DBMS > ClickHouse vs. Hive vs. Linter vs. Lovefield vs. Microsoft SQL Server

System Properties Comparison ClickHouse vs. Hive vs. Linter vs. Lovefield vs. Microsoft SQL Server

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.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

RDBMS for high security requirements

Embeddable relational database for web apps written in pure JavaScript

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

clickhouse.com

hive.apache.org

linter.ru

google.github.io/lovefield

www.microsoft.com/en-us/sql-server

Technical documentation

clickhouse.com/docs

cwiki.apache.org/confluence/display/Hive/Home

github.com/google/lovefield/blob/master/docs/spec_index.md

learn.microsoft.com/en-US/sql/sql-server

Developer

Clickhouse Inc.

Apache Software Foundation

initially developed by Facebook

relex.ru

Google

Microsoft

Initial release

2016

2012

1990

2014

1989

Current release

v24.4.1.2088-stable, May 2024

3.1.3, April 2022

2.1.12, February 2017

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2

commercial

Open Source

Apache 2.0

commercial

restricted free version is 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.

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.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

Java

C and C++

JavaScript

C++

Server operating systems

FreeBSD
Linux
macOS

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
Windows

Data scheme

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

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like DML and DDL statements

yes

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

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

JDBC
ODBC
Thrift

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

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++
Java
PHP
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

JavaScript

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax with the possibility to convert from PL/SQL

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

Source-replica replication

none

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

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, groups and roles

fine grained access rights according to SQL-standard

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