Estimating storage and query costs

On-demand queries are charged based on the number of bytes read. For current on-demand query pricing, see the Pricing page.

To estimate costs before running a query use the:

• Query validator in the GCP Console or the classic BigQuery web UI
• --dry_run flag in the CLI
• dryRun parameter when submitting a query job using the API
• Google Cloud Platform Pricing Calculator
• Client libraries

Estimating query costs

To estimate query costs:

Console

When you enter a query in the GCP Console, the query validator verifies the query syntax and provides an estimate of the number of bytes read. You can use this estimate to calculate query cost in the Pricing Calculator.

If your query processes a small amount of data, you might need to convert the bytes that are processed from KB to MB. MB is the smallest measure that is used by the Pricing Calculator.

Classic UI

When you enter a query in the GCP Console or the classic BigQuery web UI, the query validator verifies the query syntax and provides an estimate of the number of bytes read. You can use this estimate to calculate query cost in the Pricing Calculator.

If your query processes a small amount of data, you might need to convert the bytes that are processed from KB to MB. MB is the smallest measure that is used by the Pricing Calculator.

CLI

When you run a query in the CLI, you can use the --dry_run flag to estimate the number of bytes read. You can use this estimate to calculate query cost in the Pricing Calculator.

A CLI query that uses the --dry_run flag looks like the following:

bq query \
--use_legacy_sql=false \
--dry_run \
'SELECT
  column1,
  column2,
  column3
FROM
  `project_id.dataset.table`
LIMIT
  1000'

When you run the command, the response contains the estimated bytes read: Query successfully validated. Assuming the tables are not modified, running this query will process 10918 bytes of data.

If your query processes a small amount of data, you might need to convert the bytes that are processed from KB to MB. MB is the smallest measure used by the Pricing Calculator.

API

To perform a dry run by using the API, submit a query job with dryRun set to true.

go

Before trying this sample, follow the Go setup instructions in the BigQuery Quickstart Using Client Libraries . For more information, see the BigQuery Go API reference documentation .

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q := client.Query(`
	SELECT
	   name,
	   COUNT(*) as name_count
	FROM ` + "`bigquery-public-data.usa_names.usa_1910_2013`" + `
	WHERE state = 'WA'
	GROUP BY name
	`)
q.DryRun = true
// Location must match that of the dataset(s) referenced in the query.
q.Location = "US"

job, err := q.Run(ctx)
if err != nil {
	return err
}
// Dry run is not asynchronous, so get the latest status and statistics.
status := job.LastStatus()
if err != nil {
	return err
}
fmt.Printf("This query will process %d bytes\n", status.Statistics.TotalBytesProcessed)

Python

Before trying this sample, follow the Python setup instructions in the BigQuery Quickstart Using Client Libraries . For more information, see the BigQuery Python API reference documentation .

To perform a dry run using the Python client library, set the QueryJobConfig.dry_run property to True. Client.query() always returns a completed QueryJob when provided a dry run query configuration.

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# from google.cloud import bigquery
# client = bigquery.Client()

job_config = bigquery.QueryJobConfig()
job_config.dry_run = True
job_config.use_query_cache = False
query_job = client.query(
    (
        "SELECT name, COUNT(*) as name_count "
        "FROM `bigquery-public-data.usa_names.usa_1910_2013` "
        "WHERE state = 'WA' "
        "GROUP BY name"
    ),
    # Location must match that of the dataset(s) referenced in the query.
    location="US",
    job_config=job_config,
)  # API request

# A dry run query completes immediately.
assert query_job.state == "DONE"
assert query_job.dry_run

print("This query will process {} bytes.".format(query_job.total_bytes_processed))

Estimating query costs using the Google Cloud Platform Pricing Calculator

To estimate on-demand query costs in the Google Cloud Platform Pricing Calculator, enter the number of bytes that are processed by the query as MB, GB, TB, or PB. If your query processes less than 1 TB, the estimate is $0 because BigQuery provides 1 TB of on-demand query processing free per month.

To estimate the cost of a query using the pricing calculator:

1. Open the Google Cloud Platform Pricing Calculator,
2. Click BigQuery.
3. Click the On-Demand tab.
4. For Table Name, type the name of the table. For example, airports.
5. For Storage Pricing, enter 0 in the Storage field.
6. For Query Pricing, enter the estimated bytes read from your dry run or the query validator. If the value is less than 1 MB, you must convert it to MB for the pricing calculator. Using 10918 bytes as an example, the value is approximately 0.01091 MB.
7. Click Add To Estimate.
8. The estimate appears to the right. Notice that you can save or email the estimate.

In this case, the number of bytes read by the query is below the 1 TB of on-demand processing provided via the free tier. As a result, the estimated cost is $0.

Including flat-rate pricing in the Pricing Calculator

If you have flat-rate pricing applied to your billing account, you can click the Flat-Rate tab, choose your flat-rate plan, and add your storage costs to the estimate.

For more information, see Flat-rate pricing.

Estimating storage costs using the Google Cloud Platform Pricing Calculator

To estimate storage costs in the Google Cloud Platform Pricing Calculator, enter the number of bytes that are stored as MB, GB, TB, or PB. BigQuery provides 10 GB of storage free per month.

To estimate storage costs using the pricing calculator:

1. Open the Google Cloud Platform Pricing Calculator,
2. Click BigQuery.
3. Click the On-Demand tab.
4. For Table Name, type the name of the table. For example, airports.
5. For Storage Pricing, enter 100 in the Storage field. Leave the measure set to GB.
6. Click Add To Estimate.
7. The estimate appears to the right. Notice that you can save or email the estimate.