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https://issues.apache.org/jira/browse/SPARK-61292?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Tim Meehan updated SPARK-61292:
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Fix Version/s: connect-rust-4.3.0
> Ask the server in DataFrame.isLocal of the Python client, as PySpark does
> -------------------------------------------------------------------------
>
> Key: SPARK-61292
> URL: https://issues.apache.org/jira/browse/SPARK-61292
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: Tim Meehan
> Priority: Major
> Fix For: connect-rust-4.3.0
>
>
> PySpark's DataFrame.isLocal asks the server, with an AnalyzePlan request
> (pyspark/sql/connect/dataframe.py:2053), and the server answers from the plan
> it analyzes. The Python client of spark-connect-rust answers from its own
> plan instead (DataFrame::is_local,
> crates/spark-connect/src/dataframe.rs:2244): only a local relation that it
> built, or the result of a command, is local. So the answers differ where the
> server's analysis makes a local relation of another plan.
> {code:python}
> from pyspark.sql import SparkSession
> spark = SparkSession.builder.remote("sc://localhost:15002").getOrCreate()
> print(spark.sql("VALUES (1)").isLocal())
> {code}
> Against an OSS Spark 4.3.0 Connect server on loopback:
> * pyspark-client 4.3.0: True
> * spark-connect-rust master 8768cc4: False
> Both give the same answer for spark.sql("SHOW DATABASES"), spark.sql("SELECT
> 1"), createDataFrame and range. PySpark caches the answer per DataFrame
> (functools.cache), so a second call sends no request.
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