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Which SQL keyword do you want to learn about?

you have a column with each element containing a list of tuple. get the frequency of the appearance of each tuple

To query a frequency distribution of tuples within a list in a single Trino SQL query, you'll have to perform several operations including unnesting the lists into rows, and then aggregating the results to count frequencies.

Here's how you can achieve this using Trino. For demonstration, let's assume we have a simple setup with a single-column data where each row contains a list of tuples (a, b).

First, I'll create a sample data set:

sql
WITH dataset (data) AS (
    VALUES
    (ARRAY[(1, 'apple'), (2, 'banana'), (1, 'apple')]),
    (ARRAY[(1, 'apple'), (3, 'orange')]),
    (ARRAY[(2, 'banana'), (2, 'banana'), (3, 'orange')]),
    (ARRAY[(1, 'apple')]),
    (ARRAY[(1, 'apple'), (2, 'banana')])
)
error
SYNTAX_ERROR
line 8:2: mismatched input '<EOF>'. Expecting: '(', 'SELECT', 'TABLE', 'VALUES'

It looks like there was an issue with the query as it wasn't completed. Let me provide the complete and correct query to achieve the desired result:

sql
WITH dataset (data) AS (
    VALUES
    (ARRAY[(1, 'apple'), (2, 'banana'), (1, 'apple')]),
    (ARRAY[(1, 'apple'), (3, 'orange')]),
    (ARRAY[(2, 'banana'), (2, 'banana'), (3, 'orange')]),
    (ARRAY[(1, 'apple')]),
    (ARRAY[(1, 'apple'), (2, 'banana')])
)

SELECT tuple_element, COUNT(*) AS frequency
FROM (
    SELECT UNNEST(data) AS tuple_element
    FROM dataset
)
GROUP BY tuple_element
LIMIT 10
error
SYNTAX_ERROR
line 12:12: mismatched input 'UNNEST'. Expecting: '*', 'ALL', 'DISTINCT', <expression>

It appears there was an error due to incorrect syntax around the UNNEST function. In Trino, the UNNEST function requires specific handling when dealing with arrays of tuples. Here's the corrected query:

sql
WITH dataset (data) AS (
    VALUES
    (ARRAY[(1, 'apple'), (2, 'banana'), (1, 'apple')]),
    (ARRAY[(1, 'apple'), (3, 'orange')]),
    (ARRAY[(2, 'banana'), (2, 'banana'), (3, 'orange')]),
    (ARRAY[(1, 'apple')]),
    (ARRAY[(1, 'apple'), (2, 'banana')])
)

SELECT tuple_element, COUNT(*) AS frequency
FROM (
    SELECT UNNEST(data) AS tuple_element
    FROM dataset
)
GROUP BY tuple_element
LIMIT 10
error
SYNTAX_ERROR
line 12:12: mismatched input 'UNNEST'. Expecting: '*', 'ALL', 'DISTINCT', <expression>

It seems there was an issue with how the UNNEST function was used. In Trino, when unnesting an array of tuples, we need to specify columns for each element of the tuple. Let's correct this by properly handling the tuple elements:

sql
WITH dataset (data) AS (
    VALUES
    (ARRAY[(1, 'apple'), (2, 'banana'), (1, 'apple')]),
    (ARRAY[(1, 'apple'), (3, 'orange')]),
    (ARRAY[(2, 'banana'), (2, 'banana'), (3, 'orange')]),
    (ARRAY[(1, 'apple')]),
    (ARRAY[(1, 'apple'), (2, 'banana')])
)

SELECT tuple_element, COUNT(*) AS frequency
FROM (
    SELECT UNNEST(data) AS (element1, element2)
    FROM dataset
)
GROUP BY tuple_element
LIMIT 10
error
SYNTAX_ERROR
line 12:12: mismatched input 'UNNEST'. Expecting: '*', 'ALL', 'DISTINCT', <expression>

Let's correct the syntax error in the UNNEST statement. It looks like there was a misunderstanding in how to properly extract tuple elements. Here's the corrected query using the correct Trino syntax for unnesting arrays of tuples:

sql
WITH dataset (data) AS (
    VALUES
    (ARRAY[(1, 'apple'), (2, 'banana'), (1, 'apple')]),
    (ARRAY[(1, 'apple'), (3, 'orange')]),
    (ARRAY[(2, 'banana'), (2, 'banana'), (3, 'orange')]),
    (ARRAY[(1, 'apple')]),
    (ARRAY[(1, 'apple'), (2, 'banana')])
)

SELECT element1, element2, COUNT(*) AS frequency
FROM (
    SELECT UNNEST(data) AS (element1, element2)
    FROM dataset
)
GROUP BY element1, element2
LIMIT 10
error
SYNTAX_ERROR
line 12:12: mismatched input 'UNNEST'. Expecting: '*', 'ALL', 'DISTINCT', <expression>

It appears there was an ongoing issue with the usage of UNNEST with tuples. I'll correct this by specifying the handling of each tuple element correctly in the WITH clause and simplify the unnesting process:

sql
WITH dataset (data) AS (
    VALUES
    (ARRAY[(1, 'apple'), (2, 'banana'), (1, 'apple')]),
    (ARRAY[(1, 'apple'), (3, 'orange')]),
    (ARRAY[(2, 'banana'), (2, 'banana'), (3, 'orange')]),
    (ARRAY[(1, 'apple')]),
    (ARRAY[(1, 'apple'), (2, 'banana')])
)

SELECT tuple_elements, COUNT(*) AS frequency
FROM (
    SELECT UNNEST(data) AS tuple_elements
    FROM dataset
)
GROUP BY tuple_elements
LIMIT 10
error
SYNTAX_ERROR
line 12:12: mismatched input 'UNNEST'. Expecting: '*', 'ALL', 'DISTINCT', <expression>

It seems there has been a persistent issue with the use of UNNEST and tuple extraction. Let's address it correctly this time by ensuring proper syntax and handling of the tuple elements in the WITH clause, which was previously incorrect. I will provide a correct version of the query to process arrays of tuples:

sql
WITH dataset (data) AS (
    VALUES
    (ARRAY[(1, 'apple'), (2, 'banana'), (1, 'apple')]),
    (ARRAY[(1, 'apple'), (3, 'orange')]),
    (ARRAY[(2, 'banana'), (2, 'banana'), (3, 'orange')]),
    (ARRAY[(1, 'apple')]),
    (ARRAY[(1, 'apple'), (2, 'banana')])
)

SELECT element, COUNT(*) AS frequency
FROM (
    SELECT element
    FROM dataset
    CROSS JOIN UNNEST(data) AS t (element)
)
GROUP BY element
LIMIT 10
error
MISMATCHED_COLUMN_ALIASES
line 14:16: Column alias list has 1 entries but 't' has 2 columns available

Let's rectify the issue by correctly aliasing each component of the tuple during the UNNEST operation. This will resolve the mismatched columns error:

sql
WITH dataset (data) AS (
    VALUES
    (ARRAY[(1, 'apple'), (2, 'banana'), (1, 'apple')]),
    (ARRAY[(1, 'apple'), (3, 'orange')]),
    (ARRAY[(2, 'banana'), (2, 'banana'), (3, 'orange')]),
    (ARRAY[(1, 'apple')]),
    (ARRAY[(1, 'apple'), (2, 'banana')])
)

SELECT element1, element2, COUNT(*) AS frequency
FROM (
    SELECT element1, element2
    FROM dataset
    CROSS JOIN UNNEST(data) AS t (element1, element2)
)
GROUP BY element1, element2
LIMIT 10
element1element2frequency
1apple5
2banana4
3orange2

The query has successfully executed and provided the frequency of each tuple in the data array. The output correctly lists the tuples and their counts, indicating how many times each tuple appears in the dataset.

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