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https://issues.apache.org/jira/browse/SPARK-61262?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Tim Meehan updated SPARK-61262:
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Fix Version/s: connect-rust-4.3.0
> Allow Arrow batch chunking in ExecutePlan, so that a value over 128 MB can be
> collected, as PySpark does
> --------------------------------------------------------------------------------------------------------
>
> Key: SPARK-61262
> URL: https://issues.apache.org/jira/browse/SPARK-61262
> 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 Connect client asks the server to split large Arrow batches, so it
> can collect a value bigger than the 128 MB message limit. The Rust client
> does not, and fails.
> {code:python}
> from pyspark.sql import SparkSession, functions as F
> spark = SparkSession.builder.remote("sc://localhost:15002").getOrCreate()
> spark.range(1).select(F.repeat(F.lit("x"), 140 * 1024 * 1024)).collect()
> {code}
> Against an OSS Spark 4.3.0 Connect server on loopback:
> * pyspark-client 4.3.0 returns the row. Its ExecutePlan requests carry
> request_options { result_chunking_options { allow_arrow_batch_chunking: true
> } }, so the server splits the batch.
> * spark-connect-rust fails with "decoded message length too large: found
> 146801134 bytes, the limit is: 134217728 bytes" (measured at 4ad36945). At
> 8768cc4 no code under crates/ sets result_chunking_options.
> Both clients cap a message at 128 MB, and up to 100 MB both work.
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