Learn how to diagnose and resolve BigQuery quotaExceeded errors using Cloud Audit Logs and Cloud Monitoring. Optimize your Google Cloud data analysis workflow.
Table of Contents
Question
Your company uses BigQuery to store and analyze data. Upon submitting your query in BigQuery, the query fails with a quotaExceeded error. You need to diagnose the issue causing the error. What should you do? (Choose two.)
A. Use BigQuery BI Engine to analyze the issue.
B. Use the INFORMATION_SCHEMA views to analyze the underlying issue.
C. Configure Cloud Trace to analyze the issue.
D. Search errors in Cloud Audit Logs to analyze the issue.
E. View errors in Cloud Monitoring to analyze the issue.
Answer
D. Search errors in Cloud Audit Logs to analyze the issue.
E. View errors in Cloud Monitoring to analyze the issue.
Explanation
When encountering a quotaExceeded error in BigQuery, it’s crucial to diagnose the underlying cause to resolve the issue effectively. The two most appropriate methods for troubleshooting this error are:
Cloud Audit Logs (Option D):
Cloud Audit Logs provide a detailed record of administrative activities and access to your Google Cloud resources. For BigQuery, these logs capture information about query executions, including any errors that occur. By searching the Cloud Audit Logs, you can find specific details about the quotaExceeded error, such as:
- The exact time the error occurred
- The user or service account that triggered the error
- The specific quota that was exceeded (e.g., concurrent queries, bytes processed, etc.)
- Any additional context or error messages
Cloud Monitoring (Option E):
Cloud Monitoring offers real-time visibility into the performance, uptime, and overall health of your BigQuery resources. It provides:
- Predefined dashboards for BigQuery metrics
- Custom alerts for quota usage and limits
- Detailed metrics on query performance, slot utilization, and quota consumption
By using Cloud Monitoring, you can:
- Identify trends in quota usage over time
- Set up alerts to proactively notify you when quotas are approaching their limits
- Correlate the error with other performance metrics to understand the broader context
Why the other options are incorrect:
A. BigQuery BI Engine is an in-memory analysis service for BigQuery. While it can improve query performance, it’s not designed for diagnosing quota-related errors.
B. INFORMATION_SCHEMA views provide metadata about BigQuery datasets, tables, and jobs. While useful for understanding database structure and query history, they don’t directly provide information about quota errors.
C. Cloud Trace is primarily used for analyzing latency in applications and services. It’s not the appropriate tool for diagnosing BigQuery quota issues.
By leveraging both Cloud Audit Logs and Cloud Monitoring, you can gain a comprehensive understanding of the quotaExceeded error, identify its root cause, and take appropriate actions to prevent future occurrences, such as optimizing queries, adjusting quotas, or implementing more efficient data processing strategies.
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