DBMS > Alibaba Cloud AnalyticDB for MySQL vs. Apache Impala vs. ClickHouse vs. etcd

System Properties Comparison Alibaba Cloud AnalyticDB for MySQL vs. Apache Impala vs. ClickHouse vs. etcd

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Editorial information provided by DB-Engines

Name

Description

A real-time data warehousing service that can process petabytes of data with high concurrency and low latency. It is fully compatible with the MySQL protocol.

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 distributed reliable key-value store

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store

Time Series DBMS

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

Score	0.85
Rank	#207	Overall
	#96	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Website

www.alibabacloud.com/product/analyticdb-for-mysql

impala.apache.org

clickhouse.com

etcd.io
github.com/etcd-io/etcd

Technical documentation

www.alibabacloud.com/help/doc-detail/93776.htm

impala.apache.org/impala-docs.html

clickhouse.com/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

Developer

Alibaba

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Clickhouse Inc.

Initial release

2013

2016

Current release

4.1.0, June 2022

v24.4.1.2088-stable, May 2024

3.4, August 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

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.
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.

Implementation language

C++

Server operating systems

hosted

Linux

FreeBSD
Linux
macOS

FreeBSD
Linux
Windows

experimental

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

yes

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

JDBC
ODBC

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

gRPC
JSON over HTTP

Supported programming languages

C#
Java
PHP
Python

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++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

yes, watching key changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

key based and custom

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

selectable replication factor

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

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

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

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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.

More information provided by the system vendor

Specific characteristics

A real-time data warehousing service that can process PB data with high concurrency...
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Competitive advantages

TPC Benchmark: The world leading result in TPC-DS benchmark . TPC-H benchmark for...
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Licensing and pricing models

Available regions: America US Virginia US Silicon Valley Asia China Hong Kong India...
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