Introduction to partitioned tables

A partitioned table is divided into segments, called partitions, that make it easier to manage and query your data. By dividing a large table into smaller partitions, you can improve query performance and control costs by reducing the number of bytes read by a query. You partition tables by specifying a partition column which is used to segment the table.

If a query uses a qualifying filter on the value of the partitioning column, BigQuery can scan the partitions that match the filter and skip the remaining partitions. This process is called pruning.

In a partitioned table, data is stored in physical blocks, each of which holds one partition of data. Each partitioned table maintains various metadata about the sort properties across all operations that modify it. The metadata lets BigQuery more accurately estimate a query cost before the query is run.

When to use partitioning

Consider partitioning a table in the following scenarios:

  • You want to improve the query performance by only scanning a portion of a table.
  • Your table operation exceeds a standard table quota and you can scope the table operations to specific partition column values allowing higher partitioned table quotas.
  • You want to determine query costs before a query runs. BigQuery provides query cost estimates before the query is run on a partitioned table. Calculate a query cost estimate by pruning a partitioned table, then issuing a query dry run to estimate query costs.
  • You want any of the following partition-level management features:

Consider clustering a table instead of partitioning a table in the following circumstances:

  • You need more granularity than partitioning allows.
  • Your queries commonly use filters or aggregation against multiple columns.
  • The cardinality of the number of values in a column or group of columns is large.
  • You do not need strict cost estimates before query execution.
  • Partitioning results in a small amount of data per partition (approximately less than 1 GB).
  • Partitioning results in a large number of partitions, exceeding the limits on partitioned tables.
  • Your DML operations frequently modify (for example, every few minutes) most partitions in the table.

In such cases, table clustering lets you accelerate queries by clustering data in specific columns based on user-defined sort properties.

You can also combine clustering and table partitioning to achieve finer-grained sorting. For more information on this approach, see Combining clustered and partitioning tables.

Types of partitioning

This section describes the different ways to partition a table.

Integer range partitioning

You can partition a table based on ranges of values in a specific INTEGER column. To create an integer-range partitioned table, you provide:

  • The partitioning column.
  • The starting value for range partitioning (inclusive).
  • The ending value for range partitioning (exclusive).
  • The interval of each range within the partition.

For example, suppose you create an integer range partition with the following specification:

Argument Value
column name customer_id
start 0
end 100
interval 10

The table is partitioned on the customer_id column into ranges of interval 10. The values 0 to 9 go into one partition, values 10 to 19 go into the next partition, etc., up to 99. Values outside this range go into a partition named __UNPARTITIONED__. Any rows where customer_id is NULL go into a partition named __NULL__.

Time-unit column partitioning

You can partition a table on a DATE,TIMESTAMP, or DATETIME column in the table. When you write data to the table, BigQuery automatically puts the data into the correct partition, based on the values in the column.

For TIMESTAMP and DATETIME columns, the partitions can have either hourly, daily, monthly, or yearly granularity. For DATE columns, the partitions can have daily, monthly, or yearly granularity. Partitions boundaries are based on UTC time.

For example, suppose that you partition a table on a DATETIME column with monthly partitioning. If you insert the following values into the table, the rows are written to the following partitions:

Column value Partition (monthly)
DATETIME("2019-01-01") 201901
DATETIME("2019-01-15") 201901
DATETIME("2019-04-30") 201904

In addition, two special partitions are created:

  • __NULL__: Contains rows with NULL values in the partitioning column.
  • __UNPARTITIONED__: Contains rows where the value of the partitioning column is earlier than 1960-01-01 or later than 2159-12-31.

Ingestion time partitioning

When you create a table partitioned by ingestion time, BigQuery automatically assigns rows to partitions based on the time when BigQuery ingests the data. You can choose hourly, daily, monthly, or yearly granularity for the partitions. Partitions boundaries are based on UTC time.

If your data might reach the limit of 4000 partitions per table when using a finer time granularity, use a coarser granularity instead. For example, you can partition by month instead of day to reduce the number of partitions. You can also cluster the partition column to further improve performance.

An ingestion-time partitioned table has a pseudocolumn named _PARTITIONTIME. The value of this column is the ingestion time for each row, truncated to the partition boundary (such as hourly or daily). For example, suppose that you create an ingestion-time partitioned table with hourly partitioning and send data at the following times:

Ingestion time _PARTITIONTIME Partition (hourly)
2021-05-07 17:22:00 2021-05-07 17:00:00 2021050717
2021-05-07 17:40:00 2021-05-07 17:00:00 2021050717
2021-05-07 18:31:00 2021-05-07 18:00:00 2021050718

Because the table in this example uses hourly partitioning, the value of _PARTITIONTIME is truncated to an hour boundary. BigQuery uses this value to determine the correct partition for the data.

You can also write data to a specific partition. For example, you might want to load historical data or adjust for time zones. You can use any valid date between 0001-01-01 and 9999-12-31. However, DML statements cannot reference dates prior to 1970-01-01 or after 2159-12-31. For more information, see Write data to a specific partition.

Instead of using _PARTITIONTIME, you can also use _PARTITIONDATE. The _PARTITIONDATE pseudocolumn contains the UTC date corresponding to the value in the _PARTITIONTIME pseudocolumn.

Select daily, hourly, monthly, or yearly partitioning

When you partition a table by time-unit column or ingestion time, you choose whether the partitions have daily, hourly, monthly, or yearly granularity.

  • Daily partitioning is the default partitioning type. Daily partitioning is a good choice when your data is spread out over a wide range of dates, or if data is continuously added over time.

  • Choose hourly partitioning if your tables have a high volume of data that spans a short date range — typically less than six months of timestamp values. If you choose hourly partitioning, make sure the partition count stays within the partition limits.

  • Choose monthly or yearly partitioning if your tables have a relatively small amount of data for each day, but span a wide date range. This option is also recommended if your workflow requires frequently updating or adding rows that span a wide date range (for example, more than 500 dates). In these scenarios, use monthly or yearly partitioning along with clustering on the partitioning column to achieve the best performance. For more information, see Combining clustered and partitioning tables in this document.

Combining clustered and partitioned tables

You can combine table partitioning with table clustering to achieve finely grained sorting for further query optimization.

A clustered table contains clustered columns that sort data based on user-defined sort properties. Data within these clustered columns are sorted into storage blocks which are adaptively sized based on the size of the table. When you run a query that filters by the clustered column, BigQuery only scans the relevant blocks based on the clustered columns instead of the entire table or table partition. In a combined approach using both table partitioning and clustering, you first segment table data into partitions, then you cluster the data within each partition by the clustering columns.

When you create a table that is clustered and partitioned, you can achieve more finely grained sorting, as the following diagram shows:

Comparing tables that are not clustered or partitioned to tables that are clustered and partitioned.

Partitioning versus sharding

Table sharding is the practice of storing data in multiple tables, using a naming prefix such as [PREFIX]_YYYYMMDD.

Partitioning is recommended over table sharding, because partitioned tables perform better. With sharded tables, BigQuery must maintain a copy of the schema and metadata for each table. BigQuery might also need to verify permissions for each queried table. This practice also adds to query overhead and affects query performance.

If you previously created date-sharded tables, you can convert them into an ingestion-time partitioned table. For more information, see Convert date-sharded tables into ingestion-time partitioned tables.

Partition decorators

Partition decorators enable you to reference a partition in a table. For example, you can use them to write data to a specific partition.

A partition decorator has the form table_name$partition_id where the format of the partition_id segment depends on the type of partitioning:

Partitioning type Format Example
Hourly yyyymmddhh my_table$2021071205
Daily yyyymmdd my_table$20210712
Monthly yyyymm my_table$202107
Yearly yyyy my_table$2021
Integer range range_start my_table$40

Browse the data in a partition

To browse the data in a specified partition, use the bq head command with a partition decorator.

For example, the following command lists all fields in the first 10 rows of the my_dataset.my_table in the 2018-02-24 partition:

    bq head --max_rows=10 'my_dataset.my_tablee$20180224'

Export table data

Exporting all data from a partitioned table is the same process as exporting data from a non-partitioned table. For more information, see Exporting table data.

To export data from an individual partition, use the bq extract command and append the partition decorator to the table name. For example, my_table$20160201. You can also export data from the __NULL__ and __UNPARTITIONED__ partitions by appending the partition names to the table name. For example, my_table$__NULL__ or my_table$__UNPARTITIONED__.


You cannot use legacy SQL to query partitioned tables or to write query results to partitioned tables.

BigQuery does not support partitioning by multiple columns. Only one column can be used to partition a table.

Time-unit column-partitioned tables are subject to the following limitations:

  • The partitioning column must be either a scalar DATE, TIMESTAMP, or DATETIME column. While the mode of the column can be REQUIRED or NULLABLE, it cannot be REPEATED (array-based).
  • The partitioning column must be a top-level field. You cannot use a leaf field from a RECORD (STRUCT) as the partitioning column.

Integer-range partitioned tables are subject to the following limitations:

  • The partitioning column must be an INTEGER column. While the mode of the column may be REQUIRED or NULLABLE, it cannot beREPEATED (array-based).
  • The partitioning column must be a top-level field. You cannot use a leaf field from a RECORD (STRUCT) as the partitioning column.

Quotas and limits

Partitioned tables have defined limits in BigQuery.

Quotas and limits also apply to the different types of jobs you can run against partitioned tables, including:

For more information on all quotas and limits, see Quotas and limits.

Table pricing

When you create and use partitioned tables in BigQuery, your charges are based on how much data is stored in the partitions and on the queries you run against the data:

Many partitioned table operations are free, including loading data into partitions, copying partitions, and exporting data from partitions. Though free, these operations are subject to BigQuery's Quotas and limits. For information on all free operations, see Free operations on the pricing page.

For best practices for controlling costs in BigQuery, see Controlling costs in BigQuery

Table security

Access control for partitioned tables is the same as access control for standard tables. For more information, see Introduction to table access controls.

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