Read a Parquet file into a Spark DataFrame.

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

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.html#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?

Details

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.

See also

Other Spark serialization routines: spark_load_table, spark_read_csv, spark_read_jdbc, spark_read_json, spark_read_table, spark_save_table, spark_write_csv, spark_write_json, spark_write_parquet, spark_write_table