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

Example 51 with SemanticRule

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

the class TezCompiler method runDynamicPartitionPruning.

private void runDynamicPartitionPruning(OptimizeTezProcContext procCtx) throws SemanticException {
    if (!procCtx.conf.getBoolVar(ConfVars.TEZ_DYNAMIC_PARTITION_PRUNING)) {
        return;
    }
    // Sequence of TableScan operators to be walked
    Deque<Operator<?>> deque = new LinkedList<Operator<?>>();
    deque.addAll(procCtx.parseContext.getTopOps().values());
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    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
    SemanticDispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(procCtx.parseContext.getTopOps().values());
    SemanticGraphWalker ogw = new ForwardWalker(disp);
    ogw.startWalking(topNodes, null);
}

Example 52 with SemanticRule

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

the class TezCompiler method runRemoveDynamicPruningOptimization.

private void runRemoveDynamicPruningOptimization(OptimizeTezProcContext procCtx) throws SemanticException {
    // Sequence of TableScan operators to be walked
    Deque<Operator<?>> deque = new LinkedList<Operator<?>>();
    deque.addAll(procCtx.parseContext.getTopOps().values());
    // create a walker which walks the tree in a DFS manner while maintaining
    // the operator stack.
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("Remove dynamic pruning by size", AppMasterEventOperator.getOperatorName() + "%"), new RemoveDynamicPruningBySize());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    SemanticDispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(procCtx.parseContext.getTopOps().values());
    SemanticGraphWalker ogw = new ForwardWalker(disp);
    ogw.startWalking(topNodes, null);
}

Example 53 with SemanticRule

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

the class TableAccessAnalyzer method analyzeTableAccess.

public TableAccessInfo analyzeTableAccess() throws SemanticException {
    // Set up the rules for the graph walker for group by and join operators
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("R1", GroupByOperator.getOperatorName() + "%"), new GroupByProcessor(pGraphContext));
    opRules.put(new RuleRegExp("R2", JoinOperator.getOperatorName() + "%"), new JoinProcessor(pGraphContext));
    opRules.put(new RuleRegExp("R3", MapJoinOperator.getOperatorName() + "%"), new JoinProcessor(pGraphContext));
    TableAccessCtx tableAccessCtx = new TableAccessCtx();
    SemanticDispatcher disp = new DefaultRuleDispatcher(getDefaultProc(), opRules, tableAccessCtx);
    SemanticGraphWalker ogw = new DefaultGraphWalker(disp);
    // Create a list of topop nodes and walk!
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pGraphContext.getTopOps().values());
    ogw.startWalking(topNodes, null);
    return tableAccessCtx.getTableAccessInfo();
}

Example 54 with SemanticRule

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

the class MapJoinProcessor method transform.

/**
 * Transform the query tree. For each join, check if it is a map-side join (user specified). If
 * yes, convert it to a map-side join.
 *
 * @param pactx
 *          current parse context
 */
@Override
public ParseContext transform(ParseContext pactx) throws SemanticException {
    List<MapJoinOperator> listMapJoinOps = new ArrayList<MapJoinOperator>();
    // traverse all the joins and convert them if necessary
    if (pactx.getJoinOps() != null) {
        Set<JoinOperator> joinMap = new HashSet<JoinOperator>();
        Set<MapJoinOperator> mapJoinMap = pactx.getMapJoinOps();
        if (mapJoinMap == null) {
            mapJoinMap = new HashSet<MapJoinOperator>();
            pactx.setMapJoinOps(mapJoinMap);
        }
        Iterator<JoinOperator> joinCtxIter = pactx.getJoinOps().iterator();
        while (joinCtxIter.hasNext()) {
            JoinOperator joinOp = joinCtxIter.next();
            int mapJoinPos = mapSideJoin(joinOp);
            if (mapJoinPos >= 0) {
                MapJoinOperator mapJoinOp = generateMapJoinOperator(pactx, joinOp, mapJoinPos);
                listMapJoinOps.add(mapJoinOp);
                mapJoinOp.getConf().setQBJoinTreeProps(joinOp.getConf());
                mapJoinMap.add(mapJoinOp);
            } else {
                joinOp.getConf().setQBJoinTreeProps(joinOp.getConf());
                joinMap.add(joinOp);
            }
        }
        // store the new joinContext
        pactx.setJoinOps(joinMap);
    }
    // Go over the list and find if a reducer is not needed
    List<AbstractMapJoinOperator<? extends MapJoinDesc>> listMapJoinOpsNoRed = new ArrayList<AbstractMapJoinOperator<? extends MapJoinDesc>>();
    // 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<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("R0", MapJoinOperator.getOperatorName() + "%"), getCurrentMapJoin());
    opRules.put(new RuleRegExp("R1", MapJoinOperator.getOperatorName() + "%.*" + FileSinkOperator.getOperatorName() + "%"), getMapJoinFS());
    opRules.put(new RuleRegExp("R2", MapJoinOperator.getOperatorName() + "%.*" + ReduceSinkOperator.getOperatorName() + "%"), getMapJoinDefault());
    opRules.put(new RuleRegExp("R4", MapJoinOperator.getOperatorName() + "%.*" + UnionOperator.getOperatorName() + "%"), getMapJoinDefault());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    SemanticDispatcher disp = new DefaultRuleDispatcher(getDefault(), opRules, new MapJoinWalkerCtx(listMapJoinOpsNoRed, pactx));
    SemanticGraphWalker ogw = new GenMapRedWalker(disp);
    ArrayList<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(listMapJoinOps);
    ogw.startWalking(topNodes, null);
    pactx.setListMapJoinOpsNoReducer(listMapJoinOpsNoRed);
    return pactx;
}

Example 55 with SemanticRule

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

the class PartitionColumnsSeparator method transform.

@Override
public ParseContext transform(ParseContext pctx) throws SemanticException {
    // 1. Trigger transformation
    Map<SemanticRule, SemanticNodeProcessor> opRules = new LinkedHashMap<SemanticRule, SemanticNodeProcessor>();
    opRules.put(new RuleRegExp("R1", FilterOperator.getOperatorName() + "%"), new StructInTransformer());
    SemanticDispatcher disp = new DefaultRuleDispatcher(null, opRules, null);
    SemanticGraphWalker ogw = new ForwardWalker(disp);
    List<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pctx.getTopOps().values());
    ogw.startWalking(topNodes, null);
    return pctx;
}