spark_read_csv {sparklyr} | R Documentation |
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, ...)
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. |
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, ...
.
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