DBMS > 4D vs. Databricks vs. Milvus vs. PostGIS vs. RRDtool

System Properties Comparison 4D vs. Databricks vs. Milvus vs. PostGIS vs. RRDtool

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

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

Spatial extension of PostgreSQL

Industry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database.

The data is stored in a circular buffer, thus the system storage footprint remains constant over time.

Primary database model

Relational DBMS

Document store
Relational DBMS

Vector DBMS

Spatial DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	2.78
Rank	#103	Overall
	#3	Vector DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	1.90
Rank	#132	Overall
	#11	Time Series DBMS

Website

oss.oetiker.ch/rrdtool

Technical documentation

developer.4d.com

docs.databricks.com

milvus.io/docs/overview.md

postgis.net/documentation

oss.oetiker.ch/rrdtool/doc

Developer

4D, Inc

Databricks

Tobias Oetiker

Initial release

1984

2013

2019

2005

1999

Current release

v20, April 2023

2.4.4, May 2024

3.4.2, February 2024

1.8.0, 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL v2.0

Open Source

GPL V2 and FLOSS

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

C++, Go

Implementations in Java (e.g. RRD4J) and C# available

Server operating systems

OS X
Windows

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

HP-UX
Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

yes

Numeric data only

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

Exporting into and restoring from XML files possible

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

with Databricks SQL

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

in-process shared library
Pipes

Supported programming languages

4D proprietary IDE
PHP

Python
R
Scala

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

with librrd library
C#

with a different implementation of RRDTool
Java

with a different implementation of RRDTool
JavaScript (Node.js)

with a different implementation of RRDTool
Lua
Perl
PHP

with a wrapper library
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

based on PostgreSQL

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

based on PostgreSQL

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Immediate Consistency

none

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

by using the rrdcached daemon

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

Users and groups

Role based access control and fine grained access rights

yes

based on PostgreSQL

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

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

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Vector databases
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Spatial database management systems
6 April 2021, Matthias Gelbmann

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Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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