Optimizing Slow Performance on MySQL Recursive CTE Queries

MySQL recursive Common Table Expressions (CTEs) can be powerful tools for solving complex problems. However, they can also be slow and inefficient, especially when dealing with large datasets. In this article, we will explore the techniques and strategies for optimizing MySQL recursive CTE queries, using the example of calculating the 9-minute Exponential Moving Average (EMA) for a large set of minute stock data.

Understanding Recursive CTEs

A recursive CTE is a query that references itself, allowing it to iterate over a dataset multiple times. The basic syntax for a recursive CTE is as follows:

WITH RECURSIVE t AS (
    -- anchor query
    SELECT ...
    FROM ...

    UNION ALL

    -- recursive query
    SELECT ...
    FROM t
)

The anchor query defines the starting point for the recursion, while the recursive query references the previous iteration to build upon.

Optimizing MySQL Recursive CTEs

To optimize a MySQL recursive CTE query, we need to identify the performance bottlenecks and apply techniques that reduce the computational overhead. Here are some strategies for optimizing MySQL recursive CTE queries:

1. Limiting the Number of Recursions

The number of recursions determines how many times the anchor query is executed. If the number of recursions is too high, it can lead to performance issues.

To limit the number of recursions, we can use the cte_max_recursion_depth system variable:

SET GLOBAL cte_max_recursion_depth = 1000000;

However, be cautious when increasing this value, as excessive recursions can still lead to performance problems.

2. Indexing

Proper indexing can significantly improve the performance of a recursive CTE query. Create indexes on columns used in the WHERE and JOIN clauses:

CREATE INDEX idx_ticker ON min_data (ticker);
CREATE INDEX idx_quoteid ON ema (QuoteId);

3. Materializing the Recursive Query

Instead of using a recursive CTE, consider materializing the query results into a temporary table or view. This can reduce the computational overhead and improve performance.

For example:

CREATE TABLE min_data_EMA9 AS
SELECT ...
FROM (
    SELECT ...
    FROM min_data
) t;

4. Calculating EMA in Application Code

As suggested by the answer, calculating the EMA in application code can be a more efficient approach than using a recursive CTE query.

SELECT ticker, AVG(c) OVER (PARTITION BY ticker ORDER BY t) as EMA9
FROM min_data;

5. Using Interval-based Filtering

To reduce the number of rows processed, use interval-based filtering to limit the data range:

WHERE ticker = 'TOPS'
  AND t > NOW() - INTERVAL 4 DAYS
ORDER BY t;

This approach eliminates the need for sorting and indexing.

6. Storing Hourly EMAs

To reduce the computational overhead, store the EMA values at the end of each hour (including the end of the day) in a separate table:

CREATE TABLE min_data_EMA9_HR AS
SELECT ...
FROM (
    SELECT ...
    FROM min_data
) t;

This approach eliminates the need for recursive calculations.

7. Looping through Stocks

Finally, consider looping through stocks instead of using a single query with multiple conditions:

SELECT ticker, AVG(c) OVER (PARTITION BY ticker ORDER BY t) as EMA9
FROM min_data;

This approach reduces the number of rows processed and improves performance.

Conclusion

Optimizing MySQL recursive CTE queries requires a deep understanding of the underlying techniques and strategies. By applying these tips and tricks, you can significantly improve the performance of your queries and make them more efficient. Remember to carefully consider the trade-offs between complexity and performance when choosing an approach for your specific use case.

Last modified on 2023-09-30