This document describes how to create and use ingestion-time partitioned tables in BigQuery. For information on date/timestamp partitioned tables, see Creating and using date/timestamp 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 apartition_expression
- Manually by using the Cloud Console or the classic BigQuery web UI
- By using the 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 letters (upper or lower case), numbers, and underscores
Required permissions
At a minimum, to create a table, you must be granted the following permissions:
bigquery.tables.create
permissions to create the tablebigquery.tables.updateData
to write data to the table by using a load job, a query job, or a copy jobbigquery.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
may be required to
access the data you're writing to the table.
The following predefined Cloud IAM roles include both
bigquery.tables.create
and bigquery.tables.updateData
permissions:
bigquery.dataEditor
bigquery.dataOwner
bigquery.admin
The following predefined Cloud 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 Cloud 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 or the classic BigQuery web UI
- Provide the schema inline using the command-line tool
- Submit a JSON schema file using the 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
- Select a dataset from the Resources section of the navigation panel.
Click Create table on the right side of the window.
- On the Create table page, in the Source section, select Create empty table.
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.
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.
Using Add field to manually input the schema.
In the Partition and cluster settings section:
- For Partitioning, click No partitioning and select
Partition by ingestion time.
- For Partitioning, click No partitioning and select
Partition by ingestion time.
(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.(Optional) Click Advanced options and for Encryption, click Customer-managed key to use a Key Management Service key. If you leave the Google-managed key setting, BigQuery encrypts the data at rest.
Click Create table.
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 BigQuery web UI.
Classic UI
Click the down arrow icon
next to your dataset name in the navigation and click Create new table.
On the Create Table page, in the Source Data section, click Create empty table.
On the Create Table page, in the Destination Table section:
- For Table name, choose the appropriate dataset, and in the table name field, enter the name of the table you're creating.
- Verify that Table type is set to Native table.
In the Schema section, manually enter the schema definition.
You can enter schema information manually by:
Clicking Edit as text and entering the table schema as a JSON array:
Using Add Field to input the schema:
In the Options section:
- For Partitioning, click None and select Day.
- For Encryption Type, leave the
Default
option. This property is for customer-managed encryption keys. By default, BigQuery encrypts customer content stored at rest.
Click Create Table.
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 BigQuery web UI.
CLI
Use the mk
command with the --table
flag (or -t
shortcut), and the
--time_partitioning_type=DAY
flag. Currently, DAY
is the only supported
value for --time_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 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=DAY \ --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.
- 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
expiration is set to 259,200 seconds (3 days), the table's expiration is set
to 2,592,000 (1 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
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 console BigQuery web UI.
Classic UI
You cannot specify partitioning options for a destination table when you query data using the classic BigQuery web UI.
CLI
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=DAY
flag to create an ingestion-time partitioned
destination table. Currently, DAY
is the only supported value for
--time_partitioning_type
.
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=DAY \ --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 toasia-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.
- query is a query in standard SQL syntax.
Examples:
Enter the following command to write query results to an ingestion-time
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
. 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
. Since --time_partitioning_type=DAY
is currently
the only supported value, this parameter is optional. 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.
Enter the following command to convert a series of date-sharded tables to a single ingestion-time partitioned table:
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 toasia-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 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 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 partitioning expiration is 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 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
You cannot assign access controls directly to tables or partitions. You can control table access by configuring access controls at the dataset level or higher.
Instead of granting access to individual datasets, you can assign predefined, Cloud IAM roles that grant permissions at the project level or higher.
You can also create custom roles. If you create a custom role, the permissions you grant depend on the table operations you want the user, group, or service account to be able to perform.
For more information on roles and permissions, see:
- Understanding roles in the Cloud IAM documentation
- BigQuery Predefined roles and permissions
- Assigning access controls to datasets
Using ingestion-time partitioned tables
Getting information about ingestion-time partitioned tables
You can get information about tables by:
- Using the Cloud Console or the classic BigQuery web UI
- Using the
bq show
CLI command - 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 Cloud 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 Cloud 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
Open the BigQuery web UI in the Cloud Console.
Go to the Cloud ConsoleIn the navigation panel, in the Resources section, expand your project and select a dataset to list the tables and views it contains.
Click the table name.
Click Details. This tab displays the table's description and table information.
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.
Classic UI
In the navigation pane, down arrow icon
to the left of your dataset to expand it, or double-click the dataset name. This displays the tables and views in the dataset.
Click the table name.
Click Details. The Table Details page displays the table's description and table information.
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.
CLI
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, the classic BigQuery web UI, the bq ls
CLI command, 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 Cloud 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
Open the BigQuery web UI in the Cloud Console.
Go to the Cloud ConsoleIn the navigation panel, in the Resources section, expand your project and click on your dataset.
Scroll through the list to see the tables in the dataset. Tables, partitioned tables, models, and views are identified by different icons.
Classic UI
In the web UI, in the navigation pane, down arrow icon
to the left of your dataset to expand it, or double-click the dataset name. This displays the tables and views in the dataset.
Scroll through the list to see the tables in the dataset. Tables and views are identified by different icons.
CLI
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 in the Cloud Console, the classic BigQuery web UI,
using the bq query
command, 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 Cloud 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 Cloud IAM roles include bigquery.tables.getData
permissions:
bigquery.admin
bigquery.dataEditor
bigquery.dataOwner
bigquery.dataViewer
For more information on Cloud 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
Open the BigQuery web UI in the Cloud Console.
Go to the BigQuery web UIClick the Compose new query button.
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.
(Optional) Click More and select Query settings.
Click Run.
Classic UI
Go to the BigQuery web UI.
Click the Compose query button.
Enter the following text into the New Query box to query a table's
_PARTITIONTIME
pseudo column:#standardSQL 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.
Click Show Options.
(Optional) For Processing Location, click Unspecified and choose your data's location.
Click Run query.
CLI
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 toasia-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
Open the BigQuery web UI in the Cloud Console.
Go to the BigQuery web UIClick the Compose new query button.
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.
Click More and select Query settings.
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
orEU
multi-region location. When your data is in theUS
or theEU
, the processing location is automatically detected.Click Run.
Classic UI
Go to the BigQuery web UI.
Click the Compose query button.
Enter the following text into the New Query box to query a table's
__PARTITIONS_SUMMARY__
meta table:#legacySQL SELECT partition_id FROM [dataset.table$__PARTITIONS_SUMMARY__]
Where:
- dataset is the dataset that contains the table.
- table is the name of the table.
Click Show Options.
(Optional) For Processing Location, click Unspecified and choose your data's location.
Click Run query.
CLI
Enter the following query using the bq query
command:
bq --location=location query --use_legacy_sql=true ' SELECT partition_id FROM [dataset.table$__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 toasia-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 or the classic BigQuery web UI
- Using the command-line tool's
bq query
command - Calling the
jobs.insert
API method and configuring aquery
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:
SELECT column FROM [dataset.table$__PARTITIONS_SUMMARY__]
Where:
- dataset is the name of your dataset.
- table is the name of the time-partitioned table.
- column is one of the following:
Value | Description |
---|---|
project_id |
Name of the project. |
dataset_id |
Name of the dataset. |
table_id |
Name of the time-partitioned table. |
partition_id |
Name (date) of the partition. |
creation_time |
The time at which the partition was created, in milliseconds since January 1, 1970 UTC. |
last_modified_time |
The time at which the partition was last modified, in milliseconds since January 1, 1970 UTC. |
Partition meta table permissions
At a minimum, to run a query job that uses the __PARTITIONS_SUMMARY__
meta-table, you must be granted bigquery.jobs.create
permissions. The
following predefined Cloud IAM roles include bigquery.jobs.create
permissions:
bigquery.user
bigquery.jobUser
bigquery.admin
You must also be granted bigquery.tables.getData
permissions. The following
predefined Cloud IAM roles include bigquery.tables.getData
permissions:
bigquery.dataViewer
bigquery.dataEditor
bigquery.dataOwner
bigquery.admin
For more information on Cloud IAM roles in BigQuery, see Access control.
Partition meta table examples
The following query retrieves all partition metadata for a time-partitioned
table named mydataset.mytable
.
Console
#legacySQL SELECT * FROM [mydataset.mytable$__PARTITIONS_SUMMARY__]
Classic UI
#legacySQL SELECT * FROM [mydataset.mytable$__PARTITIONS_SUMMARY__]
CLI
bq query --use_legacy_sql=true ' SELECT * FROM [mydataset.mytable$__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$__PARTITIONS_SUMMARY__]
Classic UI
#legacySQL SELECT partition_id, last_modified_time FROM [mydataset.mytable$__PARTITIONS_SUMMARY__]
CLI
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__]
Classic UI
#legacySQL SELECT partition_id, FORMAT_UTC_USEC(last_modified_time*1000) AS last_modified FROM [mydataset.table1$__PARTITIONS_SUMMARY__]
CLI
bq query --use_legacy_sql=true ' SELECT partition_id, FORMAT_UTC_USEC(last_modified_time*1000) AS last_modified FROM [mydataset.mytable$__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 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.
Step 1. Create an empty ingestion-time partitioned table 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.
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'
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'
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 date/timestamp partitioned tables, see Creating and using date/timestamp partitioned tables.
- For information on integer range partitioned tables, see Creating and using integer range partitioned tables.