Typically it means that data has been fetched from cache and there was no need to re-execute given stage. It is consistent with your DAG which shows that the next stage requires shuffling (reduceByKey). Whenever there is shuffling involved Spark automatically caches generated data: Shuffle also generates a large number of intermediate files on disk. … Read more
This happened to me when I gave more memory to the worker node than it has. Since it didn’t have swap, spark crashed while trying to store objects for shuffling with no more memory left. Solution was to either add swap, or configure the worker/executor to use less memory in addition with using MEMORY_AND_DISK storage … Read more
This is happening because you’re using python 3.8. The latest pip release of pyspark (pyspark 2.4.4 at time of writing) doesn’t support python 3.8. Downgrade to python 3.7 for now, and you should be fine.
What you get is a Console Progress Bar, [Stage 7: shows the stage you are in now, and (14174 + 5) / 62500] is (numCompletedTasks + numActiveTasks) / totalNumOfTasksInThisStage]. The progress bar shows numCompletedTasks / totalNumOfTasksInThisStage. It will be shown when both spark.ui.showConsoleProgress is true (by default) and log level in conf/log4j.properties is ERROR or … Read more
UPDATE: Suggest using Dataframes, plus something like … .write.mode(SaveMode.Overwrite) …. Handy pimp: implicit class PimpedStringRDD(rdd: RDD[String]) { def write(p: String)(implicit ss: SparkSession): Unit = { import ss.implicits._ rdd.toDF().as[String].write.mode(SaveMode.Overwrite).text(p) } } For older versions try yourSparkConf.set(“spark.hadoop.validateOutputSpecs”, “false”) val sc = SparkContext(yourSparkConf) In 1.1.0 you can set conf settings using the spark-submit script with the –conf flag. … Read more
copy paste the application Id from the spark scheduler, for instance application_1428487296152_25597 connect to the server that have launch the job yarn application -kill application_1428487296152_25597
Reading the Spark documentation I found an easier solution. Since version 1.4 of spark there is a function drop(col) which can be used in pyspark on a dataframe. You can use it in two ways df.drop(‘age’) df.drop(df.age) Pyspark Documentation – Drop
For Spark 2.1.0, my suggestion would be to use head(n: Int) or take(n: Int) with isEmpty, whichever one has the clearest intent to you. df.head(1).isEmpty df.take(1).isEmpty with Python equivalent: len(df.head(1)) == 0 # or bool(df.head(1)) len(df.take(1)) == 0 # or bool(df.take(1)) Using df.first() and df.head() will both return the java.util.NoSuchElementException if the DataFrame is empty. … Read more
You have a pretty nice outline here. To answer your questions A separate task does need to be launched for each partition of data for each stage. Consider that each partition will likely reside on distinct physical locations – e.g. blocks in HDFS or directories/volumes for a local file system. Note that the submission of … Read more
You can specify whole directories, use wildcards and even CSV of directories and wildcards. E.g.: sc.textFile(“/my/dir1,/my/paths/part-00[0-5]*,/another/dir,/a/specific/file”) As Nick Chammas points out this is an exposure of Hadoop’s FileInputFormat and therefore this also works with Hadoop (and Scalding).