Query partitioned tables

This document describes some specific considerations for querying partitioned tables in BigQuery.

For general information on running queries in BigQuery, see Running interactive and batch queries.

Overview

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 partition pruning.

Partition pruning is the mechanism BigQuery uses to eliminate unnecessary partitions from the input scan. The pruned partitions are not included when calculating the bytes scanned by the query. In general, partition pruning helps reduce query cost.

Pruning behaviors vary for the different types of partitioning, so you could see a difference in bytes processed when querying tables that are partitioned differently but are otherwise identical. To estimate how many bytes a query will process, perform a dry run.

Query a time-unit column-partitioned table

To prune partitions when you query a time-unit column-partitioned table, include a filter on the partitioning column.

In the following example, assume that dataset.table is partitioned on the transaction_date column. The example query prunes dates before 2016-01-01.

SELECT * FROM dataset.table
WHERE transaction_date >= '2016-01-01'

Query an ingestion-time partitioned table

Ingestion-time partitioned tables contain a pseudocolumn named _PARTITIONTIME, which is the partitioning column. The value of the column is the UTC ingestion time for each row, truncated to the partition boundary (such as hourly or daily), as a TIMESTAMP value.

For example, if you append data on April 15, 2021, 08:15:00 UTC, the _PARTITIONTIME column for those rows contains the following values:

  • Hourly partitioned table: TIMESTAMP("2021-04-15 08:00:00")
  • Daily partitioned table: TIMESTAMP("2021-04-15")
  • Monthly partitioned table: TIMESTAMP("2021-04-01")
  • Yearly partitioned table: TIMESTAMP("2021-01-01")

If the partition granularity is daily, the table also contains a pseudocolumn named _PARTITIONDATE. The value is equal to _PARTITIONTIME truncated to a DATE value.

Both of these pseudocolumn names are reserved. You can't create a column with either name in any of your tables.

To prune partitions, filter on either of these columns. For example, the following query scans only the partitions between the dates January 1, 2016 and January 2, 2016:

SELECT
  column
FROM
  dataset.table
WHERE
  _PARTITIONTIME BETWEEN TIMESTAMP('2016-01-01') AND TIMESTAMP('2016-01-02')

To select the _PARTITIONTIME pseudocolumn, you must use an alias. For example, the following query selects _PARTITIONTIME by assigning the alias pt to the pseudocolumn:

SELECT
  _PARTITIONTIME AS pt, column
FROM
  dataset.table

For daily partitioned tables, you can select the _PARTITIONDATE pseudocolumn in the same way:

SELECT
  _PARTITIONDATE AS pd, column
FROM
  dataset.table

The _PARTITIONTIME and _PARTITIONDATE pseudocolumns are not returned by a SELECT * statement. You must select them explicitly:

SELECT
  _PARTITIONTIME AS pt, *
FROM
  dataset.table

Handle time zones in ingestion-time partitioned tables

The value of _PARTITIONTIME is based on the UTC date when the field is populated. If you want to query data based on a time zone other than UTC, choose one of the following options:

  • Adjust for time zone differences in your SQL queries.
  • Use partition decorators to load data into specific ingestion-time partitions, based on a different time zone than UTC.

Better performance with pseudocolumns

To improve query performance, use the _PARTITIONTIME pseudocolumn by itself on the left side of a comparison.

For example, the following two queries are equivalent. Depending on the table size, the second query might perform better, because it places _PARTITIONTIME by itself on the left side of the > operator. Both queries process the same amount of data.

-- Might be slower.
SELECT
  field1
FROM
  dataset.table1
WHERE
  TIMESTAMP_ADD(_PARTITIONTIME, INTERVAL 5 DAY) > TIMESTAMP("2016-04-15");

-- Often performs better.
SELECT
  field1
FROM
  dataset.table1
WHERE
  _PARTITIONTIME > TIMESTAMP_SUB(TIMESTAMP('2016-04-15'), INTERVAL 5 DAY);

To limit the partitions that are scanned in a query, use a constant expression in your filter. The following query limits which partitions are pruned based on the first filter condition in the WHERE clause. However, the second filter condition doesn't limit the scanned partitions, because it uses table values, which are dynamic.

SELECT
  column
FROM
  dataset.table2
WHERE
  -- This filter condition limits the scanned partitions:
  _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-03-01')
  -- This one doesn't, because it uses dynamic table values:
  AND _PARTITIONTIME = (SELECT MAX(timestamp) from dataset.table1)

To limit the partitions scanned, don't include any other columns in a _PARTITIONTIME filter. For example, the following query does not limit the scanned partitions, because field1 is a column in the table.

-- Scans all partitions of table2. No pruning.
SELECT
  field1
FROM
  dataset.table2
WHERE
  _PARTITIONTIME + field1 = TIMESTAMP('2016-03-28');

If you often query a particular range of times, consider creating a view that filters on the _PARTITIONTIME pseudocolumn. For example, the following statement creates a view that includes only the most recent seven days of data from a table named dataset.partitioned_table:

-- This view provides pruning.
CREATE VIEW dataset.past_week AS
  SELECT *
  FROM
    dataset.partitioned_table
  WHERE _PARTITIONTIME BETWEEN
    TIMESTAMP_TRUNC(TIMESTAMP_SUB(CURRENT_TIMESTAMP, INTERVAL 7 * 24 HOUR), DAY)
    AND TIMESTAMP_TRUNC(CURRENT_TIMESTAMP, DAY);

For information about creating views, see Creating views.

Query an integer-range partitioned table

To prune partitions when you query an integer-range partitioned table, include a filter on the integer partitioning column.

In the following example, assume that dataset.table is an integer-range partitioned table with a partitioning specification of customer_id:0:100:10 The example query scans the three partitions that start with 30, 40, and 50.

SELECT * FROM dataset.table
WHERE customer_id BETWEEN 30 AND 50

+-------------+-------+
| customer_id | value |
+-------------+-------+
|          40 |    41 |
|          45 |    46 |
|          30 |    31 |
|          35 |    36 |
|          50 |    51 |
+-------------+-------+

Partition pruning is not supported for functions over an integer range partitioned column. For example, the following query scans the entire table.

SELECT * FROM dataset.table
WHERE customer_id + 1 BETWEEN 30 AND 50

Query data in the write-optimized storage

The __UNPARTITIONED__ partition temporarily holds data that is streamed to a partitioned table while it is in the write-optimized storage. Data that is streamed directly to a specific partition of a partitioned table does not use the __UNPARTITIONED__ partition. Instead, the data is streamed directly to the partition.

Data in the write-optimized storage has NULL values in the _PARTITIONTIME and _PARTITIONDATE columns.

To query data in the __UNPARTITIONED__ partition, use the _PARTITIONTIME pseudocolumn with the NULL value. For example:

SELECT
  column
FROM dataset.table
WHERE
  _PARTITIONTIME IS NULL

For more information, see Streaming into partitioned tables.

Best practices for partition pruning

This section describes best practices for writing queries that utilize partition pruning to optimize query performance and reduce cost.

Use a constant filter expression

To limit the partitions that are scanned in a query, filter the partitioning column using a constant expression, rather than a dynamic expression.

The following query prunes partitions:

SELECT
  t1.name, t1.quantity
FROM
  table1 AS t1
WHERE
  t1.ts = CURRENT_TIMESTAMP()

In comparison, the following query doesn't prune partitions, because the predicate, WHERE t1.ts = (SELECT timestamp FROM table3 WHERE key = 2), is not a constant expression. This query compares the partitioning column to a dynamic value, what prevents partition pruning.

SELECT
  t1.name, t1.quantity
FROM
  table1 AS t1
WHERE
  t1.ts = (SELECT timestamp FROM table3 WHERE key = 2)

Additionally, a query with the following predicates don't prune partitions because they require a computation based on a second, non-constant table column ts2 or duration:

WHERE ts >= ts2

WHERE ts < CURRENT_TIMESTAMP() - duration

Isolate the partitioning column or use supported functions

To prune partitions, filter conditions must be structured so that BigQuery can determine which partitions to scan without reading table data. To achieve this, isolate the partitioning column on one side of a comparison operator, or wrap the column only in a supported built-in function. You can use dry run to verify if partition pruning is supported on your particular query.

The following built-in functions on the partitioning column support partition pruning, if their additional arguments are constant:

Other functions and complex mathematical operations will require a full table scan.

Examples

The following queries show example predicates that support partition pruning.

SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE datehour = '2025-03-30 12:00:00';
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE datehour >= '2025-03-30'
  AND datehour < TIMESTAMP_ADD('2025-03-30', INTERVAL 1 DAY);
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE DATE(datehour) = '2025-03-30';
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE EXTRACT(DATE FROM datehour) = '2025-03-30';
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE CAST(datehour AS DATE) = '2025-03-30';
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE datehour >= '2025-01-01' AND datehour < '2025-02-01';
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE TIMESTAMP_TRUNC(datehour, MONTH) >= '2025-04-01'
  AND TIMESTAMP_TRUNC(datehour, MONTH) < '2025-07-01';
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE TIMESTAMP_DIFF(datehour, '2025-01-01', DAY) < 1;
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE TIMESTAMP_ADD(datehour, INTERVAL 1 DAY) < '2025-01-03';
 
SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE TIMESTAMP_SUB(datehour, INTERVAL 1 DAY) < '2025-01-01';

The following query skips all partitions because the predicate doesn't match any row.

SELECT COUNT(*) FROM `bigquery-public-data.wikipedia.pageviews_2025`
WHERE EXTRACT(YEAR FROM datehour) = 1900;

The following query select the first day of each month in the table, and it supports partition pruning. 

SELECT COUNT(*) FROM bigquery-public-data.wikipedia.pageviews_2025
WHERE DATE(datehour) IN UNNEST(GENERATE_DATE_ARRAY(
  DATE_TRUNC(CURRENT_DATE(), YEAR),
  DATE(DATE_TRUNC(CURRENT_DATE(), YEAR) + INTERVAL 1 YEAR - INTERVAL 1 DAY),
  INTERVAL 1 MONTH
))

Queries with the following predicates doesn't prune partitions because it manipulates the partitioning column with unsupported functions:

WHERE FORMAT_DATE('%Y-%m-%d %H', ts) = '2025-03-28 20';

WHERE EXTRACT(MONTH FROM ts) = 3 AND EXTRACT(HOUR FROM ts) = 20

Similarly, a query with the following predicate doesn't prune partitions because it manipulates the partitioning column with an arithmetic operation: 

WHERE ts + INTERVAL 1 DAY > CURRENT_TIMESTAMP()

To enable partition pruning, you must rewrite the expression by isolating the partitioning column ts from the unsupported functions or arithmetic operations. For time ranges, use >= and < to capture the exact range. For arithmetic, move the operation to the other side of the comparison.

The following query allows for partition pruning by isolating the partitioning column ts for a time range:

WHERE ts >= '2025-03-28 20:00:00' AND ts < '2025-03-28 21:00:00'

The following query allows for partition pruning by isolating the partitioning column from the arithmetic operation:

WHERE ts > CURRENT_TIMESTAMP() - INTERVAL 1 DAY

Filter on multiple columns

A predicate on the partitioning column in a query doesn't restrict what else you can filter on. You can include predicates on other columns in the same WHERE clause, and partition pruning will still occur as long as the condition evaluating the partitioning column follows the best practices. Note that AND is important in the following example. If AND is changed to OR, partition pruning won't work, as even if a partition doesn't match the predicate on the partitioning column, it still cannot be pruned. Data in these partitions with meter_id = 1234 still qualifies for the query.

Note that the predicates don't need to be written in a specific order. In the following sample query, assuming partitioning on the ts column, partition pruning still occurs regardless of predicate placement.

WHERE meter_id = 1234
  AND ts >= '2025-03-28 20:00:00' AND ts < '2025-03-28 21:00:00'

Require a partition filter in queries

When you create a partitioned table, you can require the use of predicate filters by enabling the Require partition filter option. When this option is applied, attempts to query the partitioned table without specifying a WHERE clause produce the following error:

Cannot query over table 'project_id.dataset.table' without a filter that can be used for partition elimination.

This requirement also applies to queries on views and materialized views that reference the partitioned table.

There must be at least one predicate that only references a partitioning column for the filter to be considered eligible for partition elimination. For a table partitioned on column partition_id with an additional column f in its schema, both of the following WHERE clauses satisfy the requirement:

WHERE partition_id = "20221231"

WHERE partition_id = "20221231" AND f = "20221130"

However, the following is not sufficient, and will result in an error:

WHERE partition_id = "20221231" OR f = "20221130"

For ingestion-time partitioned tables, use either the _PARTITIONTIME or _PARTITIONDATE pseudocolumn.

For more information about adding the Require partition filter option when you create a partitioned table, see Creating partitioned tables. You can also update this setting on an existing table.

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