/*
 * Grakn - A Distributed Semantic Database
 * Copyright (C) 2016  Grakn Labs Limited
 *
 * Grakn is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * Grakn is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with Grakn. If not, see <http://www.gnu.org/licenses/gpl.txt>.
 */

package ai.grakn.graph.internal.computer;

import com.google.common.base.Optional;
import org.apache.commons.configuration.Configuration;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFlatMapFunction;
import org.apache.tinkerpop.gremlin.hadoop.structure.HadoopGraph;
import org.apache.tinkerpop.gremlin.hadoop.structure.io.VertexWritable;
import org.apache.tinkerpop.gremlin.process.computer.GraphFilter;
import org.apache.tinkerpop.gremlin.process.computer.MapReduce;
import org.apache.tinkerpop.gremlin.process.computer.MessageCombiner;
import org.apache.tinkerpop.gremlin.process.computer.VertexComputeKey;
import org.apache.tinkerpop.gremlin.process.computer.VertexProgram;
import org.apache.tinkerpop.gremlin.process.computer.util.ComputerGraph;
import org.apache.tinkerpop.gremlin.process.computer.util.VertexProgramHelper;
import org.apache.tinkerpop.gremlin.spark.process.computer.CombineIterator;
import org.apache.tinkerpop.gremlin.spark.process.computer.MapIterator;
import org.apache.tinkerpop.gremlin.spark.process.computer.ReduceIterator;
import org.apache.tinkerpop.gremlin.spark.process.computer.SparkMessenger;
import org.apache.tinkerpop.gremlin.spark.process.computer.payload.MessagePayload;
import org.apache.tinkerpop.gremlin.spark.process.computer.payload.Payload;
import org.apache.tinkerpop.gremlin.spark.process.computer.payload.ViewIncomingPayload;
import org.apache.tinkerpop.gremlin.spark.process.computer.payload.ViewOutgoingPayload;
import org.apache.tinkerpop.gremlin.spark.process.computer.payload.ViewPayload;
import org.apache.tinkerpop.gremlin.structure.io.gryo.kryoshim.KryoShimServiceLoader;
import org.apache.tinkerpop.gremlin.structure.util.Attachable;
import org.apache.tinkerpop.gremlin.structure.util.detached.DetachedFactory;
import org.apache.tinkerpop.gremlin.structure.util.detached.DetachedVertexProperty;
import org.apache.tinkerpop.gremlin.structure.util.star.StarGraph;
import org.apache.tinkerpop.gremlin.util.iterator.IteratorUtils;
import scala.Tuple2;

import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Set;

/**
 * <p>
 * This is a modified version of Spark Executor.
 * We change its behaviour so it can work with our own graph computer.
 * </p>
 *
 * @author Jason Liu
 * @author Marko A. Rodriguez
 */
public class GraknSparkExecutor {

    private GraknSparkExecutor() {
    }

    //////////////////
    // DATA LOADING //
    //////////////////

    public static JavaPairRDD<Object, VertexWritable> applyGraphFilter(final JavaPairRDD<Object, VertexWritable> graphRDD, final GraphFilter graphFilter) {
        return graphRDD.mapPartitionsToPair(partitionIterator -> {
            final GraphFilter gFilter = graphFilter.clone();
            return () -> IteratorUtils.filter(partitionIterator, tuple -> (tuple._2().get().applyGraphFilter(gFilter)).isPresent());
        }, true);
    }


    ////////////////////
    // VERTEX PROGRAM //
    ////////////////////

    public static <M> JavaPairRDD<Object, ViewIncomingPayload<M>> executeVertexProgramIteration(
            final JavaPairRDD<Object, VertexWritable> graphRDD,
            final JavaPairRDD<Object, ViewIncomingPayload<M>> viewIncomingRDD,
            final GraknSparkMemory memory,
            final Configuration graphComputerConfiguration,    // has the Graph/GraphComputer.configuration() information
            final Configuration vertexProgramConfiguration) { // has the VertexProgram.loadState() information

        boolean partitionedGraphRDD = graphRDD.partitioner().isPresent();

        if (partitionedGraphRDD && null != viewIncomingRDD) // the graphRDD and the viewRDD must have the same partitioner
        {assert graphRDD.partitioner().get().equals(viewIncomingRDD.partitioner().get());}
        final JavaPairRDD<Object, ViewOutgoingPayload<M>> viewOutgoingRDD = ((null == viewIncomingRDD) ?
                graphRDD.mapValues(vertexWritable -> new Tuple2<>(vertexWritable, Optional.<ViewIncomingPayload<M>>absent())) : // first iteration will not have any views or messages
                graphRDD.leftOuterJoin(viewIncomingRDD))                                                   // every other iteration may have views and messages
                // for each partition of vertices emit a view and their outgoing messages
                .mapPartitionsToPair(partitionIterator -> {
                    KryoShimServiceLoader.applyConfiguration(graphComputerConfiguration);

                    // if the partition is empty, return without starting a new VP iteration
                    if (!partitionIterator.hasNext()){
                        return Collections.emptyList();}

                    final VertexProgram<M> workerVertexProgram = VertexProgram.createVertexProgram(HadoopGraph.open(graphComputerConfiguration), vertexProgramConfiguration); // each partition(Spark)/worker(TP3) has a local copy of the vertex program (a worker's task)
                    final String[] vertexComputeKeysArray = VertexProgramHelper.vertexComputeKeysAsArray(workerVertexProgram.getVertexComputeKeys()); // the compute keys as an array
                    final SparkMessenger<M> messenger = new SparkMessenger<>();

                    workerVertexProgram.workerIterationStart(memory.asImmutable()); // start the worker
                    return () -> IteratorUtils.map(partitionIterator, vertexViewIncoming -> {
                        final StarGraph.StarVertex vertex = vertexViewIncoming._2()._1().get(); // get the vertex from the vertex writable
                        final boolean hasViewAndMessages = vertexViewIncoming._2()._2().isPresent(); // if this is the first iteration, then there are no views or messages
                        final List<DetachedVertexProperty<Object>> previousView = hasViewAndMessages ? vertexViewIncoming._2()._2().get().getView() : memory.isInitialIteration() ? new ArrayList<>() : Collections.emptyList();
                        // revive compute properties if they already exist
                        if (memory.isInitialIteration() && vertexComputeKeysArray.length > 0){
                            vertex.properties(vertexComputeKeysArray).forEachRemaining(vertexProperty -> previousView.add(DetachedFactory.detach(vertexProperty, true)));}
                        // drop any computed properties that are cached in memory
                        vertex.dropVertexProperties(vertexComputeKeysArray);
                        final List<M> incomingMessages = hasViewAndMessages ? vertexViewIncoming._2()._2().get().getIncomingMessages() : Collections.emptyList();
                        IteratorUtils.removeOnNext(previousView.iterator()).forEachRemaining(property -> property.attach(Attachable.Method.create(vertex)));  // attach the view to the vertex
                        assert previousView.isEmpty();
                        // do the vertex's vertex program iteration
                        messenger.setVertexAndIncomingMessages(vertex, incomingMessages); // set the messenger with the incoming messages
                        workerVertexProgram.execute(ComputerGraph.vertexProgram(vertex, workerVertexProgram), messenger, memory); // execute the vertex program on this vertex for this iteration
                        // assert incomingMessages.isEmpty();  // maybe the program didn't read all the messages
                        incomingMessages.clear();
                        // detached the compute property view from the vertex
                        final List<DetachedVertexProperty<Object>> nextView = vertexComputeKeysArray.length == 0 ?  // not all vertex programs have compute keys
                                Collections.emptyList() :
                                IteratorUtils.list(IteratorUtils.map(vertex.properties(vertexComputeKeysArray), vertexProperty -> DetachedFactory.detach(vertexProperty, true)));
                        // drop compute property view as it has now been detached from the vertex
                        vertex.dropVertexProperties(vertexComputeKeysArray);
                        final List<Tuple2<Object, M>> outgoingMessages = messenger.getOutgoingMessages(); // get the outgoing messages being sent by this vertex
                        if (!partitionIterator.hasNext()) {
                            workerVertexProgram.workerIterationEnd(memory.asImmutable()); // if no more vertices in the partition, end the worker's iteration}
                        }return (nextView.isEmpty() && outgoingMessages.isEmpty()) ?
                                null : // if there is no view nor outgoing messages, emit nothing
                                new Tuple2<>(vertex.id(), new ViewOutgoingPayload<>(nextView, outgoingMessages));  // else, emit the vertex id, its view, and its outgoing messages
                    });
                }, true)  // true means that the partition is preserved
                .filter(tuple -> null != tuple); // if there are no messages or views, then the tuple is null (memory optimization)
        // the graphRDD and the viewRDD must have the same partitioner
        if (partitionedGraphRDD){
            assert graphRDD.partitioner().get().equals(viewOutgoingRDD.partitioner().get());}
        /////////////////////////////////////////////////////////////
        /////////////////////////////////////////////////////////////
        final PairFlatMapFunction<Tuple2<Object, ViewOutgoingPayload<M>>, Object, Payload> messageFunction =
                tuple -> () -> IteratorUtils.concat(
                        IteratorUtils.of(new Tuple2<>(tuple._1(), tuple._2().getView())),      // emit the view payload
                        IteratorUtils.map(tuple._2().getOutgoingMessages().iterator(), message -> new Tuple2<>(message._1(), new MessagePayload<>(message._2()))));
        final MessageCombiner<M> messageCombiner = VertexProgram.<VertexProgram<M>>createVertexProgram(HadoopGraph.open(vertexProgramConfiguration), vertexProgramConfiguration).getMessageCombiner().orElse(null);
        final Function2<Payload, Payload, Payload> reducerFunction = (a, b) -> {      // reduce the view and outgoing messages into a single payload object representing the new view and incoming messages for a vertex
            if (a instanceof ViewIncomingPayload) {
                ((ViewIncomingPayload<M>) a).mergePayload(b, messageCombiner);
                return a;
            } else if (b instanceof ViewIncomingPayload) {
                ((ViewIncomingPayload<M>) b).mergePayload(a, messageCombiner);
                return b;
            } else {
                final ViewIncomingPayload<M> c = new ViewIncomingPayload<>(messageCombiner);
                c.mergePayload(a, messageCombiner);
                c.mergePayload(b, messageCombiner);
                return c;
            }
        };
        /////////////////////////////////////////////////////////////
        /////////////////////////////////////////////////////////////
        // "message pass" by reducing on the vertex object id of the view and message payloads
        final JavaPairRDD<Object, ViewIncomingPayload<M>> newViewIncomingRDD =
                (partitionedGraphRDD ?
                        viewOutgoingRDD.flatMapToPair(messageFunction).reduceByKey(graphRDD.partitioner().get(), reducerFunction) :
                        viewOutgoingRDD.flatMapToPair(messageFunction).reduceByKey(reducerFunction))
                        .mapValues(payload -> { // handle various corner cases of when views don't exist, messages don't exist, or neither exists.
                            if (payload instanceof ViewIncomingPayload) {// this happens if there is a vertex view with incoming messages
                                return (ViewIncomingPayload<M>) payload;}
                            else if (payload instanceof ViewPayload)  {  // this happens if there is a vertex view with no incoming messages
                                return new ViewIncomingPayload<>((ViewPayload) payload);}
                            else  {                                      // this happens when there is a single message to a vertex that has no view or outgoing messages
                                return new ViewIncomingPayload<>((MessagePayload<M>) payload);}
                        });
        // the graphRDD and the viewRDD must have the same partitioner
        if (partitionedGraphRDD){
            assert graphRDD.partitioner().get().equals(newViewIncomingRDD.partitioner().get());}
        newViewIncomingRDD
                .foreachPartition(partitionIterator -> {
                    KryoShimServiceLoader.applyConfiguration(graphComputerConfiguration);
                }); // need to complete a task so its BSP and the memory for this iteration is updated
        return newViewIncomingRDD;
    }

    public static <M> JavaPairRDD<Object, VertexWritable> prepareFinalGraphRDD(
            final JavaPairRDD<Object, VertexWritable> graphRDD,
            final JavaPairRDD<Object, ViewIncomingPayload<M>> viewIncomingRDD,
            final Set<VertexComputeKey> vertexComputeKeys) {
        // the graphRDD and the viewRDD must have the same partitioner
        if (graphRDD.partitioner().isPresent()){
            assert (graphRDD.partitioner().get().equals(viewIncomingRDD.partitioner().get()));}
        final String[] vertexComputeKeysArray = VertexProgramHelper.vertexComputeKeysAsArray(vertexComputeKeys); // the compute keys as an array
        return graphRDD.leftOuterJoin(viewIncomingRDD)
                .mapValues(tuple -> {
                    final StarGraph.StarVertex vertex = tuple._1().get();
                    vertex.dropVertexProperties(vertexComputeKeysArray); // drop all existing compute keys
                    // attach the final computed view to the cached graph
                    final List<DetachedVertexProperty<Object>> view = tuple._2().isPresent() ? tuple._2().get().getView() : Collections.emptyList();
                    for (final DetachedVertexProperty<Object> property : view) {
                        if (!VertexProgramHelper.isTransientVertexComputeKey(property.key(), vertexComputeKeys)){
                            property.attach(Attachable.Method.create(vertex));}
                    }
                    return tuple._1();
                });
    }

    /////////////////
    // MAP REDUCE //
    ////////////////

    public static <K, V> JavaPairRDD<K, V> executeMap(
            final JavaPairRDD<Object, VertexWritable> graphRDD, final MapReduce<K, V, ?, ?, ?> mapReduce,
            final Configuration graphComputerConfiguration) {
        JavaPairRDD<K, V> mapRDD = graphRDD.mapPartitionsToPair(partitionIterator -> {
            KryoShimServiceLoader.applyConfiguration(graphComputerConfiguration);
            return () -> new MapIterator<>(MapReduce.<MapReduce<K, V, ?, ?, ?>>createMapReduce(HadoopGraph.open(graphComputerConfiguration), graphComputerConfiguration), partitionIterator);
        });
        if (mapReduce.getMapKeySort().isPresent()){
            mapRDD = mapRDD.sortByKey(mapReduce.getMapKeySort().get(), true, 1);}
        return mapRDD;
    }

    public static <K, V, OK, OV> JavaPairRDD<OK, OV> executeCombine(final JavaPairRDD<K, V> mapRDD,
                                                                    final Configuration graphComputerConfiguration) {
        return mapRDD.mapPartitionsToPair(partitionIterator -> {
            KryoShimServiceLoader.applyConfiguration(graphComputerConfiguration);
            return () -> new CombineIterator<>(MapReduce.<MapReduce<K, V, OK, OV, ?>>createMapReduce(HadoopGraph.open(graphComputerConfiguration), graphComputerConfiguration), partitionIterator);
        });
    }

    public static <K, V, OK, OV> JavaPairRDD<OK, OV> executeReduce(
            final JavaPairRDD<K, V> mapOrCombineRDD, final MapReduce<K, V, OK, OV, ?> mapReduce,
            final Configuration graphComputerConfiguration) {
        JavaPairRDD<OK, OV> reduceRDD = mapOrCombineRDD.groupByKey().mapPartitionsToPair(partitionIterator -> {
            KryoShimServiceLoader.applyConfiguration(graphComputerConfiguration);
            return () -> new ReduceIterator<>(MapReduce.<MapReduce<K, V, OK, OV, ?>>createMapReduce(HadoopGraph.open(graphComputerConfiguration), graphComputerConfiguration), partitionIterator);
        });
        if (mapReduce.getReduceKeySort().isPresent()){
            reduceRDD = reduceRDD.sortByKey(mapReduce.getReduceKeySort().get(), true, 1);}
        return reduceRDD;
    }
}