Pricing

BigQuery offers scalable, flexible pricing options to help fit your project and your budget.

BigQuery storage costs are based solely on the amount of data you store. Storage charges can be:

• Active — A monthly charge for data stored in tables you have modified in the last 90 days.
• Long-term — A lower monthly charge for data stored in tables that have not been modified in the last 90 days.

Query costs are based on the amount of data processed by the query. Query charges can be:

• On-demand — The most flexible option. On-demand query pricing is based solely on usage.
• Flat-rate — Enterprise customers generally prefer flat-rate pricing for queries because it offers predictable, fixed month-to-month costs.

For more information on storage and query pricing, see Google Cloud Platform SKUs.

Pricing summary

The following table summarizes BigQuery pricing. BigQuery's Quotas and Limits apply to these operations.

Operation	Pricing	Details
Active storage		The first 10 GB is free each month. See Storage pricing for details.
Long-term storage		The first 10 GB is free each month. See Storage pricing for details.
Storage API		The BigQuery Storage API is not included in the free tier.
Streaming Inserts		You are charged for rows that are successfully inserted. Individual rows are calculated using a 1 KB minimum size. See Streaming pricing for details.
Queries (analysis)		First 1 TB per month is free, see On-demand pricing for details. Flat-rate pricing is also available for high-volume customers.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

How charges are billed

Each project you create has a billing account attached to it. Any charges incurred by BigQuery jobs run in the project (such as query jobs) are billed to the attached billing account. This is true even if project resources are shared with others outside your organization. BigQuery storage costs are also billed to the attached billing account.

How to analyze billing data

You can view BigQuery costs and trends by using the Cloud Billing Reports page in the Cloud Console. For information on analyzing billing data using reports, see View Your Cost Trends With Billing Reports.

For information on analyzing your billing data in BigQuery, see Export Billing Data to BigQuery in the Cloud Billing documentation.

Free operations

The following table shows BigQuery operations that are free of charge in every location. BigQuery's Quotas and Limits apply to these operations.

Operation	Details
Loading data	When you load data into BigQuery from Cloud Storage, you are not charged for the load operation, but you do incur charges for storing the data in Cloud Storage. See Data storage on the Cloud Storage pricing page for details. Once the data is loaded into BigQuery, the data is subject to BigQuery's Storage pricing. For more information, see Loading Data into BigQuery. When you create a dataset in BigQuery, you must choose a location for the data. If you choose US, you can load data into tables in the dataset from a Cloud Storage bucket in any other region. When you load data from another region into a US dataset, there is currently no charge for internet egress. If you choose a location other than US, you must do one of the following: Load data from a Cloud Storage bucket in that region (the bucket can be either a multi-regional bucket or a regional bucket in the same region as the dataset) Copy the data into a bucket in that region When you copy data from one Cloud Storage region to another, Cloud Storage network pricing applies.
Copying data	You are not charged for copying a table, but you do incur charges for storing the new table and the table you copied. For more information, see Copying an existing table.
Exporting data	When you export data from BigQuery to Cloud Storage, you are not charged for the export operation, but you do incur charges for storing the data in Cloud Storage. See Data storage on the Cloud Storage pricing page for details. For more information, see Exporting Data from BigQuery.
Deleting datasets	You are not charged for deleting a dataset.
Deleting tables, views, and partitions	You are not charged for deleting a table, deleting a view, or deleting individual table partitions.
Metadata operations	You are not charged for list, get, patch, update and delete calls. Examples include (but are not limited to): listing datasets, updating a dataset's access control list, and updating a table's description.
Reading pseudo columns	You are not charged for querying the contents of the following pseudo columns: _TABLE_SUFFIX — Used when querying wildcard tables or to achieve table decorator semantics in standard SQL _PARTITIONDATE — Used when querying ingestion-time partitioned tables _PARTITIONTIME — Used when querying ingestion-time partitioned tables _FILE_NAME — Used when querying tables based on external data sources
Reading meta tables	You are not charged for querying the contents of the following meta tables: __PARTITIONS_SUMMARY__ — Used when getting metadata about partitions in a partitioned table or an ingestion-time partitioned table __TABLES_SUMMARY__ — Used when getting metadata about the tables and views in a dataset

Always free usage limits

As part of the Google Cloud Platform Free Tier, BigQuery offers some resources free of charge up to a specific limit. These free usage limits are available during and after the free trial period. If you go over these usage limits and are no longer in the free trial period, you will be charged according to the pricing on this page.

Resource	Monthly Free Usage Limits	Details
Storage	The first 10 GB per month is free.	BigQuery ML models and training data stored in BigQuery are included in the storage free tier.
Queries (analysis)	The first 1 TB of query data processed per month is free.	Queries that use BigQuery ML prediction, inspection, and evaluation functions are included in the analysis free tier. BigQuery ML queries that contain CREATE MODEL statements are not. Flat-rate pricing is also available for high-volume customers that prefer a stable, monthly cost.
BigQuery ML CREATE MODEL queries	The first 10 GB of data processed by queries that contain CREATE MODEL statements per month is free.	BigQuery ML CREATE MODEL queries are independent of the BigQuery analysis free tier.

Query pricing

Query pricing refers to the cost of running your SQL commands and user-defined functions. BigQuery charges for queries by using one metric: the number of bytes processed (also referred to as bytes read). You are charged for the number of bytes processed whether the data is stored in BigQuery or in an external data source such as Cloud Storage, Google Drive, or Cloud Bigtable.

When you run a query, you're charged according to the total data processed in the columns you select, even if you set an explicit LIMIT on the results. The total bytes per column is calculated based on the types of data in the column. For more information about how we calculate your data size, see Data size calculation.

Query pricing is based on your usage pattern: a monthly flat rate for queries or pricing based on interactive queries. Enterprise customers generally prefer flat-rate pricing for queries because that model offers consistent month-to-month costs. On-demand (or interactive) pricing offers flexibility and is based solely on usage.

On-demand pricing

On-demand query pricing is as follows:

Operation	Pricing	Details
Queries (analysis)		First 1 TB per month is free. Flat-rate pricing is also available for high-volume customers that prefer a stable, monthly cost.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Note the following regarding query charges:

• You aren't charged for queries that return an error, or for queries that retrieve results from the cache.
• Charges are rounded to the nearest MB, with a minimum 10 MB data processed per table referenced by the query, and with a minimum 10 MB data processed per query.
• Cancelling a running query job may incur charges up to the full cost for the query were it allowed to run to completion.
• BigQuery uses a columnar data structure. You're charged according to the total data processed in the columns you select, and the total data per column is calculated based on the types of data in the column. For more information about how your data size is calculated, see data size calculation.

On-demand query cost controls

BigQuery provides cost control mechanisms that enable you to cap your query costs. You can set:

• User-level and project-level custom cost controls
• The maximum bytes billed by a query

Flat-rate pricing

BigQuery offers flat-rate pricing for high-volume or enterprise customers who prefer a stable monthly cost for queries rather than paying the on-demand price per TB of data processed. If you choose flat-rate pricing, the cost of all bytes processed is included in the monthly flat-rate price.

BigQuery automatically manages your slots quota based on customer history, usage, and spending. For customers who require consistent monthly expenditures on analytics spending, BigQuery offers flat-rate pricing. When you enroll in a flat-rate pricing plan, you purchase a dedicated number of slots to use for query processing.

Flat-rate pricing:

• Applies only to query costs, not for storage. See Storage pricing for storage costs.
• Applies to all projects that are linked to the billing account where flat-rate pricing is applied.
• Provides additional BigQuery slots. See the following table for details.
• Provides additional query concurrency for interactive queries.

Monthly cost	Number of slots	Additional slots
	2,000

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Contact your sales representative if you are interested in flat-rate pricing.

Allocation of slots in flat-rate pricing

Like other charges, flat-rate charges are incurred by the billing account you specify when you enroll in flat-rate pricing. You can have multiple projects attached to a billing account, and the projects can have datasets and query charges in different locations.

Slots are a regional resource. When you purchase a flat-rate pricing plan, you can specify the allocation of slots by location for all projects attached to that billing account.

For example, if you typically run queries in both the EU and US multi-regional locations, and you purchase a flat-rate pricing plan that includes 6,000 slots, you can specify slot allocation as follows:

• 4,000 slots in the US
• 2,000 slots in the EU

Storage pricing

Once your data is loaded into BigQuery, you are charged for storing it. Storage pricing is based on the amount of data stored in your tables when it is uncompressed.

The size of the data is calculated based on the data types of the individual columns. For a detailed explanation of how data size is calculated, see Data size calculation.

Active storage

Active storage charges are as follows:

Storage type	Pricing	Details
Active storage		The first 10 GB is free each month.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Storage pricing is prorated per MB, per second. For example, if you store:

• 100 MB for half a month, you pay $0.001 (a tenth of a cent)
• 500 GB for half a month, you pay $5
• 1 TB for a full month, you pay $20

Long-term storage

If a table is not edited for 90 consecutive days, the price of storage for that table automatically drops by approximately 50 percent. There is no degradation of performance, durability, availability, or any other functionality when a table is considered long term storage.

Long-term storage pricing is as follows:

Storage type	Pricing	Details
Long-term storage		The first 10 GB is free each month.

If the table is edited, the price reverts back to the regular storage pricing, and the 90-day timer starts counting from zero. Anything that modifies the data in a table resets the timer, including:

Action	Details
Loading data into a table	Any load or query job that appends data to a destination table or overwrites a destination table.
Copying data into a table	Any copy job appends data to a destination table or overwrites a destination table.
Writing query results to a table	Any query job that appends data to a destination table or overwrites a destination table.
Using the Data Manipulation Language (DML)	Using a DML statement to modify table data.
Using Data Definition Language (DDL)	Using a `CREATE OR REPLACE TABLE` DDL statement to replace a table.
Streaming data into the table	Ingesting data using the tabledata.insertAll API call.

All other actions do not reset the timer, including:

• Querying a table
• Creating a view that queries a table
• Exporting data from a table
• Copying a table (to another destination table)
• Patching or updating a table resource

Each partition of a partitioned table is considered separately for long-term storage pricing. If a partition hasn't been modified in the last 90 days, the data in that partition is considered long term storage and is charged at the discounted price.

For tables that reach the 90-day threshold during a billing cycle, the price is prorated accordingly.

Long term storage pricing applies only to BigQuery storage, not to data stored in external data sources such as Cloud Bigtable, Cloud Storage, and Google Drive.

BigQuery Storage API pricing

BigQuery Storage API pricing is based on the data allocated to a stream during ReadRows streaming operations. Costs are based on the incoming data size, not on the transmitted bytes.

The size of the data is calculated based on the data types of the individual columns. For a detailed explanation of how data size is calculated, see Data size calculation.

On-demand pricing

On-demand BigQuery Storage API charges are as follows:

Pricing	Details
	The BigQuery Storage API is not included in the free tier.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Flat-rate pricing

Currently, customers with flat-rate pricing can use the BigQuery Storage API to read up to 300 TB of data per month at no charge. Reads in excess of 300 TB per month are billed at the on-demand rate.

Data size calculation

When you load data into BigQuery or query the data, you're charged according to the data size. Data size is calculated based on the size of each column's data type.

The size of your stored data, and the size of the data processed by your queries is calculated in gigabytes (GB), where 1GB is 2³⁰ bytes. This unit of measurement is also known as a gibibyte (GiB). Similarly, 1TB is 2⁴⁰ bytes (1024 GBs).

The size of BigQuery's data types is as follows:

Data type	Size
INT64/INTEGER	8 bytes
FLOAT64/FLOAT	8 bytes
NUMERIC	16 bytes
BOOL/BOOLEAN	1 byte
STRING	2 bytes + the UTF-8 encoded string size
BYTES	2 bytes + the UTF-8 encoded string size
DATE	8 bytes
DATETIME	8 bytes
TIME	8 bytes
TIMESTAMP	8 bytes
STRUCT/RECORD	0 bytes + the size of the contained fields
GEOGRAPHY	16 bytes + 24 bytes * the number of vertices in the geography type (you can verify the number of vertices using the ST_NumPoints function)

Null values for any data type are calculated as 0 bytes.

A repeated column is stored as an array, and the size is calculated based on the number of values. For example, an integer column (INT64) that is repeated (ARRAY<INT64>) and contains 4 entries is calculated as 32 bytes (4 entries x 8 bytes).

Streaming pricing

Loading data into BigQuery is free, with the exception of a small charge for streamed data.

Pricing for streaming inserts is as follows:

Operation	Pricing	Details
Streaming Inserts		You are charged for rows that are successfully inserted. Individual rows are calculated using a 1 KB minimum size.

BigQuery ML pricing

Pricing applies to all currently supported models. BigQuery ML pricing is based on your usage pattern: flat-rate or on-demand.

BigQuery ML flat-rate pricing

BigQuery offers flat-rate pricing for high-volume or enterprise customers who prefer a stable monthly cost rather than paying the on-demand price for model creation, evaluation, inspection, and prediction.

Currently, there are no additional flat-rate charges for BigQuery ML. If you choose flat-rate pricing, BigQuery ML costs are included in the monthly BigQuery flat-rate price until July 31, 2019.

BigQuery ML on-demand pricing

BigQuery ML pricing for on-demand queries depends on the type of operation: model creation, model evaluation, model inspection, or model prediction.

BigQuery ML on-demand pricing is as follows:

Operation	Pricing	Details
Model creation		Limited to 50 iterations. The first 10 GB of data processed by CREATE MODEL statements per month is free under the BigQuery free tier.
Evaluation, inspection, and prediction		Charges from evaluation, inspection, and prediction queries are included in the 1 TB of data per month under the BigQuery analysis free tier.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Data Manipulation Language pricing

BigQuery charges for DML queries based on the number of bytes processed by the query.

DML pricing for non-partitioned tables

For non-partitioned tables, the number of bytes processed is calculated as follows:

DML statement	Bytes processed
INSERT	The sum of bytes processed for all the columns referenced from the tables scanned by the query.
UPDATE	The sum of bytes in all the columns referenced from the tables scanned by the query + the sum of bytes for all columns in the updated table at the time the UPDATE starts.
DELETE	The sum of bytes in all the columns referenced from the tables scanned by the query + the sum of bytes for all columns in the modified table at the time the DELETE starts.
MERGE	If there are only INSERT clauses in the MERGE statement, you are charged for the sum of bytes processed for all the columns referenced in all tables scanned by the query. If there is an UPDATE or DELETE clause in the MERGE statement, you are charged for the sum of the bytes processed for all the columns referenced in the source tables scanned by the query + the sum of bytes for all columns in the target table (at the time the MERGE starts).

DML pricing for partitioned tables

For partitioned tables, the number of bytes processed is calculated as follows:

DML statement	Bytes processed
INSERT	The sum of bytes processed for all the columns referenced in all partitions scanned by the query.
UPDATE	The sum of bytes processed for all the columns referenced in all partitions for the tables scanned by the query + the sum of bytes for all columns in the updated or scanned partitions for the table being updated (at the time the UPDATE starts).
DELETE	The sum of bytes processed for all the columns referenced in all partitions for the tables scanned by the query + the sum of bytes for all columns in the modified or scanned partitions for the table being modified (at the time the DELETE starts).
MERGE	If there are only INSERT clauses in the MERGE statement, you are charged for the sum of bytes processed for all the columns referenced in all partitions scanned by the query. If there is an UPDATE or DELETE clause in the MERGE statement, you are charged for the sum of the bytes processed for all the columns referenced in all partitions for the source tables scanned by the query + the sum of bytes for all columns in the updated, deleted or scanned partitions for the target table (at the time the MERGE starts).

Data Definition Language pricing

BigQuery charges for DDL queries based on the number of bytes processed by the query. The number of bytes processed is calculated as follows for DDL statements:

DDL statement	Bytes processed
CREATE TABLE	None.
CREATE TABLE ... AS SELECT ...	The sum of bytes processed for all the columns referenced from the tables scanned by the query.
CREATE VIEW	None.
DROP TABLE	None.
DROP VIEW	None.

Clustered table pricing

When you create and use clustered tables in BigQuery, your charges are based on how much data is stored in the tables and on the queries you run against the data. Clustered tables help you to reduce query costs by pruning data so it is not processed by the query. This process is called block pruning.

Block pruning

BigQuery sorts the data in a clustered table based on the values in the clustering columns and organizes them into blocks.

When you submit a query that contains a filter on a clustered column, BigQuery uses the clustering information to efficiently determine whether a block contains any data relevant to the query. This allows BigQuery to only scan the relevant blocks — a process referred to as block pruning.

Query pricing is based on the number of bytes processed. When you run a query against a clustered table, and the query includes a filter on the clustered columns, BigQuery uses the filter expression and the block metadata to prune the blocks scanned by the query.

When a block is pruned it is not scanned. Only the scanned blocks are used to calculate the bytes of data processed by the query. The number of bytes processed by a query against a clustered table equals the sum of the bytes read in each column referenced by the query in the scanned blocks.

If a clustered table is referenced multiple times in a query that uses several filters, BigQuery charges for scanning the columns in the appropriate blocks in each of the respective filters.

BigQuery Data Transfer Service pricing

The BigQuery Data Transfer Service charges monthly on a prorated basis. You are charged as follows:

Source application	Monthly charge (prorated)
Campaign Manager	$2.50 per unique Advertiser ID — Advertiser IDs in the impression table.
Cloud Storage	No charge. BigQuery Quotas & Limits apply.
Google Ads	$2.50 per unique Customer ID — ExternalCustomerIDs in the Customer table, including zero impression Customer IDs.
Google Ad Manager	$100 per Network ID
Google Play	No charge through August 31, 2018. Effective September 1, 2018: $25 per unique Package Name in the Installs_country table.
YouTube Channel	$5 per Channel
YouTube Content Owner	$5 per 1000 unique Channels ($0.005 per unique Channel) — Channel IDs in the content_owner_basic_a3 table.
Data warehouse migration service: Teradata	Free during the beta period. BigQuery limits on load jobs apply.

After data is transferred to BigQuery, standard BigQuery storage and query pricing applies. For additional pricing details, contact Sales.

Calculating unique IDs

Each transfer you create generates 1 or more runs per day. Each run maintains a record of each unique ID encountered and the date the transfer run completes. IDs are only counted on the day the transfer completes. For example, if a transfer run begins on July 14th but completes on July 15th, the unique IDs are counted on July 15th.

If a unique ID is encountered in more than one transfer run on a particular day, it is counted only once. Unique IDs are counted separately for different transfers. If a unique ID is encountered in runs for two separate transfers, the ID is counted twice.

Calculating backfill charges

If you schedule a backfill, a transfer run is scheduled for each day. You are then charged based on the method described in Calculating unique IDs.

Stopping BigQuery Data Transfer Service charges

To stop incurring charges, disable or delete your transfer.

Pricing examples

Estimating query costs

For query pricing examples, see Estimating query costs.

Estimating storage costs

For storage pricing examples, see Estimating storage costs.

DML pricing examples for non-partitioned tables

The following examples demonstrate how BigQuery calculates bytes read for DML statements that modify non-partitioned tables.

Example 1: Non-partitioned table UPDATE

table1 has two columns: col1 of type INTEGER and col2 of type STRING.

UPDATE table1 SET col1 = 1 WHERE col1 = 2;

Bytes processed in this example =

• sum of the number of bytes in col1 +
• sum of the number of bytes in col2

Example 2: Non-partitioned table UPDATE

table1 has two columns: col1 of type INTEGER and col2 of type STRING. table2 has one column: field1 of type INTEGER.

UPDATE table1 SET col1 = 1 WHERE col1 in (SELECT field1 from table2)

Bytes processed in this example =

• sum of the number of bytes in table1.col1 before UPDATE +
• sum of the number of bytes in table1.col2 before UPDATE +
• sum of the number of bytes in table2.field1

DML pricing examples for partitioned tables

The following examples demonstrate how BigQuery calculates bytes read for DML statements that modify ingestion-time and partitioned tables. To view the JSON schema representations for the tables used in the examples, see Tables used in examples on the Updating Partitioned Table Data Using DML Statements page.

Example 1: Ingestion-time partitioned table INSERT

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mytable has two columns: field1 of type INTEGER and field2 of type STRING.

INSERT INTO mytable (_PARTITIONTIME, field1) AS SELECT TIMESTAMP(DATE(ts)), id from mytable2

Bytes processed in this example =

• sum of the number of bytes in mytable2.ts +
• sum of the number of bytes in mytable2.id

The size of table into which the rows are inserted — mytable — does not affect the cost of the query.

Example 2: Partitioned table INSERT

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mycolumntable has four columns: field1 of type INTEGER, field2 of type STRING, field3 of type BOOLEAN, and ts of type TIMESTAMP.

INSERT INTO mycolumntable (ts, field1) AS SELECT ts, id from mytable2

Bytes processed in this example =

• sum of the number of bytes in mytable2.ts +
• sum of the number of bytes in mytable2.id

The size of table into which the rows are inserted — mycolumntable — does not affect the cost of the query.

Example 3: Ingestion-time partitioned table UPDATE

DML statement 1: Updating a single partition

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mytable has two columns: field1 of type INTEGER and field2 of type STRING.

UPDATE project.mydataset.mytable T SET T.field1 = T.field1 + 100 WHERE T._PARTITIONTIME = TIMESTAMP(“2017-05-01”) AND EXISTS (SELECT S.id from project.mydataset.mytable2 S WHERE S.id = T.field1)

Bytes processed in this example =

• sum of the number of bytes in mytable2.id +
• sum of the number of bytes in mytable.field1 in the "2017-05-01" partition +
• sum of the number of bytes in mytable.field2 in the "2017-05-01" partition

DML statement 2: Updating a partition based on another partition in the table

UPDATE project.mydataset.mytable T SET T._PARTITIONTIME = TIMESTAMP(“2017-06-01”), T.field1 = T.field1 + 100 WHERE T._PARTITIONTIME = TIMESTAMP(“2017-05-01”) AND EXISTS (SELECT 1 from project.mydataset.mytable S WHERE S.field1 = T.field1 AND S._PARTITIONTIME = TIMESTAMP("2017-06-01") )

Bytes processed in this example =

• sum of the number of bytes in mytable.field1 in the "2017-05-01" partition +
• sum of the number of bytes in mytable.field2 in the "2017-05-01" partition +
• sum of the number of bytes in mytable.field1 in the "2017-06-01" partition +
• sum of the number of bytes in mytable.field2 in the "2017-06-01" partition

In this case, the cost of the UPDATE statement is the sum of sizes of all fields in the partitions corresponding to "2017-05-01" and "2017-06-01".

Example 4: Partitioned table UPDATE

DML statement 1: Updating a single partition

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mycolumntable has four columns: field1 of type INTEGER, field2 of type STRING, field3 of type BOOLEAN, and ts of type TIMESTAMP.

UPDATE project.mydataset.mycolumntable T SET T.field1 = T.field1 + 100 WHERE DATE(T.ts) = “2017-05-01” AND EXISTS (SELECT S.id from project.mydataset.mytable2 S WHERE S.id = T.field1)

Bytes processed in this example =

• sum of the number of bytes in mytable2.id +
• sum of the number of bytes in mycolumntable.field1 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.field2 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.field3 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.ts in the "2017-05-01" partition

DML statement 2: Updating a partition based on another partition in the table

UPDATE project.mydataset.mycolumntable T SET T.ts = TIMESTAMP(“2017-06-01”), T.field1 = T.field1 + 100 WHERE DATE(T.ts) = “2017-05-01” AND EXISTS (SELECT 1 from project.mydataset.mycolumntable S WHERE S.field1 = T.field1 AND DATE(S.ts) = "2017-06-01")

Bytes processed in this example =

• sum of the number of bytes in mycolumntable.field1 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.field2 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.field3 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.ts in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.field1 in the "2017-06-01" partition +
• sum of the number of bytes in mycolumntable.field2 in the "2017-06-01" partition +
• sum of the number of bytes in mycolumntable.field3 in the "2017-06-01" partition +
• sum of the number of bytes in mycolumntable.ts in the "2017-06-01" partition

In this case, the cost of the UPDATE statement is the sum of sizes of all fields in the partitions corresponding to "2017-05-01" and "2017-06-01".

Example 5: Ingestion-time partitioned table DELETE

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mytable has two columns: field1 of type INTEGER and field2 of type STRING.

DELETE project.mydataset.mytable T WHERE T._PARTITIONTIME = TIMESTAMP(“2017-05-01”) AND EXISTS (SELECT S.id from project.mydataset.mytable2 S WHERE S.id = T.field1)

Bytes processed in this example =

• sum of the number of bytes in mytable2.id +
• sum of the number of bytes in mytable.field1 in the "2017-05-01" partition +
• sum of the number of bytes in mytable.field2 in the "2017-05-01" partition

Example 6: Partitioned table DELETE

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mycolumntable has four columns: field1 of type INTEGER, field2 of type STRING, field3 of type BOOLEAN, and ts of type TIMESTAMP.

DELETE project.mydataset.mycolumntable T WHERE DATE(T.ts) =“2017-05-01” AND EXISTS (SELECT S.id from project.mydataset.mytable2 S WHERE S.id = T.field1)

Bytes processed in this example =

• sum of the number of bytes in mytable2.id +
• sum of the number of bytes in mycolumntable.field1 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.field2 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.field3 in the "2017-05-01" partition +
• sum of the number of bytes in mycolumntable.ts in the "2017-05-01" partition

Clustered table pricing example

You have a clustered table named ClusteredSalesData. The table is partitioned by the timestamp column, and it is clustered by the customer_id column. The data is organized into the following set of blocks:

Partition identifier	Block ID	Minimum value for customer_id in the block	Maximum value for customer_id in the block
20160501	B1	10000	19999
20160501	B2	20000	24999
20160502	B3	15000	17999
20160501	B4	22000	27999

You run the following query against the table. The query contains a filter on the customer_id column.

SELECT
  SUM(totalSale)
FROM
  `mydataset.ClusteredSalesData`
WHERE
  customer_id BETWEEN 20000
  AND 23000
  AND DATE(timestamp) = "2016-05-01"

This query:

• Scans the timestamp, customer_id, and totalSale columns in blocks B2 and B4.
• Prunes the B3 block because of the DATE(timestamp) = "2016-05-01" filter predicate on the timestamp partitioning column.
• Prunes the B1 block because of the customer_id BETWEEN 20000 AND 23000 filter predicate on the customer_id clustering column.

BigQuery ML pricing example

BigQuery ML charges are not itemized separately on your billing statement. If you have a flat-rate plan, BigQuery ML charges are included in your flat-rate charges until July 31, 2019.

If you are using on-demand pricing, BigQuery ML charges are included in the BigQuery analysis (query) charges.

BigQuery ML jobs that perform inspection, evaluation, and prediction operations incur the same charges as on-demand query jobs. Because CREATE MODEL queries incur different charges, you must calculate CREATE MODEL job costs independently by using the Stackdriver audit logs. Using the audit logs, you can determine the bytes billed by the BigQuery ML service for each BigQuery ML CREATE MODEL job. Then, multiply the bytes billed by the appropriate cost for CREATE MODEL queries in your regional or multi-regional location.

For example, to determine the cost of a query job in the US that includes a BigQuery ML CREATE MODEL statement:

1. Open the Stackdriver Logging page in the GCP console.

2. Verify that the product is set to BigQuery.

3. Click the drop-down arrow in the "Filter by label or text search" box and choose Convert to advanced filter. This adds the following text to the filter:

   resource.type="bigquery_resource"
   

4. Add the following text on line two below the resource.type line:

   protoPayload.serviceData.jobCompletedEvent.job.jobConfiguration.query.statementType="CREATE_MODEL"
   

5. To the right of the Submit Filter button, choose the appropriate time frame from the drop-down list. For example, choosing Last 24 hours would display BigQuery ML CREATE MODEL jobs completed in the past 24 hours.

6. Click Submit Filter to display the jobs completed in the given time window.

7. After the data is populated, click View Options and choose Modify custom fields.

8. In the Add custom fields dialog, enter:

   protoPayload.serviceData.jobCompletedEvent.job.jobStatistics.totalBilledBytes
   

9. Click Save to update the results.

10. After the page is updated, the bytes billed by each BigQuery ML job appear to the right of the job's timestamp. If the bytes billed are included in the free tier, no value appears. For example:

    

11. To calculate the charges for the BigQuery ML CREATE MODEL job, multiply the bytes billed by the BigQuery ML on-demand price. In this example, the CREATE MODEL job processed 100873011200 bytes. To calculate the cost of this job in the US multi-regional location, divide the billed bytes by the number of bytes per TB, and multiply it by the model creation cost:

    100873011200/1099511627776 x $250.00 = $22.94

BigQuery Data Transfer Service pricing examples

Example 1: You have 1 transfer with 3 runs that complete on the same day.

• The first run records the following unique IDs: A, B, and C
• The second run records: A
• The third run records: C and D

Since all runs finish on the same day, you are charged based on 4 unique IDs — A, B, C, D. Because ID A and ID C were recorded in two different runs that completed on the same day, IDs A and C are counted only once. If the 3 transfer runs complete every day for a month, your monthly charge is based on 4 unique IDs. If the transfer runs complete fewer times than the number of days in the month in which they run, the charges are prorated.

Example 2: You have multiple transfers with runs that complete on the same day.

• Transfer 1 runs and records the following unique IDs: A, B, and C
• Transfer 2 runs and records: A
• Transfer 3 runs and records: C and D

Because the unique IDs are counted in runs for different transfers, you are charged based on 6 unique IDs — A, B, and C from transfer 1's run; A from transfer 2's run; and C and D from transfer 3's run. If the transfer runs complete fewer times than the number of days in the month in which they run, the charges are prorated.