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Home/Questions/Spark/Big Data/Suppose you have a DAG that ingests data from multiple databases. How would you increase task parallelism in Airflow to improve performance without overloading the system?

Suppose you have a DAG that ingests data from multiple databases. How would you increase task parallelism in Airflow to improve performance without overloading the system?

Spark/Big Dataeasy0.6 min read

**Situation**: Multi-DB ingestion DAG runs sequentially; SLA at risk; DB teams report connection exhaustion. **Task**: Increase throughput without overloading source DBs or Airflow. **Action**: (1) **TaskGroup per DB**—isolate connection pools; `max_active_tasks_per_dag`...

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Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Dunnhumby
Key Concepts Tested
airflowsql

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This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Dunnhumby. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (airflow, sql) will help you answer variations of this question confidently.

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Expert Answer
113 words

Situation: Multi-DB ingestion DAG runs sequentially; SLA at risk; DB teams report connection exhaustion.

Task: Increase throughput without overloading source DBs or Airflow.

Action: (1) TaskGroup per DB—isolate connection pools; max_active_tasks_per_dag limits concurrency per DB. (2) Dynamic task mapping (Airflow 2.3+): @task returns list; expand() creates N tasks. (3) Connection pooling—SQLAlchemy pool_size, max_overflow; one connection per worker, not per task. (4) CeleryExecutor—scale workers; separate queues for DB-heavy vs. light tasks. (5) Circuit breaker—pause DAG if DB error rate spikes.

Result: 3x parallelism, DB CPU within limits, SLA met.

Scalability Trade-offs: parallelism_factor = min(worker_count, DB_max_connections / tasks_per_DB). Over-parallelism causes connection timeouts.

Cost Implications: More workers = more cost; right-size from DB capacity and SLA.

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