SQL002 – Query Execution Timeout in Databricks

Posted by

Jami K

–

February 8, 2025

Table of Contents

Introduction

SQL002 – Query Execution Timeout is a common error in Databricks SQL and Spark SQL, indicating that the query execution exceeded the configured timeout. This can occur when a long-running query fails to complete within the allowed time frame, causing an automatic termination of the query.

🚨 Common symptoms:

Error: “SQL002 – Query execution timeout.”
Queries fail after a specific period of time (e.g., 5 or 10 minutes).
Dashboard or interactive SQL queries stop responding and time out.
Large datasets or complex joins cause delays and timeout errors.

Common Causes of SQL002 – Query Execution Timeout

1. Query Complexity

Complex joins, aggregations, and subqueries can significantly increase query time.
Unoptimized SQL queries with large datasets often exceed the timeout limit.

2. Timeout Configuration

The default query timeout may be set too low in Databricks SQL or Spark configurations.
Databricks SQL default timeout is 300 seconds (5 minutes) unless modified.

3. Resource Constraints

Insufficient cluster resources (CPU, memory) may prevent queries from completing on time.
Under-provisioned SQL warehouses or clusters cannot handle large workloads.

4. Data Skew or Large Shuffles

Unbalanced partitions or data skew can cause one partition to take significantly longer to process.
Large shuffles during query execution increase runtime and often lead to timeouts.

5. Concurrency Limits

Multiple concurrent queries on the same cluster or warehouse can degrade performance, causing timeouts.
Queueing and resource contention delay query execution.

Solutions for SQL002 – Query Execution Timeout

1. Optimize Query Execution

✅ Rewrite Complex Queries

Simplify joins, subqueries, and aggregations to reduce execution time.

-- Inefficient query
SELECT customer_id, COUNT(*) 
FROM sales 
JOIN customers USING (customer_id) 
GROUP BY customer_id 
HAVING COUNT(*) > 10;

-- Optimized query with fewer operations
SELECT customer_id, COUNT(*) AS order_count
FROM sales
GROUP BY customer_id
HAVING order_count > 10;

✅ Filter Data Early (Reduce Input Size)

-- Bad: Filters after join
SELECT * FROM sales JOIN customers ON sales.customer_id = customers.customer_id WHERE sales.amount > 100;

-- Good: Apply filter before join
WITH filtered_sales AS (
  SELECT * FROM sales WHERE amount > 100
)
SELECT * FROM filtered_sales JOIN customers ON filtered_sales.customer_id = customers.customer_id;

✅ Use Proper Indexing and Partitioning

Partition large tables to reduce scan times.
Z-Order clustering for Delta Lake tables improves query performance.

OPTIMIZE delta.`/mnt/delta/sales` ZORDER BY (customer_id);

2. Increase Query Timeout Configuration

✅ Databricks SQL Query Timeout

Go to Admin Console → SQL Settings and increase the query timeout.
Default timeout is 300 seconds; increase it to 600 or 900 seconds.

✅ Spark SQL Timeout Configuration

Set the spark.databricks.queryExecutionTimeout parameter in your cluster configuration.

spark.conf.set("spark.databricks.queryExecutionTimeout", "600")  # Increase to 10 minutes

3. Scale Up Resources

✅ Use a Higher SQL Warehouse Size

Switch to a larger SQL warehouse (Standard-Large, Pro-Large) for better performance.
Ensure autoscaling is enabled to handle dynamic workloads.

✅ Cluster Configuration

Use clusters with more memory and CPU resources for complex queries.
Enable auto-scaling for Spark clusters to manage concurrent queries.

4. Monitor and Resolve Data Skew

✅ Identify and Resolve Skewed Partitions

SELECT COUNT(*) AS record_count, partition_column
FROM my_table
GROUP BY partition_column
ORDER BY record_count DESC;

Use salting or repartitioning to reduce skew.

df.repartition(10, "partition_column").write.format("delta").save("/mnt/delta/optimized")

✅ Reduce Shuffle Size

spark.conf.set("spark.sql.shuffle.partitions", "200")  # Reduce from default 200 to 100 for smaller datasets

5. Manage Concurrency and Resource Contention

✅ Limit Concurrent Queries

Reduce the number of simultaneous queries to avoid overloading resources.
Monitor cluster usage and increase capacity if necessary.

✅ Use Query Scheduling and Caching

Cache frequently queried data to improve response time.

df.cache()
df.show()

Schedule large queries during off-peak hours to avoid contention.

Step-by-Step Troubleshooting Guide

1. Check Query Execution Plan

Use EXPLAIN to analyze query performance and identify slow operations.

EXPLAIN SELECT * FROM sales WHERE amount > 100;

2. Monitor Cluster and SQL Warehouse Performance

Go to Databricks UI → SQL Warehouses → Monitor to check resource usage.
Identify CPU and memory bottlenecks.

3. Check for Data Skew and Large Shuffles

Analyze Spark UI (SQL tab) for stages with high skew or shuffle size.

4. Optimize or Partition Large Tables

Use Delta Lake OPTIMIZE and ZORDER to improve query performance.

Best Practices to Avoid Query Timeout Issues

✅ Optimize SQL Queries Regularly

Simplify queries and reduce the number of joins.
Filter data as early as possible.

✅ Increase Timeout for Long-Running Queries

Adjust the default timeout setting to handle longer queries.

✅ Scale Resources Dynamically

Use autoscaling and larger SQL warehouses for resource-heavy queries.

✅ Monitor and Resolve Data Skew

Check for imbalanced partitions and repartition large datasets.

✅ Use Delta Lake for Large Datasets

Delta Lake provides improved performance and reliability for large datasets.

Conclusion

SQL002 – Query Execution Timeout in Databricks is usually caused by long-running queries, resource limitations, or unoptimized SQL statements. By optimizing queries, increasing timeout settings, scaling resources, and monitoring data skew, teams can resolve timeout errors and improve query performance.

Databricks