/***********************************************************************************************************************
* Copyright (C) 2010-2013 by the Stratosphere project (http://stratosphere.eu)
*
* Licensed 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 eu.stratosphere.compiler.costs;
import java.util.Iterator;
import eu.stratosphere.compiler.CompilerException;
import eu.stratosphere.compiler.dag.EstimateProvider;
import eu.stratosphere.compiler.dag.TempMode;
import eu.stratosphere.compiler.plan.Channel;
import eu.stratosphere.compiler.plan.PlanNode;
/**
* Abstract base class for a cost estimator. Defines cost estimation methods and implements the basic work
* method that computes the cost of an operator by adding input shipping cost, input local cost, and
* driver cost.
*/
public abstract class CostEstimator {
public abstract void addRandomPartitioningCost(EstimateProvider estimates, Costs costs);
public abstract void addHashPartitioningCost(EstimateProvider estimates, Costs costs);
public abstract void addRangePartitionCost(EstimateProvider estimates, Costs costs);
public abstract void addBroadcastCost(EstimateProvider estimates, int replicationFactor, Costs costs);
// ------------------------------------------------------------------------
public abstract void addFileInputCost(long fileSizeInBytes, Costs costs);
public abstract void addLocalSortCost(EstimateProvider estimates, long memorySize, Costs costs);
public abstract void addLocalMergeCost(EstimateProvider estimates1, EstimateProvider estimates2, long memorySize, Costs costs);
public abstract void addHybridHashCosts(EstimateProvider buildSide, EstimateProvider probeSide, long memorySize, Costs costs);
public abstract void addStreamedNestedLoopsCosts(EstimateProvider outerSide, EstimateProvider innerSide, long bufferSize, Costs costs);
public abstract void addBlockNestedLoopsCosts(EstimateProvider outerSide, EstimateProvider innerSide, long blockSize, Costs costs);
// ------------------------------------------------------------------------
public abstract void addArtificialDamCost(EstimateProvider estimates, long bufferSize, Costs costs);
// ------------------------------------------------------------------------
/**
* This method computes the cost of an operator. The cost is composed of cost for input shipping,
* locally processing an input, and running the operator.
*
* It requires at least that all inputs are set and have a proper ship strategy set,
* which is not equal to <tt>NONE</tt>.
*
* @param n The node to compute the costs for.
*/
public void costOperator(PlanNode n) {
// initialize costs objects with no costs
final Costs totalCosts = new Costs();
final long availableMemory = n.getGuaranteedAvailableMemory();
// add the shipping strategy costs
for (Iterator<Channel> channels = n.getInputs(); channels.hasNext(); ) {
final Channel channel = channels.next();
final Costs costs = new Costs();
// Plans that apply the same strategies, but at different points
// are equally expensive. For example, if a partitioning can be
// pushed below a Map function there is often no difference in plan
// costs between the pushed down version and the version that partitions
// after the Mapper. However, in those cases, we want the expensive
// strategy to appear later in the plan, as data reduction often occurs
// by large factors, while blowup is rare and typically by smaller fractions.
// We achieve this by adding a penalty to small penalty to the FORWARD strategy,
// weighted by the current plan depth (steps to the earliest data source).
// that way, later FORWARDS are more expensive than earlier forwards.
// Note that this only applies to the heuristic costs.
switch (channel.getShipStrategy()) {
case NONE:
throw new CompilerException(
"Cannot determine costs: Shipping strategy has not been set for an input.");
case FORWARD:
// costs.addHeuristicNetworkCost(channel.getMaxDepth());
break;
case PARTITION_LOCAL_HASH:
break;
case PARTITION_RANDOM:
addRandomPartitioningCost(channel, costs);
break;
case PARTITION_HASH:
addHashPartitioningCost(channel, costs);
break;
case PARTITION_RANGE:
addRangePartitionCost(channel, costs);
break;
case BROADCAST:
addBroadcastCost(channel, channel.getReplicationFactor(), costs);
break;
default:
throw new CompilerException("Unknown shipping strategy for input: " + channel.getShipStrategy());
}
switch (channel.getLocalStrategy()) {
case NONE:
break;
case SORT:
case COMBININGSORT:
addLocalSortCost(channel, availableMemory, costs);
break;
default:
throw new CompilerException("Unsupported local strategy for input: " + channel.getLocalStrategy());
}
if (channel.getTempMode() != null && channel.getTempMode() != TempMode.NONE) {
addArtificialDamCost(channel, 0, costs);
}
// adjust with the cost weight factor
if (channel.isOnDynamicPath()) {
costs.multiplyWith(channel.getCostWeight());
}
totalCosts.addCosts(costs);
}
Channel firstInput = null;
Channel secondInput = null;
Costs driverCosts = new Costs();
// get the inputs, if we have some
{
Iterator<Channel> channels = n.getInputs();
if (channels.hasNext()) {
firstInput = channels.next();
}
if (channels.hasNext()) {
secondInput = channels.next();
}
}
// determine the local costs
switch (n.getDriverStrategy()) {
case NONE:
case UNARY_NO_OP:
case BINARY_NO_OP:
case COLLECTOR_MAP:
case MAP:
case FLAT_MAP:
case ALL_GROUP_REDUCE:
case ALL_REDUCE:
// this operations does not do any actual grouping, since every element is in the same single group
case CO_GROUP:
case SORTED_GROUP_REDUCE:
case SORTED_REDUCE:
// grouping or co-grouping over sorted streams for free
case SORTED_GROUP_COMBINE:
// partial grouping is always local and main memory resident. we should add a relative cpu cost at some point
case UNION:
// pipelined local union is for free
break;
case MERGE:
addLocalMergeCost(firstInput, secondInput, availableMemory, driverCosts);
break;
case HYBRIDHASH_BUILD_FIRST:
addHybridHashCosts(firstInput, secondInput, availableMemory, driverCosts);
break;
case HYBRIDHASH_BUILD_SECOND:
addHybridHashCosts(secondInput, firstInput, availableMemory, driverCosts);
break;
case NESTEDLOOP_BLOCKED_OUTER_FIRST:
addBlockNestedLoopsCosts(firstInput, secondInput, availableMemory, driverCosts);
break;
case NESTEDLOOP_BLOCKED_OUTER_SECOND:
addBlockNestedLoopsCosts(secondInput, firstInput, availableMemory, driverCosts);
break;
case NESTEDLOOP_STREAMED_OUTER_FIRST:
addStreamedNestedLoopsCosts(firstInput, secondInput, availableMemory, driverCosts);
break;
case NESTEDLOOP_STREAMED_OUTER_SECOND:
addStreamedNestedLoopsCosts(secondInput, firstInput, availableMemory, driverCosts);
break;
default:
throw new CompilerException("Unknown local strategy: " + n.getDriverStrategy().name());
}
// adjust with the cost weight factor
if (n.isOnDynamicPath()) {
driverCosts.multiplyWith(n.getCostWeight());
}
totalCosts.addCosts(driverCosts);
n.setCosts(totalCosts);
}
}