Read a CSV file into a Spark DataFrame
Read a tabular data file into a Spark DataFrame.
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, ... )
sc |
A |
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 |
columns |
A vector of column names or a named vector of column types.
If specified, the elements can be |
infer_schema |
Boolean; should column types be automatically inferred?
Requires one extra pass over the data. Defaults to |
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 |
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. |
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
When header is FALSE, the column names are generated with a
V prefix; e.g. V1, V2, ....
Other Spark serialization routines:
collect_from_rds(),
spark_load_table(),
spark_read_avro(),
spark_read_delta(),
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()
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.