#!/usr/bin/env python

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# to you under the Apache License, Version 2.0 (the
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from __future__ import absolute_import, division, print_function

import logging

import avro.tether.tether_task

__all__ = ["WordCountTask"]


# TODO::Make the logging level a parameter we can set
# logging.basicConfig(level=logging.INFO)
class WordCountTask(avro.tether.tether_task.TetherTask):
    """
    Implements the mapper and reducer for the word count example
    """

    def __init__(self):
        """
        """

        inschema = """{"type":"string"}"""
        midschema = """{"type":"record", "name":"Pair","namespace":"org.apache.avro.mapred","fields":[
              {"name":"key","type":"string"},
              {"name":"value","type":"long","order":"ignore"}]
              }"""
        outschema = midschema
        avro.tether.tether_task.TetherTask.__init__(self, inschema, midschema, outschema)

        # keep track of the partial sums of the counts
        self.psum = 0

    def map(self, record, collector):
        """Implement the mapper for the word count example

        Parameters
        ----------------------------------------------------------------------------
        record - The input record
        collector - The collector to collect the output
        """

        words = record.split()

        for w in words:
            logging.info("WordCountTask.Map: word={0}".format(w))
            collector.collect({"key": w, "value": 1})

    def reduce(self, record, collector):
        """Called with input values to generate reducer output. Inputs are sorted by the mapper
        key.

        The reduce function is invoked once for each value belonging to a given key outputted
        by the mapper.

        Parameters
        ----------------------------------------------------------------------------
        record - The mapper output
        collector - The collector to collect the output
        """

        self.psum += record["value"]

    def reduceFlush(self, record, collector):
        """
        Called with the last intermediate value in each equivalence run.
        In other words, reduceFlush is invoked once for each key produced in the reduce
        phase. It is called after reduce has been invoked on each value for the given key.

        Parameters
        ------------------------------------------------------------------
        record - the last record on which reduce was invoked.
        """

        # collect the current record
        logging.info("WordCountTask.reduceFlush key={0} value={1}".format(record["key"], self.psum))

        collector.collect({"key": record["key"], "value": self.psum})

        # reset the sum
        self.psum = 0