SparkContext.hadoopFile(path: str, inputFormatClass: str, keyClass: str, valueClass: str, keyConverter: Optional[str] = None, valueConverter: Optional[str] = None, conf: Optional[Dict[str, str]] = None, batchSize: int = 0) → pyspark.rdd.RDD[Tuple[T, U]]

Read an ‘old’ Hadoop InputFormat with arbitrary key and value class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is the same as for SparkContext.sequenceFile().

A Hadoop configuration can be passed in as a Python dict. This will be converted into a Configuration in Java.


path to Hadoop file


fully qualified classname of Hadoop InputFormat (e.g. “org.apache.hadoop.mapreduce.lib.input.TextInputFormat”)


fully qualified classname of key Writable class (e.g. “”)


fully qualified classname of value Writable class (e.g. “”)

keyConverterstr, optional

fully qualified name of a function returning key WritableConverter (None by default)

valueConverterstr, optional

fully qualified name of a function returning value WritableConverter (None by default)

confdict, optional

Hadoop configuration, passed in as a dict (None by default)

batchSizeint, optional

The number of Python objects represented as a single Java object. (default 0, choose batchSize automatically)