sparklyr (version 0.2.26)

spark_read_csv: Read a CSV file into a Spark DataFrame

Description

Read a CSV file into a Spark DataFrame

Usage

spark_read_csv(sc, name, path, header = TRUE, delimiter = ",", quote = "\"", escape = "\\", charset = "UTF-8", null_value = NULL, 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://"

header

Should the first row of data be used as a header? Defaults to TRUE.

delimiter

The character used to delimit each column, defaults to ,.

quote

The character used as a quote, defaults to "hdfs://".

escape

The chatacter used to escape other characters, defaults to \.

charset

The character set, defaults to "UTF-8".

null_value

The character to use for default values, defaults to NULL.

options

A list of strings with additional options.

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

Value

Reference to a Spark DataFrame / dplyr tbl

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.

When header is FALSE, the column names are generated with a V prefix; e.g. V1, V2, ....

See Also

Other reading and writing data: spark_read_json, spark_read_parquet, spark_write_csv, spark_write_json, spark_write_parquet