Pyspark Scenarios 21 : Dynamically processing complex json file in pyspark #complexjson #databricks

Pyspark Scenarios 21 : Dynamically processing complex json file in pyspark #complexjson #databricks
Pyspark Interview question
Pyspark Scenario Based Interview Questions
Pyspark Scenario Based Questions
Scenario Based Questions
#PysparkScenarioBasedInterviewQuestions
#ScenarioBasedInterviewQuestions
#PysparkInterviewQuestions

Complete Pyspark Real Time Scenarios Videos.

Pyspark Scenarios 1: How to create partition by month and year in pyspark
pyspark scenarios 2 : how to read variable number of columns data in pyspark dataframe #pyspark
Pyspark Scenarios 3 : how to skip first few rows from data file in pyspark
Pyspark Scenarios 4 : how to remove duplicate rows in pyspark dataframe #pyspark #Databricks
Pyspark Scenarios 5 : how read all files from nested folder in pySpark dataframe
Pyspark Scenarios 6 How to Get no of rows from each file in pyspark dataframe
Pyspark Scenarios 7 : how to get no of rows at each partition in pyspark dataframe
Pyspark Scenarios 8: How to add Sequence generated surrogate key as a column in dataframe.
Pyspark Scenarios 9 : How to get Individual column wise null records count
Pyspark Scenarios 10:Why we should not use crc32 for Surrogate Keys Generation?
Pyspark Scenarios 11 : how to handle double delimiter or multi delimiters in pyspark
Pyspark Scenarios 12 : how to get 53 week number years in pyspark extract 53rd week number in spark
Pyspark Scenarios 13 : how to handle complex json data file in pyspark
Pyspark Scenarios 14 : How to implement Multiprocessing in Azure Databricks
Pyspark Scenarios 15 : how to take table ddl backup in databricks
Pyspark Scenarios 16: Convert pyspark string to date format issue dd-mm-yy old format
Pyspark Scenarios 17 : How to handle duplicate column errors in delta table
Pyspark Scenarios 18 : How to Handle Bad Data in pyspark dataframe using pyspark schema
Pyspark Scenarios 19 : difference between #OrderBy #Sort and #sortWithinPartitions Transformations
Pyspark Scenarios 20 : difference between coalesce and repartition in pyspark #coalesce #repartition
Pyspark Scenarios 21 : Dynamically processing complex json file in pyspark #complexjson #databricks
Pyspark Scenarios 22 : How To create data files based on the number of rows in PySpark #pyspark
pyspark tutorial,
pyspark,
pyspark tutorial for beginners,
pyspark interview questions,
pyspark project,
what is pyspark,
pyspark tutorial in telugu,
pyspark databricks tutorial,
databricks pyspark tutorial,
pyspark sql,
pyspark tutorial in hindi,
pyspark installation on windows 10,
how to install pyspark in jupyter notebook,
pyspark tutorial in tamil,
pyspark tutorial in telugu,
pyspark full course,
pyspark for data engineers,

pyspark sql
pyspark
hive
which
databricks
apache spark
sql server
spark sql functions
spark interview questions
sql interview questions
spark sql interview questions
spark sql tutorial
spark architecture
coalesce in sql
hadoop vs spark
window function in sql
which role is most likely to use azure data factory to define a data pipeline for an etl process?
what is data warehouse
broadcast variable in spark
pyspark documentation
apache spark architecture
google colab
case class in scala

RISING
broadcast variable in spark
google colab
case class in scala
pyspark documentation
spark architecture
window function in sql
apache spark architecture
hadoop vs spark
spark interview questions
databricks,
azure databricks,
databricks tutorial,
databricks tutorial for beginners,
azure databricks tutorial,
what is databricks,
azure databricks tutorial for beginners,
databricks interview questions,
databricks certification,
delta live tables databricks,
databricks sql,
databricks data engineering associate,
pyspark databricks tutorial,
databricks azure,
delta lake databricks,
snowflake vs databricks,

[bunnyvlogs]

Mind block ayyipoyindi sir....meeku oka pedda hifi... emm chesaru sir excellect work ...big fan of you since 2020

[sailpawar]

this was awesmome...i was manually extracting and giving col names

[Learner-zmwz]

Thanks for sharing awesome dynamic approach.
There is one issue with JSON data, it is not a valid JSON , it should be as, an array with separated by ", " like [ {<first object>}, {<second object>} ], then it will give the desired output.

[rahulm]

Thank you a lot, can you make a video to compare two dataframes schemas and based on it creates null columns

[manisharand]

Thanks a lot for the videos, I have watched all of the playlists. I am also having a similar type of complex json but little more complex to read, if you have time can you please help me with that. if you do it will be a great help tome.

[hritiksharma]

Please make a video for unity catalog setup for databricks

[sravankumar]

Superb explanation, i have one doubt what is if c(1):(:5) meaning and it iterstes c(0) meaning could u please clarify my doubts

[kiranshah]

Could you please make a series of video on spark framework. TIA

[anjanashetty]

This is really amazing helped me, but it's slowly down select operation not sure why

[martinandreriverarodriguez]

I really thank you so much, It helps me a lot, I just have a question, how can I do to order all the collumns? like the first schema, thanks a lot!!

[MrTigerman]

good tutorial appreciate it andi.. what if the fields in json are optional i mean same json can have an orders field and next json file for a user may not have orders array in ur example.. Will the code fail on keyerror as the array doesnt exist and the schema keep on changing in json?

[shubhamaggarwal]

Thanks for sharing the video, but infortunately its not working in my case, its sowing the same file as it was, its not flattening the data, can you please help me..
Also, can you please make a video how can we use autoloader with delta live tables for json files and how we can parse with the same.
I shall be highly highly thankful to you

[sangeetchourey]

# Following script produces same output, and will be more efficient when we have higher volume of data to process

jsnStr = """{
"name":"MSFT", "location":"Redmond", "satellites": ["Bay Area", "Shanghai"],
"goods": {
"trade":true, "customers":["government", "distributer", "retail"],
"orders":[
{"orderId":1, "orderTotal":123.34, "shipped":{"orderItems":[{"itemName":"Laptop", "itemQty":20}, {"itemName":"Charger", "itemQty":2}]}},
{"orderId":2, "orderTotal":323.34, "shipped":{"orderItems":[{"itemName":"Mice", "itemQty":2}, {"itemName":"Keyboard", "itemQty":1}]}}
]}}
{"name":"Company1", "location":"Seattle", "satellites": ["New York"],
"goods":{"trade":false, "customers":["store1", "store2"],
"orders":[
{"orderId":4, "orderTotal":123.34, "shipped":{"orderItems":[{"itemName":"Laptop", "itemQty":20}, {"itemName":"Charger", "itemQty":3}]}},
{"orderId":5, "orderTotal":343.24, "shipped":{"orderItems":[{"itemName":"Chair", "itemQty":4}, {"itemName":"Lamp", "itemQty":2}]}}
]}}
{"name": "Company2", "location": "Bellevue",
"goods": {"trade": true, "customers":["Bank"], "orders": [{"orderId": 4, "orderTotal": 123.34}]}}
{"name": "Company3", "location": "Kirkland"}"""

df =


spark.sql("""
select location, name, satellites, goods_customer, goods_orders_orderId, goods_orders_orderTotal, orderItems.itemName goods_orders_shipped_orderItems_itemName,
orderItems.itemQty from (
select
location,
name,
satellites,
goods_customer,
orders.orderId goods_orders_orderId,
orders.orderTotal goods_orders_orderTotal,
orderItems
from (
select location, name, satellites, goods_customer, explode(orders) orders from (
select location, name, satellites, explode(goods.customers) goods_customer, goods.orders from (
select location, name, explode(satellites) satellites, goods from tbl_json) iq
) iq2
) iq3
)iq4
""").display()

[thebossismael]

Te hace falta agregar esto por si al momento de aplicar esto, todos tus datos te aparezcan NULL:

spark.conf.set("spark.sql.legacy.json.allowEmptyString.enable", True)