In PySpark 2.1.0 method drop supports multiple columns: PySpark 2.0.2: DataFrame.drop(col) PySpark 2.1.0: DataFrame.drop(*cols) Example: df.drop(‘col1’, ‘col2’) or using the * operator as df.drop(*[‘col1’, ‘col2’])
I figured out the correct way to calculate a moving/rolling average using this stackoverflow: Spark Window Functions – rangeBetween dates The basic idea is to convert your timestamp column to seconds, and then you can use the rangeBetween function in the pyspark.sql.Window class to include the correct rows in your window. Here’s the solved example: … Read more
assuming that a worker wants to send 4G of data to the driver, then having spark.driver.maxResultSize=1G, will cause the worker to send 4 messages (instead of 1 with unlimited spark.driver.maxResultSize). No. If estimated size of the data is larger than maxResultSize given job will be aborted. The goal here is to protect your application from … Read more
Starting with Spark 1.0 there are two methods you can use to solve this easily: RDD.zipWithIndex is just like Seq.zipWithIndex, it adds contiguous (Long) numbers. This needs to count the elements in each partition first, so your input will be evaluated twice. Cache your input RDD if you want to use this. RDD.zipWithUniqueId also gives … Read more
Learning to performance-tune Spark requires quite a bit of investigation and learning. There are a few good resources including this video. Spark 1.4 has some better diagnostics and visualisation in the interface which can help you. In summary, you spill when the size of the RDD partitions at the end of the stage exceed the … Read more
df.fillna(0, subset=[‘a’, ‘b’]) There is a parameter named subset to choose the columns unless your spark version is lower than 1.3.1
Following steps might help: Get your hostname by using “hostname” command. Make an entry in the /etc/hosts file for your hostname if not present as follows: 127.0.0.1 your_hostname
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
reduce result datasets into single dataset