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We next present SparkR DataFrames and discuss how they can be used to address the above use cases.

In this section we present some of the design choices involved

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in building SparkR. We first present details about the SparkR DataFrames API and then present an overview of SparkR’s archi- tecture.

3.1 SparkR DataFrames API The central component of SparkR is a distributed data frame implemented on top of Spark. SparkR DataFrames have an API simi- lar to dplyr or local R data frames, but scale to large datasets using Spark’s execution engine and relational query optimizer. DataFrame Operators: SparkR’s DataFrame supports a number of methods to read input and perform structured data analysis. As shown in Figure 1, SparkR’s read.df method integrates with

1 # Load the flights CSV file using ‘read.df‘

2 df <- read.df(sqlContext, “./nycflights13.csv”,

3 “com.databricks.spark.csv”) 4

5 # Select flights from JFK. 6 jfk_flights <- filter(df, df$origin == “JFK”) 7

8 # Group and aggregate flights to each destination. 9 dest_flights <- agg(

10 groupBy(jfk_flights, jfk_flights$dest), 11 count = n(jfk_flights$dest)) 12

13 # Running SQL Queries 14 registerTempTable(df, “table”) 15 training <- sql(sqlContext, 16 “SELECT distance, depDelay, arrDelay FROM table”)

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