DBMS > 4D vs. Apache Druid vs. GreptimeDB vs. Kdb vs. TerminusDB

System Properties Comparison 4D vs. Apache Druid vs. GreptimeDB vs. Kdb vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

An open source Time Series DBMS built for increased scalability, high performance and efficiency

High performance Time Series DBMS

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Time Series DBMS

Time Series DBMS
Vector DBMS

Graph DBMS

Secondary database models

Relational DBMS

Document store
RDF store

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

Technical documentation

developer.4d.com

druid.apache.org/docs/latest/design

docs.greptime.com

code.kx.com

terminusdb.github.io/terminusdb/#

Developer

4D, Inc

Apache Software Foundation and contributors

Greptime Inc.

Kx Systems, a division of First Derivatives plc

DataChemist Ltd.

Initial release

1984

2012

2022

2000

kdb was released 2000, kdb+ in 2003

2018

Current release

v20, April 2023

29.0.1, April 2024

3.6, May 2018

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

Apache Version 2.0

commercial

free 32-bit version

Open Source

GPL V3

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.

Implementation language

Java

Rust

Prolog, Rust

Server operating systems

OS X
Windows

Linux
OS X
Unix

Android
Docker
FreeBSD
Linux
macOS
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-less columns are supported

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

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

Secondary indexes

yes

table attribute 'grouped'

SQL

Support of SQL

yes

close to SQL 92

SQL for querying

yes

SQL-like query language (q)

SQL-like query language (WOQL)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
RESTful HTTP/JSON API

gRPC
HTTP API
JDBC

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

4D proprietary IDE
PHP

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

C++
Erlang
Go
Java
JavaScript

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

JavaScript
Python

Server-side scripts

Stored procedures

yes

Python

user defined functions

yes

Triggers

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

manual/auto, time-based

Sharding

horizontal partitioning

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes, via HDFS, S3 or other storage engines

Source-replica replication

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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

in-memory journaling

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Simple rights management via user accounts

rights management via user accounts

Role-based access control

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

upon request
» 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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