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https://issues.apache.org/jira/browse/SPARK-22538?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Felix Cheung updated SPARK-22538:
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Target Version/s: 2.2.1, 2.3.0 (was: 2.3.0, 2.2.2)
> SQLTransformer.transform(inputDataFrame) uncaches inputDataFrame
> ----------------------------------------------------------------
>
> Key: SPARK-22538
> URL: https://issues.apache.org/jira/browse/SPARK-22538
> Project: Spark
> Issue Type: Bug
> Components: ML, PySpark, SQL, Web UI
> Affects Versions: 2.2.0
> Reporter: MBA Learns to Code
> Assignee: Liang-Chi Hsieh
> Fix For: 2.2.1, 2.3.0
>
>
> When running the below code on PySpark v2.2.0, the cached input DataFrame df
> disappears from SparkUI after SQLTransformer.transform(...) is called on it.
> I don't yet know whether this is only a SparkUI bug, or the input DataFrame
> df is indeed unpersisted from memory. If the latter is true, this can be a
> serious bug because any new computation using new_df would have to re-do all
> the work leading up to df.
> {code}
> import pandas
> import pyspark
> from pyspark.ml.feature import SQLTransformer
> spark = pyspark.sql.SparkSession.builder.getOrCreate()
> df = spark.createDataFrame(pandas.DataFrame(dict(x=[-1, 0, 1])))
> # after below step, SparkUI Storage shows 1 cached RDD
> df.cache(); df.count()
> # after below step, cached RDD disappears from SparkUI Storage
> new_df = SQLTransformer(statement='SELECT * FROM __THIS__').transform(df)
> {code}
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