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

Example 36 with SemanticNodeProcessor

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

the class AnnotateRunTimeStatsOptimizer method resolve.

@Override
public PhysicalContext resolve(PhysicalContext pctx) throws SemanticException {
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    SemanticDispatcher disp = new AnnotateRunTimeStatsDispatcher(pctx, opRules);
    SemanticGraphWalker ogw = new DefaultGraphWalker(disp);
    ArrayList<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pctx.getRootTasks());
    ogw.startWalking(topNodes, null);
    return pctx;
}

Example 37 with SemanticNodeProcessor

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

the class BucketingSortingInferenceOptimizer method inferBucketingSorting.

/**
 * For each map reduce task, if it has a reducer, infer whether or not the final output of the
 * reducer is bucketed and/or sorted
 *
 * @param mapRedTasks
 * @throws SemanticException
 */
private void inferBucketingSorting(List<ExecDriver> mapRedTasks) throws SemanticException {
    for (ExecDriver mapRedTask : mapRedTasks) {
        // of the outputs of intermediate map reduce jobs.
        if (!mapRedTask.getWork().isFinalMapRed()) {
            continue;
        }
        if (mapRedTask.getWork().getReduceWork() == null) {
            continue;
        }
        Operator<? extends OperatorDesc> reducer = mapRedTask.getWork().getReduceWork().getReducer();
        // uses sampling, which means it's not bucketed
        boolean disableBucketing = mapRedTask.getWork().getMapWork().getSamplingType() > 0;
        BucketingSortingCtx bCtx = new BucketingSortingCtx(disableBucketing);
        // RuleRegExp rules are used to match operators anywhere in the tree
        // RuleExactMatch rules are used to specify exactly what the tree should look like
        // In particular, this guarantees that the first operator is the reducer
        // (and its parent(s) are ReduceSinkOperators) since it begins walking the tree from
        // the reducer.
        Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
        opRules.put(new RuleRegExp("R1", SelectOperator.getOperatorName() + "%"), BucketingSortingOpProcFactory.getSelProc());
        // Matches only GroupByOperators which are reducers, rather than map group by operators,
        // or multi group by optimization specific operators
        opRules.put(new RuleExactMatch("R2", new String[] { GroupByOperator.getOperatorName() }), BucketingSortingOpProcFactory.getGroupByProc());
        // Matches only JoinOperators which are reducers, rather than map joins, SMB map joins, etc.
        opRules.put(new RuleExactMatch("R3", new String[] { JoinOperator.getOperatorName() }), BucketingSortingOpProcFactory.getJoinProc());
        opRules.put(new RuleRegExp("R5", FileSinkOperator.getOperatorName() + "%"), BucketingSortingOpProcFactory.getFileSinkProc());
        opRules.put(new RuleRegExp("R7", FilterOperator.getOperatorName() + "%"), BucketingSortingOpProcFactory.getFilterProc());
        opRules.put(new RuleRegExp("R8", LimitOperator.getOperatorName() + "%"), BucketingSortingOpProcFactory.getLimitProc());
        opRules.put(new RuleRegExp("R9", LateralViewForwardOperator.getOperatorName() + "%"), BucketingSortingOpProcFactory.getLateralViewForwardProc());
        opRules.put(new RuleRegExp("R10", LateralViewJoinOperator.getOperatorName() + "%"), BucketingSortingOpProcFactory.getLateralViewJoinProc());
        // Matches only ForwardOperators which are preceded by some other operator in the tree,
        // in particular it can't be a reducer (and hence cannot be one of the ForwardOperators
        // added by the multi group by optimization)
        opRules.put(new RuleRegExp("R11", ".+" + ForwardOperator.getOperatorName() + "%"), BucketingSortingOpProcFactory.getForwardProc());
        // Matches only ForwardOperators which are reducers and are followed by GroupByOperators
        // (specific to the multi group by optimization)
        opRules.put(new RuleExactMatch("R12", new String[] { ForwardOperator.getOperatorName(), GroupByOperator.getOperatorName() }), BucketingSortingOpProcFactory.getMultiGroupByProc());
        // The dispatcher fires the processor corresponding to the closest matching rule and passes
        // the context along
        SemanticDispatcher disp = new DefaultRuleDispatcher(BucketingSortingOpProcFactory.getDefaultProc(), opRules, bCtx);
        SemanticGraphWalker ogw = new PreOrderWalker(disp);
        // Create a list of topop nodes
        ArrayList<Node> topNodes = new ArrayList<Node>();
        topNodes.add(reducer);
        ogw.startWalking(topNodes, null);
        mapRedTask.getWork().getMapWork().getBucketedColsByDirectory().putAll(bCtx.getBucketedColsByDirectory());
        mapRedTask.getWork().getMapWork().getSortedColsByDirectory().putAll(bCtx.getSortedColsByDirectory());
    }
}

Example 38 with SemanticNodeProcessor

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

the class CorrelationOptimizer method transform.

/**
 * Detect correlations and transform the query tree.
 *
 * @param pctx
 *          current parse context
 * @throws SemanticException
 */
@Override
public ParseContext transform(ParseContext pctx) throws SemanticException {
    pCtx = pctx;
    if (HiveConf.getBoolVar(pCtx.getConf(), HiveConf.ConfVars.HIVECONVERTJOIN)) {
        findPossibleAutoConvertedJoinOperators();
    }
    // detect correlations
    CorrelationNodeProcCtx corrCtx = new CorrelationNodeProcCtx(pCtx);
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("R1", ReduceSinkOperator.getOperatorName() + "%"), new CorrelationNodeProc());
    SemanticDispatcher disp = new DefaultRuleDispatcher(getDefaultProc(), opRules, corrCtx);
    SemanticGraphWalker 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);
    // We have finished tree walking (correlation detection).
    // We will first see if we need to abort (the operator tree has not been changed).
    // If not, we will start to transform the operator tree.
    abort = corrCtx.isAbort();
    if (abort) {
        LOG.info("Abort. Reasons are ...");
        for (String reason : corrCtx.getAbortReasons()) {
            LOG.info("-- " + reason);
        }
    } else {
        // transform the operator tree
        LOG.info("Begain query plan transformation based on intra-query correlations. " + corrCtx.getCorrelations().size() + " correlation(s) to be applied");
        for (IntraQueryCorrelation correlation : corrCtx.getCorrelations()) {
            QueryPlanTreeTransformation.applyCorrelation(pCtx, corrCtx, correlation);
        }
    }
    return pCtx;
}

Example 39 with SemanticNodeProcessor

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

the class PcrExprProcFactory method walkExprTree.

/**
 * Remove partition conditions when necessary from the the expression tree.
 *
 * @param tabAlias
 *          the table alias
 * @param parts
 *          the list of all pruned partitions for the table
 * @param vcs
 *          virtual columns referenced
 * @param pred
 *          expression tree of the target filter operator
 * @return the node information of the root expression
 * @throws SemanticException
 */
public static NodeInfoWrapper walkExprTree(String tabAlias, ArrayList<Partition> parts, List<VirtualColumn> vcs, ExprNodeDesc pred) throws SemanticException {
    // Create the walker, the rules dispatcher and the context.
    PcrExprProcCtx pprCtx = new PcrExprProcCtx(tabAlias, parts, vcs);
    Map<SemanticRule, SemanticNodeProcessor> exprRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    exprRules.put(new RuleRegExp("R1", ExprNodeColumnDesc.class.getName() + "%"), getColumnProcessor());
    exprRules.put(new RuleRegExp("R2", ExprNodeFieldDesc.class.getName() + "%"), getFieldProcessor());
    exprRules.put(new RuleRegExp("R5", ExprNodeGenericFuncDesc.class.getName() + "%"), getGenericFuncProcessor());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    SemanticDispatcher disp = new DefaultRuleDispatcher(getDefaultExprProcessor(), exprRules, pprCtx);
    SemanticGraphWalker egw = new ExpressionWalker(disp);
    List<Node> startNodes = new ArrayList<Node>();
    startNodes.add(pred);
    HashMap<Node, Object> outputMap = new HashMap<Node, Object>();
    egw.startWalking(startNodes, outputMap);
    // Return the wrapper of the root node
    return (NodeInfoWrapper) outputMap.get(pred);
}

Example 40 with SemanticNodeProcessor

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

the class ReduceSinkJoinDeDuplication method transform.

@Override
public ParseContext transform(ParseContext pctx) throws SemanticException {
    pGraphContext = pctx;
    ReduceSinkJoinDeDuplicateProcCtx cppCtx = new ReduceSinkJoinDeDuplicateProcCtx(pGraphContext);
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("R1", ReduceSinkOperator.getOperatorName() + "%"), ReduceSinkJoinDeDuplicateProcFactory.getReducerMapJoinProc());
    SemanticDispatcher disp = new DefaultRuleDispatcher(ReduceSinkJoinDeDuplicateProcFactory.getDefaultProc(), opRules, cppCtx);
    SemanticGraphWalker ogw = new ForwardWalker(disp);
    // Create a list of topop nodes
    ArrayList<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pGraphContext.getTopOps().values());
    ogw.startWalking(topNodes, null);
    return pGraphContext;
}