How to Add Up Columns in Time-Series Tables with Repeating Order Numbers

Aggregating Time-Series Data with SQL for Repeated Order Numbers

This solution uses SQL to create a modular query handling non-unique order numbers with time-series aggregation.

-- Define a Common Table Expression (CTE) to track transitions between order IDs
WITH order_transitions AS (
    SELECT
        *,
        LAG(order_id) OVER (ORDER BY start) AS prev_id,
        LEAD(order_id) OVER (ORDER BY start) AS next_id
    FROM production
)
-- Create a query to handle gaps and the first line issue
SELECT
    order_id,
    MIN(start) AS start,
    MAX(end) AS end,
    SUM(count) AS total_count
FROM (
    SELECT
        order_id,
        start,
        end,
        count,
        CASE
            WHEN prev_id != order_id OR prev_id IS  THEN ROW_NUMBER() OVER (ORDER BY start)
            ELSE 
        END AS grouping_flag
    FROM order_transitions
) t
GROUP BY order_id, grouping_flag
ORDER BY start;

Using Procedural SQL with PL/pgSQL for Custom Aggregation

This approach uses PL/pgSQL in PostgreSQL for dynamic and iterative row-by-row processing.

DO $$
DECLARE
    curr_order_id INTEGER;
    curr_start TIMESTAMP;
    curr_end TIMESTAMP;
    curr_count INTEGER;
BEGIN
    -- Create a temp table to hold results
    CREATE TEMP TABLE aggregated_data (
        order_id INTEGER,
        start TIMESTAMP,
        end TIMESTAMP,
        count INTEGER
    );
    -- Loop through each row in production
    FOR row IN SELECT * FROM production ORDER BY start LOOP
        IF curr_order_id IS DISTINCT FROM row.order_id THEN
            -- Insert previous aggregated row
            INSERT INTO aggregated_data VALUES (curr_order_id, curr_start, curr_end, curr_count);
            -- Reset for new group
            curr_order_id := row.order_id;
            curr_start := row.start;
            curr_end := row.end;
            curr_count := row.count;
        ELSE
            -- Aggregate within the same group
            curr_end := row.end;
            curr_count := curr_count + row.count;
        END IF;
    END LOOP;
END $$;

JavaScript Backend Solution with Node.js and SQL Integration

This backend solution uses Node.js to process SQL data dynamically, incorporating error handling and modular functions.

const { Client } = require('pg'); // PostgreSQL client
const aggregateData = async () => {
    const client = new Client({
        user: 'user',
        host: 'localhost',
        database: 'production_db',
        password: 'password',
        port: 5432
    });
    try {
        await client.connect();
        const query = `WITH lp AS (
            SELECT *, LEAD(order_id) OVER (ORDER BY start) AS next_id FROM production
        )
        SELECT order_id, MIN(start) AS start, MAX(end) AS end, SUM(count) AS count
        FROM lp
        GROUP BY order_id
        ORDER BY MIN(start);`;
        const result = await client.query(query);
        console.log(result.rows);
    } catch (err) {
        console.error('Error executing query:', err);
    } finally {
        await client.end();
    }
};
aggregateData();

Frequently Asked Questions About SQL Time-Series Aggregation

1. What is the purpose of LEAD() and LAG() in SQL?
2. The LEAD() function fetches the value from the next row, while LAG() retrieves the value from the previous row. They are used to identify transitions or changes in rows, such as tracking changes in order_id.
3. How do I use GROUP BY for time-series data?
4. You can use GROUP BY to aggregate rows based on a common column, like order_id, while applying aggregate functions like SUM() or MAX() to combine values across the group.
5. What are the benefits of WITH Common Table Expressions (CTEs)?
6. CTEs simplify queries by allowing you to define temporary result sets that are easy to read and reuse. For instance, a CTE can identify the start and end of a group before aggregating.
7. Can I use recursive queries for time-series aggregation?
8. Yes! Recursive queries are useful for linking data rows that depend on one another. For example, you can "chain" rows with overlapping times for more complex aggregations.
9. How do I ensure accuracy when dealing with overlapping time ranges?
10. To avoid double-counting, use conditional logic in your query, such as filtering or setting boundaries. Combining CASE statements with window functions can help manage these overlaps.

Wrapping Up with SQL Aggregation Insights

Understanding how to handle repeated order_id values in time-series data is crucial for accurate data processing. This article highlighted various techniques like CTEs and window functions to simplify complex queries and ensure meaningful results. These strategies are essential for scenarios involving overlapping or fragmented orders.

Whether you’re building a production dashboard or analyzing time-sensitive data, these SQL skills will elevate your capabilities. Combining modular query design with advanced functions ensures that your solutions are both efficient and maintainable. Apply these methods in your projects to unlock the full potential of time-series data analysis! 😊