Big Query Update

[Gvantss]

My company has established a table in a BigQuery sandbox account to store data pertaining to various site events such as logins, registrations, verifications, and more. This data is crucial for our daily marketing analysis. However, we've encountered an issue where the table isn't being updated as expected on a daily basis. We would like to understand if this issue could be related to the limitations of a BigQuery sandbox account. While we are aware of the query processing limitations, typically around 1 terabyte, we are uncertain if this would affect the daily update frequency of our table. Could you please clarify whether the sandbox account limitations might be causing the delay in daily updates to our table, and if so, suggest any potential solutions or workarounds?

[ms4446 · Accepted solution]

It is possible that the BigQuery sandbox account could be causing the delay in daily updates to your table. Although the query processing limitation of 1 terabyte is substantial, it is possible that your table is encountering other sandbox limitations.

To determine if any specific limitations are causing the delay, you can use the BigQuery Audit Log to view the history of all operations performed on your table. The Audit Log will show you the type of operation, the timestamp, and the status of the operation. However, it's essential to note that detailed audit logs might not be available in the sandbox environment without the appropriate permissions or configurations. If you see any errors or failures in the Audit Log, this could indicate a limitation being exceeded.

If you suspect you are exceeding one of the sandbox limits, here are a few potential solutions or workarounds:

1. Upgrade to a Paid BigQuery Account: Paid accounts have fewer restrictions compared to the sandbox environment.
2. Batch Your Updates: Instead of performing many small updates throughout the day, batch them together into a single, larger update.
3. Use a Streaming Data Platform: If you need to update your table in real-time, consider using platforms like Cloud Dataflow or Cloud Pub/Sub. However, note that streaming data directly into BigQuery is not available in the sandbox environment.

In addition to the above, here are some other things you can check to troubleshoot the issue:

• Ensure that the user account or service account performing the updates has the necessary permissions.
• Check the BigQuery job log for any errors or warnings.
• Try querying the table directly to see if the data is up-to-date. However, if there are limitations causing the delay, querying might not provide the expected results.

[Gvantss]

Tank you for response. Also, we'd like to inquire about a different issue. Our table currently lacks pageview data, and we're curious if this absence could also be attributed to the limitations of a BigQuery sandbox account.

Furthermore, we are interested in understanding how upgrading to a paid BigQuery account or any relevant billing updates might help us overcome these limitations and enable us to include pageview data in our table. Your insights and guidance on this matter would be greatly appreciated

[ms4446]

Yes, the limitations of a BigQuery sandbox account, such as the 10GB storage limit, could be preventing you from including pageview data in your table. Pageview data can indeed be voluminous, and if your table is nearing the storage limit, adding this data could exceed the sandbox's capacity.

Upgrading to a paid BigQuery account would provide several benefits:

1. Increased Storage Limit: Paid accounts offer more storage, allowing you to include voluminous datasets like pageview data.
2. Advanced Features: With a paid account, you gain access to advanced BigQuery features such as machine learning and geospatial analysis, which can be invaluable for analyzing pageview data.
3. Improved Performance: Paid accounts can handle more substantial data operations, potentially improving the performance of your daily updates.

To further optimize your BigQuery usage:

• Partitioned Tables: Consider using partitioned tables, which can enhance performance and reduce costs. For instance, partitioning by date can make queries more efficient.

• Materialized Views: These can pre-compute results of frequently-run queries, improving query performance.