Spark Scenario Based Question | Window - Ranking Function in Spark | Using PySpark | LearntoSpark

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In this video, we will learn to apply window Ranking function in PySpark.

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  1. Azarudeen Shahul
  2. Spark Interview Question
  3. Spark
  4. LearntoSpark
  5. PySpark
  6. Spark with Scala
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Комментарии
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Good insight for one of the most asked interview questions.

sangramrajpujari
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Well done... Your playlist is great... Keep it up...

uzwalgutta
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Same question asked me in the Interview Today 😃 Answered...

mohan_sai
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Thanks for video, please do the video on Pyspark performance

sivaranganath
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is row_number, col, rank, dense_rank depreciated in 3.0.1 unable to find those. What's the alternate for that.

bhanubrahmadesam
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Great content! Concise and brief explanation 👍 Can you create video on frequently faced spark issues and how to tackle them? It can be syntax or optimization or any other issues

datum
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windows function are mostly used in pyspark ya

hanumanthchinna
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Hi nice explanation, but i have a question, why we need to use 'row_number' to remove duplicate. We can just use the dropDuplicate() function to remove the duplicated right ?

RaviKumar-oyjq
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Does this work on given daaset
because u r partiioning by name here we have two Rakesh's with different age

RAM, 28, BE, 2012
Rakesh, 53, MBA, 1985
Madhu, 22, B.Com, 2018
Rakesh, 56, MBA, 1985
Bill, 32, ME, 2007
Madhu, 22, B.Com, 2018

balinasuryachandra
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I have a question, if we have age, education, and year different but with same name then it will be capturing that record right as count wrt name column .. in that scenario out data will not be correct one. Please help my understanding

jittendrakumar
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For the groupby approach, if there are 100 columns in a table then what else can be written instead of writing 100 columns

kritibhatia
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In the windowing function line, , Why does the column Year is accessed using col("Year") whereas, the column "Name" is accessed directly instead of col(Name)?

hsakarp
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Can we get your facebook question in your linkedin?

sangramrajpujari
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win=Window.partitionBy('Name', 'Age', 'Education', 'Year').orderBy('Name', 'Age', 'Education', 'Year')
df.withColumn('new', row_number().over(win)).filter(col('new')==1).drop(col('new')).show()

mitrontech