Python filter list with wildcard

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Wildcard tables enable you to query multiple tables using concise SQL statements. Wildcard tables are available only in standard SQL. 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.

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

For information on wildcard table syntax, see Wildcard tables in the standard SQL reference.

Limitations

Wildcard table queries are subject to the following limitations.

• The wildcard table functionality does not support views. If the wildcard table matches any view in the dataset, the query returns an error. This is true whether or not your query contains a WHERE clause on the _TABLE_SUFFIX pseudo column to filter out the view.
• Currently, 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 native BigQuery storage only. You cannot use wildcards when querying an external table or a view.
• 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 pseudo columns that include JavaScript user-defined functions do not limit the number of tables scanned in a wildcard table.
• Wildcard queries are not supported for tables protected by customer-managed encryption keys [CMEK].

Before you begin

• Ensure that you are using BigQuery standard SQL. 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]:

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

Querying sets of 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, and so on.

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:

Filtering selected tables using _TABLE_SUFFIX

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

The _TABLE_SUFFIX pseudo column 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:

The corresponding _TABLE_SUFFIX pseudo column 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:

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 do not 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:

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:

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

First query:

Second query:

These example queries use the INFORMATION_SCHEMA.TABLES view. For more information on 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 pseudo column 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:

Scanning a range of ingestion-time partitioned tables using _PARTITIONTIME

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

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 pseudo column contains full table names. For example, the following FROM clause scans all tables in the GSOD dataset:

With an empty prefix, the _TABLE_SUFFIX pseudo column 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:

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 standard SQL 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. If the schema is inconsistent across tables matched by the wildcard table, BigQuery returns an error.

Best practices

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

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

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

What's next

• For more information about standard SQL, see the Standard SQL query reference.

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