Optimizing CROSS JOINs in Presto/AWS Athena: A Deep Dive into UNNEST Clauses and Partitioning Strategies

Understanding CROSS JOIN and UNNEST Clauses in Presto/AWS Athena

Introduction

Presto, a distributed SQL engine, is used by AWS Athena for big data analytics. One of the powerful features of Presto is its ability to perform complex joins and aggregations on large datasets. However, with great power comes great complexity, and understanding how to optimize queries can be challenging.

In this article, we will explore one specific optimization technique in Presto/AWS Athena: specifying the partition according to which a CROSS JOIN UNNEST clause should be applied. We will delve into the technical details of how Presto executes these clauses and provide examples and explanations to help you understand how to optimize your queries.

Understanding CROSS JOIN and UNNEST Clauses

A CROSS JOIN is an operator that combines each row of one table with each row of another table, creating a Cartesian product. The UNNEST clause in Presto allows you to unnest arrays or other nested data types into separate rows.

Here’s an example query that demonstrates the use of these clauses:

SELECT member_id, arrays_of_distinct_days_of_activity, dt
FROM activity_table
CROSS JOIN UNNEST(sequence(date '2019-07-01',date '2019-07-15',interval '1' day)) AS T(dt)

In this query, we’re joining the activity_table with a sequence of dates using CROSS JOIN and UNNEST. The resulting table will contain each member ID, their arrays of distinct days of activity, and a date column (dt) representing each day in the sequence.

Understanding the Performance Issue

When executing this query, Presto needs to check all possible combinations of member_id and arrays_of_distinct_days_of_activity. Since each member has a unique row, this creates an exponential number of possible join combinations. This results in a long calculation time.

To illustrate this issue, let’s consider the following example:

SELECT *
FROM activity_table
WHERE member_id = 'member1'
  AND arrays_of_distinct_days_of_activity @> array['2020-01-01', '2020-01-15']

In this query, we’re filtering a single row of data based on the arrays_of_distinct_days_of_activity column. However, Presto still needs to check all possible combinations of member IDs and arrays of distinct days of activity.

Specifying the Partition According to Which a CROSS JOIN UNNEST Clause Should be Applied

To optimize this query, we need to specify the partition according to which the CROSS JOIN UNNEST clause should be applied. This can help Presto reduce the number of combinations it needs to check.

In Presto/AWS Athena, you can use the PARTITION BY clause with a subquery or Common Table Expressions (CTEs) to achieve this. Here’s an example:

WITH filtered_members AS (
  SELECT member_id
  FROM activity_table
  WHERE arrays_of_distinct_days_of_activity @> array['2020-01-01', '2020-01-15']
)
SELECT m.member_id, a.arrays_of_distinct_days_of_activity, d.dt
FROM activity_table a
JOIN filtered_members m ON a.member_id = m.member_id
CROSS JOIN UNNEST(sequence(date '2019-07-01',date '2019-07-15',interval '1' day)) AS T(dt)

In this query, we’re using a CTE to filter the members based on their arrays of distinct days of activity. We then join this filtered table with the original activity_table and apply the CROSS JOIN UNNEST clause.

By specifying the partition according to which the CROSS JOIN UNNEST clause should be applied, Presto can reduce the number of combinations it needs to check, resulting in improved performance.

Additional Optimization Techniques

In addition to specifying the partition according to which a CROSS JOIN UNNEST clause should be applied, there are other optimization techniques you can use to improve performance:

Indexing: Create indexes on columns used in WHERE, JOIN, and ORDER BY clauses.
Materialized Views: Use materialized views to cache frequently queried data.
Query Optimization: Use Presto’s built-in query optimizer to rewrites queries for better performance.

Conclusion

Optimizing queries in Presto/AWS Athena requires a deep understanding of how the engine executes complex joins and aggregations. By specifying the partition according to which a CROSS JOIN UNNEST clause should be applied, you can reduce the number of combinations Presto needs to check, resulting in improved performance.

In addition to specifying partitions, there are other optimization techniques you can use to improve performance, such as indexing, materialized views, and query optimization. By applying these techniques, you can write more efficient queries that take advantage of Presto’s capabilities.

Last modified on 2023-11-15