Dynamic SQL Generation with STRING_AGG: A Powerful (and Potentially Dangerous) Technique

Introduction

Imagine you need to perform a series of updates in your database, but the exact parameters for each update are determined by data already in the database. This is a common scenario in data warehousing, ETL processes, and certain application logic. One powerful technique for achieving this is to dynamically generate SQL statements within your database. Today, we’ll dissect a query that uses STRING_AGG (or its equivalent) to create a large UPDATE statement from a list of IDs, highlighting why this approach can be “cool” (in a responsible way!) while also discussing the potential pitfalls.

The Query

Here’s the SQL query we’ll be breaking down:

SELECT concat('UPDATE hashing_monitor_salesprices hms  SET hash = concat( hash , 1), updated = now() WHERE  kafka_key IN (',publicIds,');' )
FROM (
    SELECT
        STRING_AGG( concat( '''',public_id,''''), ',') AS publicIds,
        style_number
    FROM
        active_sales_price asp
    WHERE
        style_number IN (
            10210430, 10278214, 10285290, 10289449, 10294544,
            10301577, 10303332, 10307505, 10307510, 10311353,
            10311536, 10311548, 10311669, 10315593, 10315597,
            10315603, 10315606, 10315614, 10315844, 10316068,
            10317094, 10318793, 10318856, 10318863, 10318898,
            10319048, 10320441, 10321893, 10321977, 10322680,
            10322818, 10322853, 10322890, 10322991, 10323099,
            10323359
        )
    GROUP BY style_number
) q;

This query generates a single UPDATE statement that targets records in the hashing_monitor_salesprices table based on public_id values retrieved from the active_sales_price table, filtered by a list of style_number values. The generated UPDATE statement concatenates “1” to the existing hash value and updates the updated timestamp.

Tables Involved

• active_sales_price: This table likely holds active sales price information. Key columns include public_id (presumably a unique identifier for each sales price) and style_number.
• hashing_monitor_salesprices: This table appears to be used for monitoring or auditing changes to sales prices. It has columns like hash (likely a checksum or hash value of the sales price data), kafka_key (matching the public_id from the other table), and updated (a timestamp).

Simplified Schema (Example):

active_sales_price Table:
+----------------+-------------+
| Column Name    | Data Type   |
+----------------+-------------+
| public_id      | VARCHAR     |
| style_number   | INT         |
| ... other sales price data ...| ...         |
+----------------+-------------+

hashing_monitor_salesprices Table:
+----------------+-------------+
| Column Name    | Data Type   |
+----------------+-------------+
| kafka_key      | VARCHAR     |
| hash           | VARCHAR     |
| updated        | TIMESTAMP   |
| ... other monitoring data ...| ...         |
+----------------+-------------+

Step-by-Step Breakdown

1. Innermost SELECT (Filtering):
   • SELECT STRING_AGG( concat( '''',public_id,''''), ',') AS publicIds, style_number: This is the core of the dynamic SQL generation. It selects public_id and style_number from the active_sales_price table
   • FROM active_sales_price asp: Select from the table active_sales_price.
   • WHERE style_number IN (...): This filters the active_sales_price table to only include rows where the style_number is in the provided list. This is crucial for targeting specific product styles.
   • GROUP BY style_number: It’s technically not necessary because string_agg naturally aggregates all the ids based on style number, but it is in this case, since we only want a single row to be retured, therefore only 1 update statment.
2. STRING_AGG and Concatenation:
   • STRING_AGG( concat( '''',public_id,''''), ',') AS publicIds: This is where the magic happens.
     • concat( '''',public_id,''''): This part takes each public_id and wraps it in single quotes. This is essential because the public_id values will be used in the IN clause of the dynamically generated UPDATE statement, and string literals in SQL need to be enclosed in single quotes. The triple single quotes are required to escape the single quote for the final string.
     • STRING_AGG(..., ','): This aggregates the single-quoted public_id values into a single string, separated by commas. This creates a comma-separated list suitable for the IN clause. The alias AS publicIds gives this concatenated string a name.
3. Outer SELECT (SQL Generation):
   • SELECT concat('UPDATE hashing_monitor_salesprices hms SET hash = concat( hash , 1), updated = now() WHERE kafka_key IN (',publicIds,');' ): This takes the comma-separated list of publicIds generated by the inner query and embeds it into a complete UPDATE statement.
     • concat(...): The concat() function combines the static parts of the UPDATE statement (the UPDATE keyword, table name, SET clause, WHERE clause) with the dynamically generated publicIds list. This results in a complete SQL UPDATE statement as a string.

Why is this “Cool”?

• Dynamic Updates: It allows you to update a large number of rows based on conditions derived from other tables, all in a single (generated) statement.
• Conciseness (Potentially): While this specific query might not look shorter than writing individual update statements, imagine if you had hundreds or thousands of style_number values. Generating the SQL is much more manageable than writing them out manually.
• Automation: This technique is well-suited for automating database maintenance tasks or ETL processes where update parameters change frequently.

Example Output

The query will output a single row containing a string like this:

UPDATE hashing_monitor_salesprices hms SET hash = concat( hash , 1), updated = now() WHERE kafka_key IN ('id1','id2','id3','id4','id5', ... );

Important Considerations and Potential Pitfalls

• SQL Injection Risk: This is the biggest concern. If the public_id values in the active_sales_price table are not properly sanitized, an attacker could inject malicious SQL code into the generated UPDATE statement. Never use this technique with user-supplied input without rigorous validation and escaping! Consider using parameterized queries instead, which are much safer. In many cases, if your public_id is a UUID/GUID that already mitigates this risk, however.
• Statement Size Limits: Most database systems have a limit on the maximum size of a SQL statement. If the list of public_id values becomes too long, the generated UPDATE statement might exceed this limit, causing an error. Consider breaking up the update into smaller batches if this is a concern.
• Performance: While generating a single large UPDATE can be faster than executing many individual UPDATE statements (due to reduced overhead), it can also lock the table for a longer period, potentially impacting other queries. Consider the trade-offs and test performance carefully.
• Debugging: Debugging dynamically generated SQL can be tricky. Make sure to log the generated SQL statement so you can see exactly what’s being executed.

Alternatives

• Parameterized Queries: The safest approach is to use parameterized queries (also known as prepared statements). This involves preparing a SQL statement with placeholders for the values, and then binding the actual values to the placeholders. This prevents SQL injection and can also improve performance. However, this usually requires executing multiple queries from your application code.
• Stored Procedures: You could encapsulate the logic for generating and executing the updates within a stored procedure. This can improve security and maintainability.
• Using Application logic to create update statements: The creation of the update statement and then running of it could be done outside the database.

Conclusion

Dynamically generating SQL statements with STRING_AGG is a powerful technique, but it comes with significant risks, particularly SQL injection. Use it with caution, and always prioritize security by sanitizing inputs or using parameterized queries. This example shows a specific use case; exploring other group aggregation functions and ways to optimize large-scale database updates can further enhance your data management skills. Remember to test your generated SQL thoroughly before deploying it to a production environment.