Deleting All Records Except the First of Each Month in SQLite

Understanding the Problem: Deleting Records While Preserving the First Entry of Each Month

The core issue revolves around deleting records from a SQLite table while ensuring that the first record of each month is preserved. The table contains daily records with dates formatted as YYYY-MM-DD. The goal is to clean up historical data by retaining only the first record of each month for years prior to the current year. The initial approach uses a simple DELETE query with a LIKE clause to exclude records that are not the first of the month. However, this approach fails when there is no record for the first day of the month, as it may inadvertently delete the second day’s record instead.

The challenge lies in ensuring that the query dynamically identifies the earliest available record for each month, even if the first day is missing. This requires a more sophisticated approach than simple string matching or filtering. The solution must account for gaps in the data and ensure that the earliest available record for each month is preserved, regardless of its specific date.

Exploring the Causes: Why the Initial Query Fails and What to Consider

The initial query fails because it relies on a static condition (date NOT LIKE '%-01') to identify the first day of the month. This approach assumes that every month has a record for the first day, which is not always the case. If the first day’s record is missing, the query will incorrectly delete the second day’s record, as it does not meet the LIKE condition. This highlights the importance of dynamic logic in SQL queries, especially when dealing with incomplete or irregular data.

Another consideration is performance. The table may contain a large number of records, and the query must efficiently identify and delete the appropriate rows. Indexing the date column is crucial for optimizing the query’s performance, as it allows the database to quickly locate and compare dates. Without an index, the query may degrade significantly, especially when using subqueries or window functions.

Additionally, the query must handle edge cases, such as months with no records or partitions with missing data. For example, if the table tracks multiple partitions (e.g., different categories or regions), the query must ensure that it correctly processes each partition independently. This adds complexity to the solution, as it requires partitioning the data and applying the logic separately to each group.

Crafting the Solution: A Robust Query to Preserve the Earliest Record of Each Month

To address these challenges, we can use a combination of SQLite features, including subqueries, window functions, and indexing. The goal is to dynamically identify the earliest record for each month and delete all other records for that month. Here are the key steps to achieve this:

Step 1: Identify the Earliest Record for Each Month

We can use a window function to rank records within each month based on their date. The RANK() function, combined with PARTITION BY, allows us to assign a rank to each record within its month. The earliest record for each month will have a rank of 1. For example:

SELECT rowid, date,
       RANK() OVER (PARTITION BY SUBSTR(date, 1, 7) ORDER BY date) AS rank
FROM daily
WHERE date < strftime('%Y', 'now');

This query partitions the data by month (using SUBSTR(date, 1, 7) to extract the YYYY-MM portion) and orders the records within each partition by date. The RANK() function assigns a rank to each record, with the earliest date receiving a rank of 1.

Step 2: Delete Records That Are Not the Earliest for Their Month

Once we have identified the earliest record for each month, we can delete all other records. We can achieve this by using a Common Table Expression (CTE) to store the ranked records and then deleting rows that do not have a rank of 1. For example:

WITH ranked_records AS (
    SELECT rowid,
           RANK() OVER (PARTITION BY SUBSTR(date, 1, 7) ORDER BY date) AS rank
    FROM daily
    WHERE date < strftime('%Y', 'now')
)
DELETE FROM daily
WHERE rowid IN (SELECT rowid FROM ranked_records WHERE rank <> 1);

This query first creates a CTE (ranked_records) that contains the rowid and rank of each record. It then deletes all records from the daily table where the rank is not 1.

Step 3: Optimize Performance with Indexing

To ensure the query performs well, especially on large datasets, it is essential to index the date column. An index allows the database to quickly locate and compare dates, reducing the time required for sorting and ranking. For example:

CREATE INDEX idx_daily_date ON daily(date);

This index should be created before running the deletion query. If the table already has a primary key or unique constraint on the date column, additional indexing may not be necessary.

Step 4: Handle Edge Cases and Partitions

If the table contains multiple partitions (e.g., different categories or regions), the query must be modified to handle each partition independently. This can be done by including the partition column in the PARTITION BY clause of the window function. For example:

WITH ranked_records AS (
    SELECT rowid,
           RANK() OVER (PARTITION BY partition, SUBSTR(date, 1, 7) ORDER BY date) AS rank
    FROM daily
    WHERE date < strftime('%Y', 'now')
)
DELETE FROM daily
WHERE rowid IN (SELECT rowid FROM ranked_records WHERE rank <> 1);

This query ensures that the ranking and deletion logic is applied separately to each partition.

Step 5: Test and Validate the Query

Before running the deletion query on the entire dataset, it is advisable to test it on a smaller subset of data. This allows you to verify that the query behaves as expected and does not inadvertently delete the wrong records. For example:

-- Test query to see which records would be deleted
WITH ranked_records AS (
    SELECT rowid, date,
           RANK() OVER (PARTITION BY SUBSTR(date, 1, 7) ORDER BY date) AS rank
    FROM daily
    WHERE date < strftime('%Y', 'now')
)
SELECT * FROM daily
WHERE rowid IN (SELECT rowid FROM ranked_records WHERE rank <> 1);

This query returns the records that would be deleted, allowing you to review them before proceeding with the actual deletion.

Final Thoughts: Ensuring Data Integrity and Performance

The solution outlined above provides a robust and efficient way to delete records while preserving the earliest record for each month. By using window functions and CTEs, the query dynamically identifies the records to delete, even when the first day’s record is missing. Indexing the date column ensures that the query performs well, even on large datasets. Additionally, handling partitions and edge cases ensures that the query works correctly in all scenarios.

When implementing this solution, it is important to back up the data before running the deletion query. This provides a safety net in case anything goes wrong. It is also a good practice to test the query on a subset of data and review the results before applying it to the entire dataset.

By following these steps, you can confidently clean up historical data in your SQLite database while preserving the integrity of your records.