Creating and using ingestion-time partitioned tables

This document describes how to create and use ingestion-time partitioned tables in BigQuery. For information on column-based time partitioned tables, see Creating and using column-based time partitioned tables. For information on integer range partitioned tables, see Creating and using integer range partitioned tables.

After creating an ingestion-time partitioned table, you can:

• Control access to your table data
• Get information about your partitioned tables
• List the partitioned tables in a dataset
• Get partitioned table metadata using meta-tables

For more information on managing partitioned tables including updating partitioned table properties, copying a partitioned table, and deleting a partitioned table, see Managing partitioned tables.

Creating ingestion-time partitioned tables

You can create an ingestion-time partitioned table in BigQuery:

• By using a DDL CREATE TABLE statement with a partition_expression
• Manually by using the Cloud Console
• By using the bq command-line tool's bq mk command
• Programmatically by calling the tables.insert API method
• By using the client libraries
• From query results
• When you load data
• By converting date-sharded tables into a partitioned tables

Table naming

When you create a table in BigQuery, the table name must be unique per dataset. The table name can:

• Contain up to 1,024 characters.
• Contain Unicode characters in category L (letter), M (mark), N (number), Pc (connector, including underscore), Pd (dash), Zs (space). For more information, see General Category.

For example, the following are all valid table names: table-01, ग्राहक, 00_お客様, étudiant.

Required permissions

At a minimum, to create a table, you must be granted the following permissions:

• bigquery.tables.create permissions to create the table
• bigquery.tables.updateData to write data to the table by using a load job, a query job, or a copy job
• bigquery.jobs.create to run a query job, load job, or copy job that writes data to the table

Additional permissions such as bigquery.tables.getData might be required to access the data you're writing to the table.

The following predefined IAM roles include both bigquery.tables.create and bigquery.tables.updateData permissions:

• bigquery.dataEditor
• bigquery.dataOwner
• bigquery.admin

The following predefined IAM roles include bigquery.jobs.create permissions:

• bigquery.user
• bigquery.jobUser
• bigquery.admin

In addition, if a user has bigquery.datasets.create permissions, when that user creates a dataset, they are granted bigquery.dataOwner access to it. bigquery.dataOwner access gives the user the ability to create and update tables in the dataset.

For more information on IAM roles and permissions in BigQuery, see Predefined roles and permissions.

Creating an empty ingestion-time partitioned table with a schema definition

When you create an empty time partitioned table with a schema definition, you can:

• Enter the schema by using the Cloud Console
• Provide the schema inline using the bq command-line tool
• Submit a JSON schema file using the bq command-line tool
• Provide the schema in a table resource when calling the API's tables.insert method

For more information on specifying a table schema, see Specifying a schema.

After the time partitioned table is created, you can:

• Load data into it
• Write query results to it
• Copy data into it

To create an empty time partitioned table with a schema definition:

Console

1. Select a dataset from the Resources section of the navigation panel. Click Create table on the right side of the window.
2. On the Create table page, in the Source section, select Create empty table.

3. On the Create table page, in the Destination section:

   • For Dataset name, choose the appropriate dataset.

   • In the Table name field, enter the name of the table you're creating in BigQuery.

   • Verify that Table type is set to Native table.

4. In the Schema section, enter the schema definition.

   • Enter schema information manually by:

     • Enabling Edit as text and entering the table schema as a JSON array.

       Note: You can view the schema of an existing table in JSON format by entering the following command in the bq command-line tool: bq show --format=prettyjson dataset.table.

     • Using Add field to manually input the schema.

5. In the Partition and cluster settings section:

   • For Partitioning, click No partitioning and select Partition by ingestion time.

6. (Optional) For Partitioning filter, click the Require partition filter box to require users to include a WHERE clause that specifies the partitions to query. Requiring a partition filter may reduce cost and improve performance. For more information, see Querying partitioned tables.

7. (Optional) Click Advanced options and for Encryption, click Customer-managed key to use a Cloud Key Management Service key. If you leave the Google-managed key setting, BigQuery encrypts the data at rest.

8. Click Create table.

Note: When you create an empty partitioned table using the Cloud Console, you cannot add a label, description, table expiration, or partition expiration. You can add these optional properties when you create a table using the bq command-line tool or the API.

After the table is created, you can update the partitioned table's table expiration, description, and labels. You cannot add a partition expiration after a table is created using the Cloud Console.

bq

Use the mk command with the --table flag (or -t shortcut), and the -- time_partitioning_type flag, set to either DAY, HOUR, MONTH, or YEAR as appropriate to your partitioning type. You can supply table's schema definition inline or via a JSON schema file. Optional parameters include --expiration, --description, --time_partitioning_expiration, --destination_kms_key, and --label. If you are creating a table in a project other than your default project, add the project ID to the dataset in the following format: project_id:dataset.

--destination_kms_key is not demonstrated here. For more information on using this flag, see Protecting data with Cloud KMS keys.

Enter the following command to create an empty ingestion-time partitioned table with a schema definition:

bq mk --table \
--expiration integer1 \
--time_partitioning_type=unit_time \
--time_partitioning_expiration integer2 \
--description "description" \
-label key:value,key:value \
project_id:dataset.table \
schema

Where:

• integer1 is the default lifetime (in seconds) for the table. The minimum value is 3600 seconds (one hour). The expiration time evaluates to the current UTC time plus the integer value. If you set the table's expiration time when you create a partitioned table, the dataset's default table expiration setting is ignored. Setting this value deletes the table and all partitions after the specified time.
• unit_time is either DAY, HOUR, MONTH, or YEAR, depending on your partitioning type. This defaults to DAY when time_partitioning_type is not specified.
• integer2 is the default lifetime (in seconds) for the table's partitions. There is no minimum value. The expiration time evaluates to the partition's date plus the integer value. The partition expiration is independent of the table's expiration but does not override it. If you set a partition expiration that is longer than the table's expiration, the table expiration takes precedence.
• description is a description of the table in quotes.
• key:value is the key:value pair that represents a label. You can enter multiple labels using a comma-separated list.
• project_id is your project ID.
• dataset is a dataset in your project.
• table is the name of the time partitioned table you're creating.
• schema is an inline schema definition in the format column:data_type,column:data_type or the path to the JSON schema file on your local machine.

When you specify the schema on the command line, you cannot include a RECORD (STRUCT) type, you cannot include a column description, and you cannot specify the column's mode. All modes default to NULLABLE. To include descriptions, modes, and RECORD types, supply a JSON schema file instead.

Examples:

Enter the following command to create an ingestion-time partitioned table named mytable in mydataset in your default project. The partitioning type is set to DAY, the partitioning expiration is set to 259,200 seconds (3 days), the table's expiration is set to 2,592,000 (one 30-day month), the description is set to This is my time partitioned table, and the label is set to organization:development. The command uses the -t shortcut instead of --table. The schema is specified inline as: qtr:STRING,sales:FLOAT,year:STRING.

bq mk -t \
--expiration 2592000 \
--time_partitioning_type=DAY \
--time_partitioning_expiration 259200 \
--description "This is my time partitioned table" \
--label organization:development \
mydataset.mytable \
qtr:STRING,sales:FLOAT,year:STRING

Enter the following command to create a time partitioned table named mytable in myotherproject, not your default project. The partitioning type is set to DAY, the partitioning expiration is set to 259,200 seconds (3 days), the description is set to This is my time partitioned table, and the label is set to organization:development. The command uses the -t shortcut instead of --table. This command does not specify a table expiration. If the dataset has a default table expiration, it is applied. If the dataset has no default table expiration, the table will never expire, but the partitions will expire in 3 days. The path to the schema file is /tmp/myschema.json.

bq mk -t \
--time_partitioning_type=DAY \
--time_partitioning_expiration 259200 \
--description "This is my time partitioned table" \
--label organization:development \
myotherproject:mydataset.mytable \
/tmp/myschema.json

After the table is created, you can update the ingestion-time partitioned table's table expiration, partition expiration, description, and labels.

API

Call the tables.insert method with a defined table resource that specifies the timePartitioning property and the schema property.

Creating an ingestion-time partitioned table from a query result

To create a partitioned table from a query result, write the results to a new destination table. You can create a new partitioned table by querying a partitioned table or a non-partitioned table. You cannot change an existing table to a partitioned table using query results.

For more information on querying time partitioned tables, see Querying partitioned tables.

Console

You cannot specify partitioning options for a destination table when you query data using the Cloud Console.

bq

Enter the bq query command, specify the --destination_table flag to create a permanent table based on the query results, and specify the --time_partitioning_type flag to create an ingestion-time partitioned destination table. DAY, HOUR, MONTH, and YEAR are the supported values for --time_partitioning_type, depending on the granularity of your partition.

Specify the use_legacy_sql=false flag to use standard SQL syntax. To write the query results to a table that is not in your default project, add the project ID to the dataset name in the following format: project_id:dataset.

(Optional) Supply the --location flag and set the value to your location.

Enter the following command to create a new, ingestion-time partitioned destination table from a query result:

bq --location=location query \
--destination_table project_id:dataset.table \
--time_partitioning_type=unit_time \
--use_legacy_sql=false \
'query'

Where:

• location is the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
• project_id is your project ID.
• dataset is the name of the dataset that will contain the new time partitioned table.
• table is the name of the time partitioned table you're creating using the query results.
  • unit_time is either DAY, HOUR, MONTH, or YEAR, depending on your partitioning type. This defaults to DAY when time_partitioning_type is unspecified.
• query is a query in standard SQL syntax.

Examples:

Note: These examples query a US-based public dataset. Because the public dataset is stored in the US multi-region location, the dataset that contains your destination table must also be in the US. You cannot query a dataset in one location and write the results to a destination table in another location.

Enter the following command to write query results to an ingestion-time daily partitioned destination table named mytable in mydataset. The dataset is in your default project. The query retrieves data from a non- partitioned table — the USA Name Data public dataset.

bq query \
--destination_table mydataset.mytable \
--time_partitioning_type=DAY \
--use_legacy_sql=false \
'SELECT
   name,
   number
 FROM
   `bigquery-public-data`.usa_names.usa_1910_current
 WHERE
   gender = "M"
 ORDER BY
   number DESC'

Enter the following command to write query results to an ingestion-time partitioned destination table named mytable in mydataset, partitioned by the date of data ingestion. The dataset is in myotherproject, not your default project. The query retrieves data from a non-partitioned table — the USA Name Data public dataset.

bq query \
--destination_table myotherproject:mydataset.mytable \
--time_partitioning_type=DAY \
--use_legacy_sql=false \
'SELECT
   name,
   number
 FROM
   `bigquery-public-data`.usa_names.usa_1910_current
 WHERE
   gender = "M"
 ORDER BY
   number DESC'

API

To save query results to a permanent, ingestion-time partitioned table, call the jobs.insert method, configure a query job, and include a value for the destinationTable and timePartitioning properties.

Creating an ingestion-time partitioned table when loading data

You can create an ingestion-time partitioned table by specifying partitioning options when you load data into a new table. You do not need to create an empty partitioned table before loading data into it. You can create the partitioned table and load your data at the same time.

When you load data into BigQuery, you can supply the table schema, or for supported data formats, you can use schema auto-detection.

Partition decorators enable you to load data into a specific partition. To adjust for timezones, use a partition decorator to load data into a partition based on your preferred timezone. For example, if you are on Pacific Standard Time (PST), load all data that is generated on May 1, 2016 PST into the partition for that date by using the corresponding partition decorator:

table_name$20160501

For more information on loading data, see Introduction to loading data into BigQuery.

Converting date-sharded tables into ingestion-time partitioned tables

If you have previously created date-sharded tables, you can convert the entire set of related tables into a single ingestion-time partitioned table by using the partition command in the bq command-line tool. The date-sharded tables must use the following naming convention: table_YYYYMMDD. For example, mytable_20160101, ... , mytable_20160331.

Optional parameters include --time_partitioning_expiration, --location, and --time_partitioning_type. If unspecified, --time_partitioning_type defaults to DAY. If your source tables or your destination table are in a project other than your default project, add the project ID to the dataset name in the following format: project_id:dataset.

Note: The --label, --expiration, and --description flags are not supported by the partition command. You can add labels, a table expiration, and a description to the partitioned table after it is created.

Enter the following command to convert a series of date-sharded tables to a single ingestion-time partitioned table, partitioned by date:

bq --location=location partition \
--time_partitioning_type=DAY \
--time_partitioning_expiration integer \
project_id:dataset.source_table_ \
project_id:dataset.destination_table

Where:

• location is the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
• integer is the default lifetime (in seconds) for the table's partitions. There is no minimum value. The expiration time evaluates to the partition's UTC date plus the integer value. The partition expiration is independent of the table's expiration but does not override it. If you set a partition expiration that is longer than the table's expiration, the table expiration takes precedence.
• project_id is your project ID.
• dataset is a dataset in your project.
• sourcetable is the prefix of your date-sharded table.
• destination_table is the name of the partitioned table you're creating.

Examples:

Enter the following command to create an ingestion-time partitioned table named mytable_partitioned in mydataset in your default project. The table will be partitioned by date, with the partitioning expiration set to 259,200 seconds (3 days). The date sharded source tables are prefixed with sourcetable_. The source tables are also in your default project.

bq partition \
--time_partitioning_type=DAY \
--time_partitioning_expiration 259200 \
mydataset.sourcetable_ \
mydataset.mytable_partitioned

When you run the command, a copy job is created that generates partitions from the sharded tables. For example, if the date-sharded tables were sourcetable_20180126 and sourcetable_20180127, the copy job would create the following partitions: mydataset.mytable_partitioned$20180126 and mydataset.mytable_partitioned$20180127.

Enter the following command to create an ingestion-time partitioned table named mytable_partitioned in mydataset. mydataset is in myotherproject, not your default project. The table will be partitioned by date, with the partitioning expiration set to 86,400 seconds (1 day). The date sharded source tables are prefixed with sourcetable_. The source tables are in your default project.

bq partition \
--time_partitioning_type=DAY \
--time_partitioning_expiration 86400 \
mydataset.sourcetable_ \
myotherproject:mydataset.mytable_partitioned

Enter the following command to create an ingestion-time partitioned table named mytable_partitioned in mydataset in your default project. mydataset was created in the asia-northeast1 region. The table will be partitioned by date, with the partitioning expiration is set to 259,200 seconds (3 days). The date sharded source tables are prefixed with sourcetable_. The source tables are also in your default project.

bq --location=asia-northeast1 partition \
--time_partitioning_type=DAY \
--time_partitioning_expiration 259200 \
mydataset.sourcetable_ \
mydataset.mytable_partitioned

Controlling access to time partitioned tables

To configure access to tables and views, you can grant an IAM role to an entity at the following levels, listed in order of range of resources allowed (largest to smallest):

• a high level in the Google Cloud resource hierarchy such as the project, folder, or organization level
• the dataset level
• the table/view level

Access with any resource protected by IAM is additive. For example, if an entity does not have access at the high level such as a project, you could grant the entity access at the dataset level, and then the entity will have access to the tables and views in the dataset. Similarly, if the entity does not have access at the high level or the dataset level, you could grant the entity access at the table of view level.

Granting IAM roles at a higher level in the Google Cloud resource hierarchy such as the project, folder, or organization level gives the entity access to a broad set of resources. For example, granting a role to an entity at the project level gives that entity permissions that apply to all datasets throughout the project.

Granting a role at the dataset level specifies the operations an entity is allowed to perform on tables and views in that specific dataset, even if the entity does not have access at a higher level. For information on configuring dataset-level access controls, see Controlling access to datasets.

Granting a role at the table or view level specifies the operations an entity is allowed to perform on specific tables and views, even if the entity does not have access at a higher level. For information on configuring table-level access controls, see Controlling access to tables and views.

You can also create IAM custom roles. If you create a custom role, the permissions you grant depend on the specific operations you want the entity to be able to perform.

You can't set a "deny" permission on any resource protected by IAM.

For more information on roles and permissions, see:

• Understanding roles in the IAM documentation
• BigQuery Predefined roles and permissions
• Controlling access to datasets
• Controlling access to tables and views
• Restricting access with BigQuery Column-level security

Using ingestion-time partitioned tables

Getting information about ingestion-time partitioned tables

You can get information about tables by:

• Using the Cloud Console
• Using the bq show command in the bq command-line tool
• Calling the tables.get API method
• Using the client libraries

Required permissions

At a minimum, to get information about tables, you must be granted bigquery.tables.get permissions. The following predefined IAM roles include bigquery.tables.get permissions:

• bigquery.metadataViewer
• bigquery.dataViewer
• bigquery.dataOwner
• bigquery.dataEditor
• bigquery.admin

In addition, if a user has bigquery.datasets.create permissions, when that user creates a dataset, they are granted bigquery.dataOwner access to it. bigquery.dataOwner access gives the user the ability to get information about tables in a dataset.

For more information on IAM roles and permissions in BigQuery, see Predefined roles and permissions.

Getting ingestion-time partitioned table information

To view information about an ingestion-time partitioned table:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. In the navigation panel, in the Resources section, expand your project and select a dataset to list the tables and views it contains.

3. Click the table name.

4. Click Details. This tab displays the table's description and table information.

   

5. Click the Schema tab to view the table's schema definition. Notice the additional pseudo column _PARTITIONTIME that contains the date-based timestamp for data that is loaded into the table.

bq

Issue the bq show command to display all table information. Use the --schema flag to display only table schema information. The --format flag can be used to control the output.

If you are getting information about a table in a project other than your default project, add the project ID to the dataset in the following format: project_id:dataset.

bq show --schema --format=prettyjson <var>project_id:dataset.table</var>

Where:

• project_id is your project ID.
• dataset is the name of the dataset.
• table is the name of the table.

Examples:

Enter the following command to display all information about mytable in mydataset. mydataset is in your default project.

bq show --format=prettyjson mydataset.mytable

Enter the following command to display all information about mytable in mydataset. mydataset is in myotherproject, not your default project.

bq show --format=prettyjson myotherproject:mydataset.mytable

Enter the following command to display only schema information about mytable in mydataset. mydataset is in myotherproject, not your default project.

When you show schema information for a time partitioned table, the _PARTITIONTIME pseudo column is not displayed.

bq show --schema --format=prettyjson myotherproject:mydataset.mytable

API

Call the bigquery.tables.get method and provide any relevant parameters.

Listing ingestion-time partitioned tables in a dataset

You can list tables in datasets (including partitioned tables) by using the Cloud Console or the bq ls command in the bq command-line tool, or by calling the tables.list API method.

Required permissions

At a minimum, to list tables in a dataset, you must be granted bigquery.tables.list permissions. The following predefined IAM roles include bigquery.tables.list permissions:

• bigquery.user
• bigquery.metadataViewer
• bigquery.dataViewer
• bigquery.dataOwner
• bigquery.dataEditor
• bigquery.admin

For more information on IAM roles and permissions in BigQuery, see Predefined roles and permissions.

Listing partitioned tables

To list the tables in a dataset (including partitioned tables):

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. In the navigation panel, in the Resources section, expand your project and click on your dataset.

3. Scroll through the list to see the tables in the dataset. Tables, partitioned tables, models, and views are identified by different icons.

bq

Issue the bq ls command. The --format flag can be used to control the output. If you are listing tables in a project other than your default project, add the project ID to the dataset in the following format: project_id:dataset.

bq ls --format=pretty project_id:dataset

Where:

• project_id is your project ID.
• dataset is the name of the dataset.

When you run the command, the Type field displays either TABLE or VIEW. For ingestion-time partitioned tables, the Time Partitioning field displays DAY and the partition expiration time in milliseconds, if a partition expiration is specified.

For example:

+-------------------------+-------+----------------------+--------------------------------+
|         tableId         | Type  |        Labels        | Time Partitioning              |
+-------------------------+-------+----------------------+--------------------------------+
| mytable                 | TABLE | department:shipping  |  DAY (expirationMs: 259200000) |
| myview                  | VIEW  |                      |                                |
+-------------------------+-------+----------------------+--------------------------------+

Examples:

Enter the following command to list tables in dataset mydataset in your default project.

bq ls --format=pretty mydataset

Enter the following command to list tables in dataset mydataset in myotherproject.

bq ls --format=pretty myotherproject:mydataset

API

To list tables using the API, call the tables.list method.

Listing partitions in ingestion-time partitioned tables

You can list the partitions in an ingestion-time partitioned table by querying the _PARTITIONTIME pseudo column in standard SQL or the __PARTITIONS_SUMMARY__ meta table in legacy SQL.

You can run the query by using the Cloud Console, by using the bq query command in the bq command-line tool, or by calling the jobs.insert method and configuring a query job.

Required permissions

To run a query job that uses the __PARTITIONS_SUMMARY__ meta table, or the _PARTITIONTIME pseudo column, you must be granted bigquery.jobs.create permissions. The following predefined IAM roles include bigquery.jobs.create permissions:

• bigquery.user
• bigquery.jobUser
• bigquery.admin

You must also be granted bigquery.tables.getData permissions. The following predefined IAM roles include bigquery.tables.getData permissions:

• bigquery.admin
• bigquery.dataEditor
• bigquery.dataOwner
• bigquery.dataViewer

For more information on IAM roles and permissions in BigQuery, see Predefined roles and permissions.

Listing partitions in an ingestion-time partitioned table

You can list partitions in an ingestion-time partitioned table using standard SQL (preferred) or legacy SQL. To list partitions:

Standard SQL:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Click the Compose new query button.

3. Enter the following text into the Query editor box to query the _PARTITIONTIME pseudo column:

   SELECT
     _PARTITIONTIME as pt
   FROM
     `dataset.table`
   GROUP BY 1
   

   Where:

   • dataset is the dataset that contains the table.
   • table is the name of the table.

4. (Optional) Click More and select Query settings.

   

5. Click Run.

bq

Enter the following query using the bq query command:

bq --location=location query --use_legacy_sql=false '
SELECT
  _PARTITIONTIME as pt
FROM
  `dataset.table`
GROUP BY 1'

Where:

• location is the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
• dataset is the dataset that contains the table.
• table is the name of the table.

API

Call the jobs.insert method and configure a query job that queries the table's _PARTITIONTIME pseudo column.

Specify your location in the location property in the jobReference section of the job resource.

Legacy SQL:

Console

1. Open the BigQuery page in the Cloud Console.

   Go to the BigQuery page

2. Click the Compose new query button.

3. Enter the following text into the Query editor box to query the __PARTITIONS_SUMMARY__ meta table:

   #legacySQL
   SELECT
     partition_id
   FROM
     [dataset.table$__PARTITIONS_SUMMARY__]
   

   Where:

   • dataset is the dataset that contains the table.
   • table is the name of the table.

4. Click More and select Query settings.

   

5. For Processing location, click Auto-select and choose your data's location. You can leave processing location set to auto-select if your data is in the US or EU multi-region location. When your data is in the US or the EU, the processing location is automatically detected.

   

6. Click Run.

bq

Enter the following query using the bq query command:

bq --location=location query --use_legacy_sql=true '
SELECT
   partition_id
 FROM
   [dataset.table$__PARTITIONS_SUMMARY__]'

Where:

• location is the name of your location. The --location flag is optional. For example, if you are using BigQuery in the Tokyo region, you can set the flag's value to asia-northeast1. You can set a default value for the location using the .bigqueryrc file.
• dataset is the dataset that contains the table.
• table is the name of the table.

API

Call the jobs.insert method and configure a query job that queries the __PARTITIONS_SUMMARY__ meta table.

Specify your location in the location property in the jobReference section of the job resource.

Getting table metadata using meta tables

You can get information about partitioned tables by using special tables called meta tables. Meta tables contain metadata such as the list of tables and views in a dataset. The meta tables are read only.

Getting partition metadata using meta tables

The __PARTITIONS_SUMMARY__ meta table is a special table whose contents represent metadata about partitions in a time-partitioned table. The __PARTITIONS_SUMMARY__ meta table is read-only.

To access metadata about the partitions in a time-partitioned table, use the __PARTITIONS_SUMMARY__ meta-table in a query's SELECT statement. You can run the query by:

• Using the Cloud Console
• Using the bq command-line tool's bq query command
• Calling the jobs.insert API method and configuring a query job
• Using the client libraries

Currently, standard SQL does not support the partition decorator separator ($) so you cannot query __PARTITIONS_SUMMARY__ in standard SQL. A legacy SQL query that uses the __PARTITIONS_SUMMARY__ meta-table looks like the following:

#legacySQL
SELECT
  column
FROM
  [dataset.table$__PARTITIONS_SUMMARY__]

Where:

• dataset is the name of your dataset.
• table is the name of the time-partitioned table.
• column is one of the following:

Value	Description
project_id	Name of the project.
dataset_id	Name of the dataset.
table_id	Name of the time-partitioned table.
partition_id	Name (date) of the partition.
creation_time	The time at which the partition was created, in milliseconds since January 1, 1970 UTC.
last_modified_time	The time at which the partition was last modified, in milliseconds since January 1, 1970 UTC.

Partition meta table permissions

At a minimum, to run a query job that uses the __PARTITIONS_SUMMARY__ meta-table, you must be granted bigquery.jobs.create permissions. The following predefined IAM roles include bigquery.jobs.create permissions:

• bigquery.user
• bigquery.jobUser
• bigquery.admin

You must also be granted bigquery.tables.getData permissions. The following predefined IAM roles include bigquery.tables.getData permissions:

• bigquery.dataViewer
• bigquery.dataEditor
• bigquery.dataOwner
• bigquery.admin

For more information on IAM roles in BigQuery, see Access control.

Partition meta table examples

The following query retrieves all partition metadata for a time-partitioned table named mydataset.mytable.

Console

#legacySQL
SELECT
  *
FROM
  [mydataset.mytable$__PARTITIONS_SUMMARY__]

bq

bq query --use_legacy_sql=true '
SELECT
  *
FROM
  [mydataset.mytable$__PARTITIONS_SUMMARY__]'

The output looks like the following:

+----------------+------------+----------------+--------------+---------------+--------------------+
|   project_id   | dataset_id |    table_id    | partition_id | creation_time | last_modified_time |
+----------------+------------+----------------+--------------+---------------+--------------------+
| myproject      | mydataset  | mytable        | 20160314     | 1517190224120 | 1517190224997      |
| myproject      | mydataset  | mytable        | 20160315     | 1517190224120 | 1517190224997      |
+----------------+------------+----------------+--------------+---------------+--------------------+

The following query lists the times when the partitions in mydataset.mytable were last modified.

Console

#legacySQL
SELECT
  partition_id,
  last_modified_time
FROM
  [mydataset.mytable$__PARTITIONS_SUMMARY__]

bq

bq query --use_legacy_sql=true '
SELECT
  partition_id,
  last_modified_time
FROM
  [mydataset.mytable$__PARTITIONS_SUMMARY__]'

The output looks like the following:

+--------------+--------------------+
| partition_id | last_modified_time |
+--------------+--------------------+
| 20160102     |      1471632556179 |
| 20160101     |      1471632538142 |
| 20160103     |      1471632570463 |
+--------------+--------------------+

To display the last_modified_time field in human-readable format, use the FORMAT_UTC_USEC function. For example:

Console

#legacySQL
SELECT
  partition_id,
  FORMAT_UTC_USEC(last_modified_time*1000) AS last_modified
FROM
  [mydataset.table1$__PARTITIONS_SUMMARY__]

bq

bq query --use_legacy_sql=true '
SELECT
  partition_id,
  FORMAT_UTC_USEC(last_modified_time*1000) AS last_modified
FROM
  [mydataset.mytable$__PARTITIONS_SUMMARY__]'

The output looks like the following:

+--------------+----------------------------+
| partition_id |       last_modified        |
+--------------+----------------------------+
| 20160103     | 2016-08-19 18:49:30.463000 |
| 20160102     | 2016-08-19 18:49:16.179000 |
| 20160101     | 2016-08-19 18:48:58.142000 |
+--------------+----------------------------+

Example: Creating an ingestion-time partitioned table using query results

In this example, you create an ingestion-time partitioned table by using the bq command-line tool, and you add data to three partitions using query results. The table will contain weather data, partitioned by date, for the first three days in 2016.

This example queries the NOAA GSOD weather dataset for temperatures for the first three days of 2016 and writes the results to the a partitioned table.

Note: These examples query a US-based public dataset. Because the public dataset is stored in the US multi-region location, the dataset that contains your destination table must also be in the US. You cannot query a dataset in one location and write the results to a destination table in another location.

Step 1. Create an empty ingestion-time table, partitioned by date, named temps in a dataset named mydataset. There is no need to specify a schema for the table. The schema from the query results is used as the schema definition for the table.

bq mk --time_partitioning_type=DAY mydataset.temps

To view the configuration settings, use the bq show command:

bq show --format=prettyjson mydataset.temps

Look for the timePartitioning entry in the bq show command output:

{
  ...
  "timePartitioning": {
    "type": "DAY"
  },
  "type": "TABLE"
}

Step 2. Query the NOAA GSOD weather dataset for temperatures for the first three days of 2016 and write the results to the respective partitions in the temps table. The following queries use the --destination_table option and standard SQL syntax to write 100 rows of query results to a partition.

1. Run the following query to write January 1, 2016 temperatures to partition mydataset.temps$20160101:

   bq query --use_legacy_sql=false \
   --destination_table 'mydataset.temps$20160101' \
   'SELECT
      stn,
      temp
    FROM
      `bigquery-public-data`.noaa_gsod.gsod2016
    WHERE
      mo="01"
      AND da="01"
    LIMIT
      100'
   

2. Run the following query to write January 2, 2016 temperatures to partition mydataset.temps$20160102:

   bq --location=US query --use_legacy_sql=false \
   --destination_table 'mydataset.temps$20160102' \
   'SELECT
      stn,
      temp
    FROM
      `bigquery-public-data`.noaa_gsod.gsod2016
    WHERE
      mo="01"
      AND da="02"
    LIMIT
      100'
   

3. Run the following query to write January 3, 2016 temperatures to partition mydataset.temps$20160103:

   bq --location=US query --use_legacy_sql=false \
   --destination_table 'mydataset.temps$20160103' \
   'SELECT
      stn,
      temp
    FROM
      `bigquery-public-data`.noaa_gsod.gsod2016
    WHERE
      mo="01"
      AND da="03"
    LIMIT
      100'
   

Step 3. Confirm that you have 300 rows in your table by using the bq show command.

bq show mydataset.temps

The results show the Schema and Total Rows.

  Last modified        Schema       Total Rows   Total Bytes     Expiration      Time Partitioning   Labels   kmsKeyName
 ----------------- ---------------- ------------ ------------- ----------------- ------------------- -------- ------------
  28 Jan 15:03:45   |- stn: string   300          4800          29 Jan 15:00:32   DAY
                    |- temp: float

Next steps

• For an overview of partitioned table support in BigQuery, see Introduction to partitioned tables.
• To learn how to manage and update partitioned tables, see Managing partitioned tables.
• For information on querying partitioned tables, see Querying partitioned tables.
• For information on column-based time partitioned tables, see Creating and using column-based time partitioned tables.
• For information on integer-range partitioned tables, see Creating and using integer-range partitioned tables.