spark_read_text

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Read a Text file into a Spark DataFrame

Read a text file into a Spark DataFrame.

Usage
spark_read_text(sc, name, path, 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://", "s3a://" and "file://" protocols.

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?

...

Optional arguments; currently unused.

Details

You can read data from HDFS (hdfs://), S3 (s3a://), as well as the local file system (file://).

If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults.conf spark.hadoop.fs.s3a.access.key, spark.hadoop.fs.s3a.secret.key or any of the methods outlined in the aws-sdk documentation Working with AWS credentials In order to work with the newer s3a:// protocol also set the values for spark.hadoop.fs.s3a.impl and spark.hadoop.fs.s3a.endpoint . In addition, to support v4 of the S3 api be sure to pass the -Dcom.amazonaws.services.s3.enableV4 driver options for the config key spark.driver.extraJavaOptions For instructions on how to configure s3n:// check the hadoop documentation: s3n authentication properties

See Also

Other Spark serialization routines: spark_load_table, spark_read_csv, spark_read_jdbc, spark_read_json, spark_read_libsvm, spark_read_orc, spark_read_parquet, spark_read_source, spark_read_table, spark_save_table, spark_write_csv, spark_write_jdbc, spark_write_json, spark_write_orc, spark_write_parquet, spark_write_source, spark_write_table, spark_write_text

Aliases
  • spark_read_text
Documentation reproduced from package sparklyr, version 0.9.2, License: Apache License 2.0 | file LICENSE

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