Why does SQL cost decrease with an 'IN' clause for user IDs?

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Introduction


When executing SQL queries, particularly those that involve views and multiple conditions, developers often encounter unexpected behaviors in execution plans. One such anomaly arises when using the IN clause in conjunction with filtering by user_id. In this article, we will explore why using an IN clause with a single existing user ID, alongside a non-existent ID, reduces execution costs in the context of selecting from a SQL view.

Understanding the Tables and Relationships


Before diving into the execution plans, it's essential to clarify the structure of the tables involved in this discussion:

user_service Table


The user_service table consists of the following columns:

• user_id: Identifies the user.
• svc_id: Denotes the service associated with the user.
• service_name: The name of the service.
• status: Indicates the current status of the service.

user_service_transaction Table


Similarly, the user_service_transaction table contains:

• user_id: References the user.
• svc_id: Links back to the respective service.
• account_id, txn_id, txn_name: Details regarding transactions associated with the user.
• other_id: Additional reference, unspecified here.
• status: The current status of the transaction.

Both tables are indexed on user_id and svc_id, facilitating faster look-ups and joins.

The View Definition


The view, VIEW_SERVICE_TRANSACTIONS, combines information from both tables and aggregates transaction names for users with active statuses in both tables. The SQL for this view can be seen below:

SELECT USER_ID, SERVICE_NAME, LISTAGG(txn_name, ',') AS TRANSACTION_NAMES
FROM (
SELECT AUS.USER_ID, AUS.SERVICE_NAME, AUST.txn_name
FROM user_service AUS
INNER JOIN user_service_transaction AUST ON
AUS.USER_ID = AUST.USER_ID AND AUS.svc_id = AUST.svc_id
WHERE AUS.STATUS='A' AND AUST.STATUS ='A'
GROUP BY AUST.USER_ID, AUST.SERVICE_NAME, AUST.txn_name
)
GROUP BY USER_ID, SERVICE_NAME


This view captures the essence of user transactions while ensuring that only users with active services are considered.

Examining the Query Performance


When querying the view for a single user ID:

SELECT *
FROM VIEW_SERVICE_TRANSACTIONS
WHERE USER_ID = 189791


You may observe a specific execution plan and cost associated with this query when compared to the following query, which includes an IN clause:

SELECT *
FROM VIEW_SERVICE_TRANSACTIONS
WHERE USER_ID IN (189791, -1)


Interestingly, executing the second query drastically reduces the execution cost, even though one of the conditions (-1) does not point to an existing user. This phenomenon can be perplexing without the appropriate understanding of SQL execution plans and optimization methodologies.

Reason for Cost Decrease


The core reason for the reduced execution cost when employing the IN clause lies in how SQL databases optimize query execution:

• Cardinality Reduction: Including a non-existent user (-1) likely prompts the SQL engine to take advantage of certain optimization techniques such as filtering based on predicate clauses earlier in the execution plan. Non-existent IDs do not add complexity to the resulting datasetin reducing cardinality leading to faster assessments.
• Iterator Strategy: The optimizer may choose a different execution path (an inner loop for the IN clause) which allows it to fetch relevant records in a more efficient manner when evaluated based on multiple user IDs, despite one being null. This leads to reduced total iterations on larger datasets.

Refactoring for Consistent Performance


To achieve consistent execution performance with single user selections without relying on potentially misleading optimizations, consider the following strategies:

• Use UNION ALL: Instead of an IN clause, break your query into a simple union for cases where you want defaults and existing IDs. This punctually informs the optimizer without querying the entire dataset.
  SELECT * FROM VIEW_SERVICE_TRANSACTIONS WHERE USER_ID = 189791
  UNION ALL
  SELECT * FROM VIEW_SERVICE_TRANSACTIONS WHERE USER_ID = -1 AND 1=0
• Materialized Views: If the dataset does not change frequently, employing materialized views can boost performance for heavy aggregate calculations.
• Index Adjustments: Consider modifying your indexing strategy based on frequent query patterns or leveraging covering indexes if necessary.

Conclusion


The cost performance associated with SQL queries can often reveal complex optimization strategies by the SQL engine. Understanding these mechanisms is crucial for writing efficient SQL queries. The peculiar case of execution costs dropping based on the IN clause provides insight into how the engine evaluates datasets and optimizes retrieval processes. Adapting your queries and employing best practices will ensure that your applications continue to perform efficiently, especially as they scale.

Frequently Asked Questions


1. Why does using IN with non-existent IDs speed up queries?

• The optimizer reduces cardinality, improving performance for indexing and execution.

2. Are there downsides to using UNION instead of IN?

• While UNION can create overhead, especially with large datasets, it may be preferable for consistent performance.

3. How can I further optimize my SQL queries?

• Consider indexing strategies, evaluating execution plans, and restructuring queries to reduce complexity based on dataset characteristics.

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