spark_read_orc

Read a ORC file into a Spark DataFrame

Description

Read a https://orc.apache.org/ORC file into a Spark DataFrame.

Usage

spark_read_orc(
  sc,
  name = NULL,
  path = name,
  options = list(),
  repartition = 0,
  memory = TRUE,
  overwrite = TRUE,
  columns = NULL,
  schema = NULL,
  ...
)

Arguments

Argument Description
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.
options A list of strings with additional options. See https://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?
columns A vector of column names or a named vector of column types. If specified, the elements can be "binary" for BinaryType, "boolean" for BooleanType, "byte" for ByteType, "integer" for IntegerType, "integer64" for LongType, "double" for DoubleType, "character" for StringType, "timestamp" for TimestampType and "date" for DateType.
schema A (java) read schema. Useful for optimizing read operation on nested data.
Optional arguments; currently unused.

Details

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

See Also

Other Spark serialization routines: collect_from_rds(), spark_load_table(), spark_read_avro(), spark_read_binary(), spark_read_csv(), spark_read_delta(), spark_read_image(), spark_read_jdbc(), spark_read_json(), spark_read_libsvm(), spark_read_parquet(), spark_read_source(), spark_read_table(), spark_read_text(), spark_read(), spark_save_table(), spark_write_avro(), spark_write_csv(), spark_write_delta(), spark_write_jdbc(), spark_write_json(), spark_write_orc(), spark_write_parquet(), spark_write_source(), spark_write_table(), spark_write_text()