Complex query performance optimization in Azure Synapse

Question

I am working on a complex ETL process using Azure Synapse Analytics and I need some expert advice on optimizing the performance of a particularly challenging query.

I have a large dataset stored in an Azure Synapse dedicated SQL pool. The dataset contains billions of rows and is partitioned by date. I need to perform a series of transformations that include multiple JOINs, aggregations, and window functions. The query looks something like this.

WITH filtered_data AS (

SELECT 

    col1, col2, col3, 

    ROW_NUMBER() OVER (PARTITION BY col1 ORDER BY col4 DESC) AS rn

FROM 

    my_table

WHERE 

    date_col BETWEEN '2023-01-01' AND '2023-12-31'

    AND col5 = 'some_condition'

), aggregated_data AS (

SELECT 

    col1, 

    SUM(col2) AS sum_col2,

    AVG(col3) AS avg_col3

FROM 

    filtered_data

WHERE 

    rn = 1

GROUP BY 

    col1

)

SELECT

a.col1, 

a.sum_col2, 

b.col2, 

b.col3

FROM

aggregated_data a

JOIN

another_table b ON a.col1 = b.col1

WHERE

b.col6 > 1000

ORDER BY

a.sum_col2 DESC;

The query execution time is significantly high. I suspect this is due to the size of the dataset and the complexity of the transformations. The query often consumes a large amount of memory and CPU, leading to resource contention with other processes. I am unsure if my current indexing and partitioning strategies are optimal for this type of query.

1. What are the best practices for optimizing complex queries in Azure Synapse Analytics, particularly those involving large datasets and multiple transformations?
2. How can I effectively utilize partitioning to improve query performance in this scenario?
3. Are there specific indexing strategies or hints that I should consider to enhance the performance of such queries?
4. How can I monitor and manage resource utilization to ensure that this query does not negatively impact other processes running on the same dedicated SQL pool?
5. Is there a way to optimize the concurrency handling to allow multiple users to run similar queries without significant performance degradation?

I appreciate any guidance. Thank you!

Answer 1

@MulluriNivas-6816

Thankyou for using Microsoft Q&A platform and thanks for posting your query here.

I understand that you are trying to know the best practices for optimizing complex queries in Azure Synapse Analytics, including partitioning, indexing strategies, and resource utilization monitoring.

Probably you can give a try to ensure filters are applied as early as possible to reduce the dataset size , example:

WITH filtered_data AS (
    SELECT 
        col1, col2, col3, col4
    FROM 
        my_table
    WHERE 
        date_col BETWEEN '2023-01-01' AND '2023-12-31'
        AND col5 = 'some_condition'
), ranked_data AS (
    SELECT 
        col1, col2, col3, 
        ROW_NUMBER() OVER (PARTITION BY col1 ORDER BY col4 DESC) AS rn
    FROM 
        filtered_data
), aggregated_data AS (
    SELECT 
        col1, 
        SUM(col2) AS sum_col2,
        AVG(col3) AS avg_col3
    FROM 
        ranked_data
    WHERE 
        rn = 1
    GROUP BY 
        col1
)
SELECT
    a.col1, 
    a.sum_col2, 
    b.col2, 
    b.col3
FROM
    aggregated_data a
    JOIN another_table b ON a.col1 = b.col1
WHERE
    b.col6 > 1000
ORDER BY
    a.sum_col2 DESC;

To your query:

"What are the best practices for optimizing complex queries in Azure Synapse Analytics, particularly those involving large datasets and multiple transformations?"

You could consider the following best practices for optimizing complex queries :

• Use appropriate distribution keys to minimize data movement.
• Leverage partitioning on date columns for partition elimination.
• Use clustered column store indexes for large fact tables.
• Apply filters as early as possible in the query.

"How can I effectively utilize partitioning to improve query performance in this scenario?" :

• Check Partition sizing : When creating partitions on clustered columnstore tables, it is important to consider how many rows belong to each partition. For optimal compression and performance of clustered columnstore tables, a minimum of 1 million rows per distribution and partition is needed. Before partitions are created, dedicated SQL pool already divides each table into 60 distributions. Any partitioning added to a table is in addition to the distributions created behind the scenes.
• Use Appropriate Partition Keys: Choose partition keys based on columns frequently used in query predicates, such as date columns. This ensures that only relevant partitions are scanned, reducing the amount of data processed and improving performance.

"Are there specific indexing strategies or hints that I should consider to enhance the performance of such queries?"

Assign a higher resource class :

EXEC sp_addrolemember 'largerc', 'your_user';

• Identify or create user which uses the right resource class
• Rebuild clustered columnstore indexes with higher resource class user
• Verify clustered columnstore segment quality has improved

"How can I monitor and manage resource utilization to ensure that this query does not negatively impact other processes running on the same dedicated SQL pool?"

You can use the Query Store feature to monitor query performance and identify resource-intensive queries. You can also use workload management to prioritize and allocate resources to different queries based on their importance.

"Is there a way to optimize the concurrency handling to allow multiple users to run similar queries without significant performance degradation?"

Concurrent slots availability is based on the DWU and resource class used. Make sure to scale up to enough service level and RC . More info here

Hope it helps. Thankyou