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id: online-compute_FlinkSQL_flinksql_002
name: Order-Payment Interval Join + Windowed Aggregation
category: online-compute/FlinkSQL
timeout_seconds: 600
modality: pure-text
engine: flink-sql
---
## Prompt
I need you to write a Flink SQL that uses two datagen built-in tables to simulate an order stream and a payment stream, performs an Interval Join on `order_id`, and then aggregates the order count and total payment amount over a 10-minute tumbling window, outputting the results to the console.
**Business Background and Objective**: Simulate an order stream and a payment stream using two datagen built-in tables, each generating 100 million records. The `order_id` and `user_id` in the order stream range from 1 to 1,000,000, and the `order_id` in the payment stream also ranges from 1 to 1,000,000. Both tables use `LOCALTIMESTAMP` as the event time and set a 5-second watermark delay.
Perform an Interval Join on the two tables using `order_id`, matching only records where the payment time falls within 10 minutes before or after the order time. Then, aggregate the successfully matched records over a 10-minute tumbling window to compute the order count and total payment amount per window, and finally output the results to the console table.
**Source Table Definitions**:
- `orders_source` (order stream, datagen connector):
- `order_id INT`: Order ID, randomly generated in the range 1–1,000,000
- `user_id INT`: User ID, randomly generated in the range 1–1,000,000
- `event_time`: Uses `LOCALTIMESTAMP` to generate event time, with a WATERMARK delay of 5 seconds
- Generation rate: `rows-per-second = 1000`
- `payments_source` (payment stream, datagen connector):
- `order_id INT`: Order ID, randomly generated in the range 1–1,000,000
- `pay_amount DOUBLE`: Payment amount, randomly generated in the range 1–100,000
- `event_time`: Uses `LOCALTIMESTAMP` to generate event time, with a WATERMARK delay of 5 seconds
- Generation rate: `rows-per-second = 1000`
**Output Table Definition**:
- `console_output` (print connector):
- `window_start VARCHAR`: Window start time
- `window_end VARCHAR`: Window end time
- `order_count BIGINT`: Order count within the window
- `total_pay_amount DOUBLE`: Total payment amount within the window
**Join + Aggregation Logic**:
- Interval Join condition: `orders_source.order_id = payments_source.order_id`
- Time window: `payments_source.event_time BETWEEN orders_source.event_time - INTERVAL '10' MINUTE AND orders_source.event_time + INTERVAL '10' MINUTE`
- Tumbling window: `TUMBLE` 10 minutes
- Aggregation metrics: `COUNT(*) AS order_count`, `SUM(pay_amount) AS total_pay_amount`
- Convert window times to `VARCHAR` for output
**Output Requirements**:
- Use `INSERT INTO console_output` to output the results.
- Output field order: `window_start`, `window_end`, `order_count`, `total_pay_amount`
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