pyspark-dataframes

问题 I am able to execute a simple SQL statement using PySpark in Azure Databricks but I want to execute a stored procedure instead. Below is the PySpark code I tried. #initialize pyspark import findspark findspark.init('C:\Spark\spark-2.4.5-bin-hadoop2.7') #import required modules from pyspark import SparkConf, SparkContext from pyspark.sql import SparkSession from pyspark.sql import * import pandas as pd #Create spark configuration object conf = SparkConf() conf.setMaster("local").setAppName("My

问题 I am able to execute a simple SQL statement using PySpark in Azure Databricks but I want to execute a stored procedure instead. Below is the PySpark code I tried. #initialize pyspark import findspark findspark.init('C:\Spark\spark-2.4.5-bin-hadoop2.7') #import required modules from pyspark import SparkConf, SparkContext from pyspark.sql import SparkSession from pyspark.sql import * import pandas as pd #Create spark configuration object conf = SparkConf() conf.setMaster("local").setAppName("My

问题 I am able to execute a simple SQL statement using PySpark in Azure Databricks but I want to execute a stored procedure instead. Below is the PySpark code I tried. #initialize pyspark import findspark findspark.init('C:\Spark\spark-2.4.5-bin-hadoop2.7') #import required modules from pyspark import SparkConf, SparkContext from pyspark.sql import SparkSession from pyspark.sql import * import pandas as pd #Create spark configuration object conf = SparkConf() conf.setMaster("local").setAppName("My

问题 I am able to execute a simple SQL statement using PySpark in Azure Databricks but I want to execute a stored procedure instead. Below is the PySpark code I tried. #initialize pyspark import findspark findspark.init('C:\Spark\spark-2.4.5-bin-hadoop2.7') #import required modules from pyspark import SparkConf, SparkContext from pyspark.sql import SparkSession from pyspark.sql import * import pandas as pd #Create spark configuration object conf = SparkConf() conf.setMaster("local").setAppName("My

问题 How can I create a pyspark data frame with 2 JSON files? file1: this file has complete data file2: this file has only the schema of file1 data. file1 {"RESIDENCY":"AUS","EFFDT":"01-01-1900","EFF_STATUS":"A","DESCR":"Australian Resident","DESCRSHORT":"Australian"} file2 [{"fields":[{"metadata":{},"name":"RESIDENCY","nullable":true,"type":"string"},{"metadata":{},"name":"EFFDT","nullable":true,"type":"string"},{"metadata":{},"name":"EFF_STATUS","nullable":true,"type":"string"},{"metadata":{},

问题 How can I create a pyspark data frame with 2 JSON files? file1: this file has complete data file2: this file has only the schema of file1 data. file1 {"RESIDENCY":"AUS","EFFDT":"01-01-1900","EFF_STATUS":"A","DESCR":"Australian Resident","DESCRSHORT":"Australian"} file2 [{"fields":[{"metadata":{},"name":"RESIDENCY","nullable":true,"type":"string"},{"metadata":{},"name":"EFFDT","nullable":true,"type":"string"},{"metadata":{},"name":"EFF_STATUS","nullable":true,"type":"string"},{"metadata":{},

问题 Can it iterate through the Pyspark groupBy dataframe without aggregation or count? For example code in Pandas: for i, d in df2: mycode .... ^^ if using pandas ^^ Is there a difference in how to iterate groupby in Pyspark or have to use aggregation and count? 回答1: At best you can use .first , .last to get respective values from the groupBy but not all in the way you can get in pandas. ex: from pyspark.sql import functions as f df.groupBy(df['some_col']).agg(f.first(df['col1']), f.first(df[

问题 How to parse and transform json string from spark dataframe rows in pyspark? I'm looking for help how to parse: json string to json struct output 1 transform json string to columns a, b and id output 2 Background: I get via API json strings with a large number of rows ( jstr1 , jstr2 , ...), which are saved to spark df . I can read schema for each row separately, but this is not the solution as it is very slow as schema has a large number of rows. Each jstr has the same schema, columns/keys a

问题 For example, I have one full set of nested JSON, I need to update this JSON with the latest values from another nested JSON. Can anyone help me with this? I want to implement this in Pyspark. Full Set Json look like this: { "email": "abctest@xxx.com", "firstName": "name01", "id": 6304, "surname": "Optional", "layer01": { "key1": "value1", "key2": "value2", "key3": "value3", "key4": "value4", "layer02": { "key1": "value1", "key2": "value2" }, "layer03": [ { "inner_key01": "inner value01" }, {

问题 For example, I have one full set of nested JSON, I need to update this JSON with the latest values from another nested JSON. Can anyone help me with this? I want to implement this in Pyspark. Full Set Json look like this: { "email": "abctest@xxx.com", "firstName": "name01", "id": 6304, "surname": "Optional", "layer01": { "key1": "value1", "key2": "value2", "key3": "value3", "key4": "value4", "layer02": { "key1": "value1", "key2": "value2" }, "layer03": [ { "inner_key01": "inner value01" }, {