DBMS > ClickHouse vs. ToroDB vs. Trino vs. Valentina Server

System Properties Comparison ClickHouse vs. ToroDB vs. Trino vs. Valentina Server

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Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

A MongoDB-compatible JSON document store, built on top of PostgreSQL

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

Object-relational database and reports server

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Time Series 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	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Score	0.18
Rank	#313	Overall
	#140	Relational DBMS

Website

clickhouse.com

github.com/torodb/server

trino.io

www.valentina-db.net

Technical documentation

clickhouse.com/docs

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

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Clickhouse Inc.

8Kdata

Trino Software Foundation

Paradigma Software

Initial release

2016

2012

2020 rebranded from PrestoSQL

1999

Current release

v24.6.2.17-stable, July 2024

5.7.5

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL-V3

Open Source

Apache Version 2.0

commercial

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.

DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.

Implementation language

C++

Java

Server operating systems

FreeBSD
Linux
macOS

All OS with a Java 7 VM

Linux
macOS

for devlopment

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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.

Secondary indexes

yes

depending on connected data-source

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

APIs and other access methods

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

JDBC
RESTful HTTP API
Trino CLI

ODBC

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

Go
Java
JavaScript (Node.js)
Python
R
Ruby

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

yes, depending on connected data-source

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Source-replica replication

depending on connected data-source

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

depending on connected data-source

Foreign keys

Referential integrity

yes

Transaction concepts

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

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

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

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users and roles

SQL standard access control

fine grained access rights according to SQL-standard

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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