spark_read_orc
Read a ORC file into a Spark DataFrame
Read a 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
- 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 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?
- columns
A vector of column names or a named vector of column types. If specified, the elements can be
"binary"
forBinaryType
,"boolean"
forBooleanType
,"byte"
forByteType
,"integer"
forIntegerType
,"integer64"
forLongType
,"double"
forDoubleType
,"character"
forStringType
,"timestamp"
forTimestampType
and"date"
forDateType
.- 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:
spark_load_table()
,
spark_read_avro()
,
spark_read_csv()
,
spark_read_delta()
,
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()