Pyspark Advanced interview questions part 1

Pyspark Advanced interview questions part 1
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PySpark Interview Questions and Answers for beginners and experts. List of frequently asked PySpark Interview Questions with Answers by Besant Technologies. We hope these PySpark Interview Questions and Answers are useful and will help you to get the best job in the networking industry. This PySpark interview questions and answers are prepared by PySpark Professionals based on MNC Companies’ expectations. Stay tune we will update New PySpark Interview questions with Answers Frequently
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[@abhilash0410]

Bro bring more real-time interview questions like these thank you so much !

[@vedanthasm2659]

One of the best explanation. Bro..Please make more videos on Pyspark

[@sjitghosh]

You are doing an excellent work. Helping a lot!!

[@saachinileshpatil]

Thanks for sharing 👍, very informative

[@janardhanreddy3267]

nice explanation ,please attach csv file or json in description to practice

[@rocku4evr]

Great......fortunate to be your subscriber

[@seshuseshu4106]

Very good detailed explanation, thanks for your efforts, keep continue ..

[@akashpb4044]

Awesome video... Cleared my doubts 👍👍👍

[@achintamondal1494]

Awesome video. Could you please share the notebook, it will really help.

[@nsrchndshkh]

Thanks Man. This was some detailed explanation. Kudos

[@TRRaveendra]

Ur welcome 👍

[@fratkalkan7850]

very clean explanation thank you sir

[@rajanib9057]

can you pleaae explain how did spark filter those 2 colums as bad data? I don't see any where condition mentioned for the corrupt column

[@sanooosai]

great thank you

[@janardhanreddy3267]

please upload all pyspark interview questions videos

[@balajia8376]

seems querying _corrupt_record is not working. I tried it today and not allowing me to query with the column name.cust_df.filter("_corrupt_record is not null"). AnalysisException: Since Spark 2.3, the queries from raw JSON/CSV files are disallowed when the referenced columns only include the internal corrupt record column (named _corrupt_record by default). For example: spark.read.schema(schema).csv(file).filter($"_corrupt_record".isNotNull).count() and spark.read.schema(schema).csv(file).select("_corrupt_record").show(). Instead, you can cache or save the parsed results and then send the same query. For example, val df = spark.read.schema(schema).csv(file).cache() and then df.filter($"_corrupt_record".isNotNull).count().

[@TRRaveendra]

cust_df.cache() Cache dataframe and it's won't raise exception

[@balajia8376]

@@TRRaveendra Yes I did, even after that also not allowing to write a query on _corrupt_record is null or not null.

[@balajia8376]

seems badRecordsPath is only the solution.

[@varuns4472]

Nice one

[@balajia8376]

cust_df.select("_corrupt_record").show() is working but not allowing is null or not null. cust_df.select("_corrupt_record is null").show(). let me know if this is working for you. thank you.

[@shreekrishnavani7868]

Nice explanation 👌 thanks

[@johnsonrajendran6194]

are any such mode options available while reading parquet files?

[@rahulyeole6411]

Please share basic big data video

[@swagatikatripathy4917]

Why do we write inferschema= true

[@TRRaveendra]

InferSchema =True Creating datatypes based on data. Header = True creating columns from file first line

[@naveendayyala1484]

plz share the notebook in .dbc format

[@sachintiwari6846]

Woah what a explanation

[@srikanthbachina7764]

Hi pls share ur contact details I am looking for python, pyspark, databricks training

[@balajia8376]

root |-- cust_id: integer (nullable = true) |-- cust_name: string (nullable = true) |-- manager: string (nullable = true) |-- city: string (nullable = true) |-- phno: long (nullable = true) |-- _corrupt_record: string (nullable = true) . display(cust_df.filter("_corrupt_record is not null")). FileReadException: Error while reading file dbfs:/FileStore/tables/csv_with_bad_records.csv. Caused by: IllegalArgumentException: _corrupt_record does not exist. Available: cust_id, cust_name, manager, city, phno