Schema
root
|-- userId: string (nullable = true)
|-- languageknowList: array (nullable = true)
| |-- element: struct (containsNull = false)
| | |-- code: string (nullable = false)
| | |-- description: string (nullable = false)
| | |-- name: string (nullable = false)
The df has userId and languageknownList. Every user should know English, so English language is not present in languageknowList I have to add.
English
code: 10
description: English Language
name: English
Any one please help me.
You can create a new array of structs column and concat to the existing column:
import pyspark.sql.functions as F
english = F.struct(F.lit('10').alias('code'),
F.lit('English Language').alias('description'),
F.lit('English').alias('name')
)
df2 = df.withColumn(
'languageknowList',
F.when(
~F.array_contains(F.col('languageknowList'), english),
F.concat(
F.col('languageknowList'),
F.array(english)
)
).otherwise(
F.col('languageknowList')
)
)
Related
I have below sample data & structure and trying to play around to better understand SparkSQL,Pyspark commands.
schemaTest="`id` BIGINT NOT NULL,`name` STRING,`address` STRUCT<`number`: INT, `road`: STRING,
`city`: STRUCT<`name`: STRING, `postcode`: BIGINT>>,`numbers` ARRAY<INT>"
data = [(1,"Smith",(1200,"North Custer RD",("Sugar Land TX",75034)),[2815,2133])]
this is what I get from printSchema:
root
|-- id: long (nullable = false)
|-- name: string (nullable = true)
|-- address: struct (nullable = true)
| |-- number: integer (nullable = true)
| |-- road: string (nullable = true)
| |-- city: struct (nullable = true)
| | |-- name: string (nullable = true)
| | |-- postcode: long (nullable = true)
|-- numbers: array (nullable = true)
| |-- element: integer (containsNull = true)
when I query the df , this is how it's represented and I am trying to re-format the "address" column for a better representation:
+---+-----+-----------------------------------------------+------------+
|id |name |address |numbers |
+---+-----+-----------------------------------------------+------------+
|1 |Smith|{1200, North Custer RD, {Sugar Land TX, 75034}}|[2815, 2133]|
+---+-----+-----------------------------------------------+------------+
I want it to be more like this:
+---+-----+------------------------------------------+------------+
|id |name |address |numbers |
+---+-----+------------------------------------------+------------+
|1 |Smith|1200 North Custer RD, Sugar Land TX, 75034|[2815, 2133]|
+---+-----+------------------------------------------+------------+
I tried explode to see if I can extract but it says mismatch (I am assuming cannot perform explode on structType).
can someone give me an example using withColumn how to reformat the "Address" column?. or if you have any other approach?
You can use concat built-in function to create a string from several columns, as follows:
from pyspark.sql import functions as F
result = input_df.withColumn(
'address',
F.concat(
F.col('address.number'),
F.lit(' '),
F.col('address.road'),
F.lit(', '),
F.col('address.city.name'),
F.lit(', '),
F.col('address.city.postcode')
)
)
I am at work and I need immediate help please
I have a parquet file and I need to convert it to csv. could u please help me?
error:
AnalysisException: CSV data source does not support array<struct<company:string,dateRange:string,description:string,location:string,title:string>> data type.
I have never worked with this format so I can't even print schema. sorry
printshema:
root
|-- _id: string (nullable = true)
|-- Locale: string (nullable = true)
|-- workExperience: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- company: string (nullable = true)
| | |-- dateRange: string (nullable = true)
| | |-- description: string (nullable = true)
| | |-- location: string (nullable = true)
| | |-- title: string (nullable = true)
The parquet schema can be flattened using explode:
df=spark.read.parquet(...)
flattened_df = df.withColumn("tmp", F.explode("workExperience")) \
.selectExpr("_id", "Locale", "tmp.*")
flattened_df.write.csv(...)
You can't save a dataframe which contains column with array/struct type to CSV. You need to cast the column to string before writing.
df.withColumn('workExperience', col('workExperience').cast('string')).write.csv('path')
I have a column that contains array of structs. It looks like this:
|-- Network: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- Code: string (nullable = true)
| | |-- Signal: string (nullable = true)
This is just a small sample, there are many more columns inside the struct than this. Is there a way to take the arrays in the column for each row, concatenate them and make them into one string? For example, we could have something like this:
[["example", 2], ["example2", 3]]
Is there a way to make into:
"example2example3"?
Assuming having a dataframe df with the following schema:
df.printSchema
df with sample data:
df.show(false)
You need to first explode the Network array to select the struct elements Code and signal.
var myDf = df.select(explode($"Network").as("Network"))
Then you need to concat the two columns using the concat() function and then pass the output to the collect_list() function which will aggregate all rows into one row of type array<string>
myDf = myDf.select(collect_list(concat($"Network.code",$"Network.signal")).as("data"))
Finally, you need to concat into the required format which can be done using concat_ws() function which takes two arguments, the first being the separator to be placed between two string and the second argument being a column with array<string> type which is our output from our previous step. As per your use case, we don't need any separator to be placed between two concatenates strings hence we keep the separator argument as an empty quote.
myDf = myDf.select(concat_ws("",$"data").as("data"))
All the above steps can be done in one line
myDf= myDf.select(explode($"Network").as("Network")).select(concat_ws("",collect_list(concat($"Network.code",$"Network.signal"))).as("data")).show(false)
If you want the output directly into a String variable then use:
val myStr = myDf.first.get(0).toString
print(myStr)
There is a library called spark-hats (Github, small article) that you might find very useful in these situations.
With its use, you can map the array easily and output the concatenation next to the elements or even somewhere else if you provide a fully qualified name.
Setup
import org.apache.spark.sql.functions._
import za.co.absa.spark.hats.Extensions._
scala> df.printSchema
root
|-- info: struct (nullable = true)
| |-- drivers: struct (nullable = true)
| | |-- carName: string (nullable = true)
| | |-- carNumbers: string (nullable = true)
| | |-- driver: string (nullable = true)
|-- teamName: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- team1: string (nullable = true)
| | |-- team2: string (nullable = true)
scala> df.show(false)
+---------------------------+------------------------------+
|info |teamName |
+---------------------------+------------------------------+
|[[RB7, 33, Max Verstappen]]|[[Redbull, rb], [Monster, mt]]|
+---------------------------+------------------------------+
Command you are looking for
scala> val dfOut = df.nestedMapColumn(inputColumnName = "teamName", outputColumnName = "nextElementInArray", expression = a => concat(a.getField("team1"), a.getField("team2")) )
dfOut: org.apache.spark.sql.DataFrame = [info: struct<drivers: struct<carName: string, carNumbers: string ... 1 more field>>, teamName: array<struct<team1:string,team2:string,nextElementInArray:string>>]
Output
scala> dfOut.printSchema
root
|-- info: struct (nullable = true)
| |-- drivers: struct (nullable = true)
| | |-- carName: string (nullable = true)
| | |-- carNumbers: string (nullable = true)
| | |-- driver: string (nullable = true)
|-- teamName: array (nullable = true)
| |-- element: struct (containsNull = false)
| | |-- team1: string (nullable = true)
| | |-- team2: string (nullable = true)
| | |-- nextElementInArray: string (nullable = true)
scala> dfOut.show(false)
+---------------------------+----------------------------------------------------+
|info |teamName |
+---------------------------+----------------------------------------------------+
|[[RB7, 33, Max Verstappen]]|[[Redbull, rb, Redbullrb], [Monster, mt, Monstermt]]|
+---------------------------+----------------------------------------------------+
I currently had created a struct field in this way:
df = df.withColumn('my_struct', struct(
col('id').alias('id_test')
col('value').alias('value_test')
).alias('my_struct'))
The think is that now I need to add and extra field to my_struct called "optional". This field must be there when it exits and remove it when it's not. Sadly values like null/none not an option.
So far I have two different dataframes, one with the desired value and the column by id and another one without the value/column and all the information.
df_optional = df_optional.select('id','optional')
df = df.select('id','value','my_struct')
I want to add into df.my_struct the optional value when df_optional.id join df.join plus the rest.
Till this point I have this:
df_with_option = df.join(df_optional,on=['id'],how='inner') \
.withColumn('my_struct', struct(
col('id').alias('id_test')
col('value').alias('value_test')
col(optional).alias('optional')
).alias('my_struct')).drop('optional')
df_without = df.join(df_optional,on=['id'],how='leftanti') # it already have my_struct
But union should have similar columns so my code breaks.
df_result = df_without .unionByName(df_with_option)
I want to union both dataframes because at the end I write a json file partitioned by id:
df_result.repartitionByRange(df_result.count(),df['id']).write.format('json').mode('overwrite').save('my_path')
Those json files should have the 'optional' column when it has values, otherwise it should be out of the schema.
Any help will be appreciate.
--ADITIONAL INFO.
Schema input:
df_root
|-- id: string (nullable = true)
|-- optional: string (nullable = true)
df_optional
|-- id: string (nullable = true)
|-- value: string (nullable = true)
|-- my_struct: struct (nullable = true)
| |-- id: string (nullable = true)
| |-- value: string (nullable = true)
Schema output:
df_result
|-- id: string (nullable = true)
|-- value: string (nullable = true)
|-- my_struct: struct (nullable = true)
| |-- id: string (nullable = true)
| |-- value: string (nullable = true)
| |-- optional: string (nullable = true) (*)
(*) Only when it exists.
--UPDATE
I think that it jsut not posible in that way. I probably need to keep both dataframes appart and just write it two times. Something like this:
df_without.repartitionByRange(df_result.count(),df['id']).write.format('json').mode('overwrite').save('my_path')
df_with_option.repartitionByRange(df_result.count(),df['id']).write.format('json').mode('append').save('my_path')
Then I will had in my path the files by it's own way.
I have a schema and I would like to check the array if it has columns inside before exploding it. my schema looks like this
|-- CaseNumber: string (nullable = true)
|-- Interactions: struct (nullable = true)
| |-- EmailInteractions: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- CreatedBy: string (nullable = true)
| | | |-- CreatedOn: string (nullable = true)
| | | |-- Direction: string (nullable = true)
| |-- PhoneInteractions: array (nullable = true)
| | |-- element: string (containsNull = true)
| |-- WebInteractions: array (nullable = true)
| | |-- element: string (containsNull = true)
|-- EntityAction: string (nullable = true)
I would like to check if "EmailInteractions" has elements under it before I run the job that will explode it,
I have edited the question for clarity
1. check if email interactions array exist and check if it has columns, if both true, explode the array and finish, if one of the conditions is false, pass to step 2
2.check if phone interactions array exist and check if it has columns, if both true, explode the array and finish, if one of the conditions is false, pass to step 3
3.check if web interactions exist and check if it has columns, if both true, explode the array and finish, if one of the conditions is false, finish
I am new to coding and data bricks, please help on this.
This is one way of doing it. Unfortunately information under StructField is not searchable easily, I converted it to a string and search the filed by field name or keyword "StructField" to know that it contains a field.
val jsonWithNull = """{"a": 123,"b":null,"EmailInteractions":[{"CreatedBy":"test"}]}"""
val jsonWithoutNull = """{"a": 123,"b":3,"EmailInteractions":[{"CreatedBy":"test1"}]}"""
import spark.implicits._
val df = spark.read.json(Seq(jsonWithNull,jsonWithoutNull).toDS)
df.printSchema()
val field = df.schema.filter { f =>
if (f.dataType.typeName == "array" && f.toString().contains("CreatedBy")){
true
}
else{
false
}
}
println(field)
// check for field value being not null then explode
Result List(StructField(EmailInteractions,ArrayType(StructType(StructField(CreatedBy,StringType,true)),true),true))