Strange behavior when using bigquery metadata refreshing

[Stev0198]

I have set up a large number of tables as biglake tables connected to cloud storage uri's, and then I have set up a large number of materialised views on top of the biglake tables.

The problem I have seen is that when the "last modified date" of the biglake table is later than that of the materialised view linked to it, it fails to read from it and I get a Metadata cache unused reasons: OTHER REASON followed by the table name.

This is manageable, as I can set the biglake and materialised view refreshing to Manual to control when I refresh them to ensure that the materialised view reads from the biglake table.

But, sometimes, I get an issue where I would create the biglake table and then create the materialised view a bit later, meaning it works and uses the metadata cache successfully. For a while. And then exactly one hour after the biglake table was created its "last modified date" gets updated. And I can't find any CALL BQ.REFRESH_EXTERNAL_METADATA_CACHE calls in the project history at these timestamps. Once this happens the biglake table last modified date is later than that of the materialised view linked to it, it fails to read from it and I get a Metadata cache unused reasons: OTHER REASON followed by the table name.

Has anyone run into this issue and is there a way to prevent the biglake tables from updating their last modified dates exactly one hour after they're created without indication [no visibility in project history]? 

[ms4446]

Navigating BigLake Tables and Materialized Views in Google BigQuery

Dealing with BigLake tables in Google BigQuery, especially when it comes to metadata cache issues affecting materialized views, presents nuanced challenges. This guide offers an overview of both technical solutions and strategic considerations, emphasizing the iterative nature of data management in cloud environments.

Understanding Cache Dynamics

BigQuery's metadata cache is crucial for external tables, including those in BigLake, enhancing query performance by storing essential details. However, automatic refreshes can desynchronize materialized views if the base table's metadata is updated without a corresponding refresh of the views.

Technical Solutions & Strategic Workarounds

• Manual & Scheduled Updates: Manually refresh metadata and automate updates using tools like Cloud Functions. Align these actions with your broader data management strategy for optimal results.

Beyond Technical Workarounds

• Strategic Modeling: Reassess the structure and access patterns of your BigLake tables. Implementing data partitioning, such as by date, can reduce reliance on materialized views while maintaining desired performance levels.

• Understanding Requirements: Critically evaluate the necessity of materialized views in your architecture. In some scenarios, alternative strategies might offer a streamlined pipeline and fulfill your requirements more efficiently.

• Google Cloud Engagement: Actively engage with Google Cloud support for insights into best practices and information on upcoming features that could benefit your specific use case.

A Continuous Journey

• Iterative Process: The management of metadata and optimization of queries in cloud environments is a continuous journey. Adapt your strategies based on new insights, evolving data needs, and updates from Google Cloud.

Addressing challenges with BigLake tables and materialized views in Google BigQuery necessitates a combination of immediate technical interventions and long-term strategic planning. Effective data management is characterized by ongoing adaptation and learning, leveraging both specific workarounds and broader strategies such as strategic data modeling and proactive communication with Google Cloud. This balanced, iterative approach ensures robust, scalable solutions for complex data environments.

Your revised guide and the addition of a Cloud Functions example for automating metadata refreshes in Google BigQuery provide a comprehensive and practical approach to managing BigLake tables and materialized views. The enhancements you've made, particularly around configuration, error handling, and deployment guidance, significantly improve the utility and adaptability of the solution. Here's a polished version of the final section, incorporating your updates and providing a clear, step-by-step guide for implementation:

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Automating Metadata Refresh with Cloud Functions

To effectively manage metadata synchronization for BigLake tables in Google BigQuery, automating the refresh process using Cloud Functions can be a robust solution. Here's an enhanced example that incorporates best practices for configuration, error handling, and deployment:

import os
from google.cloud import bigquery, logging

# Initialize a logging client
logging_client = logging.Client()
logger = logging_client.logger("metadata_refresh_function")

def refresh_metadata(event, context):
try:
client = bigquery.Client()

# Load configuration from environment variables
dataset_id = os.environ.get('DATASET_ID')
table_id = os.environ.get('EXTERNAL_TABLE_ID')

# Ensure configuration was loaded correctly
if not dataset_id or not table_id:
raise ValueError("Missing configuration. Set DATASET_ID and EXTERNAL_TABLE_ID environment variables.")

table_fq_id = f"{client.project}.{dataset_id}.{table_id}"
query = f"SELECT * FROM `{table_fq_id}` LIMIT 0"
query_job = client.query(query)
query_job.result()

logger.log_text(f"Refreshed metadata for {table_fq_id}", severity="INFO")

except Exception as e:
logger.log_text(f"Error refreshing metadata: {str(e)}", severity="ERROR")