spark_read_parquet

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Read a Parquet file into a Spark DataFrame

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
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_json, spark_save_table, spark_write_csv, spark_write_json, spark_write_parquet

Aliases
  • spark_read_parquet
Documentation reproduced from package sparklyr, version 0.5, License: Apache License 2.0 | file LICENSE

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