BigQuery analysis cost across regions

Hi,

We have been trying to understand our BigQuery costs, specifically those due to queries, which I believe in GCP Billing is the equivalent to `BigQuery Analysis SKU`?

For some of our projects, this cost is reported for different regions; for example, europe-west1 and europe-west-3. I would like to understand what are possible causes for having costs across regions. Does it depend on the region used during creation of datasets, or is it something users have to/can configure in their GCP account? or are other things which I am missing?

Another question is that by dividing the cost by usage, I see that the ratio is different for different regions. Is this correct? Is there a source where I can see such prices listed, because so far I only found this source, which reports one value per location: https://cloud.google.com/bigquery/pricing#analysis_pricing_models, and my understanding was that it should not matter which region is used.

Thanks in advance!

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You're right. In Google Cloud Billing, the "BigQuery Analysis SKU" refers specifically to the costs incurred when you run queries against your data. This is essentially the computational cost of processing your BigQuery queries.

The primary reason you'll see BigQuery analysis costs in different regions (e.g., europe-west1 and europe-west3) is the location of your BigQuery datasets. Here's how it works:

  • Dataset Location: When you create a BigQuery dataset, you specify its geographic location. This choice influences where the data is physically stored and where queries against that dataset are executed.
  • Query Execution: When you run a query against a dataset, the query processing happens in the same region where the dataset resides. This is done to minimize latency and improve performance.

Key Points

  • Data Location Matters: The location of your BigQuery datasets determines the regions where you'll incur BigQuery Analysis costs.
  • User Control: You have control over this when creating datasets, and you can choose regions that align with your cost and performance goals.
  • No Separate User Configuration: There isn't a separate setting in your GCP account to control this; it's tied to the dataset location.

You are correct that the analysis cost per byte can differ slightly between regions. This is because Google Cloud's infrastructure costs can vary slightly depending on the specific region. However, the price differences are generally minor.

 

While the link you provided gives a general overview, it's best to check the detailed pricing information for the specific regions you are using. You can usually find this within the Google Cloud Pricing Calculator.

Example

Let's say you have the following:

  • Dataset A: Located in europe-west1
  • Dataset B: Located in europe-west3

When you run queries against Dataset A, the analysis costs will show up under europe-west1. Queries against Dataset B will incur costs under europe-west3.

Strategies for Cost Optimization

  • Dataset Placement: Carefully consider the location of your datasets. If cost is a significant concern, choose regions with lower analysis pricing if it aligns with your latency requirements.
  • Query Optimization: Efficient query design can reduce the amount of data processed, thus lowering costs regardless of region.
     

 

 

 

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2 REPLIES 2

You're right. In Google Cloud Billing, the "BigQuery Analysis SKU" refers specifically to the costs incurred when you run queries against your data. This is essentially the computational cost of processing your BigQuery queries.

The primary reason you'll see BigQuery analysis costs in different regions (e.g., europe-west1 and europe-west3) is the location of your BigQuery datasets. Here's how it works:

  • Dataset Location: When you create a BigQuery dataset, you specify its geographic location. This choice influences where the data is physically stored and where queries against that dataset are executed.
  • Query Execution: When you run a query against a dataset, the query processing happens in the same region where the dataset resides. This is done to minimize latency and improve performance.

Key Points

  • Data Location Matters: The location of your BigQuery datasets determines the regions where you'll incur BigQuery Analysis costs.
  • User Control: You have control over this when creating datasets, and you can choose regions that align with your cost and performance goals.
  • No Separate User Configuration: There isn't a separate setting in your GCP account to control this; it's tied to the dataset location.

You are correct that the analysis cost per byte can differ slightly between regions. This is because Google Cloud's infrastructure costs can vary slightly depending on the specific region. However, the price differences are generally minor.

 

While the link you provided gives a general overview, it's best to check the detailed pricing information for the specific regions you are using. You can usually find this within the Google Cloud Pricing Calculator.

Example

Let's say you have the following:

  • Dataset A: Located in europe-west1
  • Dataset B: Located in europe-west3

When you run queries against Dataset A, the analysis costs will show up under europe-west1. Queries against Dataset B will incur costs under europe-west3.

Strategies for Cost Optimization

  • Dataset Placement: Carefully consider the location of your datasets. If cost is a significant concern, choose regions with lower analysis pricing if it aligns with your latency requirements.
  • Query Optimization: Efficient query design can reduce the amount of data processed, thus lowering costs regardless of region.
     

 

 

 

Thanks for the detailed response. It is very helpful.