View source: R/data_interface.R
spark_read_json | R Documentation |
Read a table serialized in the JavaScript Object Notation format into a Spark DataFrame.
spark_read_json(
sc,
name = NULL,
path = name,
options = list(),
repartition = 0,
memory = TRUE,
overwrite = TRUE,
columns = NULL,
...
)
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. |
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? |
columns |
A vector of column names or a named vector of column types.
If specified, the elements can be |
... |
Optional arguments; currently unused. |
You can read data from HDFS (hdfs://
), S3 (s3a://
), as well as
the local file system (file://
).
Other Spark serialization routines:
collect_from_rds()
,
spark_insert_table()
,
spark_load_table()
,
spark_read()
,
spark_read_avro()
,
spark_read_binary()
,
spark_read_csv()
,
spark_read_delta()
,
spark_read_image()
,
spark_read_jdbc()
,
spark_read_libsvm()
,
spark_read_orc()
,
spark_read_parquet()
,
spark_read_source()
,
spark_read_table()
,
spark_read_text()
,
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()
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