Recommended order of reading

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Recommended order of reading:

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5 ways to calculate max of row in Spark

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5 ways to calculate max of row in Spark/PySpark In this post I tested the performance of 5 different methods to calculate a maximal value of a single row. You might encounter this situation before: you have multiple columns with some value, integer or float for example, and you want to calculate the maximum value per row for all the data frame and append it as additional column. We will test 5 different ways to calculate maximum value: udf - user define function 'array' and 'array_max' functions 'greatest' function rdd 'map' transformation data frame groupBy and max-aggregation For each of these methods will count the time it take to read a CSV file, calculate the maximal value and add it as 'max' column. For the data, we are going to generate 10 columns of integer values over 1 million rows via Python code. The values will be in the range of 1 to 1000. import random def gen_csv (): f = open ( "../data_10x1m.csv...
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Create Custom Datasource With Spark 3 - Part 1

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This article is the first of a series on how to create a custom data source that will plug in to Spark mechanism. The idea is to write something like: Dataset < Row > df = sparkSession . read () . option ( "filename" , "path-to-data-file" ) . option ( "other-options" , "some value" ) . format ( "com.roizaig.mydatasource" ) . load (); To read a file, parse it according to some custom logic and return a dataset that represents it. Why Create Custom Data Source? Spark support out of the box in Parquet , CSV , JSON and ORC  and more. In most cases these formats will be more than okay and if not, you can always read it as a text file and do some string manipulation in Spark. However, there are some cases where we would like to "teach" Spark to read our own custom format. Flexibility is a key point, with a custom datasource you can read every format y...
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Spark - How to read a JSON file

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First thing to notice it the file structure. Its actually a collection of json objects *each one in a single line*. { "field_name": "value", "field_name": "value", ... } { "field_name": "value", "field_name": "value", ... } { "field_name": "value", "field_name": "value", ... } Unlike csv() method the json() method does not have inferSchema parameter. The schema is inferred from the json types. from pyspark.sql import SparkSession import pyspark.sql.types as t import pyspark.sql.functions as f spark = SparkSession . builder \ . master( 'local' ) \ . appName( 'Read JSON file' ) \ . getOrCreate() df = spark . read \ . json( 'data.json' , multiLine = True ) df . printSchema() df . show(truncate = False ) root |-- Address: string (nullable = true) |-- Age: long (nullable = true) |-- Birth Date: string (nu...
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