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ClickHouse is a columnar database designed for analytics and data warehousing. It is optimized for queries over massive datasets, making it a great choice for applications built with Spring Boot that need to handle reporting, dashboards, and business intelligence use cases.

Setting Up ClickHouse

First, install ClickHouse and ensure the server is running. In your Spring Boot project, add the ClickHouse JDBC driver dependency to connect with the database. 
<dependency>
  <groupId>ru.yandex.clickhouse</groupId>
  <artifactId>clickhouse-jdbc</artifactId>
  <version>0.3.2</version>
</dependency>
Then, configure the connection in application.properties: 
spring.datasource.url=jdbc:clickhouse://localhost:8123/default
spring.datasource.username=default
spring.datasource.password=

Creating Tables

ClickHouse tables are designed differently from relational databases. A common choice for a warehouse is the MergeTree engine, which supports indexing, partitioning, and efficient storage. 
CREATE TABLE sales_data (
  order_id UInt64,
  order_date Date,
  customer_id String,
  product String,
  amount Float64
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(order_date)
ORDER BY (order_date, customer_id);
This schema allows fast queries on time-based data and customer analytics.

Writing Data

Data can be inserted directly through Spring JDBC templates or batch jobs. Using bulk inserts increases performance when loading large datasets into the warehouse. 
jdbcTemplate.batchUpdate(
  "INSERT INTO sales_data (order_id, order_date, customer_id, product, amount) VALUES (?, ?, ?, ?, ?)",
  salesList
);

Querying Data

Spring Boot services can expose REST APIs to fetch aggregated results from ClickHouse. For example, retrieving monthly revenue: 
SELECT toYYYYMM(order_date) AS month, sum(amount) AS total
FROM sales_data
GROUP BY month
ORDER BY month;
Spring Data repositories or JdbcTemplate can be used to run these queries and return results to clients.

Benefits of ClickHouse as a Warehouse

  • High Performance: Columnar storage accelerates analytical queries.
  • Efficient Storage: Data compression reduces disk usage.
  • Scalability: Handles billions of rows with ease.
  • Integration: Works smoothly with Spring Boot APIs and dashboards.
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