Read from Delta Lake into a Spark DataFrame.
spark_read_delta(
sc,
path,
name = NULL,
version = NULL,
timestamp = NULL,
options = list(),
repartition = 0,
memory = TRUE,
overwrite = TRUE,
...
)A spark_connection.
The path to the file. Needs to be accessible from the cluster. Supports the "hdfs://", "s3a://" and "file://" protocols.
The name to assign to the newly generated table.
The version of the delta table to read.
The timestamp of the delta table to read. For example,
"2019-01-01" or "2019-01-01'T'00:00:00.000Z".
A list of strings with additional options.
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?
Optional arguments; currently unused.
Other Spark serialization routines:
collect_from_rds(),
spark_insert_table(),
spark_load_table(),
spark_read_avro(),
spark_read_binary(),
spark_read_csv(),
spark_read_image(),
spark_read_jdbc(),
spark_read_json(),
spark_read_libsvm(),
spark_read_orc(),
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()