Using Arrays in Athena SQL: Concatenating Distinct Values and Partitioning by Specific Dimensions

Working with Arrays in Athena SQL: Concatenating Distinct Values and Partitioning by Specific Dimensions

As a data analyst or scientist, working with data can be a daunting task, especially when dealing with large datasets. In Amazon Athena, one of the powerful features is the ability to work with arrays, which allows you to perform complex operations on your data. In this article, we’ll explore how to concatenate distinct values in an array and partition by specific dimensions using Athena SQL.

Understanding Arrays in Athena SQL

In Athena SQL, arrays are a data type that allows you to store multiple values in a single column. These arrays can be used to perform various operations such as aggregations, filtering, and grouping. One of the most commonly used array functions is ARRAY_AGG, which allows you to aggregate an array by a specified dimension.

Concatenating Distinct Values in an Array

Let’s start with concatenating distinct values in an array. We can use the ARRAY_JOIN function to concatenate arrays, but only when they have the same structure and length.

Suppose we have a table called your_table that contains customer information, including products purchased. The column products stores the product names as an array. We want to concatenate all distinct product names for each customer.

SELECT 
    customer_id,
    date_range,
    ARRAYJOIN(ARRAY_DISTINCT(ARRAY_AGG(product)), ', ') AS concatenated_products
FROM 
    your_table
GROUP BY 
    customer_id, date_range;

However, this query does not partition the results by customer_id and date_range. To achieve this, we need to use a combination of array functions and grouping.

Partitioning by Specific Dimensions

To partition the results by specific dimensions, we can use the PARTITION BY clause. However, in Athena SQL, we cannot directly use PARTITION BY with arrays. Instead, we need to first convert the array to a string using the ARRAY_TO_STRING function.

Here’s an example:

SELECT 
    customer_id,
    date_range,
    ARRAYJOIN(ARRAY_DISTINCT(ARRAY_AGG(product)) || ',') AS concatenated_products
FROM 
    your_table
GROUP BY 
    customer_id, date_range;

In this query, we’re converting each array to a string by using || (the concatenation operator) and then joining the arrays together with commas. However, this will not give us the desired output.

To achieve the correct output, we need to use a combination of functions that are not directly available in Athena SQL. This is where we need to get creative with our approach.

Using a User-Defined Function (UDF)

One way to solve this problem is by creating a user-defined function (UDF) in Apache Spark SQL. UDFs allow us to create custom functions that can be used within the query engine.

Here’s an example of how you could implement a UDF in Spark SQL:

CREATE TEMPORARY FUNCTION array_concat(arr IN ARRAY<STRING>)RETURNS STRING AS $$
BEGIN
    RETURN CONCATENATE(arr);
END;
$$ LANGUAGE java;

Once we’ve created the UDF, we can use it within our Athena query.

Using a Spark UDAF

Another way to solve this problem is by using a Spark User-Defined Aggregate Function (UDAF). A UDAF allows us to create custom aggregate functions that can be used within the query engine.

Here’s an example of how you could implement a UDAF in Spark SQL:

CREATE TEMPORARY FUNCTION array_concat(arr IN ARRAY<STRING>)RETURNS STRING AS $$
BEGIN
    RETURN CONCATENATE(arr);
END;
$$ LANGUAGE java;

CREATE TEMPORARY FUNCTION concat_products(customer_id IN INT, date_range IN DATE)RETURNS STRING AS $$
BEGIN
    WITH data AS (
        SELECT 
            customer_id,
            date_range,
            array_agg(product) as products_array
        FROM 
            your_table
        GROUP BY 
            customer_id, date_range
    )
    RETURN concat_products_array(data);
END;
$$ LANGUAGE java;

CREATE TEMPORARY FUNCTION concat_products_array(arr IN ARRAY<STRING>)RETURNS STRING AS $$
BEGIN
    FOR product IN arr LOOP
        RETURN product || ',';
    END LOOP;
END;
$$ LANGUAGE java;

Once we’ve created the UDAF, we can use it within our Athena query.

Using a Spark UDAF with Athena

To use the Spark UDAF in Athena, you need to create a Spark UDF that is compatible with Athena’s Spark engine. This involves modifying the UDAF to accept and return arrays of strings.

Here’s an example:

CREATE TEMPORARY FUNCTION array_concat(arr IN ARRAY<STRING>)RETURNS STRING AS $$
BEGIN
    RETURN CONCATENATE(arr);
END;
$$ LANGUAGE java;

CREATE TEMPORARY FUNCTION concat_products(customer_id IN INT, date_range IN DATE)RETURNS STRING AS $$
BEGIN
    WITH data AS (
        SELECT 
            customer_id,
            date_range,
            array_agg(product) as products_array
        FROM 
            your_table
        GROUP BY 
            customer_id, date_range
    )
    RETURN concat_products_array(data);
END;
$$ LANGUAGE java;

CREATE TEMPORARY FUNCTION concat_products_array(arr IN ARRAY<STRING>)RETURNS STRING AS $$
BEGIN
    FOR product IN arr LOOP
        RETURN product || ',';
    END LOOP;
END;
$$ LANGUAGE java;

CREATE OR REPLACE FUNCTION concat_products(customer_id INT, date_range DATE)
RETURNS STRING AS (
    concat_products customer_id, date_range
);

Now we can use the concat_products function in our Athena query:

SELECT 
    customer_id,
    date_range,
    concat_products(customer_id, date_range) AS concatenated_products
FROM 
    your_table;

This is just one possible solution to concatenating distinct values in an array and partitioning by specific dimensions in Athena SQL. While it may require some creative thinking and coding, the end result can be a powerful and flexible data processing tool that meets your needs.

Conclusion

In this article, we explored how to concatenate distinct values in an array and partition by specific dimensions using Athena SQL. We discussed several approaches, including using ARRAY_AGG and ARRAY_JOIN, creating user-defined functions (UDFs) and Spark User-Defined Aggregate Functions (UDAFs), and modifying these UDFs for compatibility with Athena’s Spark engine.

Whether you’re a data analyst or scientist, working with large datasets can be challenging. By understanding how to work with arrays in Athena SQL, you’ll be better equipped to handle complex queries and extract insights from your data.

References

[Amazon Athena Documentation](https://docs.aws.amazon.com/athena/latest/dg/tutorial- getting-started.html)
Apache Spark SQL Documentation
Java Language Reference

Last modified on 2024-10-02