spark_read_source

Read from a generic source into a Spark DataFrame.

Description

Read from a generic source into a Spark DataFrame.

Usage

spark_read_source(
  sc,
  name = NULL,
  path = name,
  source,
  options = list(),
  repartition = 0,
  memory = TRUE,
  overwrite = TRUE,
  columns = 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. source | A data source capable of reading data. 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. … | Optional arguments; currently unused.

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_orc(), spark_read_parquet(), 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()