Maryann Xue created SPARK-24613:
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Summary: Cache with UDF could not be matched with subsequent
dependent caches
Key: SPARK-24613
URL: https://issues.apache.org/jira/browse/SPARK-24613
Project: Spark
Issue Type: Improvement
Components: SQL
Affects Versions: 2.3.0
Reporter: Maryann Xue
Fix For: 2.4.0
When caching a query, we generate its execution plan from the query's logical
plan. However, the logical plan we get from the Dataset has already been
analyzed, and when we try the get the execution plan, this already analyzed
logical plan will be analyzed again in the new QueryExecution object, and
unfortunately some rules have side effects if applied multiple times, which in
this case, is the {{HandleNullInputsForUDF}} rule. The re-analyzed plan now has
an extra null-check and can't be matched against the same plan. The following
test would fail since {{df2}}'s execution plan inside the CacheManager does not
depend on {{df1}}.
{code:java}
test("cache UDF result correctly 2") {
val expensiveUDF = udf({x: Int => Thread.sleep(10000); x})
val df = spark.range(0, 10).toDF("a").withColumn("b", expensiveUDF($"a"))
val df2 = df.agg(sum(df("b")))
df.cache()
df.count()
df2.cache()
// udf has been evaluated during caching, and thus should not be re-evaluated
here
failAfter(5 seconds) {
df2.collect()
}
}
{code}
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