How do we optimize BigQuery performance for petabyte-scale Data Warehousing in Google Cloud?

All answers to this question.

At 2 petabytes, you absolutely should be using Clustered Tables alongside your partitions. Partitioning is great for "time" slices, but clustering organizes your data based on the values in specific columns (like customer_id or region), which drastically reduces the amount of data scanned during joins and filters. Regarding pricing, if your baseline slot usage is consistently above 100-200 slots, moving to Flex Slots or Annual Commitments will save you a fortune compared to the $5 per TB on-demand model. Use the "BigQuery Reservation" feature to allocate specific slot capacities to different teams so a "rogue" analyst doesn't eat up your entire budget with a single bad query.

   Answered 2024-06-10 by Mary Taylor

Have you tried using "Search Indexes" in BigQuery for those specific columns that your analysts are frequently filtering by? It can speed up point-lookups significantly.

   Answered 2024-06-12 by Charles Wilson

• Charles, we just implemented Search Indexes on our 'Email' and 'TransactionID' columns, and the performance gain for our support team's lookups was incredible—from 15 seconds down to under 2 seconds. The best part is that it didn't require us to rewrite any SQL; BigQuery's optimizer just picked up the index automatically. It does add a bit to the storage cost, but for the user experience improvement, it was a no-brainer for our high-concurrency workloads.

     Commented 2024-06-13 by Joseph Allen

Avoid "SELECT *" at all costs! In a columnar database like BigQuery, it is the number one cause of unnecessary spend and slow performance.

   Answered 2024-06-14 by Sarah Jenkins

• I agree with Sarah. We actually set up a "Query Linter" that blocks any PR containing a "SELECT *" in our production data pipelines. It's a simple rule that saves us thousands of dollars.

     Commented 2024-06-15 by Anthony Davis