Exploring Spark data types

You've already seen (back in Chapter 1) src_tbls() for listing the DataFrames on Spark that sparklyr can see. You've also seen glimpse() for exploring the columns of a tibble on the R side.

sparklyr has a function named sdf_schema() for exploring the columns of a tibble on the R side. It's easy to call; and a little painful to deal with the return value.

sdf_schema(a_tibble)

The return value is a list, and each element is a list with two elements, containing the name and data type of each column. The exercise shows a data transformation to more easily view the data types.

Here is a comparison of how R data types map to Spark data types. Other data types are not currently supported by sparklyr.

R type	Spark type
logical	BooleanType
numeric	DoubleType
integer	IntegerType
character	StringType
list	ArrayType

Diese Übung ist Teil des Kurses

Introduction to Spark with sparklyr in R

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Anleitung zur Übung

A Spark connection has been created for you as spark_conn. A tibble attached to the track metadata stored in Spark has been pre-defined as track_metadata_tbl.

Call sdf_schema() to get the schema of the track metadata.
Run the transformation code on schema to see it in a more readable tibble format.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# track_metadata_tbl has been pre-defined
track_metadata_tbl

# Get the schema
(schema <- ___(___))

# Transform the schema
schema %>%
  lapply(function(x) do.call(data_frame, x)) %>%
  bind_rows()

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