Spark uses a master/slave architecture. As you can see in the figure, it has one central coordinator (Driver) that communicates with many distributed workers (executors). The driver and each of the executors run in their own Java processes. DRIVER The driver is the process where the main method runs. First it converts the user program … Read more
I have a few suggestions: If your nodes are configured to have 6g maximum for Spark (and are leaving a little for other processes), then use 6g rather than 4g, spark.executor.memory=6g. Make sure you’re using as much memory as possible by checking the UI (it will say how much mem you’re using) Try using more … Read more
results.show(20, false) will not truncate. Check the source 20 is the default number of rows displayed when show() is called without any arguments.
Here is an example of the difference, as a spark-shell session: First, some data – two lines of text: val rdd = sc.parallelize(Seq(“Roses are red”, “Violets are blue”)) // lines rdd.collect res0: Array[String] = Array(“Roses are red”, “Violets are blue”) Now, map transforms an RDD of length N into another RDD of length N. For … Read more
There are many ways to do that: Option 1. Using selectExpr. data = sqlContext.createDataFrame([(“Alberto”, 2), (“Dakota”, 2)], [“Name”, “askdaosdka”]) data.show() data.printSchema() # Output #+——-+———-+ #| Name|askdaosdka| #+——-+———-+ #|Alberto| 2| #| Dakota| 2| #+——-+———-+ #root # |– Name: string (nullable = true) # |– askdaosdka: long (nullable = true) df = data.selectExpr(“Name as name”, “askdaosdka as … Read more
First thing is DataFrame was evolved from SchemaRDD. Yes.. conversion between Dataframe and RDD is absolutely possible. Below are some sample code snippets. df.rdd is RDD[Row] Below are some of options to create dataframe. 1) yourrddOffrow.toDF converts to DataFrame. 2) Using createDataFrame of sql context val df = spark.createDataFrame(rddOfRow, schema) where schema can be from … Read more
It avoids a full shuffle. If it’s known that the number is decreasing then the executor can safely keep data on the minimum number of partitions, only moving the data off the extra nodes, onto the nodes that we kept. So, it would go something like this: Node 1 = 1,2,3 Node 2 = 4,5,6 … Read more