I have few csv files with headers but I found out that some files have different column orders. Is there a way to handle this with Spark where I can define select order for each file so that the master DF doesn't have mismatch where col x might have values from col y?
My current read -
val masterDF = spark.read.option("header", "true").csv(allFiles:_*)
Extract all file names and store into list variable.
Then define schema of with all the columns in it.
iterate through each file using header true, so we are reading each file separately.
unionAll the new dataframe with the existing dataframe.
Example:
file_lst=['<path1>','<path2>']
from pyspark.sql.functions import *
from pyspark.sql.types import *
#define schema for the required columns
schema = StructType([StructField("column1",StringType(),True),StructField("column2",StringType(),True)])
#create an empty dataframe
df=spark.createDataFrame([],schema)
for i in file_lst:
tmp_df=spark.read.option("header","true").csv(i).select("column1","column2")
df=df.unionAll(tmp_df)
#display results
df.show()
Related
What is needed to a seq of s3 location is given. The difference for any two location is the partition column value of the table.
Each parquet folder has same schema .
So we need to loop the sequence of s3 parquet file path with same schema and save in a single dataframe in scala along.
If you have an array with all of directories you want to import you can iterate over that array and make a collection of dataframes and then union them into a single one.
Try something like this.
//You have now a collection of dataframes
val dataframes = directories.map(dir =>
spark.read.parquet(dir))
//Let's union them into one
val df_union = dataframes.reduce(_ union _)
If you turn on the options, then simply you can load the files recursively.
spark.read.parquet("s3a://path/to/root/")
The options are as follows.
spark.hive.mapred.supports.subdirectories true
spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive true
This can be used in a way that
import org.apache.spark.{SparkContext, SparkConf}
import org.apache.spark.sql.SparkSession
val conf = new SparkConf()
.setMaster("local[2]")
.setAppName("test")
.set("spark.hive.mapred.supports.subdirectories","true")
.set("spark.hadoop.mapreduce.input.fileinputformat.input.dir.recursive","true")
val spark = SparkSession.builder.config(conf).getOrCreate()
val df = spark.read.parquet("s3a://path/to/root/")
I have all those supporting libraries in pyspark and I am able to create dataframe for parent-
def xmlReader(root, row, filename):
df = spark.read.format("com.databricks.spark.xml").options(rowTag=row,rootTag=root).load(filename)
xref = df.select("genericEntity.entityId", "genericEntity.entityName","genericEntity.entityType","genericEntity.inceptionDate","genericEntity.updateTimestamp","genericEntity.entityLongName")
return xref
df1 = xmlReader("BOBML","entityTransaction","s3://dev.xml")
df1.head()
I am unable to create child dataframe-
def xmlReader(root, row, filename):
df2 = spark.read.format("com.databricks.spark.xml").options(rowTag=row, rootTag=root).load(filename)
xref = df2.select("genericEntity.entityDetail", "genericEntity.entityDetialId","genericEntity.updateTimestamp")
return xref
df3 = xmlReader("BOBML","s3://dev.xml")
df3.head()
I am not getting any output and I was planning to do union between parent and child dataframe. Any help will be truly appreciated!
After more than 24 hours, I am able to solve the problem and thanks to all whoever at least look at my problem.
Solution:
Step 1: Upload couple of libraries
from pyspark.sql import SparkSession
from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
Step2 (Parents):Read xml files, print schema, register temp tables, and create dataframe.
Step3 (Child): Repeat step 2.
Step4: Create final Dataframe by joining Child and parent dataframes.
Step5: Load data into S3 (write.csv/S3://Path) or Database.
I am new to Spark.
I have loaded a CSV file into a Spark DataFrame, say OriginalDF
Now I want to
1. filter out some columns from it and create a new dataframe of the originalDF
2. create a dataFrame out of the extracted columns
How can these 2 dataframes be created in spark scala?
using select, you can select what columns you want.
val df2 = OriginalDF.select($"col1",$"col2",$"col3")
using filter you should able to filter the rows.
val df3 = OriginalDF.where($"col1" < 10)
another way to filter data is using where. Both filter and where are synonyms so you can use them interchangeably.
val df3 = OriginalDF.filter($"col1" < 10)
Note select and filter returns a new dataframe as a result.
I am new to Scala. I need to read data from csv file which has two header columns named Name and Marks, based on the Marks column I want to show the result in a 3rd column; pass or fail (<35 fail, >35pass).
The data looks like this:
Name,Marks
x,10
y,50
z,80
Result should be:
Name Marks Result
x 10 Fail
Y 50 Pass
z 80 Pass
You can read the csv file with header, then add a column by using when and otherwise to give different values depending on the marks.
import spark.implicits._
val df = spark.read.option("header", true).csv("/path/to/csv") // read csv
val df2 = df.withColumn("Result", when($"Marks" < 35, "Fail").otherwise("Pass"))
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession
val spark = SparkSession.builder.master("local")
.appName("").config("spark.sql.warehouse.dir", "C:/temp").getOrCreate()
val df = spark.read.option("header",true).csv("file path")
val resul = df.withColumn("Result", when(col("Marks").cast("Int")>=35, "PASS").otherwise("FAIL"))
I'm new to Pyspark and I'm trying to add a new column to my existing dataframe. The new column should contain only 4 fixed values (e.g. 1,2,3,4) and I'd like to randomly pick one of the values for each row.
How can I do that?
Pyspark dataframes are immutable, so you have to return a new one (e.g. you can't just assign to it the way you can with Pandas dataframes). To do what you want use a udf:
from pyspark.sql.functions import udf
import numpy as np
df = <original df>
udf_randint = udf(np.random.randint(1, 4))
df_new = df.withColumn("random_num": udf_randint)