Examples with RuleRegExp org.apache.hadoop.hive.ql.lib.RuleRegExp used on opensource projects

Example 26 with RuleRegExp

use of org.apache.hadoop.hive.ql.lib.RuleRegExp in project hive by apache.

the class SparkCompiler method runRemoveDynamicPruning.

private void runRemoveDynamicPruning(OptimizeSparkProcContext procCtx) throws SemanticException {
    ParseContext pCtx = procCtx.getParseContext();
    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp("Disabling Dynamic Partition Pruning", SparkPartitionPruningSinkOperator.getOperatorName() + "%"), new SparkRemoveDynamicPruning());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
    GraphWalker ogw = new DefaultGraphWalker(disp);
    // Create a list of topop nodes
    ArrayList<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pCtx.getTopOps().values());
    ogw.startWalking(topNodes, null);
}

Example 27 with RuleRegExp

use of org.apache.hadoop.hive.ql.lib.RuleRegExp in project hive by apache.

the class SparkCompiler method generateTaskTree.

/**
 * TODO: need to turn on rules that's commented out and add more if necessary.
 */
@Override
protected void generateTaskTree(List<Task<? extends Serializable>> rootTasks, ParseContext pCtx, List<Task<MoveWork>> mvTask, Set<ReadEntity> inputs, Set<WriteEntity> outputs) throws SemanticException {
    PERF_LOGGER.PerfLogBegin(CLASS_NAME, PerfLogger.SPARK_GENERATE_TASK_TREE);
    GenSparkUtils utils = GenSparkUtils.getUtils();
    utils.resetSequenceNumber();
    ParseContext tempParseContext = getParseContext(pCtx, rootTasks);
    GenSparkProcContext procCtx = new GenSparkProcContext(conf, tempParseContext, mvTask, rootTasks, inputs, outputs, pCtx.getTopOps());
    // -------------------------------- First Pass ---------------------------------- //
    // Identify SparkPartitionPruningSinkOperators, and break OP tree if necessary
    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp("Clone OP tree for PartitionPruningSink", SparkPartitionPruningSinkOperator.getOperatorName() + "%"), new SplitOpTreeForDPP());
    Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
    GraphWalker ogw = new GenSparkWorkWalker(disp, procCtx);
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pCtx.getTopOps().values());
    ogw.startWalking(topNodes, null);
    // -------------------------------- Second Pass ---------------------------------- //
    // Process operator tree in two steps: first we process the extra op trees generated
    // in the first pass. Then we process the main op tree, and the result task will depend
    // on the task generated in the first pass.
    topNodes.clear();
    topNodes.addAll(procCtx.topOps.values());
    generateTaskTreeHelper(procCtx, topNodes);
    // the partitions used.
    if (!procCtx.clonedPruningTableScanSet.isEmpty()) {
        SparkTask pruningTask = SparkUtilities.createSparkTask(conf);
        SparkTask mainTask = procCtx.currentTask;
        pruningTask.addDependentTask(procCtx.currentTask);
        procCtx.rootTasks.remove(procCtx.currentTask);
        procCtx.rootTasks.add(pruningTask);
        procCtx.currentTask = pruningTask;
        topNodes.clear();
        topNodes.addAll(procCtx.clonedPruningTableScanSet);
        generateTaskTreeHelper(procCtx, topNodes);
        procCtx.currentTask = mainTask;
    }
    // we need to clone some operator plans and remove union operators still
    for (BaseWork w : procCtx.workWithUnionOperators) {
        GenSparkUtils.getUtils().removeUnionOperators(procCtx, w);
    }
    // we need to fill MapWork with 'local' work and bucket information for SMB Join.
    GenSparkUtils.getUtils().annotateMapWork(procCtx);
    // finally make sure the file sink operators are set up right
    for (FileSinkOperator fileSink : procCtx.fileSinkSet) {
        GenSparkUtils.getUtils().processFileSink(procCtx, fileSink);
    }
    // Process partition pruning sinks
    for (Operator<?> prunerSink : procCtx.pruningSinkSet) {
        utils.processPartitionPruningSink(procCtx, (SparkPartitionPruningSinkOperator) prunerSink);
    }
    PERF_LOGGER.PerfLogEnd(CLASS_NAME, PerfLogger.SPARK_GENERATE_TASK_TREE);
}

Example 28 with RuleRegExp

use of org.apache.hadoop.hive.ql.lib.RuleRegExp in project hive by apache.

the class SparkCompiler method runDynamicPartitionPruning.

private void runDynamicPartitionPruning(OptimizeSparkProcContext procCtx) throws SemanticException {
    if (!conf.isSparkDPPAny()) {
        return;
    }
    ParseContext parseContext = procCtx.getParseContext();
    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp(new String("Dynamic Partition Pruning"), FilterOperator.getOperatorName() + "%"), new DynamicPartitionPruningOptimization());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
    GraphWalker ogw = new ForwardWalker(disp);
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(parseContext.getTopOps().values());
    ogw.startWalking(topNodes, null);
}

Example 29 with RuleRegExp

use of org.apache.hadoop.hive.ql.lib.RuleRegExp in project hive by apache.

the class ConstantPropagate method transform.

/**
 * Transform the query tree.
 *
 * @param pactx
 *        the current parse context
 */
@Override
public ParseContext transform(ParseContext pactx) throws SemanticException {
    pGraphContext = pactx;
    // generate pruned column list for all relevant operators
    ConstantPropagateProcCtx cppCtx = new ConstantPropagateProcCtx(constantPropagateOption);
    // create a walker which walks the tree in a DFS manner while maintaining
    // the operator stack. The dispatcher
    // generates the plan from the operator tree
    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp("R1", FilterOperator.getOperatorName() + "%"), ConstantPropagateProcFactory.getFilterProc());
    opRules.put(new RuleRegExp("R2", GroupByOperator.getOperatorName() + "%"), ConstantPropagateProcFactory.getGroupByProc());
    opRules.put(new RuleRegExp("R3", SelectOperator.getOperatorName() + "%"), ConstantPropagateProcFactory.getSelectProc());
    opRules.put(new RuleRegExp("R4", FileSinkOperator.getOperatorName() + "%"), ConstantPropagateProcFactory.getFileSinkProc());
    opRules.put(new RuleRegExp("R5", ReduceSinkOperator.getOperatorName() + "%"), ConstantPropagateProcFactory.getReduceSinkProc());
    opRules.put(new RuleRegExp("R6", JoinOperator.getOperatorName() + "%"), ConstantPropagateProcFactory.getJoinProc());
    opRules.put(new RuleRegExp("R7", TableScanOperator.getOperatorName() + "%"), ConstantPropagateProcFactory.getTableScanProc());
    opRules.put(new RuleRegExp("R8", ScriptOperator.getOperatorName() + "%"), ConstantPropagateProcFactory.getStopProc());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(ConstantPropagateProcFactory.getDefaultProc(), opRules, cppCtx);
    GraphWalker ogw = new ConstantPropagateWalker(disp);
    // Create a list of operator nodes to start the walking.
    ArrayList<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pGraphContext.getTopOps().values());
    ogw.startWalking(topNodes, null);
    for (Operator<? extends Serializable> opToDelete : cppCtx.getOpToDelete()) {
        if (opToDelete.getParentOperators() == null || opToDelete.getParentOperators().size() != 1) {
            throw new RuntimeException("Error pruning operator " + opToDelete + ". It should have only 1 parent.");
        }
        opToDelete.getParentOperators().get(0).removeChildAndAdoptItsChildren(opToDelete);
    }
    cppCtx.getOpToDelete().clear();
    return pGraphContext;
}

Example 30 with RuleRegExp

use of org.apache.hadoop.hive.ql.lib.RuleRegExp in project hive by apache.

the class CountDistinctRewriteProc method transform.

@Override
public ParseContext transform(ParseContext pctx) throws SemanticException {
    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    // process group-by pattern
    opRules.put(new RuleRegExp("R1", GroupByOperator.getOperatorName() + "%" + ReduceSinkOperator.getOperatorName() + "%" + GroupByOperator.getOperatorName() + "%"), getCountDistinctProc(pctx));
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(getDefaultProc(), opRules, null);
    GraphWalker ogw = new DefaultGraphWalker(disp);
    // Create a list of topop nodes
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pctx.getTopOps().values());
    ogw.startWalking(topNodes, null);
    return pctx;
}