Every row in the dataframe contains a csv formatted string line plus another simple string, so what I'm trying to get at the end is a dataframe composed of the fields extracted from the line string together with category.
So I proceeded as follows to explode the line string
val df = stream.toDF("line","category")
.map(x => x.getString(0))......
At the end I manage to get a new dataframe composed of the line fields but I can't return the category to the new dataframe
I can't join the new dataframe with the initial one since the common field id was not a separate column at first.
Sample of input :
line | category
"'1';'daniel';'dan#gmail.com'" | "premium"
Sample of output:
id | name | email | category
1 | "daniel"| "dan#gmail.com"| "premium"
Any suggestions, thanks in advance.
If the structure of strings in line column is fixed as mentioned in the question, then following simple solution should work where split inbuilt function is used to split the string into array and then finally selecting the elements from the array and aliasing to get the final dataframe
import org.apache.spark.sql.functions._
df.withColumn("line", split(col("line"), ";"))
.select(col("line")(0).as("id"), col("line")(1).as("name"), col("line")(2).as("email"), col("category"))
.show(false)
which should give you
+---+--------+---------------+--------+
|id |name |email |category|
+---+--------+---------------+--------+
|'1'|'daniel'|'dan#gmail.com'|premium |
+---+--------+---------------+--------+
I hope the answer is helpful
Related
I have a dataframe having a row value "My name is Rahul" I want to split "my name is" in one column and "Rahul" in another column. Here is no delimiter to use the split function. How can I do it in spark?
Instead of Split function, Use regexp_extract function in Spark.
Regex Explanation:
(.*)\\s(.*) //capture everything into 1 capture group until last space(\s) then capture everything after into 2 capture group.
Example:
val df= Seq(("My name is Rahul")).toDF("text") //sample string
df.withColumn("col1",regexp_extract($"text","(.*)\\s(.*)",1)).
withColumn("col2",regexp_extract($"text","(.*)\\s(.*)",2)).
show()
Result:
+----------------+----------+-----+
| text| col1| col2|
+----------------+----------+-----+
|My name is Rahul|My name is|Rahul|
+----------------+----------+-----+
I need to add a new column to dataframe DF1 but the new column's value should be calculated using other columns' value present in that DF. Which of the other columns to be used will be given in another dataframe DF2.
eg. DF1
|protocolNo|serialNum|testMethod |testProperty|
+----------+---------+------------+------------+
|Product1 | AB |testMethod1 | TP1 |
|Product2 | CD |testMethod2 | TP2 |
DF2-
|action| type| value | exploded |
+------------+---------------------------+-----------------+
|append|hash | [protocolNo] | protocolNo |
|append|text | _ | _ |
|append|hash | [serialNum,testProperty] | serialNum |
|append|hash | [serialNum,testProperty] | testProperty |
Now the value of exploded column in DF2 will be column names of DF1 if value of type column is hash.
Required -
New column should be created in DF1. the value should be calculated like below-
hash[protocolNo]_hash[serialNumTestProperty] ~~~ here on place of column their corresponding row values should come.
eg. for Row1 of DF1, col value should be
hash[Product1]_hash[ABTP1]
this will result into something like this abc-df_egh-45e after hashing.
The above procedure should be followed for each and every row of DF1.
I've tried using map and withColumn function using UDF on DF1. But in UDF, outer dataframe value is not accessible(gives Null Pointer Exception], also I'm not able to give DataFrame as input to UDF.
Input DFs would be DF1 and DF2 as mentioned above.
Desired Output DF-
|protocolNo|serialNum|testMethod |testProperty| newColumn |
+----------+---------+------------+------------+----------------+
|Product1 | AB |testMethod1 | TP1 | abc-df_egh-4je |
|Product2 | CD |testMethod2 | TP2 | dfg-df_ijk-r56 |
newColumn value is after hashing
Instead of DF2, you can translate DF2 to case class like Specifications, e.g
case class Spec(columnName:String,inputColumns:Seq[String],action:String,action:String,type:String*){}
Create instances of above class
val specifications = Seq(
Spec("new_col_name",Seq("serialNum","testProperty"),"hash","append")
)
Then you can process the below columns
val transformed = specifications
.foldLeft(dtFrm)((df: DataFrame, spec: Specification) => df.transform(transformColumn(columnSpec)))
def transformColumn(spec: Spec)(df: DataFrame): DataFrame = {
spec.type.foldLeft(df)((df: DataFrame, type : String) => {
type match {
case "append" => {have a case match of the action and do that , then append with df.withColumn}
}
}
Syntax may not be correct
Since DF2 has the column names that will be used to calculate a new column from DF1, I have made this assumption that DF2 will not be a huge Dataframe.
First step would be to filter DF2 and get the column names that we want to pick from DF1.
val hashColumns = DF2.filter('type==="hash").select('exploded).collect
Now, hashcolumns will have the columns that we want to use to calculate hash in the newColumn. The hashcolumns is an Array of Row. We need this to be a Column that will be applied while creating the newColumn in DF1.
val newColumnHash = hashColumns.map(f=>hash(col(f.getString(0)))).reduce(concat_ws("_",_,_))
The above line will convert the Row to a Column with hash function applied to it. And we reduce it while concatenating _. Now, the task becomes simple. We just need to apply this to DF1.
DF1.withColumn("newColumn",newColumnHash).show(false)
Hope this helps!
I have a dataframe which contains only one row with the column name: source_column in the below format:
forecast_id:bigInt|period:numeric|name:char(50)|location:char(50)
I want to retrieve this value into a String and then split it on the regex |
First I tried converting the row from the DataFrame into the String by following way so that I can check if the row is converted to String:
val sourceColDataTypes = sourceCols.select("source_columns").rdd.map(x => x.toString()).collect()
When I try to print: println(sourceColDataTypes) to check the content, I see [Ljava.lang.String;#19bbb216
I couldn't understand the mistake here. Could anyone let me know how can I properly fetch a row from a dataframe and convert it to String.
You can also try this:
df.show()
//Input data
//+-----------+----------+--------+--------+
//|forecast_id|period |name |location|
//+-----------+----------+--------+--------+
//|1000 |period1000|name1000|loc1000 |
//+-----------+----------+--------+--------+
df.map(_.mkString(",")).show(false)
//Output:
//+--------------------------------+
//|value |
//+--------------------------------+
//|1000,period1000,name1000,loc1000|
//+--------------------------------+
df.rdd.map(_.mkString(",")).collect.foreach(println)
//1000,period1000,name1000,loc1000
I have the following question :
Actually I am working with the following csv file:
""job"";""marital"""
""management"";""married"""
""technician"";""single"""
I loaded it into a spark dataframe as follows:
My aim is to check the length and type of each field in the dataframe following the set od rules below :
col type
job char10
marital char7
I started implementing the check of the length of each field but I am getting a compilation error :
val data = spark.read.option("inferSchema", "true").option("header", "true").csv("file:////home/user/Desktop/user/file.csv")
data.map(line => {
val fields = line.toString.split(";")
fields(0).size
fields(1).size
})
The expected output should be:
List(10,10)
As for the check of the types I don't have any idea about how to implement it as we are using dataframes. Any idea about a function verifying the data format ?
Thanks a lot in advance for your replies.
ata
I see you are trying to use Dataframe, But if there are multiple double quotes then you can read as a textFile and remove them and convert to Dataframe as below
import org.apache.spark.sql.functions._
import spark.implicits._
val raw = spark.read.textFile("path to file ")
.map(_.replaceAll("\"", ""))
val header = raw.first
val data = raw.filter(row => row != header)
.map { r => val x = r.split(";"); (x(0), x(1)) }
.toDF(header.split(";"): _ *)
You get with data.show(false)
+----------+-------+
|job |marital|
+----------+-------+
|management|married|
|technician|single |
+----------+-------+
To calculate the size you can use withColumn and length function and play around as you need.
data.withColumn("jobSize", length($"job"))
.withColumn("martialSize", length($"marital"))
.show(false)
Output:
+----------+-------+-------+-----------+
|job |marital|jobSize|martialSize|
+----------+-------+-------+-----------+
|management|married|10 |7 |
|technician|single |10 |6 |
+----------+-------+-------+-----------+
All the column type are String.
Hope this helps!
You are using a dataframe. So when you use the map method, you are processing Row in your lambda.
so line is a Row.
Row.toString will return a string representing the Row, so in your case 2 structfields typed as String.
If you want to use map and process your Row, you have to get the vlaue inside the fields manually. with getAsString and getAsString.
Usually when you use Dataframes, you have to work in column's logic as in SQL using select, where... or directly the SQL syntax.
I am a beginner of Spark. Please help me out with a solution.
The csv file contains the text in the form of key:value paring delimited by a comma. And in some lines, the keys(or columns) may be missing.
I have loaded this file into a single column of a dataframe. I want to segregate these keys as columns and values associated to it as data into that column. And when there are some columns missing i want to add a new column and a dummy data to that.
Dataframe
+----------------------------------------------------------------+
| _c0 |
+----------------------------------------------------------------+
|name:Pradnya,IP:100.0.0.4, college: SDM, year:2018 |
|name:Ram, IP:100.10.10.5, college: BVB, semester:IV, year:2018 |
+----------------------------------------------------------------+
I want the output in this form
+----------- ----------------------------------------------
| name | IP | College | Semester | year |
+-----------+-------------------------+-----------+-------+
| Pradnya |100.0.0.4 | SDM | null | 2018 |
+-----------+-------------+-----------+-----------+-------+
| Ram | 100.10.10.5 | BVB | IV |2018 |
+-----------+-------------+-----------+-----------+-------+
Thanks.
Pyspark won't recognize the key:value pairing. One workaround is convert the file int json format and then read the json file.
content of raw.txt:
name:Pradnya,IP:100.0.0.4, college: SDM, year:2018
name:Ram, IP:100.10.10.5, college: BVB, semester:IV, year:2018
Following code will create the json file :
with open('raw.json', 'w') as outfile:
json.dump([dict([p.split(':') for p in l.split(',')]) for l in open('raw.txt')], outfile)
Now you can create the pyspark dataframe using following code :
df = spark.read.format('json').load('raw.json')
If you know all field names and keys/values do not contain embedded delimiters. then you can probably convert the key/value lines into Row object through RDD's map function.
from pyspark.sql import Row
from string import lower
# assumed you already defined SparkSession named `spark`
sc = spark.sparkContext
# initialize the RDD
rdd = sc.textFile("key-value-file")
# define a list of all field names
columns = ['name', 'IP', 'College', 'Semester', 'year']
# set Row object
def setRow(x):
# convert line into key/value tuples. strip spaces and lowercase the `k`
z = dict((lower(k.strip()), v.strip()) for e in x.split(',') for k,v in [ e.split(':') ])
# make sure all columns shown in the Row object
return Row(**dict((c, z[c] if c in z else None) for c in map(lower, columns)))
# map lines to Row objects and then convert the result to dataframe
rdd.map(setRow).toDF().show()
#+-------+-----------+-------+--------+----+
#|college| ip| name|semester|year|
#+-------+-----------+-------+--------+----+
#| SDM| 100.0.0.4|Pradnya| null|2018|
#| BVB|100.10.10.5| Ram| IV|2018|
#+-------+-----------+-------+--------+----+