Read a Parquet file into a Spark DataFrame.
spark_read_parquet(
sc,
name = NULL,
path = name,
options = list(),
repartition = 0,
memory = TRUE,
overwrite = TRUE,
columns = NULL,
schema = NULL,
...
)A spark_connection.
The name to assign to the newly generated table.
The path to the file. Needs to be accessible from the cluster. Supports the "hdfs://", "s3a://" and "file://" protocols.
A list of strings with additional options. See https://spark.apache.org/docs/latest/sql-programming-guide.html#configuration.
The number of partitions used to distribute the generated table. Use 0 (the default) to avoid partitioning.
Boolean; should the data be loaded eagerly into memory? (That is, should the table be cached?)
Boolean; overwrite the table with the given name if it already exists?
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.
A (java) read schema. Useful for optimizing read operation on nested data.
Optional arguments; currently unused.
You can read data from HDFS (hdfs://), S3 (s3a://), as well as
the local file system (file://).
Other Spark serialization routines:
collect_from_rds(),
spark_insert_table(),
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_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()