Read a table serialized in the http://www.json.org/ format into a Spark DataFrame.

spark_read_json(sc, name, path, options = list(), 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://", "s3n://" and "file://" protocols.
options
A list of strings with additional options.
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?

Details

You can read data from HDFS (hdfs://), S3 (s3n://), as well as the local file system (file://).

If you are reading from a secure S3 bucket be sure that the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables are both defined.

See also

Other Spark serialization routines: spark_load_table, spark_read_csv, spark_read_parquet, spark_save_table, spark_write_csv, spark_write_json, spark_write_parquet