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Asked: 2024-09-25T15:31:59+05:30 In:

How can I flatten an array in Flink SQL, join it with another table, and then perform an aggregation on the joined data?

I’ve been diving into Flink SQL for a project I’m working on, and I’ve hit a bit of a wall that I hope you all can help me with. Here’s the scenario: I’ve got a data stream where one of the columns is an array, and I need to flatten that array out first. Let’s say my input data looks something like this:

“`json
{“id”: 1, “values”: [10, 20, 30]}
{“id”: 2, “values”: [15, 25]}
{“id”: 3, “values”: [5, 10, 15, 20]}
“`

So the goal here is to get each value from the “values” array into its own row while maintaining the “id” association. I’m wondering how to do that effectively in Flink SQL. I’ve tried using the `UNNEST` function, but I’m not quite sure if that’s the right approach.

Once I have my flattened data, the next step is to join it with another table that contains some additional information. This second table has user information with ids that look like this:

“`json
{“id”: 1, “user”: “Alice”}
{“id”: 2, “user”: “Bob”}
{“id”: 3, “user”: “Charlie”}
“`

After I flatten and join the two datasets on the “id”, I’d like to perform an aggregation to sum the values for each user. Something like getting the total of all the “values” associated with each user.

Now, I’m feeling a bit lost here. How do I structure my SQL queries to achieve this? Are there any specific Flink SQL features or functions I should be using, or is there an efficient way to handle this kind of operation? I’ve read a bit about windowing functions, but I’m not sure they apply here since it’s not about time series data.

If anyone has tackled a similar problem or has insights into the best way to go about this – or even a small code example that illustrates the whole flow from flattening the array to the final aggregation – I would really appreciate it! It’d really help me move forward on this, and I’m sure others in the community might benefit from your suggestions too. Thanks in advance!

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    2 Answers

    1. To flatten an array in Flink SQL, you can indeed use the `UNNEST` function effectively. Given your input data with an array of values, the following SQL query can be used to achieve the desired flattening while maintaining the association with the “id”:

      SELECT id, value
FROM input_table
CROSS JOIN UNNEST(values) AS T(value);

      Once you have flattened the data, the next step is to join it with your user information table. You can achieve this by using a standard SQL `JOIN` operation. Afterward, you can use the `SUM` function to aggregate the values associated with each user. The complete SQL query for this would look something like this:

      SELECT u.user, SUM(v.value) AS total_value
FROM (
    SELECT id, value
    FROM input_table
    CROSS JOIN UNNEST(values) AS T(value)
) AS v
JOIN user_table AS u ON v.id = u.id
GROUP BY u.user;


    2. To flatten your data stream in Flink SQL, you can indeed use the `UNNEST` function! It’s perfect for getting each element of the array into its own row while keeping the “id” intact.

      Here’s a sample SQL query that should help you with the flattening part:

      
SELECT id, value
FROM your_table_name
CROSS JOIN UNNEST(values) AS value

      This query will produce a result where each “value” from the “values” array has its own row, linked to the original “id”.

      Next, to join this flattened data with your user information table, you can do something like this:

      
SELECT u.user, SUM(f.value) AS total_value
FROM (
    SELECT id, value
    FROM your_table_name
    CROSS JOIN UNNEST(values) AS value
) AS f
JOIN user_table AS u ON f.id = u.id
GROUP BY u.user

      Here, we’re joining the flattened results (aliased as f) with your user table (aliased as u) on the “id” column and then grouping by user. The SUM(f.value) will give you the total of all “values” for each user. Pretty neat, right?

      Using CROSS JOIN UNNEST() is the way to go, and you don’t really need window functions for this kind of aggregation. Just remember to replace your_table_name and user_table with the actual names of your tables. Hope that helps you move forward! Good luck!


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