google-bigquery

Error: Scalar subquery produced more than one element


We just started migrating our queries from Legacy to Standard SQL so we are learning on how to process nested data and arrays now.

Basically what we want to do is to retrieve from ga_sessions table the following data:

visitor id, session id, array of skus
visitor 1, session 1, [sku_0, sku_1, (...), sku_n]
visitor 1, session 2, [skus]

To do so we ran this simple query:

  WITH
  customers_data AS(
  SELECT
    fullvisitorid fv,
    visitid v,
    ARRAY_AGG((
      SELECT
        prods.productsku
      FROM
        UNNEST(hits.product) prods)) sku
  FROM
    `dataset_id.ga_sessions_*`,
    UNNEST(hits) hits
  WHERE
    1 = 1
    AND _table_suffix BETWEEN FORMAT_DATE("%Y%m%d", DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
    AND FORMAT_DATE("%Y%m%d", DATE_SUB(CURRENT_DATE(), INTERVAL 0 DAY))
    --and (select count(productsku) from unnest(hits.product) where productsku is not null) = 1
  GROUP BY
    fv,
    v
  LIMIT
    100 )
SELECT
  *
FROM
  customers_data

But we get this error:

Error: Scalar subquery produced more than one element

The data that comes from the hits field looks something like this:

enter image description here

So when we addded back the where clause:

and (select count(productsku) from unnest(hits.product) where productsku is not null) = 1

It does not give an error but the results have duplicated skus and we also lost the skus inside the bigger arrays.

Is there some mistake in our query preventing the arrays of being unnested?


Solution

  • If I understand correctly, I think you want something like this:

    WITH customers_data AS (
      SELECT
        fullvisitorid fv,
        visitid v,
        ARRAY_CONCAT_AGG(ARRAY(
          SELECT productsku FROM UNNEST(hits.product))) sku
      FROM
        `dataset_id.ga_sessions_*`,
        UNNEST(hits) hits
      WHERE
        _table_suffix BETWEEN
          FORMAT_DATE("%Y%m%d", DATE_SUB(CURRENT_DATE(), INTERVAL 1 DAY))
          AND FORMAT_DATE("%Y%m%d", DATE_SUB(CURRENT_DATE(), INTERVAL 0 DAY))
      GROUP BY
        fv,
        v
      LIMIT
        100
    )
    SELECT
      *
    FROM
      customers_data;
    

    This preserves all of the SKUs through the use of ARRAY_CONCAT_AGG over an ARRAY subquery that extracts the SKUs for each row. If you want to deduplicate all of the SKUs across rows, you can replace

    SELECT
      *
    FROM
      customers_data;
    

    with:

    SELECT *
      REPLACE (ARRAY(SELECT DISTINCT s FROM UNNEST(sku) AS s) AS sku)
    FROM
      customers_data;
    

    Edit: For more reading, take a look at types of expression subqueries in the documentation. In your case, you needed an ARRAY subquery, since the idea was to take an ARRAY<STRUCT<...>> in each row and transform it into an ARRAY of the field type in order to concatenate the arrays across rows.

    ARRAY_AGG creates an array from individual elements, whereas ARRAY_CONCAT_AGG creates an array from the concatenation of arrays. The difference between them is similar to the difference between the array literal constructor [] and ARRAY_CONCAT, except that the _AGG versions are aggregate functions.

    As a standalone example, you can try:

    WITH T AS (
      SELECT ARRAY<STRUCT<x INT64, y INT64>>[(1, 10), (2, 11), (3, 12)] AS arr UNION ALL
      SELECT ARRAY<STRUCT<x INT64, y INT64>>[(4, 13)] UNION ALL
      SELECT ARRAY<STRUCT<x INT64, y INT64>>[(5, 14), (6, 15)]
    )
    SELECT ARRAY(SELECT x FROM UNNEST(arr)) AS x_array
    FROM T;
    

    This returns a column x_array where the elements in each array are those of the x field from each element in arr. To concatenate all of the arrays so that there is a single row in the result, use ARRAY_CONCAT_AGG, e.g.:

    WITH T AS (
      SELECT ARRAY<STRUCT<x INT64, y INT64>>[(1, 10), (2, 11), (3, 12)] AS arr UNION ALL
      SELECT ARRAY<STRUCT<x INT64, y INT64>>[(4, 13)] UNION ALL
      SELECT ARRAY<STRUCT<x INT64, y INT64>>[(5, 14), (6, 15)]
    )
    SELECT ARRAY_CONCAT_AGG(ARRAY(SELECT x FROM UNNEST(arr))) AS x_array
    FROM T;
    

    For your other question, REPLACE accepts a list of expressions paired with the columns that they are meant to replace. The expression can be something simple such as a literal, or it can be something more complicated such as an ARRAY subquery, which is what I used. For example:

    WITH T AS (
      SELECT 1 AS x, 'foo' AS y, true AS z UNION ALL
      SELECT 2, 'bar', false UNION ALL
      SELECT 3, 'baz', true
    )
    SELECT * REPLACE(1 - x AS x, CAST(x AS STRING) AS y)
    FROM T;
    

    This replaces the original x and y columns that would have been returned from the SELECT * with the results of 1 - x and CAST(x AS STRING) instead.