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https://issues.apache.org/jira/browse/SPARK-35089?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17364277#comment-17364277
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Robert Joseph Evans commented on SPARK-35089:
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[~Tonzetic], I don't know what you mean by an error. You have asked Spark to
calculate something that has multiple "correct" answers. Each time Spark runs
one of those "correct" answers is selected, somewhat at random. The
{{monotonically_increasing_id()}} change reduces the number of "correct"
answers to 1.
For example
| Start | User | Type |
|40|Anna| TypeA |
|41|Anna| TypeB |
|40|Anna| TypeB |
You are asking Spark to sort the data by Start, and then do a window operation
that depends on the order of the data. But there are two correct answers to
sorting the data.
| Start | User | Type |
|40|Anna| *TypeA* |
|40|Anna| *TypeB* |
|41|Anna| TypeB |
| Start | User | Type |
|40|Anna| *TypeB* |
|40|Anna| *TypeA* |
|41|Anna| TypeB |
So which of these is the "correct" way to sort the data? Because each of these
will produce a different answer from the window operation, and because The
order of {{TypeA}} vs {{TypeB}} is different between the two the relative
distance between {{start}} and {{end}} will be different (In this case 0 vs 1).
So if you can tell me what the correct ordering should be, then I can tell you
if adding the new id has made it correct, or if it is just consistent.
> non consistent results running count for same dataset after filter and lead
> window function
> -------------------------------------------------------------------------------------------
>
> Key: SPARK-35089
> URL: https://issues.apache.org/jira/browse/SPARK-35089
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.1, 3.1.1
> Reporter: Domagoj
> Priority: Major
>
> **** edit 2021-05-18
> I have make it simpler to reproduce; I've put already generated data on s3
> bucket that is publicly available with 24.000.000 records
> Now all you need to do is run this code:
> {code:java}
> import org.apache.spark.sql.expressions.Window
> import org.apache.spark.sql._
> import org.apache.spark.sql.functions._
> val w = Window.partitionBy("user").orderBy("start")
> val ts_lead = coalesce(lead("start", 1) .over(w), lit(30000000))
> spark.read.orc("s3://dtonzetic-spark-sample-data/sample-data.orc").
> withColumn("end", ts_lead).
> withColumn("duration", col("end")-col("start")).
> where("type='TypeA' and duration>4").count()
> {code}
>
> this were my results:
> - run 1: 2547559
> - run 2: 2547559
> - run 3: 2547560
> - run 4: 2547558
> - run 5: 2547558
> - run 6: 2547559
> - run 7: 2547558
> This results are from new EMR cluster, version 6.3.0, so nothing changed.
> **** end edit 2021-05-18
> I have found an inconsistency with count function results after lead window
> function and filter.
>
> I have a dataframe (this is simplified version, but it's enough to reproduce)
> with millions of records, with these columns:
> * df1:
> ** start(timestamp)
> ** user_id(int)
> ** type(string)
> I need to define duration between two rows, and filter on that duration and
> type. I used window lead function to get the next event time (that define end
> for current event), so every row now gets start and stop times. If NULL (last
> row for example), add next midnight as stop. Data is stored in ORC file
> (tried with Parquet format, no difference)
> This only happens with multiple cluster nodes, for example AWS EMR cluster or
> local docker cluster setup. If I run it on single instance (local on laptop),
> I get consistent results every time. Spark version is 3.0.1, both in AWS and
> local and docker setup.
> Here is some simple code that you can use to reproduce it, I've used
> jupyterLab notebook on AWS EMR. Spark version is 3.0.1.
>
>
> {code:java}
> import org.apache.spark.sql.expressions.Window
> // this dataframe generation code should be executed only once, and data have
> to be saved, and then opened from disk, so it's always same.
> val getRandomUser = udf(()=>{
> val users = Seq("John","Eve","Anna","Martin","Joe","Steve","Katy")
> users(scala.util.Random.nextInt(7))
> })
> val getRandomType = udf(()=>{
> val types = Seq("TypeA","TypeB","TypeC","TypeD","TypeE")
> types(scala.util.Random.nextInt(5))
> })
> val getRandomStart = udf((x:Int)=>{
> x+scala.util.Random.nextInt(47)
> })
> // for loop is used to avoid out of memory error during creation of dataframe
> for( a <- 0 to 23){
> // use iterator a to continue with next million, repeat 1 mil times
> val x=Range(a*1000000,(a*1000000)+1000000).toDF("id").
> withColumn("start",getRandomStart(col("id"))).
> withColumn("user",getRandomUser()).
> withColumn("type",getRandomType()).
> drop("id")
> x.write.mode("append").orc("hdfs:///random.orc")
> }
> // above code should be run only once, I used a cell in Jupyter
> // define window and lead
> val w = Window.partitionBy("user").orderBy("start")
> // if null, replace with 30.000.000
> val ts_lead = coalesce(lead("start", 1) .over(w), lit(30000000))
> // read data to dataframe, create stop column and calculate duration
> val fox2 = spark.read.orc("hdfs:///random.orc").
> withColumn("end", ts_lead).
> withColumn("duration", col("end")-col("start"))
> // repeated executions of this line returns different results for count
> // I have it in separate cell in JupyterLab
> fox2.where("type='TypeA' and duration>4").count()
> {code}
> My results for three consecutive runs of last line were:
> * run 1: 2551259
> * run 2: 2550756
> * run 3: 2551279
> It's very important to say that if I use filter:
> fox2.where("type='TypeA' ")
> or
> fox2.where("duration>4"),
>
> each of them can be executed repeatedly and I get consistent result every
> time.
> I can save dataframe after crating stop and duration columns, and after that,
> I get consistent results every time.
> It is not very practical workaround, as I need a lot of space and time to
> implement it.
> This dataset is really big (in my eyes at least, aprox 100.000.000 new
> records per day).
> If I run this same example on my local machine using master = local[*],
> everything works as expected, it's just on cluster setup. I tried to create
> cluster using docker on my local machine, created 3.0.1 and 3.1.1 clusters
> with one master and two workers, and have successfully reproduced issue.
>
>
>
>
>
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