Read a Parquet file into a Spark DataFrame.

  name = NULL,
  path = name,
  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


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. If specified, the elements can be "binary" for BinaryType, "boolean" for BooleanType, "byte" for ByteType, "integer" for IntegerType, "integer64" for LongType, "double" for DoubleType, "character" for StringType, "timestamp" for TimestampType and "date" for DateType.


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 driver options for the config key spark.driver.extraJavaOptions For instructions on how to configure s3n:// check the hadoop documentation: s3n authentication properties

See also