sparklyr (version 0.3.5)

spark_read_parquet: Read a Parquet file into a Spark DataFrame

Description

Read a Parquet file into a Spark DataFrame

Usage

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

Arguments

sc

The Spark connection

name

Name of table

path

The path to the file. Needs to be accessible from the cluster. Supports: "hdfs://" or "s3n://"

options

A list of strings with additional options. See http://spark.apache.org/docs/latest/sql-programming-guide.html#configuration.

repartition

Total of partitions used to distribute table or 0 (default) to avoid partitioning

memory

Load data eagerly into memory

overwrite

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 reading and writing data: spark_read_csv, spark_read_json, spark_write_csv, spark_write_json, spark_write_parquet