Read Apache Avro data into a Spark DataFrame.
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()