MongoPaginateByCountPartitioner NoSuchElementException for any spark action

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    • Type: Bug
    • Resolution: Fixed
    • Priority: Major - P3
    • 2.2.2
    • Affects Version/s: 2.2.1
    • Component/s: Partitioners
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      Conditions (Spark conf):

      sparkConf.set("spark.mongodb.input.uri", inDBUrl + ".session");
// partitioner config
sparkConf.set("spark.mongodb.input.partitioner", "MongoPaginateByCountPartitioner");
sparkConf.set("spark.mongodb.input.partitionerOptions.partitionKey", "_id");
sparkConf.set("spark.mongodb.input.partitionerOptions.numberOfPartitions",String.valueOf(partitionCnt)); // I tried 1 and 10 value for numberOfPartitions

      Using the Connector, I'm getting:

      1. data for a wide time period (for example, the whole day),
      2. looping on previous whole data for getting subset for short time period (for example, for every 5 minutes of the day)

      If the whole day period contains appropriate data (filtered by mongo aggregation pipeline), but the certain 5 minutes period doesn't contain, I'm getting error when calling any JavaRDD<Document> action, for example rdd.count():

          java.util.NoSuchElementException: next on empty iterator
	at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
	at scala.collection.Iterator$$anon$2.next(Iterator.scala:37)
	at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63)
	at scala.collection.IterableLike$class.head(IterableLike.scala:107)
	at scala.collection.mutable.ArrayOps$ofRef.scala$collection$IndexedSeqOptimized$$super$head(ArrayOps.scala:186)
	at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:126)
	at scala.collection.mutable.ArrayOps$ofRef.head(ArrayOps.scala:186)
	at com.mongodb.spark.rdd.partitioner.PartitionerHelper$.setLastBoundaryToLessThanOrEqualTo(PartitionerHelper.scala:127)
	at com.mongodb.spark.rdd.partitioner.MongoPaginateByCountPartitioner.partitions(MongoPaginateByCountPartitioner.scala:85)
	at com.mongodb.spark.rdd.MongoRDD.getPartitions(MongoRDD.scala:137)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
	at scala.Option.getOrElse(Option.scala:121)
	at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
	at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
	at scala.Option.getOrElse(Option.scala:121)
	at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
	at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
	at scala.Option.getOrElse(Option.scala:121)
	at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
	at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
	at scala.Option.getOrElse(Option.scala:121)
	at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
	at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:252)
	at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:250)
	at scala.Option.getOrElse(Option.scala:121)
	at org.apache.spark.rdd.RDD.partitions(RDD.scala:250)
	at org.apache.spark.Partitioner$$anonfun$defaultPartitioner$2.apply(Partitioner.scala:66)
	at org.apache.spark.Partitioner$$anonfun$defaultPartitioner$2.apply(Partitioner.scala:66)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
	at scala.collection.immutable.List.foreach(List.scala:381)
	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
	at scala.collection.immutable.List.map(List.scala:285)
	at org.apache.spark.Partitioner$.defaultPartitioner(Partitioner.scala:66)
	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$groupByKey$3.apply(PairRDDFunctions.scala:644)
	at org.apache.spark.rdd.PairRDDFunctions$$anonfun$groupByKey$3.apply(PairRDDFunctions.scala:644)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
	at org.apache.spark.rdd.PairRDDFunctions.groupByKey(PairRDDFunctions.scala:643)
	at org.apache.spark.api.java.JavaPairRDD.groupByKey(JavaPairRDD.scala:559)
	at MyCode: rdd.count() // any JavaRDD<Document> action cause this error

      Example, how to use use method getDataReadyToAgg (see code stub below) for the error reproducing:

      1. getting whole day data. Input: 
        JavaMongoRDD<Document> preaggData = MongoSpark.load(sparkContext);
periodStart=2018-03-11T00:00:00+03, periodEnd=2018-03-12T00:00:00+03

        If I call resultRdd.collect(), I see few records.

      1. use the same method for certain time. Input: 
        JavaMongoRDD<Document> preaggData = result of previous call (the whole day data)
periodStart=2018-03-11T00:00:00+03, periodEnd=2018-03-11T00:05:00+03}}.

        If data don't exist for this certain period, I'm getting NoSuchElementException

      getDataReadyToAgg.java
          /**
     * gets from pre-aggregated only rows, that fall into periodStart-periodEnd intersection
     *
     * @param preaggData  pre-aggregated data (by session)
     * @param attemptItem attempt info
     */
    private JavaMongoRDD<Document> getDataReadyToAgg(JavaMongoRDD<Document> preaggData,
                                                     OttAggAttemptItem attemptItem) {

        long periodStart = attemptItem.getPeriodStart()
                .getTime();
        long periodEnd = attemptItem.getPeriodEnd()
                .getTime();

        List<Document> pipeline = new ArrayList<>();

        // where by date
        String matchDate = "{$match:{$or:[" +
                "{start : {$gte:{$date:" + periodStart + "}, $lte:{$date:" + periodEnd + "}}}," +
                "{end : {$gte:{$date:" + periodStart + "}, $lte:{$date:" + periodEnd + "}}}";

        if (period != PERIOD_MONTH) {
            matchDate = matchDate + ",\n" + "{start : {$lt:{$date:" + periodStart + "}}, end: {$gt:{$date:" + periodEnd + "}}}";
        }

        matchDate = matchDate + "]}}";

        pipeline.add(Document.parse(matchDate));

        log.debug("pipeline: {}", pipeline);

        return preaggData.withPipeline(pipeline);

    }

            Assignee:
            Ross Lawley
            Reporter:
            Alex Ryasn
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              Created:
              Updated:
              Resolved: