calling a pyspark stored procedure in bigquery takes a long time

[smithj51]

is there a way to speed this up? the script itself is very fast, but the action of actually calling the stored procedure takes over 60 seconds. For example, I run the CALL statement starting at 3:24:38PM. The first log statement is not printed until 3:25:53pm. What is happening in those 75 seconds, between starting the run with the CALL statement, and actually executing the pyspark script? 

Is it possible the 75 seconds is the execution of the script, and the log statements (print statements within the pyspark script) are all dumped at the end? The first and last log statements are tagged within the same second, even though when I'm watching the log stream they do not come in at the same second.

Creation time: Jun 13, 2024, 3:24:38 PM UTC-4

Start time: Jun 13, 2024, 3:24:38 PM UTC-4

End time: Jun 13, 2024, 3:26:18 PM UTC-4

Duration: 1 min 40 sec

First log statement:

2024-06-13 15:25:53.000 EDT: Using the default container image
(^ Jun 13, 2024, 3:25:53 PM UTC-4)
Last log statement:
2024-06-13T19:25:53.000 EDT: success
(^ Jun 13, 2024, 3:25:53 PM UTC-4)

[ms4446]

The 75-second delay you're experiencing between the CALL statement and the first log output in BigQuery when running a PySpark stored procedure is likely due to a combination of factors:

1. Session Initialization:

   • Resource Provisioning: BigQuery needs to set up a Spark session for your stored procedure, which involves provisioning computational resources, launching the Spark driver and executors, and loading your PySpark code into the execution environment.

2. Dependency Resolution:

   • Library Installation: If your PySpark script relies on external libraries (e.g., from PyPI), BigQuery must download and install these libraries into the Spark environment, which can add to the initialization time.

3. Data Preparation:

   • Data Loading: If your stored procedure reads data from BigQuery tables or external sources, there could be additional overhead in loading and preparing this data for processing within Spark.

4. Logging Buffering:

   • Buffered Logs: BigQuery might be buffering the log output from your PySpark script and only flushing it periodically. This could explain why the timestamps for your first and last log statements appear close together even though the script execution takes longer.

Strategies for Optimization

Here are some strategies to explore for speeding up the execution of your PySpark stored procedure:

1. Container Image Optimization:

   • Custom Container: If your PySpark script uses multiple libraries, consider building a custom container image with these dependencies pre-installed. This can significantly reduce the startup time as the environment will be ready for immediate use.

2. Code Optimization:

   • Code Review: Review your PySpark code for potential optimizations. Focus on parallelizing operations, minimizing data shuffling, and using efficient data structures to enhance performance.

3. Data Caching:

   • Spark Caching: If your stored procedure frequently reads the same data, consider caching it within Spark for faster access, reducing the need for repeated data loading.

4. Resource Configuration:

   • Allocate Resources: Ensure you're allocating sufficient resources (e.g., memory, CPU) to your BigQuery Spark session. These settings can be adjusted in the stored procedure definition to better match your workload requirements.

5. Logging Configuration:

   • Optimize Logging: If logging is causing a bottleneck, explore ways to adjust the logging level or write logs to a more efficient destination (e.g., Google Cloud Storage).

Troubleshooting Log Timing

To better understand the true timing of your log statements, you can try the following:

1. Structured Logging:

   • Use Structured Logging: Instead of using print statements, employ a structured logging framework (e.g., Python's logging module) and configure it to write logs to a BigQuery table or Google Cloud Logging. This allows for more accurate querying and analysis of log timestamps.

2. Flush Logs:

   • Explicit Log Flushing: Add explicit calls to flush the log output after key statements in your PySpark code to ensure logs are written immediately.

import logging

# ... your PySpark code ...

logging.info("Starting data processing")
logging.getLogger().handlers[0].flush()  # Flush the log

# ... more code ...

[smithj51]

thank you for the detailed response! we did try a custom container image but couldn't get it to work. everything worked perfectly when we ran it locally, but when we tried to run it in BigQuery we got an error: Job failed. Please check logs at EXECUTION DETAILS from the console or Cloud Logging.

There were no other log statements and no further details so we could not debug.

[ms4446]

Here are some steps, you can take to debug and help you resolve issues with your custom container image:

1. Verify Container Image Compatibility:

   • Base Image: Ensure your custom container is based on an image compatible with BigQuery's requirements. Use official images or images known to work with BigQuery.
   • PySpark Version: Confirm that the version of PySpark and other dependencies are compatible with the environment BigQuery expects.

2. Inspect Container Configuration:

   • Entry Point: Make sure the entry point or command specified in the Dockerfile is correctly set up to run PySpark jobs.
   • Environment Variables: Ensure that all required environment variables are correctly set up. BigQuery might rely on specific variables that need to be present.

3. Check Permissions and Access:

   • IAM Permissions: Verify that the service account used by BigQuery has the necessary permissions to pull and use the custom container from your container registry (e.g., Container Registry, Artifact Registry).
   • Network Access: Ensure that the container has the necessary network access if it needs to connect to external services or databases.

4. Test Container Image in a Similar Environment:

   • Cloud Run or Kubernetes: Deploy and test the container image on Cloud Run or a Kubernetes cluster with similar configurations to validate that it works outside of BigQuery. This can help isolate if the issue is specific to BigQuery or the container image.

5. Enable Detailed Logging:

   • Container Logs: Modify your container to log detailed information during startup and execution. Redirect logs to stdout/stderr so they can be captured by Cloud Logging.
   • Debug Mode: If applicable, run the container in debug mode to capture more verbose output that might indicate where the problem lies.

6. Inspect BigQuery Execution Details:

   • Check Job Logs: Go to BigQuery's job execution details in the console or Cloud Logging. Look for any error messages or hints that could indicate what went wrong.
   • Execution ID: Use the execution ID to search for logs in Cloud Logging. Sometimes the logs are stored under different resource types or filters.

Steps to Create a Custom Container Image for BigQuery

Here's an example of how to create and use a custom container image for BigQuery:

1. Create a Dockerfile:

# Use a base image compatible with BigQuery
FROM gcr.io/deeplearning-platform-release/spark:latest

# Install additional dependencies
RUN pip install --no-cache-dir pandas numpy

# Set the entry point for PySpark
ENTRYPOINT ["spark-submit"]

2. Build and Push the Docker Image:

docker build -t gcr.io/your-project-id/your-image:tag .
docker push gcr.io/your-project-id/your-image:tag

3. Configure BigQuery to Use the Image:
   • Job Submission: When submitting the PySpark job in BigQuery, specify the custom container image in the job configuration.

CREATE OR REPLACE PROCEDURE my_dataset.my_stored_procedure()
BEGIN
  CALL my_spark_job();
END;

CREATE OR REPLACE SPARK JOB my_spark_job
OPTIONS (
  container_image = 'gcr.io/your-project-id/your-image:tag'
) AS
SELECT * FROM my_table;

Example Debugging Process

Suppose your Dockerfile is set up correctly, but you're still facing issues. Here's a more specific example of a debugging approach:

1. Update Dockerfile for Detailed Logging:

# Use a base image compatible with BigQuery
FROM gcr.io/deeplearning-platform-release/spark:latest

# Install additional dependencies
RUN pip install --no-cache-dir pandas numpy

# Enable detailed logging
ENV PYSPARK_SUBMIT_ARGS="--conf spark.eventLog.enabled=true --conf spark.eventLog.dir=file:/tmp/spark-events"

# Set the entry point for PySpark
ENTRYPOINT ["spark-submit"]

2. Run the Container Locally with Logging:

docker run -it --rm -p 4040:4040 gcr.io/your-project-id/your-image:tag \
  --master local \
  your_script.py

3. Deploy and Test in a Cloud Environment:

   • Use Cloud Run to deploy the container and test it. Ensure you have logging enabled to capture all output.

4. Inspect Cloud Logging:

   • In Google Cloud Console, go to Logging > Logs Explorer and filter by resource type to find logs related to your container execution.

Additional Resources

• Cloud Container Registry: For managing container images.
• Cloud Run: For deploying and testing container images.
• BigQuery Spark Connector Documentation: For detailed instructions and configuration options.

If you continue to face challenges, consider reaching out to Google Cloud support.