DBMS > ClickHouse vs. Linter vs. Realm vs. SAP SQL Anywhere vs. Warp 10

System Properties Comparison ClickHouse vs. Linter vs. Realm vs. SAP SQL Anywhere vs. Warp 10

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.

RDBMS for high security requirements

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Secondary database models

Time Series 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	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

clickhouse.com

linter.ru

realm.io

www.sap.com/products/technology-platform/sql-anywhere.html

www.warp10.io

Technical documentation

clickhouse.com/docs

realm.io/docs

help.sap.com/docs/SAP_SQL_Anywhere

www.warp10.io/content/02_Getting_started

Developer

Clickhouse Inc.

relex.ru

Realm, acquired by MongoDB in May 2019

SAP

formerly Sybase

SenX

Initial release

2016

1990

2014

1992

2015

Current release

v24.4.1.2088-stable, May 2024

17, July 2015

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

commercial

Open Source

Apache License 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.

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

C and C++

Java

Server operating systems

FreeBSD
Linux
macOS

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

Android
Backend: server-less
iOS
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

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)

yes

APIs and other access methods

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

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

ADO.NET
HTTP API
JDBC
ODBC

HTTP API
Jupyter
WebSocket

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#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

.Net
Java

with Android only
Objective-C
React Native
Swift

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

runs within the applications so server-side scripts are unnecessary

yes, in C/C++, Java, .Net or Perl

yes

WarpScript

Triggers

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

none

Sharding

based on HBase

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

none

Source-replica replication

Database mirroring

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

based on HBase

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 capabilities

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

yes

In-Memory realm

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.

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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