This is a user-list question, not a dev-list question. Moving this conversation to the user list and BCC-ing the dev list.
Also, this statement > We are not validating against table or column existence. is not correct. When you call spark.sql(…), Spark will lookup the table references and fail with TABLE_OR_VIEW_NOT_FOUND if it cannot find them. Also, when you run DDL via spark.sql(…), Spark will actually run it. So spark.sql(“drop table my_table”) will actually drop my_table. It’s not a validation-only operation. This question of validating SQL is already discussed on Stack Overflow <https://stackoverflow.com/q/46973729/877069>. You may find some useful tips there. Nick > On Dec 24, 2023, at 4:52 AM, Mich Talebzadeh <mich.talebza...@gmail.com> > wrote: > > > Yes, you can validate the syntax of your PySpark SQL queries without > connecting to an actual dataset or running the queries on a cluster. > PySpark provides a method for syntax validation without executing the query. > Something like below > ____ __ > / __/__ ___ _____/ /__ > _\ \/ _ \/ _ `/ __/ '_/ > /__ / .__/\_,_/_/ /_/\_\ version 3.4.0 > /_/ > > Using Python version 3.9.16 (main, Apr 24 2023 10:36:11) > Spark context Web UI available at http://rhes75:4040 <http://rhes75:4040/> > Spark context available as 'sc' (master = local[*], app id = > local-1703410019374). > SparkSession available as 'spark'. > >>> from pyspark.sql import SparkSession > >>> spark = SparkSession.builder.appName("validate").getOrCreate() > 23/12/24 09:28:02 WARN SparkSession: Using an existing Spark session; only > runtime SQL configurations will take effect. > >>> sql = "SELECT * FROM <TABLE> WHERE <COLUMN> = some value" > >>> try: > ... spark.sql(sql) > ... print("is working") > ... except Exception as e: > ... print(f"Syntax error: {e}") > ... > Syntax error: > [PARSE_SYNTAX_ERROR] Syntax error at or near '<'.(line 1, pos 14) > > == SQL == > SELECT * FROM <TABLE> WHERE <COLUMN> = some value > --------------^^^ > > Here we only check for syntax errors and not the actual existence of query > semantics. We are not validating against table or column existence. > > This method is useful when you want to catch obvious syntax errors before > submitting your PySpark job to a cluster, especially when you don't have > access to the actual data. > In summary > Theis method validates syntax but will not catch semantic errors > If you need more comprehensive validation, consider using a testing framework > and a small dataset. > For complex queries, using a linter or code analysis tool can help identify > potential issues. > HTH > > Mich Talebzadeh, > Dad | Technologist | Solutions Architect | Engineer > London > United Kingdom > > view my Linkedin profile > <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> > > https://en.everybodywiki.com/Mich_Talebzadeh > > > Disclaimer: Use it at your own risk. Any and all responsibility for any loss, > damage or destruction of data or any other property which may arise from > relying on this email's technical content is explicitly disclaimed. The > author will in no case be liable for any monetary damages arising from such > loss, damage or destruction. > > > > On Sun, 24 Dec 2023 at 07:57, ram manickam <ramsidm...@gmail.com > <mailto:ramsidm...@gmail.com>> wrote: >> Hello, >> Is there a way to validate pyspark sql to validate only syntax errors?. I >> cannot connect do actual data set to perform this validation. Any help >> would be appreciated. >> >> >> Thanks >> Ram
