/*
* 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 org.apache.flink.compiler;
import java.util.ArrayDeque;
import java.util.ArrayList;
import java.util.Deque;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.Set;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.apache.flink.api.common.InvalidProgramException;
import org.apache.flink.api.common.Plan;
import org.apache.flink.api.common.operators.GenericDataSinkBase;
import org.apache.flink.api.common.operators.GenericDataSourceBase;
import org.apache.flink.api.common.operators.Operator;
import org.apache.flink.api.common.operators.Union;
import org.apache.flink.api.common.operators.base.BulkIterationBase;
import org.apache.flink.api.common.operators.base.CoGroupOperatorBase;
import org.apache.flink.api.common.operators.base.CrossOperatorBase;
import org.apache.flink.api.common.operators.base.DeltaIterationBase;
import org.apache.flink.api.common.operators.base.FilterOperatorBase;
import org.apache.flink.api.common.operators.base.FlatMapOperatorBase;
import org.apache.flink.api.common.operators.base.GroupReduceOperatorBase;
import org.apache.flink.api.common.operators.base.JoinOperatorBase;
import org.apache.flink.api.common.operators.base.MapOperatorBase;
import org.apache.flink.api.common.operators.base.MapPartitionOperatorBase;
import org.apache.flink.api.common.operators.base.PartitionOperatorBase;
import org.apache.flink.api.common.operators.base.ReduceOperatorBase;
import org.apache.flink.api.common.operators.base.BulkIterationBase.PartialSolutionPlaceHolder;
import org.apache.flink.api.common.operators.base.DeltaIterationBase.SolutionSetPlaceHolder;
import org.apache.flink.api.common.operators.base.DeltaIterationBase.WorksetPlaceHolder;
import org.apache.flink.compiler.costs.CostEstimator;
import org.apache.flink.compiler.costs.DefaultCostEstimator;
import org.apache.flink.compiler.dag.BinaryUnionNode;
import org.apache.flink.compiler.dag.BulkIterationNode;
import org.apache.flink.compiler.dag.BulkPartialSolutionNode;
import org.apache.flink.compiler.dag.CoGroupNode;
import org.apache.flink.compiler.dag.CollectorMapNode;
import org.apache.flink.compiler.dag.CrossNode;
import org.apache.flink.compiler.dag.DataSinkNode;
import org.apache.flink.compiler.dag.DataSourceNode;
import org.apache.flink.compiler.dag.FilterNode;
import org.apache.flink.compiler.dag.FlatMapNode;
import org.apache.flink.compiler.dag.GroupReduceNode;
import org.apache.flink.compiler.dag.IterationNode;
import org.apache.flink.compiler.dag.MapNode;
import org.apache.flink.compiler.dag.MapPartitionNode;
import org.apache.flink.compiler.dag.MatchNode;
import org.apache.flink.compiler.dag.OptimizerNode;
import org.apache.flink.compiler.dag.PactConnection;
import org.apache.flink.compiler.dag.PartitionNode;
import org.apache.flink.compiler.dag.ReduceNode;
import org.apache.flink.compiler.dag.SinkJoiner;
import org.apache.flink.compiler.dag.SolutionSetNode;
import org.apache.flink.compiler.dag.TempMode;
import org.apache.flink.compiler.dag.WorksetIterationNode;
import org.apache.flink.compiler.dag.WorksetNode;
import org.apache.flink.compiler.deadlockdetect.DeadlockPreventer;
import org.apache.flink.compiler.plan.BinaryUnionPlanNode;
import org.apache.flink.compiler.plan.BulkIterationPlanNode;
import org.apache.flink.compiler.plan.BulkPartialSolutionPlanNode;
import org.apache.flink.compiler.plan.Channel;
import org.apache.flink.compiler.plan.IterationPlanNode;
import org.apache.flink.compiler.plan.NAryUnionPlanNode;
import org.apache.flink.compiler.plan.OptimizedPlan;
import org.apache.flink.compiler.plan.PlanNode;
import org.apache.flink.compiler.plan.SinkJoinerPlanNode;
import org.apache.flink.compiler.plan.SinkPlanNode;
import org.apache.flink.compiler.plan.SolutionSetPlanNode;
import org.apache.flink.compiler.plan.SourcePlanNode;
import org.apache.flink.compiler.plan.WorksetIterationPlanNode;
import org.apache.flink.compiler.plan.WorksetPlanNode;
import org.apache.flink.compiler.postpass.OptimizerPostPass;
import org.apache.flink.configuration.ConfigConstants;
import org.apache.flink.configuration.GlobalConfiguration;
import org.apache.flink.runtime.operators.shipping.ShipStrategyType;
import org.apache.flink.runtime.operators.util.LocalStrategy;
import org.apache.flink.util.InstantiationUtil;
import org.apache.flink.util.Visitor;
/**
* The optimizer that takes the user specified program plan and creates an optimized plan that contains
* exact descriptions about how the physical execution will take place. It first translates the user
* program into an internal optimizer representation and then chooses between different alternatives
* for shipping strategies and local strategies.
* <p>
* The basic principle is taken from optimizer works in systems such as Volcano/Cascades and Selinger/System-R/DB2. The
* optimizer walks from the sinks down, generating interesting properties, and ascends from the sources generating
* alternative plans, pruning against the interesting properties.
* <p>
* The optimizer also assigns the memory to the individual tasks. This is currently done in a very simple fashion: All
* sub-tasks that need memory (e.g. reduce or join) are given an equal share of memory.
*/
public class PactCompiler {
// ------------------------------------------------------------------------
// Constants
// ------------------------------------------------------------------------
/**
* Compiler hint key for the input channel's shipping strategy. This String is a key to the operator's stub
* parameters. The corresponding value tells the compiler which shipping strategy to use for the input channel.
* If the operator has two input channels, the shipping strategy is applied to both input channels.
*/
public static final String HINT_SHIP_STRATEGY = "INPUT_SHIP_STRATEGY";
/**
* Compiler hint key for the <b>first</b> input channel's shipping strategy. This String is a key to
* the operator's stub parameters. The corresponding value tells the compiler which shipping strategy
* to use for the <b>first</b> input channel. Only applicable to operators with two inputs.
*/
public static final String HINT_SHIP_STRATEGY_FIRST_INPUT = "INPUT_LEFT_SHIP_STRATEGY";
/**
* Compiler hint key for the <b>second</b> input channel's shipping strategy. This String is a key to
* the operator's stub parameters. The corresponding value tells the compiler which shipping strategy
* to use for the <b>second</b> input channel. Only applicable to operators with two inputs.
*/
public static final String HINT_SHIP_STRATEGY_SECOND_INPUT = "INPUT_RIGHT_SHIP_STRATEGY";
/**
* Value for the shipping strategy compiler hint that enforces a <b>Forward</b> strategy on the
* input channel, i.e. no redistribution of any kind.
*
* @see #HINT_SHIP_STRATEGY
* @see #HINT_SHIP_STRATEGY_FIRST_INPUT
* @see #HINT_SHIP_STRATEGY_SECOND_INPUT
*/
public static final String HINT_SHIP_STRATEGY_FORWARD = "SHIP_FORWARD";
/**
* Value for the shipping strategy compiler hint that enforces a random repartition strategy.
*
* @see #HINT_SHIP_STRATEGY
* @see #HINT_SHIP_STRATEGY_FIRST_INPUT
* @see #HINT_SHIP_STRATEGY_SECOND_INPUT
*/
public static final String HINT_SHIP_STRATEGY_REPARTITION= "SHIP_REPARTITION";
/**
* Value for the shipping strategy compiler hint that enforces a hash-partition strategy.
*
* @see #HINT_SHIP_STRATEGY
* @see #HINT_SHIP_STRATEGY_FIRST_INPUT
* @see #HINT_SHIP_STRATEGY_SECOND_INPUT
*/
public static final String HINT_SHIP_STRATEGY_REPARTITION_HASH = "SHIP_REPARTITION_HASH";
/**
* Value for the shipping strategy compiler hint that enforces a range-partition strategy.
*
* @see #HINT_SHIP_STRATEGY
* @see #HINT_SHIP_STRATEGY_FIRST_INPUT
* @see #HINT_SHIP_STRATEGY_SECOND_INPUT
*/
public static final String HINT_SHIP_STRATEGY_REPARTITION_RANGE = "SHIP_REPARTITION_RANGE";
/**
* Value for the shipping strategy compiler hint that enforces a <b>broadcast</b> strategy on the
* input channel.
*
* @see #HINT_SHIP_STRATEGY
* @see #HINT_SHIP_STRATEGY_FIRST_INPUT
* @see #HINT_SHIP_STRATEGY_SECOND_INPUT
*/
public static final String HINT_SHIP_STRATEGY_BROADCAST = "SHIP_BROADCAST";
/**
* Compiler hint key for the operator's local strategy. This String is a key to the operator's stub
* parameters. The corresponding value tells the compiler which local strategy to use to process the
* data inside one partition.
* <p>
* This hint is ignored by operators that do not have a local strategy (such as <i>Map</i>), or by operators that
* have no choice in their local strategy (such as <i>Cross</i>).
*/
public static final String HINT_LOCAL_STRATEGY = "LOCAL_STRATEGY";
/**
* Value for the local strategy compiler hint that enforces a <b>sort based</b> local strategy.
* For example, a <i>Reduce</i> operator will sort the data to group it.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_SORT = "LOCAL_STRATEGY_SORT";
/**
* Value for the local strategy compiler hint that enforces a <b>sort based</b> local strategy.
* During sorting a combine method is repeatedly applied to reduce the data volume.
* For example, a <i>Reduce</i> operator will sort the data to group it.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_COMBINING_SORT = "LOCAL_STRATEGY_COMBINING_SORT";
/**
* Value for the local strategy compiler hint that enforces a <b>sort merge based</b> local strategy on both
* inputs with subsequent merging of inputs.
* For example, a <i>Match</i> or <i>CoGroup</i> operator will use a sort-merge strategy to find pairs
* of matching keys.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_SORT_BOTH_MERGE = "LOCAL_STRATEGY_SORT_BOTH_MERGE";
/**
* Value for the local strategy compiler hint that enforces a <b>sort merge based</b> local strategy.
* The the first input is sorted, the second input is assumed to be sorted. After sorting both inputs are merged.
* For example, a <i>Match</i> or <i>CoGroup</i> operator will use a sort-merge strategy to find pairs
* of matching keys.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_SORT_FIRST_MERGE = "LOCAL_STRATEGY_SORT_FIRST_MERGE";
/**
* Value for the local strategy compiler hint that enforces a <b>sort merge based</b> local strategy.
* The the second input is sorted, the first input is assumed to be sorted. After sorting both inputs are merged.
* For example, a <i>Match</i> or <i>CoGroup</i> operator will use a sort-merge strategy to find pairs
* of matching keys.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_SORT_SECOND_MERGE = "LOCAL_STRATEGY_SORT_SECOND_MERGE";
/**
* Value for the local strategy compiler hint that enforces a <b>merge based</b> local strategy.
* Both inputs are assumed to be sorted and are merged.
* For example, a <i>Match</i> or <i>CoGroup</i> operator will use a merge strategy to find pairs
* of matching keys.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_MERGE = "LOCAL_STRATEGY_MERGE";
/**
* Value for the local strategy compiler hint that enforces a <b>hash based</b> local strategy.
* For example, a <i>Match</i> operator will use a hybrid-hash-join strategy to find pairs of
* matching keys. The <b>first</b> input will be used to build the hash table, the second input will be
* used to probe the table.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_HASH_BUILD_FIRST = "LOCAL_STRATEGY_HASH_BUILD_FIRST";
/**
* Value for the local strategy compiler hint that enforces a <b>hash based</b> local strategy.
* For example, a <i>Match</i> operator will use a hybrid-hash-join strategy to find pairs of
* matching keys. The <b>second</b> input will be used to build the hash table, the first input will be
* used to probe the table.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_HASH_BUILD_SECOND = "LOCAL_STRATEGY_HASH_BUILD_SECOND";
/**
* Value for the local strategy compiler hint that chooses the outer side of the <b>nested-loop</b> local strategy.
* A <i>Cross</i> operator will process the data of the <b>first</b> input in the outer-loop of the nested loops.
* Hence, the data of the first input will be is streamed though, while the data of the second input is stored on
* disk
* and repeatedly read.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_NESTEDLOOP_STREAMED_OUTER_FIRST = "LOCAL_STRATEGY_NESTEDLOOP_STREAMED_OUTER_FIRST";
/**
* Value for the local strategy compiler hint that chooses the outer side of the <b>nested-loop</b> local strategy.
* A <i>Cross</i> operator will process the data of the <b>second</b> input in the outer-loop of the nested loops.
* Hence, the data of the second input will be is streamed though, while the data of the first input is stored on
* disk
* and repeatedly read.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_NESTEDLOOP_STREAMED_OUTER_SECOND = "LOCAL_STRATEGY_NESTEDLOOP_STREAMED_OUTER_SECOND";
/**
* Value for the local strategy compiler hint that chooses the outer side of the <b>nested-loop</b> local strategy.
* A <i>Cross</i> operator will process the data of the <b>first</b> input in the outer-loop of the nested loops.
* Further more, the first input, being the outer side, will be processed in blocks, and for each block, the second
* input,
* being the inner side, will read repeatedly from disk.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_NESTEDLOOP_BLOCKED_OUTER_FIRST = "LOCAL_STRATEGY_NESTEDLOOP_BLOCKED_OUTER_FIRST";
/**
* Value for the local strategy compiler hint that chooses the outer side of the <b>nested-loop</b> local strategy.
* A <i>Cross</i> operator will process the data of the <b>second</b> input in the outer-loop of the nested loops.
* Further more, the second input, being the outer side, will be processed in blocks, and for each block, the first
* input,
* being the inner side, will read repeatedly from disk.
*
* @see #HINT_LOCAL_STRATEGY
*/
public static final String HINT_LOCAL_STRATEGY_NESTEDLOOP_BLOCKED_OUTER_SECOND = "LOCAL_STRATEGY_NESTEDLOOP_BLOCKED_OUTER_SECOND";
/**
* The log handle that is used by the compiler to log messages.
*/
public static final Logger LOG = LoggerFactory.getLogger(PactCompiler.class);
// ------------------------------------------------------------------------
// Members
// ------------------------------------------------------------------------
/**
* The statistics object used to obtain statistics, such as input sizes,
* for the cost estimation process.
*/
private final DataStatistics statistics;
/**
* The cost estimator used by the compiler.
*/
private final CostEstimator costEstimator;
/**
* The default degree of parallelism for jobs compiled by this compiler.
*/
private int defaultDegreeOfParallelism;
// ------------------------------------------------------------------------
// Constructor & Setup
// ------------------------------------------------------------------------
/**
* Creates a new compiler instance. The compiler has no access to statistics about the
* inputs and can hence not determine any properties. It will perform all optimization with
* unknown sizes and default to the most robust execution strategies. The
* compiler also uses conservative default estimates for the operator costs, since
* it has no access to another cost estimator.
* <p>
* The address of the job manager (to obtain system characteristics) is determined via the global configuration.
*/
public PactCompiler() {
this(null, new DefaultCostEstimator());
}
/**
* Creates a new compiler instance that uses the statistics object to determine properties about the input.
* Given those statistics, the compiler can make better choices for the execution strategies.
* as if no filesystem was given. The compiler uses conservative default estimates for the operator costs, since
* it has no access to another cost estimator.
* <p>
* The address of the job manager (to obtain system characteristics) is determined via the global configuration.
*
* @param stats
* The statistics to be used to determine the input properties.
*/
public PactCompiler(DataStatistics stats) {
this(stats, new DefaultCostEstimator());
}
/**
* Creates a new compiler instance. The compiler has no access to statistics about the
* inputs and can hence not determine any properties. It will perform all optimization with
* unknown sizes and default to the most robust execution strategies. It uses
* however the given cost estimator to compute the costs of the individual operations.
* <p>
* The address of the job manager (to obtain system characteristics) is determined via the global configuration.
*
* @param estimator
* The <tt>CostEstimator</tt> to use to cost the individual operations.
*/
public PactCompiler(CostEstimator estimator) {
this(null, estimator);
}
/**
* Creates a new compiler instance that uses the statistics object to determine properties about the input.
* Given those statistics, the compiler can make better choices for the execution strategies.
* as if no filesystem was given. It uses the given cost estimator to compute the costs of the individual
* operations.
* <p>
* The address of the job manager (to obtain system characteristics) is determined via the global configuration.
*
* @param stats
* The statistics to be used to determine the input properties.
* @param estimator
* The <tt>CostEstimator</tt> to use to cost the individual operations.
*/
public PactCompiler(DataStatistics stats, CostEstimator estimator) {
this.statistics = stats;
this.costEstimator = estimator;
// determine the default parallelization degree
this.defaultDegreeOfParallelism = GlobalConfiguration.getInteger(ConfigConstants.DEFAULT_PARALLELIZATION_DEGREE_KEY,
ConfigConstants.DEFAULT_PARALLELIZATION_DEGREE);
if (defaultDegreeOfParallelism < 1) {
LOG.warn("Config value " + defaultDegreeOfParallelism + " for option "
+ ConfigConstants.DEFAULT_PARALLELIZATION_DEGREE + " is invalid. Ignoring and using a value of 1.");
this.defaultDegreeOfParallelism = 1;
}
}
// ------------------------------------------------------------------------
// Getters / Setters
// ------------------------------------------------------------------------
public int getDefaultDegreeOfParallelism() {
return defaultDegreeOfParallelism;
}
public void setDefaultDegreeOfParallelism(int defaultDegreeOfParallelism) {
if (defaultDegreeOfParallelism > 0) {
this.defaultDegreeOfParallelism = defaultDegreeOfParallelism;
} else {
throw new IllegalArgumentException("Default parallelism cannot be zero or negative.");
}
}
// ------------------------------------------------------------------------
// Compilation
// ------------------------------------------------------------------------
/**
* Translates the given plan in to an OptimizedPlan, where all nodes have their local strategy assigned
* and all channels have a shipping strategy assigned. The compiler connects to the job manager to obtain information
* about the available instances and their memory and then chooses an instance type to schedule the execution on.
* <p>
* The compilation process itself goes through several phases:
* <ol>
* <li>Create an optimizer data flow representation of the program, assign parallelism and compute size estimates.</li>
* <li>Compute interesting properties and auxiliary structures.</li>
* <li>Enumerate plan alternatives. This cannot be done in the same step as the interesting property computation (as
* opposed to the Database approaches), because we support plans that are not trees.</li>
* </ol>
*
* @param program The program to be translated.
* @return The optimized plan.
* @throws CompilerException
* Thrown, if the plan is invalid or the optimizer encountered an inconsistent
* situation during the compilation process.
*/
public OptimizedPlan compile(Plan program) throws CompilerException {
// -------------------- try to get the connection to the job manager ----------------------
// --------------------------to obtain instance information --------------------------------
final OptimizerPostPass postPasser = getPostPassFromPlan(program);
return compile(program, postPasser);
}
/**
* Translates the given pact plan in to an OptimizedPlan, where all nodes have their local strategy assigned
* and all channels have a shipping strategy assigned. The process goes through several phases:
* <ol>
* <li>Create <tt>OptimizerNode</tt> representations of the PACTs, assign parallelism and compute size estimates.</li>
* <li>Compute interesting properties and auxiliary structures.</li>
* <li>Enumerate plan alternatives. This cannot be done in the same step as the interesting property computation (as
* opposed to the Database approaches), because we support plans that are not trees.</li>
* </ol>
*
* @param program The program to be translated.
* @param postPasser The function to be used for post passing the optimizer's plan and setting the
* data type specific serialization routines.
* @return The optimized plan.
*
* @throws CompilerException
* Thrown, if the plan is invalid or the optimizer encountered an inconsistent
* situation during the compilation process.
*/
private OptimizedPlan compile(Plan program, OptimizerPostPass postPasser) throws CompilerException {
if (program == null || postPasser == null) {
throw new NullPointerException();
}
if (LOG.isDebugEnabled()) {
LOG.debug("Beginning compilation of program '" + program.getJobName() + '\'');
}
// set the default degree of parallelism
int defaultParallelism = program.getDefaultParallelism() > 0 ?
program.getDefaultParallelism() : this.defaultDegreeOfParallelism;
// log the output
if (LOG.isDebugEnabled()) {
LOG.debug("Using a default degree of parallelism of " + defaultParallelism + '.');
}
// the first step in the compilation is to create the optimizer plan representation
// this step does the following:
// 1) It creates an optimizer plan node for each operator
// 2) It connects them via channels
// 3) It looks for hints about local strategies and channel types and
// sets the types and strategies accordingly
// 4) It makes estimates about the data volume of the data sources and
// propagates those estimates through the plan
GraphCreatingVisitor graphCreator = new GraphCreatingVisitor(defaultParallelism);
program.accept(graphCreator);
// if we have a plan with multiple data sinks, add logical optimizer nodes that have two data-sinks as children
// each until we have only a single root node. This allows to transparently deal with the nodes with
// multiple outputs
OptimizerNode rootNode;
if (graphCreator.sinks.size() == 1) {
rootNode = graphCreator.sinks.get(0);
} else if (graphCreator.sinks.size() > 1) {
Iterator<DataSinkNode> iter = graphCreator.sinks.iterator();
rootNode = iter.next();
while (iter.hasNext()) {
rootNode = new SinkJoiner(rootNode, iter.next());
}
} else {
throw new CompilerException("Bug: The optimizer plan representation has no sinks.");
}
// now that we have all nodes created and recorded which ones consume memory, tell the nodes their minimal
// guaranteed memory, for further cost estimations. we assume an equal distribution of memory among consumer tasks
rootNode.accept(new IdAndEstimatesVisitor(this.statistics));
// Now that the previous step is done, the next step is to traverse the graph again for the two
// steps that cannot directly be performed during the plan enumeration, because we are dealing with DAGs
// rather than a trees. That requires us to deviate at some points from the classical DB optimizer algorithms.
//
// 1) propagate the interesting properties top-down through the graph
// 2) Track information about nodes with multiple outputs that are later on reconnected in a node with
// multiple inputs.
InterestingPropertyVisitor propsVisitor = new InterestingPropertyVisitor(this.costEstimator);
rootNode.accept(propsVisitor);
BranchesVisitor branchingVisitor = new BranchesVisitor();
rootNode.accept(branchingVisitor);
// perform a sanity check: the root may not have any unclosed branches
if (rootNode.getOpenBranches() != null && rootNode.getOpenBranches().size() > 0) {
throw new CompilerException("Bug: Logic for branching plans (non-tree plans) has an error, and does not " +
"track the re-joining of branches correctly.");
}
// the final step is now to generate the actual plan alternatives
List<PlanNode> bestPlan = rootNode.getAlternativePlans(this.costEstimator);
if (bestPlan.size() != 1) {
throw new CompilerException("Error in compiler: more than one best plan was created!");
}
// check if the best plan's root is a data sink (single sink plan)
// if so, directly take it. if it is a sink joiner node, get its contained sinks
PlanNode bestPlanRoot = bestPlan.get(0);
List<SinkPlanNode> bestPlanSinks = new ArrayList<SinkPlanNode>(4);
if (bestPlanRoot instanceof SinkPlanNode) {
bestPlanSinks.add((SinkPlanNode) bestPlanRoot);
} else if (bestPlanRoot instanceof SinkJoinerPlanNode) {
((SinkJoinerPlanNode) bestPlanRoot).getDataSinks(bestPlanSinks);
}
DeadlockPreventer dp = new DeadlockPreventer();
dp.resolveDeadlocks(bestPlanSinks);
// finalize the plan
OptimizedPlan plan = new PlanFinalizer().createFinalPlan(bestPlanSinks, program.getJobName(), program);
// swap the binary unions for n-ary unions. this changes no strategies or memory consumers whatsoever, so
// we can do this after the plan finalization
plan.accept(new BinaryUnionReplacer());
// post pass the plan. this is the phase where the serialization and comparator code is set
postPasser.postPass(plan);
return plan;
}
/**
* This function performs only the first step to the compilation process - the creation of the optimizer
* representation of the plan. No estimations or enumerations of alternatives are done here.
*
* @param program The plan to generate the optimizer representation for.
* @return The optimizer representation of the plan, as a collection of all data sinks
* from the plan can be traversed.
*/
public static List<DataSinkNode> createPreOptimizedPlan(Plan program) {
GraphCreatingVisitor graphCreator = new GraphCreatingVisitor(1);
program.accept(graphCreator);
return graphCreator.sinks;
}
// ------------------------------------------------------------------------
// Visitors for Compilation Traversals
// ------------------------------------------------------------------------
/**
* This utility class performs the translation from the user specified program to the optimizer plan.
* It works as a visitor that walks the user's job in a depth-first fashion. During the descend, it creates
* an optimizer node for each operator, respectively data source or -sink. During the ascend, it connects
* the nodes to the full graph.
* <p>
* This translator relies on the <code>setInputs</code> method in the nodes. As that method implements the size
* estimation and the awareness for optimizer hints, the sizes will be properly estimated and the translated plan
* already respects all optimizer hints.
*/
private static final class GraphCreatingVisitor implements Visitor<Operator<?>> {
private final Map<Operator<?>, OptimizerNode> con2node; // map from the operator objects to their
// corresponding optimizer nodes
private final List<DataSourceNode> sources; // all data source nodes in the optimizer plan
private final List<DataSinkNode> sinks; // all data sink nodes in the optimizer plan
private final int defaultParallelism; // the default degree of parallelism
private final GraphCreatingVisitor parent; // reference to enclosing creator, in case of a recursive translation
private final boolean forceDOP;
private GraphCreatingVisitor(int defaultParallelism) {
this(null, false, defaultParallelism, null);
}
private GraphCreatingVisitor(GraphCreatingVisitor parent, boolean forceDOP,
int defaultParallelism, HashMap<Operator<?>, OptimizerNode> closure) {
if (closure == null){
con2node = new HashMap<Operator<?>, OptimizerNode>();
} else {
con2node = closure;
}
this.sources = new ArrayList<DataSourceNode>(4);
this.sinks = new ArrayList<DataSinkNode>(2);
this.defaultParallelism = defaultParallelism;
this.parent = parent;
this.forceDOP = forceDOP;
}
@SuppressWarnings("deprecation")
@Override
public boolean preVisit(Operator<?> c) {
// check if we have been here before
if (this.con2node.containsKey(c)) {
return false;
}
final OptimizerNode n;
// create a node for the operator (or sink or source) if we have not been here before
if (c instanceof GenericDataSinkBase) {
DataSinkNode dsn = new DataSinkNode((GenericDataSinkBase<?>) c);
this.sinks.add(dsn);
n = dsn;
}
else if (c instanceof GenericDataSourceBase) {
DataSourceNode dsn = new DataSourceNode((GenericDataSourceBase<?, ?>) c);
this.sources.add(dsn);
n = dsn;
}
else if (c instanceof MapOperatorBase) {
n = new MapNode((MapOperatorBase<?, ?, ?>) c);
}
else if (c instanceof MapPartitionOperatorBase) {
n = new MapPartitionNode((MapPartitionOperatorBase<?, ?, ?>) c);
}
else if (c instanceof org.apache.flink.api.common.operators.base.CollectorMapOperatorBase) {
n = new CollectorMapNode((org.apache.flink.api.common.operators.base.CollectorMapOperatorBase<?, ?, ?>) c);
}
else if (c instanceof FlatMapOperatorBase) {
n = new FlatMapNode((FlatMapOperatorBase<?, ?, ?>) c);
}
else if (c instanceof FilterOperatorBase) {
n = new FilterNode((FilterOperatorBase<?, ?>) c);
}
else if (c instanceof ReduceOperatorBase) {
n = new ReduceNode((ReduceOperatorBase<?, ?>) c);
}
else if (c instanceof GroupReduceOperatorBase) {
n = new GroupReduceNode((GroupReduceOperatorBase<?, ?, ?>) c);
}
else if (c instanceof JoinOperatorBase) {
n = new MatchNode((JoinOperatorBase<?, ?, ?, ?>) c);
}
else if (c instanceof CoGroupOperatorBase) {
n = new CoGroupNode((CoGroupOperatorBase<?, ?, ?, ?>) c);
}
else if (c instanceof CrossOperatorBase) {
n = new CrossNode((CrossOperatorBase<?, ?, ?, ?>) c);
}
else if (c instanceof BulkIterationBase) {
n = new BulkIterationNode((BulkIterationBase<?>) c);
}
else if (c instanceof DeltaIterationBase) {
n = new WorksetIterationNode((DeltaIterationBase<?, ?>) c);
}
else if (c instanceof Union){
n = new BinaryUnionNode((Union<?>) c);
}
else if (c instanceof PartitionOperatorBase) {
n = new PartitionNode((PartitionOperatorBase<?>) c);
}
else if (c instanceof PartialSolutionPlaceHolder) {
if (this.parent == null) {
throw new InvalidProgramException("It is currently not supported to create data sinks inside iterations.");
}
final PartialSolutionPlaceHolder<?> holder = (PartialSolutionPlaceHolder<?>) c;
final BulkIterationBase<?> enclosingIteration = holder.getContainingBulkIteration();
final BulkIterationNode containingIterationNode =
(BulkIterationNode) this.parent.con2node.get(enclosingIteration);
// catch this for the recursive translation of step functions
BulkPartialSolutionNode p = new BulkPartialSolutionNode(holder, containingIterationNode);
p.setDegreeOfParallelism(containingIterationNode.getDegreeOfParallelism());
n = p;
}
else if (c instanceof WorksetPlaceHolder) {
if (this.parent == null) {
throw new InvalidProgramException("It is currently not supported to create data sinks inside iterations.");
}
final WorksetPlaceHolder<?> holder = (WorksetPlaceHolder<?>) c;
final DeltaIterationBase<?, ?> enclosingIteration = holder.getContainingWorksetIteration();
final WorksetIterationNode containingIterationNode =
(WorksetIterationNode) this.parent.con2node.get(enclosingIteration);
// catch this for the recursive translation of step functions
WorksetNode p = new WorksetNode(holder, containingIterationNode);
p.setDegreeOfParallelism(containingIterationNode.getDegreeOfParallelism());
n = p;
}
else if (c instanceof SolutionSetPlaceHolder) {
if (this.parent == null) {
throw new InvalidProgramException("It is currently not supported to create data sinks inside iterations.");
}
final SolutionSetPlaceHolder<?> holder = (SolutionSetPlaceHolder<?>) c;
final DeltaIterationBase<?, ?> enclosingIteration = holder.getContainingWorksetIteration();
final WorksetIterationNode containingIterationNode =
(WorksetIterationNode) this.parent.con2node.get(enclosingIteration);
// catch this for the recursive translation of step functions
SolutionSetNode p = new SolutionSetNode(holder, containingIterationNode);
p.setDegreeOfParallelism(containingIterationNode.getDegreeOfParallelism());
n = p;
}
else {
throw new IllegalArgumentException("Unknown operator type: " + c);
}
this.con2node.put(c, n);
// set the parallelism only if it has not been set before. some nodes have a fixed DOP, such as the
// key-less reducer (all-reduce)
if (n.getDegreeOfParallelism() < 1) {
// set the degree of parallelism
int par = c.getDegreeOfParallelism();
if (par > 0) {
if (this.forceDOP && par != this.defaultParallelism) {
par = this.defaultParallelism;
LOG.warn("The degree-of-parallelism of nested Dataflows (such as step functions in iterations) is " +
"currently fixed to the degree-of-parallelism of the surrounding operator (the iteration).");
}
} else {
par = this.defaultParallelism;
}
n.setDegreeOfParallelism(par);
}
return true;
}
@Override
public void postVisit(Operator<?> c) {
OptimizerNode n = this.con2node.get(c);
// first connect to the predecessors
n.setInput(this.con2node);
n.setBroadcastInputs(this.con2node);
// if the node represents a bulk iteration, we recursively translate the data flow now
if (n instanceof BulkIterationNode) {
final BulkIterationNode iterNode = (BulkIterationNode) n;
final BulkIterationBase<?> iter = iterNode.getIterationContract();
// pass a copy of the no iterative part into the iteration translation,
// in case the iteration references its closure
HashMap<Operator<?>, OptimizerNode> closure = new HashMap<Operator<?>, OptimizerNode>(con2node);
// first, recursively build the data flow for the step function
final GraphCreatingVisitor recursiveCreator = new GraphCreatingVisitor(this, true,
iterNode.getDegreeOfParallelism(), closure);
BulkPartialSolutionNode partialSolution = null;
iter.getNextPartialSolution().accept(recursiveCreator);
partialSolution = (BulkPartialSolutionNode) recursiveCreator.con2node.get(iter.getPartialSolution());
OptimizerNode rootOfStepFunction = recursiveCreator.con2node.get(iter.getNextPartialSolution());
if (partialSolution == null) {
throw new CompilerException("Error: The step functions result does not depend on the partial solution.");
}
OptimizerNode terminationCriterion = null;
if (iter.getTerminationCriterion() != null) {
terminationCriterion = recursiveCreator.con2node.get(iter.getTerminationCriterion());
// no intermediate node yet, traverse from the termination criterion to build the missing parts
if (terminationCriterion == null) {
iter.getTerminationCriterion().accept(recursiveCreator);
terminationCriterion = recursiveCreator.con2node.get(iter.getTerminationCriterion());
}
}
iterNode.setPartialSolution(partialSolution);
iterNode.setNextPartialSolution(rootOfStepFunction, terminationCriterion);
// go over the contained data flow and mark the dynamic path nodes
StaticDynamicPathIdentifier identifier = new StaticDynamicPathIdentifier(iterNode.getCostWeight());
rootOfStepFunction.accept(identifier);
if(terminationCriterion != null){
terminationCriterion.accept(identifier);
}
}
else if (n instanceof WorksetIterationNode) {
final WorksetIterationNode iterNode = (WorksetIterationNode) n;
final DeltaIterationBase<?, ?> iter = iterNode.getIterationContract();
// we need to ensure that both the next-workset and the solution-set-delta depend on the workset. One check is for free
// during the translation, we do the other check here as a pre-condition
{
WorksetFinder wsf = new WorksetFinder();
iter.getNextWorkset().accept(wsf);
if (!wsf.foundWorkset) {
throw new CompilerException("In the given program, the next workset does not depend on the workset. This is a prerequisite in delta iterations.");
}
}
// calculate the closure of the anonymous function
HashMap<Operator<?>, OptimizerNode> closure = new HashMap<Operator<?>, OptimizerNode>(con2node);
// first, recursively build the data flow for the step function
final GraphCreatingVisitor recursiveCreator = new GraphCreatingVisitor(this, true, iterNode.getDegreeOfParallelism(), closure);
// descend from the solution set delta. check that it depends on both the workset
// and the solution set. If it does depend on both, this descend should create both nodes
iter.getSolutionSetDelta().accept(recursiveCreator);
final WorksetNode worksetNode = (WorksetNode) recursiveCreator.con2node.get(iter.getWorkset());
if (worksetNode == null) {
throw new CompilerException("In the given program, the solution set delta does not depend on the workset. This is a prerequisite in delta iterations.");
}
iter.getNextWorkset().accept(recursiveCreator);
SolutionSetNode solutionSetNode = (SolutionSetNode) recursiveCreator.con2node.get(iter.getSolutionSet());
if (solutionSetNode == null || solutionSetNode.getOutgoingConnections() == null || solutionSetNode.getOutgoingConnections().isEmpty()) {
solutionSetNode = new SolutionSetNode((SolutionSetPlaceHolder<?>) iter.getSolutionSet(), iterNode);
}
else {
for (PactConnection conn : solutionSetNode.getOutgoingConnections()) {
OptimizerNode successor = conn.getTarget();
if (successor.getClass() == MatchNode.class) {
// find out which input to the match the solution set is
MatchNode mn = (MatchNode) successor;
if (mn.getFirstPredecessorNode() == solutionSetNode) {
mn.makeJoinWithSolutionSet(0);
} else if (mn.getSecondPredecessorNode() == solutionSetNode) {
mn.makeJoinWithSolutionSet(1);
} else {
throw new CompilerException();
}
}
else if (successor.getClass() == CoGroupNode.class) {
CoGroupNode cg = (CoGroupNode) successor;
if (cg.getFirstPredecessorNode() == solutionSetNode) {
cg.makeCoGroupWithSolutionSet(0);
} else if (cg.getSecondPredecessorNode() == solutionSetNode) {
cg.makeCoGroupWithSolutionSet(1);
} else {
throw new CompilerException();
}
}
else {
throw new CompilerException("Error: The only operations allowed on the solution set are Join and CoGroup.");
}
}
}
final OptimizerNode nextWorksetNode = recursiveCreator.con2node.get(iter.getNextWorkset());
final OptimizerNode solutionSetDeltaNode = recursiveCreator.con2node.get(iter.getSolutionSetDelta());
// set the step function nodes to the iteration node
iterNode.setPartialSolution(solutionSetNode, worksetNode);
iterNode.setNextPartialSolution(solutionSetDeltaNode, nextWorksetNode);
// go over the contained data flow and mark the dynamic path nodes
StaticDynamicPathIdentifier pathIdentifier = new StaticDynamicPathIdentifier(iterNode.getCostWeight());
nextWorksetNode.accept(pathIdentifier);
iterNode.getSolutionSetDelta().accept(pathIdentifier);
}
}
};
private static final class StaticDynamicPathIdentifier implements Visitor<OptimizerNode> {
private final Set<OptimizerNode> seenBefore = new HashSet<OptimizerNode>();
private final int costWeight;
private StaticDynamicPathIdentifier(int costWeight) {
this.costWeight = costWeight;
}
@Override
public boolean preVisit(OptimizerNode visitable) {
return this.seenBefore.add(visitable);
}
@Override
public void postVisit(OptimizerNode visitable) {
visitable.identifyDynamicPath(this.costWeight);
}
}
/**
* Simple visitor that sets the minimal guaranteed memory per task based on the amount of available memory,
* the number of memory consumers, and on the task's degree of parallelism.
*/
private static final class IdAndEstimatesVisitor implements Visitor<OptimizerNode> {
private final DataStatistics statistics;
private int id = 1;
private IdAndEstimatesVisitor(DataStatistics statistics) {
this.statistics = statistics;
}
@Override
public boolean preVisit(OptimizerNode visitable) {
if (visitable.getId() != -1) {
// been here before
return false;
}
return true;
}
@Override
public void postVisit(OptimizerNode visitable) {
// the node ids
visitable.initId(this.id++);
// connections need to figure out their maximum path depths
for (PactConnection conn : visitable.getIncomingConnections()) {
conn.initMaxDepth();
}
for (PactConnection conn : visitable.getBroadcastConnections()) {
conn.initMaxDepth();
}
// the estimates
visitable.computeOutputEstimates(this.statistics);
// if required, recurse into the step function
if (visitable instanceof IterationNode) {
((IterationNode) visitable).acceptForStepFunction(this);
}
}
}
/**
* Visitor that computes the interesting properties for each node in the plan. On its recursive
* depth-first descend, it propagates all interesting properties top-down.
*/
public static final class InterestingPropertyVisitor implements Visitor<OptimizerNode> {
private CostEstimator estimator; // the cost estimator for maximal costs of an interesting property
/**
* Creates a new visitor that computes the interesting properties for all nodes in the plan.
* It uses the given cost estimator used to compute the maximal costs for an interesting property.
*
* @param estimator
* The cost estimator to estimate the maximal costs for interesting properties.
*/
public InterestingPropertyVisitor(CostEstimator estimator) {
this.estimator = estimator;
}
@Override
public boolean preVisit(OptimizerNode node) {
// The interesting properties must be computed on the descend. In case a node has multiple outputs,
// that computation must happen during the last descend.
if (node.getInterestingProperties() == null && node.haveAllOutputConnectionInterestingProperties()) {
node.computeUnionOfInterestingPropertiesFromSuccessors();
node.computeInterestingPropertiesForInputs(this.estimator);
return true;
} else {
return false;
}
}
@Override
public void postVisit(OptimizerNode visitable) {}
}
/**
* On its re-ascend (post visit) this visitor, computes auxiliary maps that are needed to support plans
* that are not a minimally connected DAG (Such plans are not trees, but at least one node feeds its
* output into more than one other node).
*/
private static final class BranchesVisitor implements Visitor<OptimizerNode> {
@Override
public boolean preVisit(OptimizerNode node) {
return node.getOpenBranches() == null;
}
@Override
public void postVisit(OptimizerNode node) {
if (node instanceof IterationNode) {
((IterationNode) node).acceptForStepFunction(this);
}
node.computeUnclosedBranchStack();
}
}
/**
* Utility class that traverses a plan to collect all nodes and add them to the OptimizedPlan.
* Besides collecting all nodes, this traversal assigns the memory to the nodes.
*/
private static final class PlanFinalizer implements Visitor<PlanNode> {
private final Set<PlanNode> allNodes; // a set of all nodes in the optimizer plan
private final List<SourcePlanNode> sources; // all data source nodes in the optimizer plan
private final List<SinkPlanNode> sinks; // all data sink nodes in the optimizer plan
private final Deque<IterationPlanNode> stackOfIterationNodes;
private int memoryConsumerWeights; // a counter of all memory consumers
/**
* Creates a new plan finalizer.
*/
private PlanFinalizer() {
this.allNodes = new HashSet<PlanNode>();
this.sources = new ArrayList<SourcePlanNode>();
this.sinks = new ArrayList<SinkPlanNode>();
this.stackOfIterationNodes = new ArrayDeque<IterationPlanNode>();
}
private OptimizedPlan createFinalPlan(List<SinkPlanNode> sinks, String jobName, Plan originalPlan) {
this.memoryConsumerWeights = 0;
// traverse the graph
for (SinkPlanNode node : sinks) {
node.accept(this);
}
// assign the memory to each node
if (this.memoryConsumerWeights > 0) {
for (PlanNode node : this.allNodes) {
// assign memory to the driver strategy of the node
final int consumerWeight = node.getMemoryConsumerWeight();
if (consumerWeight > 0) {
final double relativeMem = (double)consumerWeight / this.memoryConsumerWeights;
node.setRelativeMemoryPerSubtask(relativeMem);
if (LOG.isDebugEnabled()) {
LOG.debug("Assigned " + relativeMem + " of total memory to each subtask of " +
node.getPactContract().getName() + ".");
}
}
// assign memory to the local and global strategies of the channels
for (Channel c : node.getInputs()) {
if (c.getLocalStrategy().dams()) {
final double relativeMem = 1.0 / this.memoryConsumerWeights;
c.setRelativeMemoryLocalStrategy(relativeMem);
if (LOG.isDebugEnabled()) {
LOG.debug("Assigned " + relativeMem + " of total memory to each local strategy " +
"instance of " + c + ".");
}
}
if (c.getTempMode() != TempMode.NONE) {
final double relativeMem = 1.0/ this.memoryConsumerWeights;
c.setRelativeTempMemory(relativeMem);
if (LOG.isDebugEnabled()) {
LOG.debug("Assigned " + relativeMem + " of total memory to each instance of the temp " +
"table" +
" " +
"for " + c + ".");
}
}
}
}
}
return new OptimizedPlan(this.sources, this.sinks, this.allNodes, jobName, originalPlan);
}
@Override
public boolean preVisit(PlanNode visitable) {
// if we come here again, prevent a further descend
if (!this.allNodes.add(visitable)) {
return false;
}
if (visitable instanceof SinkPlanNode) {
this.sinks.add((SinkPlanNode) visitable);
}
else if (visitable instanceof SourcePlanNode) {
this.sources.add((SourcePlanNode) visitable);
}
else if (visitable instanceof BulkPartialSolutionPlanNode) {
// tell the partial solution about the iteration node that contains it
final BulkPartialSolutionPlanNode pspn = (BulkPartialSolutionPlanNode) visitable;
final IterationPlanNode iteration = this.stackOfIterationNodes.peekLast();
// sanity check!
if (iteration == null || !(iteration instanceof BulkIterationPlanNode)) {
throw new CompilerException("Bug: Error finalizing the plan. " +
"Cannot associate the node for a partial solutions with its containing iteration.");
}
pspn.setContainingIterationNode((BulkIterationPlanNode) iteration);
}
else if (visitable instanceof WorksetPlanNode) {
// tell the partial solution about the iteration node that contains it
final WorksetPlanNode wspn = (WorksetPlanNode) visitable;
final IterationPlanNode iteration = this.stackOfIterationNodes.peekLast();
// sanity check!
if (iteration == null || !(iteration instanceof WorksetIterationPlanNode)) {
throw new CompilerException("Bug: Error finalizing the plan. " +
"Cannot associate the node for a partial solutions with its containing iteration.");
}
wspn.setContainingIterationNode((WorksetIterationPlanNode) iteration);
}
else if (visitable instanceof SolutionSetPlanNode) {
// tell the partial solution about the iteration node that contains it
final SolutionSetPlanNode sspn = (SolutionSetPlanNode) visitable;
final IterationPlanNode iteration = this.stackOfIterationNodes.peekLast();
// sanity check!
if (iteration == null || !(iteration instanceof WorksetIterationPlanNode)) {
throw new CompilerException("Bug: Error finalizing the plan. " +
"Cannot associate the node for a partial solutions with its containing iteration.");
}
sspn.setContainingIterationNode((WorksetIterationPlanNode) iteration);
}
// double-connect the connections. previously, only parents knew their children, because
// one child candidate could have been referenced by multiple parents.
for (Channel conn : visitable.getInputs()) {
conn.setTarget(visitable);
conn.getSource().addOutgoingChannel(conn);
}
for (Channel c : visitable.getBroadcastInputs()) {
c.setTarget(visitable);
c.getSource().addOutgoingChannel(c);
}
// count the memory consumption
this.memoryConsumerWeights += visitable.getMemoryConsumerWeight();
for (Channel c : visitable.getInputs()) {
if (c.getLocalStrategy().dams()) {
this.memoryConsumerWeights++;
}
if (c.getTempMode() != TempMode.NONE) {
this.memoryConsumerWeights++;
}
}
for (Channel c : visitable.getBroadcastInputs()) {
if (c.getLocalStrategy().dams()) {
this.memoryConsumerWeights++;
}
if (c.getTempMode() != TempMode.NONE) {
this.memoryConsumerWeights++;
}
}
// pass the visitor to the iteraton's step function
if (visitable instanceof IterationPlanNode) {
// push the iteration node onto the stack
final IterationPlanNode iterNode = (IterationPlanNode) visitable;
this.stackOfIterationNodes.addLast(iterNode);
// recurse
((IterationPlanNode) visitable).acceptForStepFunction(this);
// pop the iteration node from the stack
this.stackOfIterationNodes.removeLast();
}
return true;
}
@Override
public void postVisit(PlanNode visitable) {}
}
/**
* A visitor that traverses the graph and collects cascading binary unions into a single n-ary
* union operator. The exception is, when on of the union inputs is materialized, such as in the
* static-code-path-cache in iterations.
*/
private static final class BinaryUnionReplacer implements Visitor<PlanNode> {
private final Set<PlanNode> seenBefore = new HashSet<PlanNode>();
@Override
public boolean preVisit(PlanNode visitable) {
if (this.seenBefore.add(visitable)) {
if (visitable instanceof IterationPlanNode) {
((IterationPlanNode) visitable).acceptForStepFunction(this);
}
return true;
} else {
return false;
}
}
@Override
public void postVisit(PlanNode visitable) {
if (visitable instanceof BinaryUnionPlanNode) {
final BinaryUnionPlanNode unionNode = (BinaryUnionPlanNode) visitable;
final Channel in1 = unionNode.getInput1();
final Channel in2 = unionNode.getInput2();
PlanNode newUnionNode;
List<Channel> inputs = new ArrayList<Channel>();
collect(in1, inputs);
collect(in2, inputs);
newUnionNode = new NAryUnionPlanNode(unionNode.getOptimizerNode(), inputs, unionNode.getGlobalProperties());
for (Channel c : inputs) {
c.setTarget(newUnionNode);
}
for(Channel channel : unionNode.getOutgoingChannels()){
channel.swapUnionNodes(newUnionNode);
}
}
}
private void collect(Channel in, List<Channel> inputs) {
if (in.getSource() instanceof NAryUnionPlanNode) {
// sanity check
if (in.getShipStrategy() != ShipStrategyType.FORWARD) {
throw new CompilerException("Bug: Plan generation for Unions picked a ship strategy between binary plan operators.");
}
if (!(in.getLocalStrategy() == null || in.getLocalStrategy() == LocalStrategy.NONE)) {
throw new CompilerException("Bug: Plan generation for Unions picked a local strategy between binary plan operators.");
}
inputs.addAll(((NAryUnionPlanNode) in.getSource()).getListOfInputs());
} else {
// is not a union node, so we take the channel directly
inputs.add(in);
}
}
}
private static final class WorksetFinder implements Visitor<Operator<?>> {
private final Set<Operator<?>> seenBefore = new HashSet<Operator<?>>();
private boolean foundWorkset;
@Override
public boolean preVisit(Operator<?> visitable) {
if (visitable instanceof WorksetPlaceHolder) {
foundWorkset = true;
}
return (!foundWorkset) && seenBefore.add(visitable);
}
@Override
public void postVisit(Operator<?> visitable) {}
}
// ------------------------------------------------------------------------
// Miscellaneous
// ------------------------------------------------------------------------
private OptimizerPostPass getPostPassFromPlan(Plan program) {
final String className = program.getPostPassClassName();
if (className == null) {
throw new CompilerException("Optimizer Post Pass class description is null");
}
try {
Class<? extends OptimizerPostPass> clazz = Class.forName(className).asSubclass(OptimizerPostPass.class);
try {
return InstantiationUtil.instantiate(clazz, OptimizerPostPass.class);
} catch (RuntimeException rtex) {
// unwrap the source exception
if (rtex.getCause() != null) {
throw new CompilerException("Cannot instantiate optimizer post pass: " + rtex.getMessage(), rtex.getCause());
} else {
throw rtex;
}
}
} catch (ClassNotFoundException cnfex) {
throw new CompilerException("Cannot load Optimizer post-pass class '" + className + "'.", cnfex);
} catch (ClassCastException ccex) {
throw new CompilerException("Class '" + className + "' is not an optimizer post passer.", ccex);
}
}
}