bigquery-standard-sql

So I'm writing a BigQuery query and basically just need to be able to check if any of a number of strings are present as elements in one of the columns of the table, where the cared-about column itself contains arrays of strings. Just for context, I'm writing the query as part of a little automated Python job and am using standard SQL. I couldn't find anything that would explicitly check for array inclusion here: https://cloud.google.com/bigquery/docs/reference/standard-sql/functions-and-operators So I came up with a solution that employs a pretty hacky regex, specifically: ...other query

I am trying to find the table of the current date of SELECT * FROM `da`.`m`.`ga_realtime_20190306` but not working SELECT * FROM `da`.`m`.`CONCAT('ga_realtime_', FORMAT_DATE('%Y%m%d', CURRENT_DATE())` How can I dynamically select a table with CURRENT_DATE and the BigQuery Standard query? Use wildcard and _TABLE_SUFFIX SELECT field1, field2, field3 FROM `my_dataset.ga_realtime_*` WHERE _TABLE_SUFFIX = FORMAT_DATE('%Y%m%d', CURRENT_DATE()) 来源： https://stackoverflow.com/questions/57817881/bigquery-dynamically-select-table-by-the-current-date-in-standard-sql

It so happens I have a stringified array in a field in BigQuery '["a","b","c"]' and I want to convert it to an array that BigQuery understands. I want to be able to do this in standard SQL: with k as (select '["a","b","c"]' as x) select x from k, unnest(x) x I have tried JSON_EXTRACT('["a","b","c"]','$') and everythig else I could find online. Any ideas? Below is for BigQuery Standard SQL #standardSQL WITH k AS ( SELECT 1 AS id, '["a","b","c"]' AS x UNION ALL SELECT 2, '["x","y"]' ) SELECT id, ARRAY(SELECT * FROM UNNEST(SPLIT(SUBSTR(x, 2 , LENGTH(x) - 2)))) AS x FROM k It transforms your

I'm trying to get the number of unique events on a specific date, rolling 90/30/7 days back. I've got this working on a limited number of rows with the query bellow but for large data sets I get memory errors from the aggregated string which becomes massive. I'm looking for a more effective way of achieving the same result. Table looks something like this: +---+------------+-------------+ | | date | userid | +---+------------+-------------+ | 1 | 2013-05-14 | xxxxx | | 2 | 2017-03-14 | xxxxx | | 3 | 2018-01-24 | xxxxx | | 4 | 2013-03-21 | xxxxx | | 5 | 2014-03-19 | xxxxx | | 6 | 2015-09-03 |