Solved: Re: Weird Query planning when adding simple Alias

samirlv · 11-23-2023 06:21 AM

Hello, i have a resource memory limit issue due to ORDER BY when adding a layer of a simple alias.

Error : "Resources exceeded during query execution: The query could not be executed in the allotted memory. Peak usage: 121% of limit. Top memory consumer(s): ORDER BY operations: 99% other/unattributed: 1%"

What is interesting is that removing 1 level of alias runs the query successfully. inspecting the query plan reveals that the query without the additional alias is applying additional steps in the query plan that seems to be optimizing the query.

Question: can you explain why/how this happens? Thanks

here is both queries:

working query :

WITH product AS (
    SELECT
        is_product,
        product_id,
        X,
        Y
    FROM A
)
SELECT *
FROM product
ORDER BY is_product DESC

Failing query :

WITH product AS (
    SELECT
        is_product,
        product_id,
        X,
        Y
    FROM A
),
allias AS (
SELECT *
FROM product
ORDER BY is_product DESC)
Select * from allias

ms4446

The inefficiency in the failing query is primarily due to the additional layer of abstraction introduced by the extra Common Table Expression (CTE), not merely the use of an alias. In SQL, aliases are typically used for renaming columns or tables for clarity and do not inherently impact performance. However, when an alias is part of an additional CTE or subquery, it can add complexity to the query.

In your failing query, the extra CTE (allias) encapsulates the ORDER BY operation. This additional layer can obscure the overall data flow and relationships in the query, potentially leading to a less efficient execution plan.

Query Planning in BigQuery:

BigQuery's query planner optimizes the execution of SQL queries based on various factors, including the structure of the query, the size of the datasets, and the operations involved. Simplified queries often allow for more straightforward and efficient optimization.

In the working query, the absence of the extra CTE allows the query planner to directly apply optimizations, particularly to the ORDER BY clause, leading to more efficient resource usage.

Strategies for Efficient Query Execution:

Minimize Complexity: Keep queries as simple and direct as possible. Avoid unnecessary layers of CTEs or subqueries unless they add value in organizing or segmenting the data logically.
Understand BigQuery's Behavior: BigQuery manages resources differently from traditional SQL databases. It's designed for handling large-scale data with specific optimization mechanisms. Familiarize yourself with these mechanisms, such as table partitioning and clustering, to write more efficient queries.
Data Processing Considerations: When using ORDER BY, be mindful of the data volume. Large sorts can be resource-intensive. If applicable, use LIMIT to reduce the amount of data sorted, but note that this might not always decrease memory usage significantly.

Additional Note:

Query Execution Plan Analysis: Regularly review the execution plans of your queries in BigQuery. Understanding how different query structures are interpreted and optimized by BigQuery can provide insights into writing more efficient queries.

The key to efficient query execution in BigQuery lies in understanding how additional layers of abstraction, like extra CTEs, can affect the query planner's ability to optimize, and structuring your queries accordingly to leverage BigQuery's unique optimization capabilities.

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ms4446