Notice this functionality requires the Spark connection sc to be instantiated with either
an explicitly specified Spark version (i.e.,
spark_connect(..., version = <version>, packages = c("avro", <other package(s)>), ...))
or a specific version of Spark avro package to use (e.g.,
spark_connect(..., packages = c("org.apache.spark:spark-avro_2.12:3.0.0", <other package(s)>), ...)).
spark_read_avro(
sc,
name = NULL,
path = name,
avro_schema = NULL,
ignore_extension = TRUE,
repartition = 0,
memory = TRUE,
overwrite = TRUE
)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.
Optional Avro schema in JSON format
If enabled, all files with and without .avro extension
are loaded (default: TRUE)
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?
Other Spark serialization routines:
collect_from_rds(),
spark_insert_table(),
spark_load_table(),
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_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()