DBMS > 4D vs. Apache Druid vs. TerminusDB vs. Vertica

System Properties Comparison 4D vs. Apache Druid vs. TerminusDB vs. Vertica

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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

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

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Relational DBMS
Time Series DBMS

Graph DBMS

Relational DBMS

Column oriented

Secondary database models

Document store
RDF store

Spatial DBMS
Time Series DBMS

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

Score	2.68
Rank	#110	Overall
	#54	Relational DBMS

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	0.22
Rank	#323	Overall
	#28	Graph DBMS

Score	11.40
Rank	#43	Overall
	#27	Relational DBMS

Website

Technical documentation

developer.4d.com

druid.apache.org/docs/latest/design

terminusdb.github.io/terminusdb/#

vertica.com/documentation

Developer

4D, Inc

Apache Software Foundation and contributors

DataChemist Ltd.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

1984

2012

2018

2005

Current release

v20, April 2023

29.0.1, April 2024

11.0.0, January 2023

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache license v2

Open Source

GPL V3

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Prolog, Rust

C++

Server operating systems

OS X
Windows

Linux
OS X
Unix

Linux

Data scheme

yes

schema-less columns are supported

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

close to SQL 92

SQL for querying

SQL-like query language (WOQL)

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
RESTful HTTP/JSON API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

4D proprietary IDE
PHP

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

JavaScript
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

manual/auto, time-based

Graph Partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes, via HDFS, S3 or other storage engines

Journaling Streams

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

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

yes

In-memory capabilities

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

User concepts

Access control

Users and groups

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

Role-based access control

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenarios

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Licensing and pricing models

Cost-based models and subscription-based models are both available. One license is...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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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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