Github user viirya commented on the issue:
https://github.com/apache/spark/pull/18687
There is another case we should fix. You can see the storage level of ds2
is StorageLevel.NONE, but its executed plan is still cached version.
scala> Seq("1", "2").toDF().write.saveAsTable("t")
scala> val ds1 = spark.table("t")
ds1: org.apache.spark.sql.DataFrame = [value: string]
scala> val ds2 = spark.table("t")
ds2: org.apache.spark.sql.DataFrame = [value: string]
scala> ds1.persist()
res1: ds1.type = [value: string]
scala> ds2.queryExecution.executedPlan
res2: org.apache.spark.sql.execution.SparkPlan =
InMemoryTableScan [value#11]
+- InMemoryRelation [value#11], true, 10000, StorageLevel(disk,
memory, deserialized, 1 replicas)
+- *FileScan parquet default.t[value#7] Batched: true, Format:
Parquet, Location:
InMemoryFileIndex[file:/root/repos/spark-1/spark-warehouse/t],
PartitionFilters: [], PushedFilters: [], ReadSchema: struct<value:string>
scala> ds1.unpersist()
res3: ds1.type = [value: string]
scala> ds2.queryExecution.executedPlan
res4: org.apache.spark.sql.execution.SparkPlan =
InMemoryTableScan [value#11]
+- InMemoryRelation [value#11], true, 10000, StorageLevel(disk,
memory, deserialized, 1 replicas)
+- *FileScan parquet default.t[value#7] Batched: true, Format:
Parquet, Location:
InMemoryFileIndex[file:/root/repos/spark-1/spark-warehouse/t],
PartitionFilters: [], PushedFilters: [], ReadSchema: struct<value:string>
scala> ds2.storageLevel
res5: org.apache.spark.storage.StorageLevel = StorageLevel(1 replicas)
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