Understanding Prepared Statements and Index Usage in MySQL

As a developer, it’s not uncommon to encounter performance issues when working with large datasets. One common technique used to improve query performance is the use of prepared statements. However, in this case, we’re dealing with a peculiar behavior where the prepared statement seems to ignore the index that should be used.

Background on Prepared Statements

A prepared statement is a SQL statement that has already been compiled and stored for reuse. This can improve performance by reducing the time it takes to execute a SQL query. When a prepared statement is executed, MySQL recompiles the query and then executes it.

In our example, we’re using the PREPARE statement to define a prepared statement. We then execute this prepared statement with specific parameters using the EXECUTE statement.

The Issue at Hand

Our issue revolves around a seemingly unrelated index that’s being ignored when using a prepared statement. Let’s dive deeper into the details of the query plan and index usage in MySQL.

Query Plan Analysis

When analyzing the query plan, we can see that the index used is indeed samples_reverse_device_id_created_at_organization_id_index. However, the plan shows that this index is not being used for the filtering predicate device_id = 5852.

Instead, the plan uses a file sort of the entire table to retrieve the results. This is a significant performance degradation compared to using the index.

Index Definition

The index definition is crucial in understanding why the index isn’t being used. The index samples_reverse_device_id_created_at_organization_id_index is defined as:

CREATE INDEX samples_reverse_device_id_created_at_organization_id_index ON samples (
  device_id,
  created_at DESC,
  organization_id
);

As you can see, this is a multi-column index with the columns in reverse order. This is an important detail because it affects how MySQL resolves equality conditions for these columns.

Equality Conditions and Index Usage

In general, when using indexes to filter rows based on an equality condition, MySQL uses a technique called “index scanning.” The idea is to scan the index to find matching values, rather than scanning the actual data in the table.

However, when dealing with multi-column indexes like ours, MySQL needs to consider how to resolve equality conditions for each column. In our case, the columns are defined in reverse order, which means that MySQL uses a technique called “reverse index scan.”

Reverse Index Scan

During a reverse index scan, MySQL scans the index from bottom to top (i.e., from the most specific to the least specific) to find matching values. This is necessary because of how the indexes are defined.

In our case, since we’re using the created_at DESC clause in the index definition, MySQL needs to start by scanning the most recent rows first and then move backward to find matches for the device_id = 5852 condition.

The Problem with Prepared Statements

Now that we understand how indexes work in MySQL, let’s revisit our prepared statement. When using a prepared statement, MySQL recompiles the query from scratch each time it’s executed. This means that any optimizations or simplifications that were made during compilation are lost.

In our case, this might explain why the index isn’t being used as expected. Since the query is being recompiled each time it’s executed, any index considerations or optimizations made by MySQL are ignored.

Solution: Delaying Predicates and Index Coverage

One solution to our problem is to delay the predicate id != 188315308 as much as possible. This can be achieved by rephrasing our query using a subquery.

Here’s an example:

SELECT *
FROM (
  SELECT *
  FROM `samples`
  WHERE `samples`.`device_id` = 5852
  ORDER BY `created_at` DESC
  LIMIT 100
) x
WHERE `id` != 188315308
ORDER BY `created_at` DESC
LIMIT 1;

By delaying the predicate as much as possible, we’re giving MySQL more flexibility to optimize the query plan and use the available indexes.

Creating a Covering Index

Another solution is to create a covering index that covers all columns used in the filtering predicates. This can be achieved by adding an index with the following definition:

CREATE INDEX ix1 ON samples (device_id, created_at, id);

This index includes all three columns used in our query: device_id, created_at, and id. By using a covering index, we’re ensuring that MySQL can retrieve all the necessary data without having to access the underlying table.

Conclusion

In conclusion, our issue with prepared statements and index usage in MySQL is more complex than it initially seemed. By understanding how indexes work in MySQL and how prepared statements affect query compilation, we’ve identified a solution to our problem.

By delaying predicates as much as possible or using covering indexes, we can optimize our queries and ensure that the available indexes are being used efficiently.

Additional Considerations

There are several additional considerations when working with large datasets and performance-critical queries:

• Index Maintenance: Regularly maintaining and analyzing indexes can help identify performance bottlenecks.

• **Query Optimization**: Using tools like EXPLAIN or OPTIMIZE can help optimize query plans and reduce execution times.
  

• Data Distribution: Ensuring that data is evenly distributed across columns used in filtering predicates can improve index usage.

By understanding these considerations, you can further optimize your queries and improve performance when working with large datasets.

Last modified on 2024-04-24