spark_read_avro
Read Apache Avro data into a Spark DataFrame.
Description
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)>), ...)
).
Usage
spark_read_avro(
sc,
name = NULL,
path = name,
avro_schema = NULL,
ignore_extension = TRUE,
repartition = 0,
memory = TRUE,
overwrite = TRUE
)
Arguments
Argument | Description |
---|---|
sc | A spark_connection . |
name | The name to assign to the newly generated table. |
path | The path to the file. Needs to be accessible from the cluster. |
Supports the “hdfs://”, “s3a://” and “file://” protocols. avro_schema | Optional Avro schema in JSON format ignore_extension | If enabled, all files with and without .avro extension are loaded (default: TRUE
) repartition | The number of partitions used to distribute the generated table. Use 0 (the default) to avoid partitioning. memory | Boolean; should the data be loaded eagerly into memory? (That is, should the table be cached?) overwrite | Boolean; overwrite the table with the given name if it already exists?
See Also
Other Spark serialization routines: collect_from_rds()
, 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()