ClickHouse Data Partitioning Strategies in Spring Boot Applications

December 11, 2025

#1

Partitioning in ClickHouse helps manage large datasets efficiently. Let’s see strategies to apply partitioning with Spring Boot applications.

1. Create a Partitioned Table

CREATE TABLE orders (
  id UInt32,
  region String,
  created_at DateTime,
  amount Float32
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(created_at)
ORDER BY id;

Data is split by month.

2. Insert Data from Spring Boot

jdbcTemplate.update(
  "INSERT INTO orders (id, region, created_at, amount) VALUES (?,?,?,?)",
  101, "US-East", LocalDateTime.now(), 250.0
);

Data goes into the correct partition automatically.

3. Query Specific Partitions

List<Map<String,Object>> results = jdbcTemplate.queryForList(
  "SELECT * FROM orders WHERE created_at >= '2025-08-01' AND created_at < '2025-09-01'"
);

Only the targeted partitions are scanned.

4. Alternative Partition Strategies

• By region: PARTITION BY region for geographically distributed data.
• By year: PARTITION BY toYYYY(created_at) for long history storage.
• By custom keys: user-defined hash or categorical fields.

5. Expose Partitioned Data APIs

@GetMapping("/api/orders/month/{month}")
public List<Map<String,Object>> getMonthlyOrders(@PathVariable String month) {
    return jdbcTemplate.queryForList(
      "SELECT * FROM orders WHERE toYYYYMM(created_at)=?", month
    );
}

Provides fast, partition-aware queries.