Michael Chen created SPARK-34780:
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Summary: Cached Table (parquet) with old Configs Used
Key: SPARK-34780
URL: https://issues.apache.org/jira/browse/SPARK-34780
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 2.4.4
Reporter: Michael Chen
When a dataframe is cached, the logical plan can contain copies of the spark
session meaning the SQLConfs are stored. Then if a different dataframe can
replace parts of it's logical plan with a cached logical plan, the cached
SQLConfs will be used for the evaluation of the cached logical plan. This is
because HadoopFsRelation ignores sparkSession for equality checks (introduced
in https://issues.apache.org/jira/browse/SPARK-17358).
{code:java}
test("cache uses old SQLConf") {
import testImplicits._
withTempDir { dir =>
val tableDir = dir.getAbsoluteFile + "/table"
val df = Seq("a").toDF("key")
df.write.parquet(tableDir)
SQLConf.get.setConfString(SQLConf.FILE_COMPRESSION_FACTOR.key, "1")
val compression1Stats = spark.read.parquet(tableDir).select("key").
queryExecution.optimizedPlan.collect {
case l: LogicalRelation => l
case m: InMemoryRelation => m
}.map(_.computeStats())
SQLConf.get.setConfString(SQLConf.FILE_COMPRESSION_FACTOR.key, "10")
val df2 = spark.read.parquet(tableDir).select("key")
df2.cache()
val compression10Stats = df2.queryExecution.optimizedPlan.collect {
case l: LogicalRelation => l
case m: InMemoryRelation => m
}.map(_.computeStats())
SQLConf.get.setConfString(SQLConf.FILE_COMPRESSION_FACTOR.key, "1")
val compression1StatsWithCache = spark.read.parquet(tableDir).select("key").
queryExecution.optimizedPlan.collect {
case l: LogicalRelation => l
case m: InMemoryRelation => m
}.map(_.computeStats())
// I expect these stats to be the same because file compression factor is
the same
assert(compression1Stats == compression1StatsWithCache)
// Instead, we can see the file compression factor is being cached and used
along with
// the logical plan
assert(compression10Stats == compression1StatsWithCache)
}
}{code}
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