DBMS > Apache Impala vs. ClickHouse vs. Informix vs. MySQL vs. NCache

System Properties Comparison Apache Impala vs. ClickHouse vs. Informix vs. MySQL vs. NCache

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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 secure embeddable database from IBM, positioned besides IBM Db2 as a relatively low-cost product optimized for OLTP and Internet of Things data

Widely used open source RDBMS

Open-Source and Enterprise in-memory Key-Value Store

Primary database model

Relational DBMS

Since Version 12.10 support for JSON/BSON datatypes compatible with MongoDB

Relational DBMS

Key/Value like access via memcached API

Key-value store

Secondary database models

Document store

Time Series DBMS

Document store
Spatial DBMS
Time Series DBMS

with Informix TimeSeries Extension

Document store
Spatial DBMS

Document store
Search engine

Using distributed Lucene

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	17.12
Rank	#34	Overall
	#21	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Website

impala.apache.org

clickhouse.com

www.ibm.com/products/informix

www.mysql.com

www.alachisoft.com/ncache

Technical documentation

impala.apache.org/impala-docs.html

clickhouse.com/docs

informix.hcldoc.com
www.ibm.com/support/knowledgecenter/SSGU8G/welcomeIfxServers.html

dev.mysql.com/doc

www.alachisoft.com/resources/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Clickhouse Inc.

IBM, HCL Technologies

Effective May 1st, 2017, HCL took on development, technical support, and product management teams, and works jointly with IBM on product strategy, marketing, and sales.

Oracle

since 2010, originally MySQL AB, then Sun

Alachisoft

Initial release

2013

2016

1984

1995

2005

Current release

4.1.0, June 2022

v24.4.2.141-stable, June 2024

14.10.FC5, November 2020

8.4.0, April 2024

5.3.3, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

free developer edition available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Enterprise Edition 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.

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.
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 MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C++

C, C++ and Java

C and C++

C#, .NET, .NET Core, Java

Server operating systems

Linux

FreeBSD
Linux
macOS

AIX
HP-UX
Linux
macOS
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Since Version 12.10 support for JSON/BSON datatypes

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

yes

with proprietary extensions

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

APIs and other access methods

JDBC
ODBC

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

JDBC
JSON API

MongoDB compatible
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

support for stored procedures with SQL-Server CLR

Triggers

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

Multi-source replication
Source-replica replication

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

optimistic locking and pessimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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.

Users with fine-grained authentication, authorization, and auditing controls

Users with fine-grained authorization concept

no user groups or roles

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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