pyspark.SparkContext.sequenceFile

SparkContext.sequenceFile(path, keyClass=None, valueClass=None, keyConverter=None, valueConverter=None, minSplits=None, batchSize=0)[source]

Read a Hadoop SequenceFile with arbitrary key and value Writable class from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI. The mechanism is as follows:

  1. A Java RDD is created from the SequenceFile or other InputFormat, and the key and value Writable classes

  2. Serialization is attempted via Pyrolite pickling

  3. If this fails, the fallback is to call ‘toString’ on each key and value

  4. PickleSerializer is used to deserialize pickled objects on the Python side

Parameters
  • path – path to sequncefile

  • keyClass – fully qualified classname of key Writable class (e.g. “org.apache.hadoop.io.Text”)

  • valueClass – fully qualified classname of value Writable class (e.g. “org.apache.hadoop.io.LongWritable”)

  • keyConverter

  • valueConverter

  • minSplits – minimum splits in dataset (default min(2, sc.defaultParallelism))

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