Read a Parquet file into a Spark DataFrame.
spark_read_parquet(sc, name, path, options = list(), repartition = 0,
memory = TRUE, overwrite = TRUE, columns = NULL, schema = NULL, ...)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.
A list of strings with additional options. See http://spark.apache.org/docs/latest/sql-programming-guide.html#configuration.
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?
A vector of column names or a named vector of column types.
A (java) read schema. Useful for optimizing read operation on nested data.
Optional arguments; currently unused.
You can read data from HDFS (hdfs://), S3 (s3a://), as well as
the local file system (file://).
If you are reading from a secure S3 bucket be sure to set the following in your spark-defaults.conf
spark.hadoop.fs.s3a.access.key, spark.hadoop.fs.s3a.secret.key or any of the methods outlined in the aws-sdk
documentation Working with AWS credentials
In order to work with the newer s3a:// protocol also set the values for spark.hadoop.fs.s3a.impl and spark.hadoop.fs.s3a.endpoint .
In addition, to support v4 of the S3 api be sure to pass the -Dcom.amazonaws.services.s3.enableV4 driver options
for the config key spark.driver.extraJavaOptions
For instructions on how to configure s3n:// check the hadoop documentation:
s3n authentication properties
Other Spark serialization routines: spark_load_table,
spark_read_csv,
spark_read_jdbc,
spark_read_json,
spark_read_libsvm,
spark_read_source,
spark_read_table,
spark_read_text,
spark_save_table,
spark_write_csv,
spark_write_jdbc,
spark_write_json,
spark_write_parquet,
spark_write_source,
spark_write_table,
spark_write_text