DBMS > ClickHouse vs. mSQL vs. Postgres-XL vs. Quasardb vs. Sphinx

System Properties Comparison ClickHouse vs. mSQL vs. Postgres-XL vs. Quasardb vs. Sphinx

Editorial information provided by DB-Engines

Name

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.

mSQL (Mini SQL) is a simple and lightweight RDBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Distributed, high-performance timeseries database

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Time Series DBMS

Search engine

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	16.20
Rank	#37	Overall
	#23	Relational DBMS

Score	1.67
Rank	#151	Overall
	#70	Relational DBMS

Score	0.52
Rank	#256	Overall
	#117	Relational DBMS

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

clickhouse.com

hughestech.com.au/products/msql

www.postgres-xl.org

quasar.ai

sphinxsearch.com

Technical documentation

clickhouse.com/docs

www.postgres-xl.org/documentation

doc.quasar.ai/master

sphinxsearch.com/docs

Developer

Clickhouse Inc.

Hughes Technologies

quasardb

Sphinx Technologies Inc.

Initial release

2016

1994

2014

since 2012, originally named StormDB

2009

2001

Current release

v23.12.1.1368-stable, December 2023

4.4, October 2021

10 R1, October 2018

3.14.1, January 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free licenses can be provided

Open Source

Mozilla public license

commercial

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

Open Source

GPL version 2, commercial licence available

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.

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Implementation language

C++

Server operating systems

FreeBSD
Linux
macOS

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
macOS

BSD
Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

XML type, but no XML query functionality

Secondary indexes

yes

with tags

yes

full-text index on all search fields

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

distributed, parallel query execution

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

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

JDBC
ODBC

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

HTTP API

Proprietary protocol

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

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

none

horizontal partitioning

Sharding

consistent hashing

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Source-replica replication with selectable replication factor

none

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

The original contents of fields are not stored in the Sphinx index.

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

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.

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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