spark_read_csv

Read a CSV file into a Spark DataFrame

Description

Read a tabular data file into a Spark DataFrame.

Usage

spark_read_csv(
  sc,
  name = NULL,
  path = name,
  header = TRUE,
  columns = NULL,
  infer_schema = is.null(columns),
  delimiter = ",",
  quote = "\"",
  escape = "\\",
  charset = "UTF-8",
  null_value = NULL,
  options = list(),
  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. header | Boolean; should the first row of data be used as a header? Defaults to TRUE. columns | 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. infer_schema | Boolean; should column types be automatically inferred? Requires one extra pass over the data. Defaults to is.null(columns). delimiter | The character used to delimit each column. Defaults to ‘,’. quote | The character used as a quote. Defaults to ’“‘. escape | The character used to escape other characters. Defaults to’'. charset | The character set. Defaults to”UTF-8”. null_value | The character to use for null, or missing, values. Defaults to NULL. 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? … | Optional arguments; currently unused.

Details

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 https://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/credentials.htmlWorking 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: https://hadoop.apache.org/docs/stable/hadoop-aws/tools/hadoop-aws/index.html#Authentication_propertiess3n authentication properties

When header is FALSE, the column names are generated with a V prefix; e.g. V1, V2, ....

See Also

Other Spark serialization routines: collect_from_rds(), spark_load_table(), spark_read_avro(), spark_read_binary(), 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()