DBMS > Alibaba Cloud Log Service vs. Apache IoTDB vs. ClickHouse vs. KairosDB

System Properties Comparison Alibaba Cloud Log Service vs. Apache IoTDB vs. ClickHouse vs. KairosDB

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Editorial information provided by DB-Engines

Name

Description

A complete real-time data logging service that supports collection, consumption, shipping, search, and analysis of logs.

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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.

Distributed Time Series DBMS based on Cassandra or H2

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.47
Rank	#263	Overall
	#18	Search engines

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Website

www.alibabacloud.com/product/log-service

iotdb.apache.org

clickhouse.com

github.com/kairosdb/kairosdb

Technical documentation

www.alibabacloud.com/help/en/sls

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

clickhouse.com/docs

kairosdb.github.io

Developer

AlibabaCloud Inc.

Apache Software Foundation

Clickhouse Inc.

Initial release

2016

2018

2016

2013

Current release

1.1.0, April 2023

v24.4.1.2088-stable, May 2024

1.2.2, November 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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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.
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Implementation language

Java

C++

Java

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL for querying

SQL-like query language

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

HTTP REST

JDBC
Native API

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

Graphite protocol
HTTP REST
Telnet API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

key based and custom

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

3 replicas

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

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

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

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

Alibaba Cloud user access control

yes

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.

simple password-based access control

More information provided by the system vendor

Specific characteristics

A real-time service for telementry data (log, metric, trace). Alibaba Cloud Log Service...
» more

Competitive advantages

* Ease of use: Completes data collections from more than 30 data sources within five...
» more

Typical application scenarios

* Log/Trace/Event Search & Analysis * Application Monitoring * Operation Analysis...
» more

Key customers

* Alibaba & Ant-Finance：he infrastructure service for the Corp * Over 22k Subscriber...
» more

Licensing and pricing models

Pay-As-You-Go https://www.alibabacloud.com/product/log-service/pricing
» more

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