pyspark

问题 I have spark installed. And, I can go into the bin folder within my spark version, and run ./spark-shell and it runs correctly. But, for some reason, I am unable to launch pyspark and any of the submodules. So, I go into bin and launch ./pyspark and it tells me that my path is incorrect. The current path I have for PYSPARK_PYTHON is the same as where I'm running the pyspark executable script from. What is the correct path for PYSPARK_PYTHON ? Shouldn't it be the path that leads to the

问题 I have spark installed. And, I can go into the bin folder within my spark version, and run ./spark-shell and it runs correctly. But, for some reason, I am unable to launch pyspark and any of the submodules. So, I go into bin and launch ./pyspark and it tells me that my path is incorrect. The current path I have for PYSPARK_PYTHON is the same as where I'm running the pyspark executable script from. What is the correct path for PYSPARK_PYTHON ? Shouldn't it be the path that leads to the

问题 I have spark installed. And, I can go into the bin folder within my spark version, and run ./spark-shell and it runs correctly. But, for some reason, I am unable to launch pyspark and any of the submodules. So, I go into bin and launch ./pyspark and it tells me that my path is incorrect. The current path I have for PYSPARK_PYTHON is the same as where I'm running the pyspark executable script from. What is the correct path for PYSPARK_PYTHON ? Shouldn't it be the path that leads to the

问题 I have pyspark dataframe with multiple columns (Around 30) of nested structs, that I want to write into csv. (struct In order to do it, I want to stringify all of the struct columns. I've checked several answers here: Pyspark converting an array of struct into string PySpark: DataFrame - Convert Struct to Array PySpark convert struct field inside array to string This is the structure of my dataframe (with around 30 complex keys): root |-- 1_simple_key: string (nullable = true) |-- 2_simple

问题 This question already has answers here : pyspark parse fixed width text file (2 answers) Closed last year . The data is as below. [Row(_c0='ACW00011604 17.1167 -61.7833 10.1 ST JOHNS COOLIDGE FLD '), Row(_c0='ACW00011647 17.1333 -61.7833 19.2 ST JOHNS '), Row(_c0='AE000041196 25.3330 55.5170 34.0 SHARJAH INTER. AIRP GSN 41196')] I have defined the schema_stn with correct column widths etc as per the documentation. My code for reading it into a dataframe using pyspark is as under: df.select(

问题 This question already has answers here : pyspark parse fixed width text file (2 answers) Closed last year . The data is as below. [Row(_c0='ACW00011604 17.1167 -61.7833 10.1 ST JOHNS COOLIDGE FLD '), Row(_c0='ACW00011647 17.1333 -61.7833 19.2 ST JOHNS '), Row(_c0='AE000041196 25.3330 55.5170 34.0 SHARJAH INTER. AIRP GSN 41196')] I have defined the schema_stn with correct column widths etc as per the documentation. My code for reading it into a dataframe using pyspark is as under: df.select(

问题 Say I have a list of magazine subscriptions, like so: subscription_id user_id created_at 12384 1 2018-08-10 83294 1 2018-06-03 98234 1 2018-04-08 24903 2 2018-05-08 32843 2 2018-03-06 09283 2 2018-04-07 Now I want to add a column that states how many previous subscriptions a user had, before this current subscription. For example, if this is the user's first subscription, the new column's value should be 0. If they had one subscription starting before this subscription, the new column's value

问题 I have installed below on my windows 10 machine to use the Apache Spark. Java, Python 3.6 and Spark (spark-2.3.1-bin-hadoop2.7) I am trying to write pyspark related code in VSCode. It is showing red underline under the 'from ' and showing error message E0401:Unable to import 'pyspark' I have also used ctrl+Shift+P and select "Python:Update workspace Pyspark libraries". It is showing notification message Make sure you have SPARK_HOME environment variable set to the root path of the local spark

问题 I am using pyspark-sql to create rows in a remote mysql db, using JDBC. I have two tables, parent_table(id, value) and child_table(id, value, parent_id) , so each row of parent_id may have as many rows in child_id associated to it as needed. Now I want to create some new data and insert it into the database. I'm using the code guidelines here for the write opperation, but I would like to be able to do something like: parentDf = sc.parallelize([5, 6, 7]).toDF(('value',)) parentWithIdDf =

问题 Imagine you are loading a large dataset by the SparkContext and Hive. So this dataset is then distributed in your Spark cluster. For instance a observations (values + timestamps) for thousands of variables. Now you would use some map/reduce methods or aggregations to organize/analyze your data. For instance grouping by variable name. Once grouped, you could get all observations (values) for each variable as a timeseries Dataframe. If you now use DataFrame.toPandas def myFunction(data_frame):