DBMS > 4D vs. BigchainDB vs. Heroic vs. Milvus vs. XTDB

System Properties Comparison 4D vs. BigchainDB vs. Heroic vs. Milvus vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	0.79
Rank	#212	Overall
	#36	Document stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.4d.com

www.bigchaindb.com

github.com/spotify/heroic

milvus.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

developer.4d.com

bigchaindb.readthedocs.io/en/latest

spotify.github.io/heroic

milvus.io/docs/overview.md

www.xtdb.com/docs

Developer

4D, Inc

Spotify

Juxt Ltd.

Initial release

1984

2016

2014

2019

Current release

v20, April 2023

2.3.4, January 2024

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

AGPL v3

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

MIT License

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

Python

Java

C++, Go

Clojure

Server operating systems

OS X
Windows

Linux

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes, extensible-data-notation format

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

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

close to SQL 92

limited SQL, making use of Apache Calcite

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

CLI Client
RESTful HTTP API

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

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

4D proprietary IDE
PHP

Go
Haskell
Java
JavaScript
Python
Ruby

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

Clojure
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

yes

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Users and groups

yes

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

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

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More resources

DB-Engines blog posts

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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Vector databases
2 June 2023, Matthias Gelbmann

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Recent citations in the news

An Introduction to BigchainDB, a Popular Blockchain Database
17 September 2020, Open Source For You

Exploring the 10 BEST Python Libraries for Blockchain Applications
9 September 2023, DataDrivenInvestor

Blockchain Database Startup BigchainDB Raises €3 Million
27 September 2016, CoinDesk

Using BigchainDB: A Database with Blockchain Characteristics
20 January 2022, Open Source For You

What is BigchainDB Technology & How it works and the Characteristics?
26 August 2017, Blockchain Council

provided by Google News

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

What Is Milvus Vector Database?
6 October 2023, The New Stack

Zilliz Unveils Game-Changing Features for Vector Search
22 March 2024, Datanami

AI-Powered Search Engine With Milvus Vector Database on Vultr
31 January 2024, SitePoint

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

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

provided by Google News

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