/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing,
 * software distributed under the License is distributed on an
 * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
 * KIND, either express or implied.  See the License for the
 * specific language governing permissions and limitations
 * under the License.
 */
package com.datatorrent.apps.logstream;

import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import java.util.Map.Entry;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import org.apache.apex.malhar.lib.logs.DimensionObject;
import org.apache.commons.lang.mutable.MutableDouble;

import com.datatorrent.api.Context.OperatorContext;
import com.datatorrent.api.DefaultOutputPort;
import com.datatorrent.api.Operator.Unifier;

import com.datatorrent.apps.logstream.LogstreamUtil.AggregateOperation;

/**
 * Unifies the output of dimension operator for every window.
 *
 * @since 0.9.4
 */
public class DimensionOperatorUnifier implements Unifier<Map<String, DimensionObject<String>>>
{
  public final transient DefaultOutputPort<Map<String, DimensionObject<String>>> aggregationsOutput = new DefaultOutputPort<Map<String, DimensionObject<String>>>();

  Map<String, DimensionObject<String>> unifiedOutput;
  private Map<String, Map<String, Map<AggregateOperation, Number>>> unifiedCache = new HashMap<String, Map<String, Map<AggregateOperation, Number>>>();
  private transient boolean firstTuple = true;
  private HashMap<String, Number> recordType = new HashMap<String, Number>();
  private static final Logger logger = LoggerFactory.getLogger(DimensionOperatorUnifier.class);

  @Override
  public void process(Map<String, DimensionObject<String>> tuple)
  {
    if (firstTuple) {
      extractType(tuple);
      firstTuple = false;
    }

    // tuple will have one record each per metric type. currently 3 record one for each of SUM, COUNT, AVERAGE
    Iterator<Entry<String, DimensionObject<String>>> iterator = tuple.entrySet().iterator();
    String randomKey = null;
    String key = null;

    if (iterator.hasNext()) {
      randomKey = iterator.next().getKey();
      String[] split = randomKey.split("\\.");
      key = split[0];
    }

    String[] split = randomKey.split("\\|");
    Number receivedFilter = new Integer(split[3]);
    Number expectedFilter = recordType.get(LogstreamUtil.FILTER);

    if (!receivedFilter.equals(expectedFilter)) {
      logger.error("Unexpected tuple");
      logger.error("expected filter = {} received = {}", expectedFilter, receivedFilter);
    }

    computeAddition(tuple, AggregateOperation.SUM, key);
    computeAddition(tuple, AggregateOperation.COUNT, key);
    computeAverage(tuple, AggregateOperation.AVERAGE, key);
  }

  private void computeAddition(Map<String, DimensionObject<String>> tuple, AggregateOperation opType, String key)
  {
    String finalKey = key + "." + opType.name();
    if (tuple.containsKey(finalKey)) {

      DimensionObject<String> dimObj = tuple.get(finalKey);
      if (unifiedCache.containsKey(key)) {
        Map<String, Map<AggregateOperation, Number>> cacheAggrs = unifiedCache.get(key);
        if (cacheAggrs.containsKey(dimObj.getVal())) {
          Map<AggregateOperation, Number> cacheAggr = cacheAggrs.get(dimObj.getVal());
          if (cacheAggr.containsKey(opType)) {
            double cacheVal = cacheAggr.get(opType).doubleValue();
            double newVal = dimObj.getCount().doubleValue();
            double finalVal = cacheVal + newVal;

            cacheAggr.put(opType, finalVal);
          }
          else {
            cacheAggr.put(opType, dimObj.getCount().doubleValue());
          }
        }
        else {
          Map<AggregateOperation, Number> newAggrs = new HashMap<AggregateOperation, Number>();
          newAggrs.put(opType, dimObj.getCount().doubleValue());
          cacheAggrs.put(dimObj.getVal(), newAggrs);
        }
      }
      else {
        Map<AggregateOperation, Number> newAggrs = new HashMap<AggregateOperation, Number>();
        Map<String, Map<AggregateOperation, Number>> cacheAggrs = new HashMap<String, Map<AggregateOperation, Number>>();

        newAggrs.put(opType, dimObj.getCount().doubleValue());
        cacheAggrs.put(dimObj.getVal(), newAggrs);
        unifiedCache.put(key, cacheAggrs);
      }
    }

  }

  private void computeAverage(Map<String, DimensionObject<String>> tuple, AggregateOperation opType, String key)
  {
    String finalKey = key + "." + opType.name();
    if (tuple.containsKey(finalKey)) {

      DimensionObject<String> dimObj = tuple.get(finalKey);
      if (unifiedCache.containsKey(key)) {
        Map<String, Map<AggregateOperation, Number>> cacheAggrs = unifiedCache.get(key);
        if (cacheAggrs.containsKey(dimObj.getVal())) {
          Map<AggregateOperation, Number> cacheAggr = cacheAggrs.get(dimObj.getVal());
          if (cacheAggr.containsKey(opType)) {
            double cacheAvg = cacheAggr.get(opType).doubleValue();
            double cacheCount = cacheAggr.get(AggregateOperation.COUNT).doubleValue(); // this is total count, should be computed before calling average

            double newAvg = dimObj.getCount().doubleValue();
            double newCount = tuple.get(key + "." + AggregateOperation.COUNT.name()).getCount().doubleValue();

            double finalVal = (cacheAvg * (cacheCount - newCount) + newAvg * newCount) / cacheCount;

            cacheAggr.put(opType, finalVal);
          }
          else {
            cacheAggr.put(opType, dimObj.getCount().doubleValue());
          }
        }
        else {
          Map<AggregateOperation, Number> newAggrs = new HashMap<AggregateOperation, Number>();
          newAggrs.put(opType, dimObj.getCount().doubleValue());
          cacheAggrs.put(dimObj.getVal(), newAggrs);
        }
      }
      else {
        Map<AggregateOperation, Number> newAggrs = new HashMap<AggregateOperation, Number>();
        Map<String, Map<AggregateOperation, Number>> cacheAggrs = new HashMap<String, Map<AggregateOperation, Number>>();

        newAggrs.put(opType, dimObj.getCount().doubleValue());
        cacheAggrs.put(dimObj.getVal(), newAggrs);
        unifiedCache.put(key, cacheAggrs);
      }
    }

  }

  @Override
  public void beginWindow(long l)
  {
    unifiedCache = new HashMap<String, Map<String, Map<AggregateOperation, Number>>>();
  }

  @Override
  public void endWindow()
  {
    Map<String, DimensionObject<String>> outputAggregationsObject;

    for (Entry<String, Map<String, Map<AggregateOperation, Number>>> keys : unifiedCache.entrySet()) {
      String key = keys.getKey();
      Map<String, Map<AggregateOperation, Number>> dimValues = keys.getValue();

      for (Entry<String, Map<AggregateOperation, Number>> dimValue : dimValues.entrySet()) {
        String dimValueName = dimValue.getKey();
        Map<AggregateOperation, Number> operations = dimValue.getValue();

        outputAggregationsObject = new HashMap<String, DimensionObject<String>>();

        for (Entry<AggregateOperation, Number> operation : operations.entrySet()) {
          AggregateOperation aggrOperationType = operation.getKey();
          Number aggr = operation.getValue();

          String outKey = key + "." + aggrOperationType.name();
          DimensionObject<String> outDimObj = new DimensionObject<String>(new MutableDouble(aggr), dimValueName);

          outputAggregationsObject.put(outKey, outDimObj);

        }
        aggregationsOutput.emit(outputAggregationsObject);
      }

    }
  }

  @Override
  public void setup(OperatorContext t1)
  {
  }

  @Override
  public void teardown()
  {
  }

  private void extractType(Map<String, DimensionObject<String>> tuple)
  {
    Iterator<Entry<String, DimensionObject<String>>> iterator = tuple.entrySet().iterator();
    String randomKey = null;

    if (iterator.hasNext()) {
      randomKey = iterator.next().getKey();
    }
    String[] split = randomKey.split("\\|");
    Number filterId = new Integer(split[3]);

    recordType.put(LogstreamUtil.FILTER, filterId);
  }

}