partitioning

When I execute below command: scala> val rdd = sc.parallelize(List((1,2),(3,4),(3,6)),4).partitionBy(new HashPartitioner(10)).persist() rdd: org.apache.spark.rdd.RDD[(Int, Int)] = ShuffledRDD[10] at partitionBy at <console>:22 scala> rdd.partitions.size res9: Int = 10 scala> rdd.partitioner.isDefined res10: Boolean = true scala> rdd.partitioner.get res11: org.apache.spark.Partitioner = org.apache.spark.HashPartitioner@a It says that there are 10 partitions and partitioning is done using HashPartitioner . But When I execute below command: scala> val rdd = sc.parallelize(List((1,2),(3,4),(3,6))

I have a set of distinct values. I am looking for a way to generate all partitions of this set, i.e. all possible ways of dividing the set into subsets. For instance, the set {1, 2, 3} has the following partitions: { {1}, {2}, {3} }, { {1, 2}, {3} }, { {1, 3}, {2} }, { {1}, {2, 3} }, { {1, 2, 3} }. As these are sets in the mathematical sense, order is irrelevant. For instance, {1, 2}, {3} is the same as {3}, {2, 1} and should not be a separate result. A thorough definition of set partitions can be found on Wikipedia . I've found a straightforward recursive solution. First, let's solve a

This question already has an answer here: Split List into Sublists with LINQ 27 answers How would one take a List (using LINQ) and break it into a List of Lists partitioning the original list on every 8th entry? I imagine something like this would involve Skip and/or Take, but I'm still pretty new to LINQ. Edit: Using C# / .Net 3.5 Edit2: This question is phrased differently than the other "duplicate" question. Although the problems are similar, the answers in this question are superior: Both the "accepted" answer is very solid (with the yield statement) as well as Jon Skeet's suggestion to

I am trying to move data from a table in PostgreSQL table to a Hive table on HDFS. To do that, I came up with the following code: val conf = new SparkConf().setAppName("Spark-JDBC").set("spark.executor.heartbeatInterval","120s").set("spark.network.timeout","12000s").set("spark.sql.inMemoryColumnarStorage.compressed", "true").set("spark.sql.orc.filterPushdown","true").set("spark.serializer", "org.apache.spark.serializer.KryoSerializer").set("spark.kryoserializer.buffer.max","512m").set("spark.serializer", classOf[org.apache.spark.serializer.KryoSerializer].getName).set("spark.streaming