Package org.apache.hadoop.hive.ql.lib

Examples of org.apache.hadoop.hive.ql.lib.RuleRegExp

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  public void invokeRewriteQueryProc(
      Operator<? extends Serializable> topOp) throws SemanticException{
    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();

    // replace scan operator containing original table with index table
    opRules.put(new RuleRegExp("R1", "TS%"),
        RewriteQueryUsingAggregateIndex.getReplaceTableScanProc());
    //rule that replaces index key selection with
    //sum(`_count_of_indexed_column`) function in original query
    opRules.put(new RuleRegExp("R2", "SEL%"),
        RewriteQueryUsingAggregateIndex.getNewQuerySelectSchemaProc());
    //Manipulates the ExprNodeDesc from GroupByOperator aggregation list
    opRules.put(new RuleRegExp("R3", "GBY%"),
        RewriteQueryUsingAggregateIndex.getNewQueryGroupbySchemaProc());

    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(getDefaultProc(), opRules, this);
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   * @param topOp
   * @throws SemanticException
   */
  void populateRewriteVars(Operator<? extends Serializable> topOp) throws SemanticException{
    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp("R1", "FIL%"),
        RewriteCanApplyProcFactory.canApplyOnFilterOperator());
    opRules.put(new RuleRegExp("R2", "GBY%"),
        RewriteCanApplyProcFactory.canApplyOnGroupByOperator());
    opRules.put(new RuleRegExp("R3", "SEL%"),
        RewriteCanApplyProcFactory.canApplyOnSelectOperator());

    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(getDefaultProc(), opRules, this);
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    // 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", "FIL%"), ColumnPrunerProcFactory
        .getFilterProc());
    opRules.put(new RuleRegExp("R2", "GBY%"), ColumnPrunerProcFactory
        .getGroupByProc());
    opRules.put(new RuleRegExp("R3", "RS%"), ColumnPrunerProcFactory
        .getReduceSinkProc());
    opRules.put(new RuleRegExp("R4", "SEL%"), ColumnPrunerProcFactory
        .getSelectProc());
    opRules.put(new RuleRegExp("R5", "JOIN%"), ColumnPrunerProcFactory
        .getJoinProc());
    opRules.put(new RuleRegExp("R6", "MAPJOIN%"), ColumnPrunerProcFactory
        .getMapJoinProc());
    opRules.put(new RuleRegExp("R7", "TS%"), ColumnPrunerProcFactory
        .getTableScanProc());
    opRules.put(new RuleRegExp("R8", "LVJ%"), ColumnPrunerProcFactory
        .getLateralViewJoinProc());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(ColumnPrunerProcFactory
        .getDefaultProc(), opRules, cppCtx);
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    // create a the context for walking operators
    SamplePrunerCtx samplePrunerCtx = new SamplePrunerCtx(pctx
        .getOpToSamplePruner());

    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp("R1", "(TS%FIL%FIL%|TS%FIL%)"), getFilterProc());

    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(getDefaultProc(), opRules,
        samplePrunerCtx);
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// generate pruned column list for all relevant operators
    ReduceSinkDeduplicateProcCtx cppCtx = new ReduceSinkDeduplicateProcCtx(pGraphContext);

    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp("R1", "RS%.*RS%"), ReduceSinkDeduplicateProcFactory
        .getReducerReducerProc());

    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(ReduceSinkDeduplicateProcFactory
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    List<PcrOpWalkerCtx.OpToDeleteInfo> opToRemove =
        new ArrayList<PcrOpWalkerCtx.OpToDeleteInfo>();
    PcrOpWalkerCtx opWalkerCtx = new PcrOpWalkerCtx(pctx, opToRemove);

    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp("R1", "(TS%FIL%)|(TS%FIL%FIL%)"), PcrOpProcFactory
        .getFilterProc());

    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(PcrOpProcFactory.getDefaultProc(),
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    // 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> exprRules = new LinkedHashMap<Rule, NodeProcessor>();
    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
    Dispatcher disp = new DefaultRuleDispatcher(getDefaultExprProcessor(),
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    // create a the context for walking operators
    OpWalkerInfo opWalkerInfo = new OpWalkerInfo(pGraphContext);

    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    opRules.put(new RuleRegExp("R1", "FIL%"), OpProcFactory.getFilterProc());
    opRules.put(new RuleRegExp("R3", "JOIN%"), OpProcFactory.getJoinProc());
    opRules.put(new RuleRegExp("R4", "RS%"), OpProcFactory.getRSProc());
    opRules.put(new RuleRegExp("R5", "TS%"), OpProcFactory.getTSProc());
    opRules.put(new RuleRegExp("R6", "SCR%"), OpProcFactory.getSCRProc());
    opRules.put(new RuleRegExp("R6", "LIM%"), OpProcFactory.getLIMProc());
    opRules.put(new RuleRegExp("R7", "UDTF%"), OpProcFactory.getUDTFProc());
    opRules.put(new RuleRegExp("R8", "LVF%"), OpProcFactory.getLVFProc());

    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(OpProcFactory.getDefaultProc(),
        opRules, opWalkerInfo);
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    // 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> exprRules = new LinkedHashMap<Rule, NodeProcessor>();
    exprRules.put(
        new RuleRegExp("R1", ExprNodeColumnDesc.class.getName() + "%"),
        getColumnProcessor());
    exprRules.put(
        new RuleRegExp("R2", ExprNodeFieldDesc.class.getName() + "%"),
        getFieldProcessor());
    exprRules.put(new RuleRegExp("R3", ExprNodeGenericFuncDesc.class.getName()
        + "%"), getGenericFuncProcessor());

    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(getDefaultExprProcessor(),
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    Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
    GroupByOptProcCtx groupByOptimizeCtx = new GroupByOptProcCtx();

    // process group-by pattern
    opRules.put(new RuleRegExp("R1", "GBY%RS%GBY%"),
        getMapAggreSortedGroupbyProc(pctx));

    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(getDefaultProc(), opRules,
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Related Classes of org.apache.hadoop.hive.ql.lib.RuleRegExp

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  • org.apache.hadoop.hive.ql.optimizer.BucketMapJoinOptimizer
  • org.apache.hadoop.hive.ql.optimizer.ColumnPruner
  • org.apache.hadoop.hive.ql.optimizer.ConstantPropagate
  • org.apache.hadoop.hive.ql.optimizer.correlation.CorrelationOptimizer
  • org.apache.hadoop.hive.ql.optimizer.correlation.ReduceSinkDeDuplication
  • org.apache.hadoop.hive.ql.optimizer.DynamicPartitionPruningOptimization
  • org.apache.hadoop.hive.ql.optimizer.GroupByOptimizer
  • org.apache.hadoop.hive.ql.optimizer.index.RewriteCanApplyCtx
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