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https://issues.apache.org/jira/browse/SPARK-60909?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon resolved SPARK-60909.
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Resolution: Fixed
Issue resolved by pull request 152
[https://github.com/apache/spark-connect-rust/pull/152]
> Run spark.sql() statements eagerly in Spark Connect Rust as PySpark does
> ------------------------------------------------------------------------
>
> Key: SPARK-60909
> URL: https://issues.apache.org/jira/browse/SPARK-60909
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: Hyukjin Kwon
> Assignee: Hyukjin Kwon
> Priority: Major
> Labels: pull-request-available
> Fix For: connect-rust-4.3.0
>
>
> In PySpark Connect, {{SparkSession.sql}} sends the statement to the server at
> once, so a command takes effect when {{sql()}} is called. The Rust-backed
> Python client builds a lazy relation instead, so a command only runs when an
> action is taken on the returned DataFrame:
> * After {{spark.sql("CREATE OR REPLACE TEMP VIEW v AS SELECT 1 AS x")}},
> {{spark.table("v")}} fails with {{TABLE_OR_VIEW_NOT_FOUND}}; with PySpark
> 4.3.0 it returns the row.
> * After {{spark.sql("SET spark.sql.shuffle.partitions=7")}},
> {{spark.conf.get("spark.sql.shuffle.partitions")}} is still the default; with
> PySpark it is {{7}}.
> DDL such as {{CREATE TABLE}} and {{USE}} behaves the same way. Run the
> statement at {{sql()}}, as PySpark does, and build the DataFrame from the
> relation that the server returns.
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