How to Start a New PySpark Job

Dec. 4, 2019

Read time: 34 minutes

I’ve been to Spark and back. But I did leave some of my soul.

According to Apache, Spark was developed to “write applications quickly in Java, Scala, Python, R, and SQL”

And I’m sure it’s true. Or at least I’m sure their intentions were noble.

I’m not talking about Scala yet, or Java, those are whole other language. I’m talking about Spark with python. Or PySpark, as the Olgivy inspired geniuses at Apache marketing call it.

The learning curve is not easy my pretties, but luckily for you, I’ve managed to sort out some of the basic ecosystem and how it all operates. Brevity is my goal.

This doesn’t include MLib, or GraphX, or streaming, just the basics

Import some data

train ="header", "true")\ .option("inferSchema", "true")\ .format("csv")\ .load("train_V2.csv")\ .limit(20000) Show the head of a dataframe

train.head(5) List the columns and their value types

train.printSchema() Show a number of rows in a better format,truncate= True) Count the number of rows

train.count() List column names

train.columns Show mean, medium, st, etc...

train.describe().show() Show mean, medium, st, etc... of just one column

train.describe('kills').show() Show only certain columns'kills','headshotKills').show(5) Get the distinct values of a column'boosts').distinct().count()'boosts').distinct() That's it for now...


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