pyspark.SparkContext.hadoopFile¶
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SparkContext.hadoopFile(path, inputFormatClass, keyClass, valueClass, keyConverter=None, valueConverter=None, conf=None, batchSize=0)[source]¶ 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.
- pathstr
path to Hadoop file
- inputFormatClassstr
fully qualified classname of Hadoop InputFormat (e.g. “org.apache.hadoop.mapreduce.lib.input.TextInputFormat”)
- keyClassstr
fully qualified classname of key Writable class (e.g. “org.apache.hadoop.io.Text”)
- valueClassstr
fully qualified classname of value Writable class (e.g. “org.apache.hadoop.io.LongWritable”)
- 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)