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: spark_load_table
,
spark_read_csv
,
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_save_table
,
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