Query multiple tables using a wildcard table

Wildcard tables enable you to query multiple tables using concise SQL statements. Wildcard tables are available only in GoogleSQL. For equivalent functionality in legacy SQL, see Table wildcard functions.

A wildcard table represents a union of all the tables that match the wildcard expression. For example, the following FROM clause uses the wildcard expression gsod* to match all tables in the noaa_gsod dataset that begin with the string gsod.

FROM
  `bigquery-public-data.noaa_gsod.gsod*`

Each row in the wildcard table contains a special column, _TABLE_SUFFIX, which contains the value matched by the wildcard character.

Limitations

Wildcard table queries are subject to the following limitations.

The wildcard table functionality doesn't support views. If the wildcard table matches any view in the dataset, the query returns an error even if your query contains a WHERE clause on the _TABLE_SUFFIX pseudocolumn to filter out the view.
Cached results are not supported for queries against multiple tables using a wildcard even if the Use Cached Results option is checked. If you run the same wildcard query multiple times, you are billed for each query.
Wildcard tables support built-in BigQuery storage only. You cannot use wildcards to query an external table or a view.
You cannot use wildcard queries over tables with incompatible partitioning or a mix of partitioned and non-partitioned tables. The queried tables also need to have identical clustering specifications.
You can use wildcard tables with partitioned tables, and both partition pruning and cluster pruning are supported. However, tables that are clustered but not partitioned don't get the full cluster pruning benefit from wildcard usage.
Queries that contain data manipulation language (DML) statements cannot use a wildcard table as the target of the query. For example, a wildcard table may be used in the FROM clause of an UPDATE query, but a wildcard table cannot be used as the target of the UPDATE operation.
Filters on the _TABLE_SUFFIX or _PARTITIONTIME pseudocolumns that include JavaScript user-defined functions don't limit the number of tables scanned in a wildcard table.
Wildcard queries are not supported for tables protected by customer-managed encryption keys (CMEK).
All tables referenced in a wildcard query must have exactly the same set of tag keys and values.
When using wildcard tables, all the tables in the dataset that begin with the table name before * are scanned even if _TABLE_SUFFIX is used in combination with REGEXP_CONTAINS and is provided a regular expression, such as ^[0-9]{2}$. For example:
```
SELECT *
FROM `my_project.my_dataset.my_table_*`
WHERE REGEXP_CONTAINS(_TABLE_SUFFIX, '^[0-9]{2}$');
```
If a single scanned table has a schema mismatch (that is, a column with the same name is of a different type), the query fails with the error Cannot read field of type X as Y Field: column_name. All tables are matched even if you are using the equality operator =. For example, in the following query, the table my_dataset.my_table_03_backup is also scanned. Thus, the query may fail due to schema mismatch. However, if there is no schema mismatch, the results come from the table my_dataset.my_table_03 only, as expected.
```
SELECT *
FROM my_project.my_dataset.my_table_*
WHERE _TABLE_SUFFIX = '03'
```

Before you begin

Ensure that you are using GoogleSQL. For more information, see Switching SQL dialects.
If you are using legacy SQL, see Table wildcard functions.
Many of the examples on this page use a public dataset from the National Oceanic and Atmospheric Administration (NOAA). For more information about the data, see NOAA Global Surface Summary of the Day Weather Data.

When to use wildcard tables

Wildcard tables are useful when a dataset contains multiple, similarly named tables that have compatible schemas. Typically, such datasets contain tables that each represent data from a single day, month, or year. For example, a public dataset hosted by BigQuery, the NOAA Global Surface Summary of the Day Weather Data, contains a table for each year from 1929 through the present.

A query that scans all the table IDs from 1929 through 1940 would be very long if you have to name all 12 tables in the FROM clause (most of the tables are omitted in this sample):

#standardSQL
SELECT
  max,
  ROUND((max-32)*5/9,1) celsius,
  mo,
  da,
  year
FROM (
  SELECT
    *
  FROM
    `bigquery-public-data.noaa_gsod.gsod1929` UNION ALL
  SELECT
    *
  FROM
    `bigquery-public-data.noaa_gsod.gsod1930` UNION ALL
  SELECT
    *
  FROM
    `bigquery-public-data.noaa_gsod.gsod1931` UNION ALL

  # ... Tables omitted for brevity

  SELECT
    *
  FROM
    `bigquery-public-data.noaa_gsod.gsod1940` )
WHERE
  max != 9999.9 # code for missing data
ORDER BY
  max DESC

The same query using a wildcard table is much more concise:

#standardSQL
SELECT
  max,
  ROUND((max-32)*5/9,1) celsius,
  mo,
  da,
  year
FROM
  `bigquery-public-data.noaa_gsod.gsod19*`
WHERE
  max != 9999.9 # code for missing data
  AND _TABLE_SUFFIX BETWEEN '29'
  AND '40'
ORDER BY
  max DESC

Wildcard tables support built-in BigQuery storage only. You cannot use wildcards when querying an external table or a view.

Wildcard table syntax

Wildcard table syntax:

SELECT
FROM
  `<project-id>.<dataset-id>.<table-prefix>*`
WHERE
  bool_expression

<project-id>: Cloud Platform project ID. Optional if you use your default project ID.
<dataset-id>: BigQuery dataset ID.
<table-prefix>: A string that is common across all tables that are matched by the wildcard character. The table prefix is optional. Omitting the table prefix matches all tables in the dataset.
* (wildcard character): The wildcard character, "*", represents one or more characters of a table name. The wildcard character can appear only as the final character of a wildcard table name.

Queries with wildcard tables support the _TABLE_SUFFIX pseudocolumn in the WHERE clause. This column contains the values matched by the wildcard character, so that queries can filter which tables are accessed. For example, the following WHERE clauses use comparison operators to filter the matched tables:

WHERE
  _TABLE_SUFFIX BETWEEN '29' AND '40'

WHERE
  _TABLE_SUFFIX = '1929'

WHERE
  _TABLE_SUFFIX < '1941'

For more information about the _TABLE_SUFFIX pseudocolumn, see Filtering selected tables using _TABLE_SUFFIX.

Enclose table names with wildcards in backticks

The wildcard table name contains the special character (*), which means that you must enclose the wildcard table name in backtick (`) characters. For example, the following query is valid because it uses backticks:

#standardSQL
/* Valid SQL query */
SELECT
  max
FROM
  `bigquery-public-data.noaa_gsod.gsod*`
WHERE
  max != 9999.9 # code for missing data
  AND _TABLE_SUFFIX = '1929'
ORDER BY
  max DESC

The following query is NOT valid because it isn't properly quoted with backticks:

#standardSQL
/* Syntax error: Expected end of statement but got "-" at [4:11] */
SELECT
  max
FROM
  # missing backticks
  bigquery-public-data.noaa_gsod.gsod*
WHERE
  max != 9999.9 # code for missing data
  AND _TABLE_SUFFIX = '1929'
ORDER BY
  max DESC

Quotation marks don't work:

#standardSQL
/* Syntax error: Unexpected string literal: 'bigquery-public-data.noaa_gsod.gsod*' at [4:3] */
SELECT
  max
FROM
  # quotes are not backticks
  'bigquery-public-data.noaa_gsod.gsod*'
WHERE
  max != 9999.9 # code for missing data
  AND _TABLE_SUFFIX = '1929'
ORDER BY
  max DESC

Query tables using wildcard tables

Wildcard tables enable you to query several tables concisely. For example, a public dataset hosted by BigQuery, the NOAA Global Surface Summary of the Day Weather Data, contains a table for each year from 1929 through the present that all share the common prefix gsod followed by the four-digit year. The tables are named gsod1929, gsod1930, gsod1931, etc.

To query a group of tables that share a common prefix, use the table wildcard symbol (*) after the table prefix in your FROM statement. For example, the following query finds the maximum temperature reported during the 1940s:

#standardSQL
SELECT
  max,
  ROUND((max-32)*5/9,1) celsius,
  mo,
  da,
  year
FROM
  `bigquery-public-data.noaa_gsod.gsod194*`
WHERE
  max != 9999.9 # code for missing data
ORDER BY
  max DESC

Filter selected tables using _TABLE_SUFFIX

To restrict a query so that it scans only a specified set of tables, use the _TABLE_SUFFIX pseudocolumn in a WHERE clause with a condition that is a constant expression.

The _TABLE_SUFFIX pseudocolumn contains the values matched by the table wildcard. For example, the previous sample query, which scans all tables from the 1940s, uses a table wildcard to represent the last digit of the year:

FROM
  `bigquery-public-data.noaa_gsod.gsod194*`

The corresponding _TABLE_SUFFIX pseudocolumn contains values in the range 0 through 9, representing the tables gsod1940 through gsod1949. These _TABLE_SUFFIX values can be used in a WHERE clause to filter for specific tables.

For example, to filter for the maximum temperature in the years 1940 and 1944, use the values 0 and 4 for _TABLE_SUFFIX:

#standardSQL
SELECT
  max,
  ROUND((max-32)*5/9,1) celsius,
  mo,
  da,
  year
FROM
  `bigquery-public-data.noaa_gsod.gsod194*`
WHERE
  max != 9999.9 # code for missing data
  AND ( _TABLE_SUFFIX = '0'
    OR _TABLE_SUFFIX = '4' )
ORDER BY
  max DESC

Using _TABLE_SUFFIX can greatly reduce the number of bytes scanned, which helps reduce the cost of running your queries.

However, filters on _TABLE_SUFFIX that include conditions without constant expressions don't limit the number of tables scanned in a wildcard table. For example, the following query does not limit the tables scanned for the wildcard table bigquery-public-data.noaa_gsod.gsod19* because the filter uses the dynamic value of the table_id column:

#standardSQL
# Scans all tables that match the prefix `gsod19`
SELECT
  ROUND((max-32)*5/9,1) celsius
FROM
  `bigquery-public-data.noaa_gsod.gsod19*`
WHERE
  _TABLE_SUFFIX = (SELECT SUBSTR(MAX(table_name), LENGTH('gsod19') + 1)
      FROM `bigquery-public-data.noaa_gsod.INFORMATION_SCHEMA.TABLES`
      WHERE table_name LIKE 'gsod194%')

As another example, the following query limits the scan based on the first filter condition, _TABLE_SUFFIX BETWEEN '40' and '60', because it is a constant expression. However, the following query does not limit the scan based on the second filter condition, _TABLE_SUFFIX = (SELECT SUBSTR(MAX(table_name), LENGTH('gsod19') + 1) FROM bigquery-public-data.noaa_gsod.INFORMATION_SCHEMA.TABLES WHERE table_name LIKE 'gsod194%'), because it is a dynamic expression:

#standardSQL
# Scans all tables with names that fall between `gsod1940` and `gsod1960`
SELECT
  ROUND((max-32)*5/9,1) celsius
FROM
  `bigquery-public-data.noaa_gsod.gsod19*`
WHERE
  _TABLE_SUFFIX BETWEEN '40' AND '60'
  AND _TABLE_SUFFIX = (SELECT SUBSTR(MAX(table_name), LENGTH('gsod19') + 1)
      FROM `bigquery-public-data.noaa_gsod.INFORMATION_SCHEMA.TABLES`
      WHERE table_name LIKE 'gsod194%')

As a workaround, you can perform two separate queries instead; for example:

First query:

#standardSQL
# Get the list of tables that match the required table name prefixes
SELECT SUBSTR(MAX(table_name), LENGTH('gsod19') + 1)
      FROM `bigquery-public-data.noaa_gsod.INFORMATION_SCHEMA.TABLES`
      WHERE table_name LIKE 'gsod194%'

Second query:

#standardSQL
# Construct the second query based on the values from the first query
SELECT
  ROUND((max-32)*5/9,1) celsius
FROM
  `bigquery-public-data.noaa_gsod.gsod19*`
WHERE _TABLE_SUFFIX = '49'

These example queries use the INFORMATION_SCHEMA.TABLES view. For more information about the INFORMATION_SCHEMA table, see Getting table metadata using INFORMATION_SCHEMA.

Scanning a range of tables using _TABLE_SUFFIX

To scan a range of tables, use the _TABLE_SUFFIX pseudocolumn along with the BETWEEN clause. For example, to find the maximum temperature reported in the years between 1929 and 1935 inclusive, use the table wildcard to represent the last two digits of the year:

#standardSQL
SELECT
  max,
  ROUND((max-32)*5/9,1) celsius,
  mo,
  da,
  year
FROM
  `bigquery-public-data.noaa_gsod.gsod19*`
WHERE
  max != 9999.9 # code for missing data
  AND _TABLE_SUFFIX BETWEEN '29' and '35'
ORDER BY
  max DESC

Scanning a range of ingestion-time partitioned tables using _PARTITIONTIME

To scan a range of ingestion-time partitioned tables, use the _PARTITIONTIME pseudocolumn with the _TABLE_SUFFIX pseudocolumn. For example, the following query scans the January 1, 2017 partition in the table my_dataset.mytable_id1.

#standardSQL
SELECT
  field1,
  field2,
  field3
FROM
  `my_dataset.mytable_*`
WHERE
  _TABLE_SUFFIX = 'id1'
  AND _PARTITIONTIME = TIMESTAMP('2017-01-01')

Querying all tables in a dataset

To scan all tables in a dataset, you can use an empty prefix and the table wildcard, which means that the _TABLE_SUFFIX pseudocolumn contains full table names. For example, the following FROM clause scans all tables in the GSOD dataset:

FROM
  `bigquery-public-data.noaa_gsod.*`

With an empty prefix, the _TABLE_SUFFIX pseudocolumn contains full table names. For example, the following query is equivalent to the previous example that finds the maximum temperature between the years 1929 and 1935, but uses full table names in the WHERE clause:

#standardSQL
SELECT
  max,
  ROUND((max-32)*5/9,1) celsius,
  mo,
  da,
  year
FROM
  `bigquery-public-data.noaa_gsod.*`
WHERE
  max != 9999.9 # code for missing data
  AND _TABLE_SUFFIX BETWEEN 'gsod1929' and 'gsod1935'
ORDER BY
  max DESC

Note, however, that longer prefixes generally perform better. For more information, see Best practices.

Query execution details

Schema used for query evaluation

In order to execute a GoogleSQL query that uses a wildcard table, BigQuery automatically infers the schema for that table. BigQuery uses the schema for the most recently created table that matches the wildcard as the schema for the wildcard table. Even if you restrict the number of tables that you want to use from the wildcard table using the _TABLE_SUFFIX pseudocolumn in a WHERE clause, BigQuery uses the schema for the most recently created table that matches the wildcard.

If a column from the inferred schema doesn't exist in a matched table, then BigQuery returns NULL values for that column in the rows for the table that is missing the column.

If the schema is inconsistent across the tables matched by the wildcard query, then BigQuery returns an error. This is the case when the columns of the matched tables have different data types, or when the columns which are not present in all of the matched tables cannot be assumed to have a null value.

Best practices

Longer prefixes generally perform better than shorter prefixes. For example, the following query uses a long prefix (gsod200):

#standardSQL
SELECT
max
FROM
`bigquery-public-data.noaa_gsod.gsod200*`
WHERE
max != 9999.9 # code for missing data
AND _TABLE_SUFFIX BETWEEN '0' AND '1'
ORDER BY
max DESC

The following query generally performs worse because it uses an empty prefix:

#standardSQL
SELECT
max
FROM
`bigquery-public-data.noaa_gsod.*`
WHERE
max != 9999.9 # code for missing data
AND _TABLE_SUFFIX BETWEEN 'gsod2000' AND 'gsod2001'
ORDER BY
max DESC

Partitioning is recommended over sharding, because partitioned tables perform better. Sharding reduces performance while creating more tables to manage. For more information, see Partitioning versus sharding.

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

What's next

For more information about GoogleSQL, see the GoogleSQL query reference.