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https://issues.apache.org/jira/browse/SPARK-40154?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean R. Owen reassigned SPARK-40154:
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Assignee: Paul Staab
> PySpark: DataFrame.cache docstring gives wrong storage level
> ------------------------------------------------------------
>
> Key: SPARK-40154
> URL: https://issues.apache.org/jira/browse/SPARK-40154
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 3.3.0
> Reporter: Paul Staab
> Assignee: Paul Staab
> Priority: Minor
> Labels: pull-request-available
>
> The docstring of the `DataFrame.cache()` method currently states that it uses
> a serialized storage level
> {code:java}
> Persists the :class:`DataFrame` with the default storage level
> (`MEMORY_AND_DISK`).
> [...]
> - The default storage level has changed to `MEMORY_AND_DISK` to match
> Scala in 2.0.{code}
> while `DataFrame.persist()` states that it uses a deserialized storage level
> {code:java}
> If no storage level is specified defaults to (`MEMORY_AND_DISK_DESER`)
> [...]
> The default storage level has changed to `MEMORY_AND_DISK_DESER` to match
> Scala in 3.0.{code}
>
> However, in practice both `.cache()` and `.persist()` use deserialized
> storage levels:
> {code:java}
> import pyspark
> from pyspark.sql import SparkSession
> from pyspark import StorageLevel
> print(pyspark.__version__)
> # 3.3.0
> spark = SparkSession.builder.master("local[2]").getOrCreate()
> df = spark.createDataFrame(zip(["A"] * 1000, ["B"] * 1000), ["col_a",
> "col_b"])
> df = df.cache()
> df.count()
> # Storage level in Spark UI: "Disk Memory Deserialized 1x Replicated"
> df = spark.createDataFrame(zip(["A"] * 1000, ["B"] * 1000), ["col_a",
> "col_b"])
> df = df.persist()
> df.count()
> # Storage level in Spark UI: "Disk Memory Deserialized 1x Replicated"
> df = spark.createDataFrame(zip(["A"] * 1000, ["B"] * 1000), ["col_a",
> "col_b"])
> df = df.persist(StorageLevel.MEMORY_AND_DISK)
> df.count()
> # Storage level in Spark UI: "Disk Memory Serialized 1x Replicated"{code}
>
>
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