Read a Parquet file into a Spark DataFrame

Read a Parquet file into a Spark DataFrame.

spark_read_parquet(sc, name, path, options = list(), repartition = 0,
  memory = TRUE, overwrite = TRUE, columns = NULL, ...)

Arguments

sc	A `spark_connection`.
name	The name to assign to the newly generated table.
path	The path to the file. Needs to be accessible from the cluster. Supports the `"hdfs://"`, `"s3n://"` and `"file://"` protocols.
options	A list of strings with additional options. See http://spark.apache.org/docs/latest/sql-programming-guide#configuration.
repartition	The number of partitions used to distribute the generated table. Use 0 (the default) to avoid partitioning.
memory	Boolean; should the data be loaded eagerly into memory? (That is, should the table be cached?)
overwrite	Boolean; overwrite the table with the given name if it already exists?
columns	A vector of column names or a named vector of column types.
...	Optional arguments; currently unused.

You can read data from HDFS (hdfs://), S3 (s3n://), as well as the local file system (file://).

If you are reading from a secure S3 bucket be sure that the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables are both defined.