Examples with GraphCreatingVisitor org.apache.flink.optimizer.traversals.GraphCreatingVisitor used on opensource projects

Example 1 with GraphCreatingVisitor

use of org.apache.flink.optimizer.traversals.GraphCreatingVisitor in project flink by apache.

the class Optimizer method createPreOptimizedPlan.

/**
 * 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, null);
    program.accept(graphCreator);
    return graphCreator.getSinks();
}

Example 2 with GraphCreatingVisitor

use of org.apache.flink.optimizer.traversals.GraphCreatingVisitor in project flink by apache.

the class PipelineBreakingTest method convertPlan.

private static List<DataSinkNode> convertPlan(Plan p) {
    GraphCreatingVisitor dagCreator = new GraphCreatingVisitor(17, p.getExecutionConfig().getExecutionMode());
    // create the DAG
    p.accept(dagCreator);
    List<DataSinkNode> sinks = dagCreator.getSinks();
    // build a single root and run the branch tracking logic
    OptimizerNode rootNode;
    if (sinks.size() == 1) {
        rootNode = sinks.get(0);
    } else {
        Iterator<DataSinkNode> iter = sinks.iterator();
        rootNode = iter.next();
        while (iter.hasNext()) {
            rootNode = new SinkJoiner(rootNode, iter.next());
        }
    }
    rootNode.accept(new IdAndEstimatesVisitor(null));
    rootNode.accept(new BranchesVisitor());
    return sinks;
}

Example 3 with GraphCreatingVisitor

use of org.apache.flink.optimizer.traversals.GraphCreatingVisitor in project flink by apache.

the class Optimizer method compile.

/**
 * Translates the given program to an OptimizedPlan. The optimized plan describes for each
 * operator which strategy to use (such as hash join versus sort-merge join), what data exchange
 * method to use (local pipe forward, shuffle, broadcast), what exchange mode to use (pipelined,
 * batch), where to cache intermediate results, etc,
 *
 * <p>The optimization happens in multiple phases:
 *
 * <ol>
 *   <li>Create optimizer dag implementation of the program.
 *       <p><tt>OptimizerNode</tt> representations of the PACTs, assign parallelism and compute
 *       size estimates.
 *   <li>Compute interesting properties and auxiliary structures.
 *   <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.
 * </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() + '\'');
    }
    final ExecutionMode defaultDataExchangeMode = program.getExecutionConfig().getExecutionMode();
    final int defaultParallelism = program.getDefaultParallelism() > 0 ? program.getDefaultParallelism() : this.defaultParallelism;
    // log the default settings
    LOG.debug("Using a default parallelism of {}", defaultParallelism);
    LOG.debug("Using default data exchange mode {}", defaultDataExchangeMode);
    // 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, defaultDataExchangeMode);
    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.getSinks().size() == 1) {
        rootNode = graphCreator.getSinks().get(0);
    } else if (graphCreator.getSinks().size() > 1) {
        Iterator<DataSinkNode> iter = graphCreator.getSinks().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));
    // We need to enforce that union nodes always forward their output to their successor.
    // Any partitioning must be either pushed before or done after the union, but not on the
    // union's output.
    UnionParallelismAndForwardEnforcer unionEnforcer = new UnionParallelismAndForwardEnforcer();
    rootNode.accept(unionEnforcer);
    // We are dealing with operator DAGs, rather than operator trees.
    // That requires us to deviate at some points from the classical DB optimizer algorithms.
    // This step builds auxiliary structures to help track branches and joins in the DAG
    BranchesVisitor branchingVisitor = new BranchesVisitor();
    rootNode.accept(branchingVisitor);
    // Propagate the interesting properties top-down through the graph
    InterestingPropertyVisitor propsVisitor = new InterestingPropertyVisitor(this.costEstimator);
    rootNode.accept(propsVisitor);
    // 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);
    }
    // finalize the plan
    OptimizedPlan plan = new PlanFinalizer().createFinalPlan(bestPlanSinks, program.getJobName(), program);
    plan.accept(new BinaryUnionReplacer());
    plan.accept(new RangePartitionRewriter(plan));
    // post pass the plan. this is the phase where the serialization and comparator code is set
    postPasser.postPass(plan);
    return plan;
}