Pyspark Scenarios 18 : How to Handle Bad Data in pyspark dataframe using pyspark schema

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Pyspark Scenarios 18 : How to Handle Bad Data in pyspark dataframe using pyspark schema

Рет қаралды 14,431

Жыл бұрын

GitHub location :
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Complete Pyspark Real Time Scenarios Videos.
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Pyspark Scenarios 16: Convert pyspark string to date format issue dd-mm-yy old format
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Pyspark Scenarios 18 : How to Handle Bad Data in pyspark dataframe using pyspark schema
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Пікірлер: 36

@Technology_of_world5 Жыл бұрын

Good massage, thankyou lot 👍

@anandattagasam7037 Жыл бұрын

Thanks for your brief explaination, i would go with 4th option (BadRecords path) instead of 5th (ColumnNnamedBadRecords).

@tusharhatwar Жыл бұрын

This channel is Goldmine for Pyspark Data engineers.

@Jgiga Жыл бұрын

Thanks for sharing

@manjulakumarisammidi1833 9 ай бұрын

instead of caching the dataframe @14:17 defining bad_data_df before good_data_df will also work, just another approach. Thanks for the video sir.

@arshiyakub17 Жыл бұрын

Thank you so much for the video on this. I have been searching for this for a long time and finally got what I needed from this video.

@sravankumar1767 Жыл бұрын

Nice explanation 👌 👍 👏

@mesukanya9828 10 ай бұрын

Thank you so much... very well explained :)

@TRRaveendra 10 ай бұрын

Thank you 🙏

@muruganc2350 Жыл бұрын

Thanks. good to learn!

@shayankabasi160 Жыл бұрын

Very nice

@jobiquirobi123 Жыл бұрын

Just find out your tutoriales, they look pretty nice, thank you!

@TRRaveendra Жыл бұрын

Thank You 👍

@Basket-hb5jc 4 ай бұрын

very valuable

@mehmetkaya4330 Жыл бұрын

Thank you for the great tutorials!

@TRRaveendra Жыл бұрын

Thanks for watching my channel videos

@mohitupadhayay1439 3 ай бұрын

Can we do the same for XML and JSON files?

@srijitachaturvedi7738 Жыл бұрын

Is this approach works while reading json data instead of csvs?

@TRRaveendra Жыл бұрын

Yes for normal json you can use the same option. for multiline json you can use option("multiline","true") otherwise it will create default _corrupt_record column.

@ketanmehta3058 Жыл бұрын

Excellent ! Clearly explained each and every option to load the data. @TeckLake Can we use this option with the JSON data as well?

@bharathsai232 Жыл бұрын

Permissive mode is not detecting malformed date types i mean if we have date as 2013-02-30 spark read in permissive mode is not detecting this as bad data

@mohitupadhayay1439 2 ай бұрын

Still we could not find the proper reason why the records went into corrupt when the column are very huge

@user-gs8xh9ww9i 2 ай бұрын

i got , query returns no records

@saisaranv Жыл бұрын

Hi TechLake Team, thanks for the wonderful video and helped a lot. can you please help me with 2 errors which am facing right now : 1. "cannot cast string into integer type" even after specific data schema defined 2. complex json flattening (i had gone through video 13 but my data is too complex in nature to flatten). appreciated in help please

@TRRaveendra Жыл бұрын

Tgrappstech@gmail.con ping me ur schema or sample data i can verify

@saisaranv Жыл бұрын

@@TRRaveendra Done..please check one. Thank you for your reply :)

@hannawg7747 Жыл бұрын

Hi Sir, Do you provide training Azure ADB ADF ?

@TRRaveendra Жыл бұрын

Yes I do. please reach me on tgrappstech@gmail.com

@chriskathumbi2292 Жыл бұрын

Hello, good video. I have a question concerning spark. When I use local data like parquet and csv and make a tempview or or just normal spark, and try to use distinct/group by or window functions, I get an error and I've seen this on my windows/linux and docker container. What could be causing this?

@TRRaveendra Жыл бұрын

what kind of error are you getting. is it related to datafile path? or is it related to missing columns or wrong group by query?

@chriskathumbi2292 Жыл бұрын

@@TRRaveendra if I use df.show() and the df contains group by, window function or distinct Py4JJavaError: An error occurred while calling o69.showString.

@chriskathumbi2292 Жыл бұрын

@@TRRaveendra Funny thing is that on Google Colabs where I have to install pyspark on launch, doesn't have this issue

@chimorammohan8392 Жыл бұрын

@@chriskathumbi2292 this might be code error pls share the code

@chriskathumbi2292 Жыл бұрын

@@chimorammohan8392 spark_df = spark.read.csv("./test/fm_file_dir_map.csv", header=True) spark_df.createOrReplaceTempView("spark_df_temp_v1") { "works": [ spark.sql( """ select distinct file_id from spark_df_temp_v1 """ ), spark_df.select(f.col("file_id")).distinct(), ], "fails": [ spark.sql( """ select distinct file_id from spark_df_temp_v1 """ ).show(), spark_df.select(f.col("file_id")).distinct().show(), ], }

@Ameem-rw4ir 3 ай бұрын

bro, thanks for your inputs. can you please help me how to handle this? empid,fname|lname@sal#deptid 1,mohan|kumar@5000#100 2,karna|varadan@3489#101 3,kavitha|gandan@6000#102 Expected output empid,fname,lname,sal,deptid 1,mohan,kumar,5000,100 2,karan,varadan,3489,101 3,kavitha,gandan,6000,102