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How to run Spark SQL query in Synapse via an API

Narender Kumar 70 Reputation points
2023-10-05T09:44:31.0966667+00:00

I am building a frontend which can take a Spark SQL query and validate and execute the SQL query and provide some sample data.

For this my backend datalake is built using Synapse Spark Pools.

So I want to run the query provided by user in Synapse Spark Pool and show the sample data response back to user.

So I need a Synapse API that can take my Spark SQL query and return the response.

I could find 2 ways to do it:

  1. Use the Synapse Serverless API to execute SQL that returns response immediately but it support TSQL and not Spark SQL
  2. Use the Synapse Notebook or Pipeline API and pass a parameterized SQL query. This method works but Spark Pool takes 3-4 minutes to start the new session and then execute the query.

Is there a way I can execute my Spark SQL query quickly on Synapse using any API?

Or Is there a way/any library to covert Spark SQL to TSQL so that I can use option 1?

Azure Synapse Analytics
Azure Synapse Analytics
An Azure analytics service that brings together data integration, enterprise data warehousing, and big data analytics. Previously known as Azure SQL Data Warehouse.
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  1. QuantumCache 20,261 Reputation points
    2023-10-05T19:30:56.11+00:00

    Hello @narender kumar

    Use the Synapse Notebook or Pipeline API and pass a parameterized SQL query. This method works but Spark Pool takes 3-4 minutes to start the new session and then execute the query.

    Regarding the second point related to the startup time, you can consider using a persistent Spark session. A persistent Spark session is a long-running session that remains active even after the query has completed. You can reuse the persistent Spark session for subsequent queries, which can reduce the overall query response time. You can find more information about persistent Spark sessions in the Azure documentation here.
    https://learn.microsoft.com/en-us/azure/synapse-analytics/spark/apache-spark-concepts#examples

    However, keep in mind that using a persistent Spark session may increase the cost of your Spark Pool, as the session will continue to consume resources even when it is not executing a query. Therefore, you should carefully consider the trade-offs between query response time and cost when deciding whether to use a persistent Spark session.

    Both options have their own advantages and disadvantages, and the best option depends on your specific use case and requirements.

    Option 1: Use the Synapse Serverless API to execute SQL that returns response immediately but it supports TSQL and not Spark SQL.

    Advantages:

    • The response is returned immediately, without the need to start a new Spark session.
    • The API supports TSQL, which is a widely used SQL dialect.

    Disadvantages:

    • The API does not support Spark SQL, which may limit the functionality and performance of the queries.
    • The API may not be suitable for complex queries or large datasets.

    Option 2: Use the Synapse Notebook or Pipeline API and pass a parameterized SQL query. This method works but Spark Pool takes 3-4 minutes to start the new session and then execute the query.

    Advantages:

    • The API supports Spark SQL, which provides more functionality and performance than TSQL.
    • The API can handle complex queries and large datasets.

    Disadvantages:

    • The response time may be slower due to the need to start a new Spark session.
    • The API may require more resources and configuration than the Serverless API.

    Regarding the question of whether there is a way to convert Spark SQL to TSQL, there is no direct way to do this as Spark SQL and TSQL are different SQL dialects. However, you can manually convert the Spark SQL queries to TSQL queries if needed.
    Please refer this similar thread which is other way: Sql to sparksql
    You may want to post new question on this forum with 'Azure SQL Database' tag and the community will help.

    In summary, if you need to execute simple queries and require a fast response time, the Synapse Serverless API may be the best option. If you need to execute complex queries and require more functionality and performance, the Synapse Notebook or Pipeline API may be the best option.

    Further Community posts:
    https://db-engines.com/en/system/Microsoft+SQL+Server%3BSpark+SQL
    https://towardsdatascience.com/sql-to-pyspark-d7966e3c15b3
    https://arulmouzhi.wordpress.com/2021/07/26/t-sql-queries-vs-spark-sql-queries/

    I hope this helps with your initial query, please do let us know in the comments section.