Audits Expanding @this_model Into A Select * Subquery Is Potentially Inefficient

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Introduction

In the context of database auditing, it's essential to ensure that the queries used are efficient and optimized for performance. One common issue that can arise is when using a subquery with a SELECT * statement, which can lead to inefficiencies if the underlying database engine optimizer does not prune the projection. In this article, we'll explore the potential issues with expanding @this_model into a subquery with SELECT * and discuss ways to optimize this to reduce reliance on the DB optimizer.

Context

The unique values audit has @this_model expanded to a subquery, as shown in the code snippet below. The issue with this approach is that using SELECT * in the subquery can lead to inefficiencies if the database engine optimizer does not prune the projection. This can result in slower query performance and increased resource utilization.

Audit Code

# unique_values(columns=(column_1, column_2))
unique_values_audit = ModelAudit(
    name="unique_values",
    defaults={"condition": exp.true()},
    query="""
SELECT *
FROM (
  SELECT
    @EACH(
      @columns,
      c -> row_number() OVER (PARTITION BY c ORDER BY c) AS rank_@c
    )
  FROM @this_model
  WHERE @condition
)
WHERE @REDUCE(
  @EACH(
    @columns,
    c -> rank_@c > 1
  ),
  (l, r) -> l OR r
)
    """,
)

Expanded Example

The expanded example of the subquery is shown below:

SELECT
  COUNT(*)
FROM (
  SELECT
    *
  FROM (
    SELECT
      ROW_NUMBER() OVER (PARTITION BY "id" ORDER BY "id" NULLS FIRST ) AS "rank_id"
    FROM (
      SELECT
        *
      FROM
        "db"."sqlmesh__mart_common"."mart_common__table__931970220" AS "mart_common__table__931970220") AS "_q_0"
    WHERE
      $1) AS "_q_1"
  WHERE
    "rank_id" > $2) AS "audit"

Understanding the Issue

The issue with expanding @this_model into a subquery with SELECT * is that it can lead to inefficiencies if the database engine optimizer does not prune the projection. This can result in slower query performance and increased resource utilization. There are several reasons why this can happen:

  • Projection Pruning: The database engine optimizer may not be able to prune the projection, which means that it will not be able to eliminate unnecessary columns from the subquery. This can lead to slower query performance and increased resource utilization.
  • Subquery Optimization: The subquery may not be optimized correctly, which can lead to inefficiencies in the query execution plan.
  • Indexing: The underlying table may not have the necessary indexes, which can lead to slower query performance.

Optimizing the Query

To optimize the query and reduce reliance on the DB optimizer, we can use several techniques:

  • Specify the Columns: Instead of using SELECT *, specify the columns that are required for the subquery. This can help the database engine optimizer to prune the projection and eliminate unnecessary columns.
  • Use Indexes: Create indexes on the columns that are used in the subquery. This can help the database engine optimizer to optimize the query execution plan and reduce the number of rows that need to be scanned.
  • Optimize the Subquery: Optimize the subquery by using techniques such as rewriting the query, using window functions, and eliminating unnecessary joins.
  • Use a Join: Instead of using a subquery, use a join to combine the tables. This can help the database engine optimizer to optimize the query execution plan and reduce the number of rows that need to be scanned.

Rewriting the Query

To rewrite the query and optimize it for performance, we can use the following approach:

  • Specify the Columns: Instead of using SELECT *, specify the columns that are required for the subquery.
  • Use Indexes: Create indexes on the columns that are used in the subquery.
  • Optimize the Subquery: Optimize the subquery by using techniques such as rewriting the query, using window functions, and eliminating unnecessary joins.
  • Use a Join: Instead of using a subquery, use a join to combine the tables.

Here is an example of how the rewritten query might look:

SELECT
  COUNT(*)
FROM (
  SELECT
    rank_id
  FROM (
    SELECT
      ROW_NUMBER() OVER (PARTITION BY "id" ORDER BY "id" NULLS FIRST ) AS "rank_id"
    FROM
      "db"."sqlmesh__mart_common"."mart_common__table__931970220" AS "mart_common__table__931970220"
  ) AS "_q_0"
  WHERE
    "rank_id" > $2
) AS "audit"

Conclusion

Introduction

In our previous article, we discussed the potential issues with expanding @this_model into a subquery with SELECT * and provided some techniques for optimizing the query to reduce reliance on the DB optimizer. In this article, we'll answer some frequently asked questions (FAQs) related to this topic.

*Q: What are the potential issues with expanding @this_model into a subquery with SELECT ?

A: The potential issues with expanding @this_model into a subquery with SELECT * include:

  • Projection Pruning: The database engine optimizer may not be able to prune the projection, which means that it will not be able to eliminate unnecessary columns from the subquery.
  • Subquery Optimization: The subquery may not be optimized correctly, which can lead to inefficiencies in the query execution plan.
  • Indexing: The underlying table may not have the necessary indexes, which can lead to slower query performance.

Q: How can I optimize the query to reduce reliance on the DB optimizer?

A: To optimize the query and reduce reliance on the DB optimizer, you can use several techniques such as:

  • Specify the Columns: Instead of using SELECT *, specify the columns that are required for the subquery.
  • Use Indexes: Create indexes on the columns that are used in the subquery.
  • Optimize the Subquery: Optimize the subquery by using techniques such as rewriting the query, using window functions, and eliminating unnecessary joins.
  • Use a Join: Instead of using a subquery, use a join to combine the tables.

Q: What are some common mistakes to avoid when optimizing the query?

A: Some common mistakes to avoid when optimizing the query include:

  • Not specifying the columns: Failing to specify the columns required for the subquery can lead to inefficiencies in the query execution plan.
  • Not creating indexes: Failing to create indexes on the columns used in the subquery can lead to slower query performance.
  • Not optimizing the subquery: Failing to optimize the subquery can lead to inefficiencies in the query execution plan.
  • Using a subquery instead of a join: Using a subquery instead of a join can lead to inefficiencies in the query execution plan.

Q: How can I determine if the query is optimized for performance?

A: To determine if the query is optimized for performance, you can use several techniques such as:

  • Analyzing the query execution plan: Analyzing the query execution plan can help you identify potential issues with the query.
  • Using query optimization tools: Using query optimization tools such as query analyzers and query optimizers can help you identify potential issues with the query.
  • Testing the query: Testing the query with different data sets and scenarios can help you identify potential issues with the query.

Q: What are some best practices for optimizing queries in general?

A: Some best practices for optimizing queries in general include:

  • Specify the columns: Instead of using SELECT *, specify the columns required for the query.
  • Use indexes: Create indexes on the columns used in the query.
  • Optimize the query: Optimize the query by using techniques such as rewriting the query, using window functions, and eliminating unnecessary joins.
  • Use a join: Instead of using a subquery, use a join to combine the tables.
  • Test the query: Test the query with different data sets and scenarios to ensure that it is optimized for performance.

Conclusion

In conclusion, expanding @this_model into a subquery with SELECT * can lead to inefficiencies if the underlying database engine optimizer does not prune the projection. To optimize the query and reduce reliance on the DB optimizer, we can use several techniques such as specifying the columns, using indexes, optimizing the subquery, and using a join. By following best practices for optimizing queries and avoiding common mistakes, we can improve the performance of our queries and reduce the number of rows that need to be scanned.