Use this quick overview and simple step-by-step procedures to get started with ClickHouse!
TL;DR: For developers new to ClickHouse: learn what it is, why you should care, and how to use it, including instructions for getting started. This is the first post in our developer quickstart series.
Are you a developer looking for a database management system optimized for high-performance analytical queries?
ClickHouse can be your first choice! This open source SQL database management feature is powerful, enabling lightning-fast queries on large data sets, delivering answers in milliseconds, not minutes.
In this article, we will share ClickHouse’s key features and common use cases to provide you with a stepping stone into its ecosystem.
What is ClickHouse?
ClickHouse is an open source, column-oriented SQL database management system optimized for high-performance analytical queries. It is designed to handle large amounts of data and can perform complex queries quickly and efficiently. It does this by storing data in columns instead of rows.
Why choose ClickHouse?
ClickHouse is more than just an ordinary database management system. This powerful platform offers some impressive features:
Speed and performance: In ClickHouse, data is stored in columns, and values from the same columns are grouped together. This approach can process queries up to 100 times faster than a row-oriented database management system (DBMS).
SQL support: ClickHouse supports a declarative query language based on SQL. This provides a powerful and user-friendly SQL interface that makes interaction and querying simple and efficient.
Integration flexibility: ClickHouse integrates seamlessly with a variety of data pipelines, ETL frameworks, and visualization tools. This flexibility ensures compatibility with a wide range of data processing and analysis workflows.
Cost-effective: ClickHouse’s open source nature and efficient resource utilization make it affordable. This is an especially viable option for small to mid-sized organizations or teams looking for a cost-effective and powerful solution for their analytics needs.
ClickHouse use case
ClickHouse is suitable for a range of applications across different domains. Its performance-oriented architecture and flexible design make it a top choice for many use cases:
Real-time analysis: ClickHouse excels in real-time analysis by ingesting large amounts of data at high speeds. This feature enables developers to create responsive dashboards and integrate with visualization tools such as Grafana, Tableau, and Superset.
Store logs, events, and traces: ClickHouse is ideal for storing logs, events, and traces from applications, servers, and network devices. It features efficient compression algorithms and optimized storage layout to minimize costs while ensuring fast data ingestion and retrieval.
Machine Learning & Artificial Intelligence: ClickHouse is a reliable platform for storing and preprocessing large datasets used in machine learning and AI projects. Engineers can leverage ClickHouse to prepare data before feeding it into machine learning models.
Business Intelligence: ClickHouse supports more than 70 file formats, including Parquet and JSON, making it suitable for business intelligence (BI). Integration with popular visualization tools such as Tableau and Power BI enhances its capabilities, making it an excellent choice for BI applications.
Getting Started with ClickHouse
Follow these steps to start using ClickHouse.
1. To download ClickHouse locally, run the following curl command.
It determines if your operating system is supported and then downloads the appropriate ClickHouse binary. To learn more about other installation options, visit the official installation guide.
curl <https://clickhouse.com/> | sh
2. To start the server, navigate to the directory where the clickhouse binaries are saved and run the following command.
./clickhouse server
The first time you run this command, it will also create the necessary files and folders in the current directory.
3. To connect to your ClickHouse service, open a new terminal, navigate to the directory where the clickhouse binaries are saved, and paste the following command clickhouse-client.
./clickhouse client
4. Like most databases, ClickHouse logically groups tables into databases. To create a new database in ClickHouse, use the CREATE DATABASE command and paste the following command to create your first database.
CREATE DATABASE IF NOT EXISTS helloworld
5. To create a new table, use the CREATE TABLE command. The following table is named my_first_table in the helloworld database.
CREATE TABLE helloworld.my_first_table
(
user_id UInt32,
message String,
timestamp DateTime,
metric Float32
)
ENGINE = MergeTree()
PRIMARY KEY (user_id, timestamp)
6. To insert data into the created table, use the INSERT INTO TABLE command.
INSERT INTO helloworld.my_first_table (user_id, message, timestamp, metric) VALUES
(101, 'Hello, ClickHouse!', now(), -1.0 ),
(102, 'Insert a lot of rows per batch', yesterday(), 1.41421 ),
(102, 'Sort your data based on your commonly-used queries', today(), 2.718 )，(101, 'Granules are the smallest chunks of data read', now() + 5, 3.14159 )
7. ClickHouse is a SQL database, and you can query your data by writing the same type of SELECT queries that you are already familiar with. To access the contents of the table created above, paste the following command.
SELECT * FROM helloworld.my_first_table
Conclusion
That's all you need to get started with ClickHouse. Try this powerful and user-friendly DBMS today. As you continue to explore, you'll discover more ways that ClickHouse can be a valuable tool for your data analysis needs.
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