Read a CSV file into a Spark DataFrame
Read a tabular data file into a Spark DataFrame.
spark_read_csv(sc, name, path, header = TRUE, columns = NULL,
infer_schema = TRUE, delimiter = ",", quote = "\"", escape = "\\",
charset = "UTF-8", null_value = NULL, options = list(),
repartition = 0, memory = TRUE, overwrite = TRUE, ...)Arguments
| 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://", "s3n://" 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. |
| 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. |
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.
When header is FALSE, the column names are generated with a
V prefix; e.g. V1, V2, ....
See also
Other Spark serialization routines: spark_load_table,
spark_read_jdbc,
spark_read_json,
spark_read_parquet,
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