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https://issues.apache.org/jira/browse/SPARK-20489?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15988350#comment-15988350
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Frank Rosner edited comment on SPARK-20489 at 4/28/17 7:25 AM:
---------------------------------------------------------------
In
[datetimeExpressions.scala#L569|https://github.com/apache/spark/blob/760c8d088df1d35d7b8942177d47bc1677daf143/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala#L569]
we reuse a {{SimpleDateFormat}}. Is this {{eval}} function being called
multi-threaded:
[datetimeExpressions.scala#L591|https://github.com/apache/spark/blob/760c8d088df1d35d7b8942177d47bc1677daf143/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala#L591]?
If so, the results are completely non-deterministic and possibly wrong:
https://twitter.com/FRosnerd/status/856994959425236992
Reason is that {{SimpleDateFormat}} is mutable and parsing not synchronized.
Could that be related?
was (Author: frosner):
In
[datetimeExpressions.scala#L569|https://github.com/apache/spark/blob/760c8d088df1d35d7b8942177d47bc1677daf143/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala#L569]
we reuse a {{SimpleDateFormat}}. Is this {{eval}} function being called
multi-threaded:
[datetimeExpressions.scala#L591|https://github.com/apache/spark/blob/760c8d088df1d35d7b8942177d47bc1677daf143/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala#L591]?
If so, the results are completely non-deterministic and possibly wrong:
https://twitter.com/FRosnerd/status/856994959425236992
Reason is that {{SimpleDateFormat}} is not mutable and not synchronized. Could
that be related?
> Different results in local mode and yarn mode when working with dates (race
> condition with SimpleDateFormat?)
> -------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-20489
> URL: https://issues.apache.org/jira/browse/SPARK-20489
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0, 2.0.1, 2.0.2
> Environment: yarn-client mode in Zeppelin, Cloudera
> Spark2-distribution
> Reporter: Rick Moritz
> Priority: Critical
>
> Running the following code (in Zeppelin, or spark-shell), I get different
> results, depending on whether I am using local[*] -mode or yarn-client mode:
> {code:title=test case|borderStyle=solid}
> import org.apache.spark.sql.Row
> import org.apache.spark.sql.types._
> import spark.implicits._
> val counter = 1 to 2
> val size = 1 to 3
> val sampleText = spark.createDataFrame(
> sc.parallelize(size)
> .map(Row(_)),
> StructType(Array(StructField("id", IntegerType, nullable=false))
> )
> )
> .withColumn("loadDTS",lit("2017-04-25T10:45:02.2"))
>
> val rddList = counter.map(
> count => sampleText
> .withColumn("loadDTS2",
> date_format(date_add(col("loadDTS"),count),"yyyy-MM-dd'T'HH:mm:ss.SSS"))
> .drop(col("loadDTS"))
> .withColumnRenamed("loadDTS2","loadDTS")
> .coalesce(4)
> .rdd
> )
> val resultText = spark.createDataFrame(
> spark.sparkContext.union(rddList),
> sampleText.schema
> )
> val testGrouped = resultText.groupBy("id")
> val timestamps = testGrouped.agg(
> max(unix_timestamp($"loadDTS", "yyyy-MM-dd'T'HH:mm:ss.SSS")) as
> "timestamp"
> )
> val loadDateResult = resultText.join(timestamps, "id")
> val filteredresult = loadDateResult.filter($"timestamp" ===
> unix_timestamp($"loadDTS", "yyyy-MM-dd'T'HH:mm:ss.SSS"))
> filteredresult.count
> {code}
> The expected result, *3* is what I obtain in local mode, but as soon as I run
> fully distributed, I get *0*. If Increase size to {{1 to 32000}}, I do get
> some results (depending on the size of counter) - none of which makes any
> sense.
> Up to the application of the last filter, at first glance everything looks
> okay, but then something goes wrong. Potentially this is due to lingering
> re-use of SimpleDateFormats, but I can't get it to happen in a
> non-distributed mode. The generated execution plan is the same in each case,
> as expected.
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