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
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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