tasks/online-compute/FlinkSQL/flinksql_002_en/task.md

metadata

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