How Can I Fetch the First and Last Records From Grouped Aggregate Queries in MySQL?

Fetching First and Last Records from Grouped Aggregate Queries in MySQL

In database operations, it's often necessary to retrieve not only aggregate values but also specific information about the first and last records within a grouped set. Consider a scenario where we're fetching aggregated data for a set of values grouped by a specific time period, such as week or year, and we desire to retrieve the opening and closing values for each group.

To achieve this, we might initially think of issuing multiple individual queries for each group using subqueries. However, this approach is suboptimal, especially for large datasets. MySQL offers efficient solutions for such scenarios using aggregation functions in conjunction with additional filtering and data manipulation functions.

To fetch the first and last records for each group, we can leverage the GROUP_CONCAT and SUBSTRING_INDEX functions. GROUP_CONCAT allows us to concatenate values for a specified column within each group, and SUBSTRING_INDEX lets us extract the first or last substring based on a delimiter.

For instance, to retrieve the opening and closing values for each yearly week, we modify our query as follows:

SELECT MIN(low_price), MAX(high_price),
SUBSTRING_INDEX(GROUP_CONCAT(CAST(open AS CHAR) ORDER BY datetime), ',', 1) AS open,
SUBSTRING_INDEX(GROUP_CONCAT(CAST(close AS CHAR) ORDER BY datetime DESC), ',', 1) AS close
FROM symbols
WHERE date BETWEEN(.. ..)
GROUP BY YEARWEEK(date)

This query groups the data by yearly week and calculates the minimum low_price and maximum high_price. Additionally, it uses GROUP_CONCAT to concatenate all open values for each group, sorted by datetime in ascending order, and SUBSTRING_INDEX to extract the first record as the opening value. Similarly, it concatenates all close values in descending order and extracts the last record as the closing value for each group.

By utilizing these functions, we avoid the need for subqueries and expensive processing, resulting in faster and more efficient queries, especially when working with large datasets.

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