[jira] [Issue Comment Deleted] (SPARK-4672) Cut off the super long serialization chain in GraphX to avoid the StackOverflow error

["Reynold Xin (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-4672?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Reynold Xin updated SPARK-4672:
-------------------------------
    Comment: was deleted

(was: User 'JerryLead' has created a pull request for this issue:
https://github.com/apache/spark/pull/3544)

> Cut off the super long serialization chain in GraphX to avoid the StackOverflow error
> -------------------------------------------------------------------------------------
>
>                 Key: SPARK-4672
>                 URL: https://issues.apache.org/jira/browse/SPARK-4672
>             Project: Spark
>          Issue Type: Bug
>          Components: GraphX, Spark Core
>    Affects Versions: 1.1.0
>            Reporter: Lijie Xu
>            Priority: Critical
>             Fix For: 1.2.0
>
>
> While running iterative algorithms in GraphX, a StackOverflow error will stably occur in the serialization phase at about 300th iteration. In general, these kinds of algorithms have two things in common:
> # They have a long computing chain.
> {code:borderStyle=solid}
> (e.g., “degreeGraph=>subGraph=>degreeGraph=>subGraph=>…=>”)
> {code}
> # They will iterate many times to converge. An example:
> {code:borderStyle=solid}
> //K-Core Algorithm
> val kNum = 5
> var degreeGraph = graph.outerJoinVertices(graph.degrees) {
> 		(vid, vd, degree) => degree.getOrElse(0)
> }.cache()
> 	
> do {
> 	val subGraph = degreeGraph.subgraph(
> 		vpred = (vid, degree) => degree >= KNum
> 	).cache()
> 	val newDegreeGraph = subGraph.degrees
> 	degreeGraph = subGraph.outerJoinVertices(newDegreeGraph) {
> 		(vid, vd, degree) => degree.getOrElse(0)
> 	}.cache()
> 	isConverged = check(degreeGraph)
> } while(isConverged == false)
> {code}
> After about 300 iterations, StackOverflow will definitely occur with the following stack trace:
> {code:borderStyle=solid}
> Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task serialization failed: java.lang.StackOverflowError
> java.io.ObjectOutputStream.writeNonProxyDesc(ObjectOutputStream.java:1275)
> java.io.ObjectOutputStream.writeClassDesc(ObjectOutputStream.java:1230)
> java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1426)
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177)
> {code}
> It is a very tricky bug, which only occurs with enough iterations. Since it took us a long time to find out its causes, we will detail the causes in the following 3 paragraphs. 
>  
> h3. Phase 1: Try using checkpoint() to shorten the lineage
> It's easy to come to the thought that the long lineage may be the cause. For some RDDs, their lineages may grow with the iterations. Also, for some magical references,  their lineage lengths never decrease and finally become very long. As a result, the call stack of task's serialization()/deserialization() method will be very long too, which finally exhausts the whole JVM stack.
> In deed, the lineage of some RDDs (e.g., EdgeRDD.partitionsRDD) increases 3 OneToOne dependencies in each iteration in the above example. Lineage length refers to the  maximum length of OneToOne dependencies (e.g., from the finalRDD to the ShuffledRDD) in each stage.
> To shorten the lineage, a checkpoint() is performed every N (e.g., 10) iterations. Then, the lineage will drop down when it reaches a certain length (e.g., 33). 
> However, StackOverflow error still occurs after 300+ iterations!
> h3. Phase 2:  Abnormal f closure function leads to a unbreakable serialization chain
> After a long-time debug, we found that an abnormal _*f*_ function closure and a potential bug in GraphX (will be detailed in Phase 3) are the "Suspect Zero". They together build another serialization chain that can bypass the broken lineage cut by checkpoint() (as shown in Figure 1). In other words, the serialization chain can be as long as the original lineage before checkpoint().
> Figure 1 shows how the unbreakable serialization chain is generated. Yes, the OneToOneDep can be cut off by checkpoint(). However, the serialization chain can still access the previous RDDs through the (1)->(2) reference chain. As a result, the checkpoint() action is meaningless and the lineage is as long as that before. 
> !https://raw.githubusercontent.com/JerryLead/Misc/master/SparkPRFigures/g1.png|width=100%!
> The (1)->(2) chain can be observed in the debug view (in Figure 2).
> {code:borderStyle=solid}
> _rdd (i.e., A in Figure 1, checkpointed) -> f -> $outer (VertexRDD) -> partitionsRDD:MapPartitionsRDD -> RDDs in  the previous iterations
> {code}
> !https://raw.githubusercontent.com/JerryLead/Misc/master/SparkPRFigures/g2.png|width=100%!
> More description: While a RDD is being serialized, its f function 
> {code:borderStyle=solid}
> e.g., f: (Iterator[A], Iterator[B]) => Iterator[V]) in ZippedPartitionsRDD2
> {code}
> will be serialized too. This action will be very dangerous if the f closure has a member “$outer” that references its outer class (as shown in Figure 1). This reference will be another way (except the OneToOneDependency) that a RDD (e.g., PartitionsRDD) can reference the other RDDs (e.g., VertexRDD). Note that checkpoint() only cuts off the direct lineage, while the function reference is still kept. So, serialization() can still access the other RDDs along the f references. 
> h3. Phase 3: Non-transient member variable of VertexRDD makes things worse
> "Reference (1)" in Figure 1 is caused by the abnormal f clousre, while "Reference (2)" is caused by the potential bug in GraphX: *PartitionsRDD is a non-transient member variable of VertexRDD*. 
> With this _small_ bug, the f closure itself (without OneToOne dependency) can cause StackOverflow error, as shown in the red box in Figure 3:
> # While _vertices:VertexRDD_ is being serialized, its member _PartitionsRDD_ will be serialized too.
> # Next, while serializing this _partitionsRDD_, serialization() will simultaneously serialize its f’s referenced $outer. Here, it is another _partitionsRDD_.
> # Finally, the chain 
> {code:borderStyle=solid}
> "f => f$3 => f$3 => $outer => vertices: VertexRDD => partitionsRDD => … => ShuffledRDD"
> {code}
> comes into shape. As a result, the serialization chain can be as long as the original lineage and finally triggers StackOverflow error.
>   
> !https://raw.githubusercontent.com/JerryLead/Misc/master/SparkPRFigures/g3.png|width=100%!
> h2. Conclusions
> In conclusion, the root cause of StackOverflow error is the long serialization chain, which cannot be cut off by _checkpoint()_. This long chain is caused by the multiple factors, including:
> # long lineage
> # $outer reference in the f closure
> # non-transient member variable
> h2. How to fix this error
> We propose three pull requests as follows to solve this problem thoroughly.
> # PR-3544
> In this pr, we change the "val PartitionsRDD" to be transient in EdgeRDDImpl and VertexRDDImpl. As a result, while _vertices:VertexRDD_ is being serialized, its member _PartitionsRDD_ will not be serialized. In other words, the "Reference (2)" in Figure 1 will be cut off.
> # PR-3545
> In this pr, we set "f = null" if ZippedPartitionsRDD is checkpointed. As a result, when PartitionsRDD is checkpointed, its f closure will be cleared and the "Reference (1)" (i.e., f => $outer) in Figure 1 will no exist.
> # PR-3549
> To cut off the long lineage, we need to perform checkpoint()  on PartitionsRDD. However, current checkpoint() is performed on VertexRDD and EdgeRDD themselves. As a result, we need to override the checkpoint() methods in VertexRDDImpl and EdgeRDDImpl to perform checkpoint() on PartitionsRDD.



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