pyspark

问题 I have a file with a column containing IDs. Usually, an ID appears only once, but occasionally, they're associated with multiple records. I want to count how many times a given ID appeared, and then split into two separate dfs so I can run different operations on both. One df should be where IDs only appear once, and one should be where IDs appear multiple times. I was able to successfully count the number of instances an ID appeared by grouping on ID and joining the counts back onto the

问题 I have a simple join where I limit on of the sides. In the explain plan I see that before the limit is executed there is an ExchangeSingle operation, indeed I see that at this stage there is only one task running in the cluster. This of course affects performance dramatically (removing the limit removes the single task bottleneck but lengthens the join as it works on a much larger dataset). Is limit truly not parallelizable? and if so- is there a workaround for this? I am using spark on

问题 I am trying to run wordcount test using pytest from this site - Unit testing Apache Spark with py.test. The problem is that I cannot start spark context. Code I use to run Spark Context: @pytest.fixture(scope="session") def spark_context(request): """ fixture for creating a spark context Args: request: pytest.FixtureRequest object """ conf = (SparkConf().setMaster("local[2]").setAppName("pytest-pyspark-local-testing")) sc = SparkContext(conf=conf) request.addfinalizer(lambda: sc.stop()) quiet

问题 I am trying to run wordcount test using pytest from this site - Unit testing Apache Spark with py.test. The problem is that I cannot start spark context. Code I use to run Spark Context: @pytest.fixture(scope="session") def spark_context(request): """ fixture for creating a spark context Args: request: pytest.FixtureRequest object """ conf = (SparkConf().setMaster("local[2]").setAppName("pytest-pyspark-local-testing")) sc = SparkContext(conf=conf) request.addfinalizer(lambda: sc.stop()) quiet

问题 Why does the counter I wrote with pyspark below does not always provide me with the right result, is it related to the global counter? def increment_counter(): global counter counter += 1 def get_number_of_element(rdd): global counter counter = 0 rdd.foreach(lambda x:increment_counter()) return counter 回答1: Your global variable is only defined on the driver node, which means that it will work fine until you are running on localhost. As soon as you will distribute your job to multiple

问题 Why does the counter I wrote with pyspark below does not always provide me with the right result, is it related to the global counter? def increment_counter(): global counter counter += 1 def get_number_of_element(rdd): global counter counter = 0 rdd.foreach(lambda x:increment_counter()) return counter 回答1: Your global variable is only defined on the driver node, which means that it will work fine until you are running on localhost. As soon as you will distribute your job to multiple

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . Improve this question Can anyone please explain how Spark Dataframes are better in terms of time taken for execution than Pandas Dataframes. I'm dealing with data with moderate volume and making python function powered transformations For example, I have a column with numbers from 1

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . Improve this question Can anyone please explain how Spark Dataframes are better in terms of time taken for execution than Pandas Dataframes. I'm dealing with data with moderate volume and making python function powered transformations For example, I have a column with numbers from 1

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed last year . Improve this question Can anyone please explain how Spark Dataframes are better in terms of time taken for execution than Pandas Dataframes. I'm dealing with data with moderate volume and making python function powered transformations For example, I have a column with numbers from 1

问题 I am running spark on yarn cluster. I tried to start the history server ./start-history-server.sh but got the following errors. starting org.apache.spark.deploy.history.HistoryServer, logging to /home/abc/spark/spark-1.5.1-bin-hadoop2.6/sbin/../logs/spark-abc-org.apache.spark.deploy.history.HistoryServer-1-abc-Efg.out failed to launch org.apache.spark.deploy.history.HistoryServer: at org.apache.spark.deploy.history.FsHistoryProvider.<init>(FsHistoryProvider.scala:47) ... 6 more full log in