spark_read_avro {sparklyr}

Read Apache Avro data into a Spark DataFrame.

Description

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

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_insert_table(), spark_load_table(), spark_read(), 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_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()