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

Role-based access control

Role based access control and fine grained access rights

yes

at REST API level; other APIs in progress

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

TypeDB is a polymorphic database with a conceptual data model, a strong subtyping...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

TypeDB provides a new level of expressivity, extensibility, interoperability, and...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Life sciences : TypeDB makes working with biological data much easier and accelerates...
» 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

Apache f or language drivers, and AGPL and Commercial for the database server. The...
» 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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

show all

Recent citations in the news

Event Stream Processing Market Exploring Future Growth 2023-2032 and Key Players - Cloudera, Inc., Hazelcast,
24 September 2024, EIN News

Hazelcast 5.4 real time data processing platform boosts AI and consistency
17 April 2024, VentureBeat

Hazelcast Expands Global Partner Program to Support Mission-Critical, AI Application Projects
20 August 2024, PR Newswire

Hazelcast Weaves Wider Logic Threads Through The Data Fabric
7 March 2024, Forbes

Hazelcast Showcases Real-Time Data Platform at 2024 Gartner Summit
15 May 2024, Datanami

provided by Google News

AI-Powered Search Engine With Milvus Vector Database on Vultr - SitePoint
30 August 2024, SitePoint

How NVIDIA GPU Acceleration Supercharged Milvus Vector Database
26 March 2024, The New Stack

IBM watsonx.data’s integrated vector database: unify, prepare, and deliver your data for AI
9 April 2024, ibm.com

10 top vector database options for similarity searches
8 August 2024, TechTarget

Milvus 2.4 Unveils Game-Changing Features for Enhanced Vector Search
20 March 2024, GlobeNewswire

provided by Google News

Modelling Biomedical Data for a Drug Discovery Knowledge Graph
6 October 2020, Towards Data Science

Speedb Goes Open Source With Its Speedb Data Engine, A Drop-in Replacement for RocksDB
9 November 2022, Business Wire

Bayer’s Approach to Modelling and Loading Data at Scale | by Daniel Crowe
16 August 2021, Towards Data Science

Building a Biomedical Knowledge Graph
28 June 2021, Towards Data Science

Comparing Grakn to Semantic Web Technologies — Part 2/3
26 June 2020, Towards Data Science

provided by Google News

Share this page