DBMS > 4D vs. LevelDB vs. Quasardb vs. TimescaleDB vs. Trino

System Properties Comparison 4D vs. LevelDB vs. Quasardb vs. TimescaleDB vs. Trino

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Distributed, high-performance timeseries database

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Secondary database models

Relational DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	2.37
Rank	#110	Overall
	#54	Relational DBMS

Score	2.33
Rank	#111	Overall
	#19	Key-value stores

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	4.06
Rank	#73	Overall
	#5	Time Series DBMS

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

www.4d.com

github.com/google/leveldb

quasar.ai

www.timescale.com

trino.io

Technical documentation

developer.4d.com

github.com/google/leveldb/blob/main/doc/index.md

doc.quasar.ai/master

docs.timescale.com

trino.io/broadcast
trino.io/docs/current

Developer

4D, Inc

Google

quasardb

Timescale

Trino Software Foundation

Initial release

1984

2011

2009

2017

2012

2020 rebranded from PrestoSQL

Current release

v20, April 2023

1.23, February 2021

3.14.1, January 2024

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

BSD

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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

C++

Java

Server operating systems

OS X
Windows

Illumos
Linux
OS X
Windows

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Linux
macOS

for devlopment

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

with tags

yes

depending on connected data-source

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

4D proprietary IDE
PHP

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

yes, depending on connected data-source

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

yes, across time and space (hash partitioning) attributes

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

Source-replica replication with selectable replication factor

Source-replica replication with hot standby and reads on replicas

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on connected data-source

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

by using LevelDB

yes

depending on connected data-source

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Users and groups

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

SQL standard access control

More information provided by the system vendor

News

23 is a go, keeping pace with Java
17 September 2024

65: Performance boosts
12 September 2024

64: Control with Open Policy Agent OPA
22 August 2024

63: Querying with JS
1 August 2024

62: A lakehouse that simply works at Prezi
11 July 2024

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