Examples with Operator org.apache.hadoop.hive.ql.exec.Operator used on opensource projects

Example 1 with Operator

use of org.apache.hadoop.hive.ql.exec.Operator in project hive by apache.

the class SemanticAnalyzer method genGroupByPlanGroupByOperator1.

/**
   * Generate the GroupByOperator for the Query Block (parseInfo.getXXX(dest)).
   * The new GroupByOperator will be a child of the reduceSinkOperatorInfo.
   *
   * @param parseInfo
   * @param dest
   * @param reduceSinkOperatorInfo
   * @param mode
   *          The mode of the aggregation (MERGEPARTIAL, PARTIAL2)
   * @param genericUDAFEvaluators
   *          The mapping from Aggregation StringTree to the
   *          genericUDAFEvaluator.
   * @param groupingSets
   *          list of grouping sets
   * @param groupingSetsPresent
   *          whether grouping sets are present in this query
   * @param groupingSetsNeedAdditionalMRJob
   *          whether grouping sets are consumed by this group by
   * @return the new GroupByOperator
   */
@SuppressWarnings("nls")
private Operator genGroupByPlanGroupByOperator1(QBParseInfo parseInfo, String dest, Operator reduceSinkOperatorInfo, GroupByDesc.Mode mode, Map<String, GenericUDAFEvaluator> genericUDAFEvaluators, List<Integer> groupingSets, boolean groupingSetsPresent, boolean groupingSetsNeedAdditionalMRJob) throws SemanticException {
    ArrayList<String> outputColumnNames = new ArrayList<String>();
    RowResolver groupByInputRowResolver = opParseCtx.get(reduceSinkOperatorInfo).getRowResolver();
    RowResolver groupByOutputRowResolver = new RowResolver();
    groupByOutputRowResolver.setIsExprResolver(true);
    ArrayList<ExprNodeDesc> groupByKeys = new ArrayList<ExprNodeDesc>();
    ArrayList<AggregationDesc> aggregations = new ArrayList<AggregationDesc>();
    List<ASTNode> grpByExprs = getGroupByForClause(parseInfo, dest);
    Map<String, ExprNodeDesc> colExprMap = new HashMap<String, ExprNodeDesc>();
    for (int i = 0; i < grpByExprs.size(); ++i) {
        ASTNode grpbyExpr = grpByExprs.get(i);
        ColumnInfo exprInfo = groupByInputRowResolver.getExpression(grpbyExpr);
        if (exprInfo == null) {
            throw new SemanticException(ErrorMsg.INVALID_COLUMN.getMsg(grpbyExpr));
        }
        groupByKeys.add(new ExprNodeColumnDesc(exprInfo));
        String field = getColumnInternalName(i);
        outputColumnNames.add(field);
        ColumnInfo oColInfo = new ColumnInfo(field, exprInfo.getType(), "", false);
        groupByOutputRowResolver.putExpression(grpbyExpr, oColInfo);
        addAlternateGByKeyMappings(grpbyExpr, oColInfo, reduceSinkOperatorInfo, groupByOutputRowResolver);
        colExprMap.put(field, groupByKeys.get(groupByKeys.size() - 1));
    }
    // This is only needed if a new grouping set key is being created
    int groupingSetsPosition = -1;
    // For grouping sets, add a dummy grouping key
    if (groupingSetsPresent) {
        groupingSetsPosition = groupByKeys.size();
        // This function is called for GroupBy2 to add grouping id as part of the groupby keys
        if (!groupingSetsNeedAdditionalMRJob) {
            addGroupingSetKey(groupByKeys, groupByInputRowResolver, groupByOutputRowResolver, outputColumnNames, colExprMap);
        } else {
            // The grouping set has not yet been processed. Create a new grouping key
            // Consider the query: select a,b, count(1) from T group by a,b with cube;
            // where it is being executed in 2 map-reduce jobs
            // The plan for 1st MR is TableScan -> GroupBy1 -> ReduceSink -> GroupBy2 -> FileSink
            // GroupBy1/ReduceSink worked as if grouping sets were not present
            // This function is called for GroupBy2 to create new rows for grouping sets
            // For each input row (a,b), 4 rows are created for the example above:
            // (a,b), (a,null), (null, b), (null, null)
            createNewGroupingKey(groupByKeys, outputColumnNames, groupByOutputRowResolver, colExprMap);
        }
    }
    HashMap<String, ASTNode> aggregationTrees = parseInfo.getAggregationExprsForClause(dest);
    // get the last colName for the reduce KEY
    // it represents the column name corresponding to distinct aggr, if any
    String lastKeyColName = null;
    List<ExprNodeDesc> reduceValues = null;
    if (reduceSinkOperatorInfo.getConf() instanceof ReduceSinkDesc) {
        List<String> inputKeyCols = ((ReduceSinkDesc) reduceSinkOperatorInfo.getConf()).getOutputKeyColumnNames();
        if (inputKeyCols.size() > 0) {
            lastKeyColName = inputKeyCols.get(inputKeyCols.size() - 1);
        }
        reduceValues = ((ReduceSinkDesc) reduceSinkOperatorInfo.getConf()).getValueCols();
    }
    int numDistinctUDFs = 0;
    boolean containsDistinctAggr = false;
    for (Map.Entry<String, ASTNode> entry : aggregationTrees.entrySet()) {
        ASTNode value = entry.getValue();
        String aggName = unescapeIdentifier(value.getChild(0).getText());
        ArrayList<ExprNodeDesc> aggParameters = new ArrayList<ExprNodeDesc>();
        boolean isDistinct = (value.getType() == HiveParser.TOK_FUNCTIONDI);
        containsDistinctAggr = containsDistinctAggr || isDistinct;
        // side, so always look for the parameters: d+e
        if (isDistinct) {
            // 0 is the function name
            for (int i = 1; i < value.getChildCount(); i++) {
                ASTNode paraExpr = (ASTNode) value.getChild(i);
                ColumnInfo paraExprInfo = groupByInputRowResolver.getExpression(paraExpr);
                if (paraExprInfo == null) {
                    throw new SemanticException(ErrorMsg.INVALID_COLUMN.getMsg(paraExpr));
                }
                String paraExpression = paraExprInfo.getInternalName();
                assert (paraExpression != null);
                if (isDistinct && lastKeyColName != null) {
                    // if aggr is distinct, the parameter is name is constructed as
                    // KEY.lastKeyColName:<tag>._colx
                    paraExpression = Utilities.ReduceField.KEY.name() + "." + lastKeyColName + ":" + numDistinctUDFs + "." + getColumnInternalName(i - 1);
                }
                ExprNodeDesc expr = new ExprNodeColumnDesc(paraExprInfo.getType(), paraExpression, paraExprInfo.getTabAlias(), paraExprInfo.getIsVirtualCol());
                ExprNodeDesc reduceValue = isConstantParameterInAggregationParameters(paraExprInfo.getInternalName(), reduceValues);
                if (reduceValue != null) {
                    // this parameter is a constant
                    expr = reduceValue;
                }
                aggParameters.add(expr);
            }
        } else {
            ColumnInfo paraExprInfo = groupByInputRowResolver.getExpression(value);
            if (paraExprInfo == null) {
                throw new SemanticException(ErrorMsg.INVALID_COLUMN.getMsg(value));
            }
            String paraExpression = paraExprInfo.getInternalName();
            assert (paraExpression != null);
            aggParameters.add(new ExprNodeColumnDesc(paraExprInfo.getType(), paraExpression, paraExprInfo.getTabAlias(), paraExprInfo.getIsVirtualCol()));
        }
        if (isDistinct) {
            numDistinctUDFs++;
        }
        Mode amode = groupByDescModeToUDAFMode(mode, isDistinct);
        GenericUDAFEvaluator genericUDAFEvaluator = null;
        genericUDAFEvaluator = genericUDAFEvaluators.get(entry.getKey());
        assert (genericUDAFEvaluator != null);
        GenericUDAFInfo udaf = getGenericUDAFInfo(genericUDAFEvaluator, amode, aggParameters);
        aggregations.add(new AggregationDesc(aggName.toLowerCase(), udaf.genericUDAFEvaluator, udaf.convertedParameters, (mode != GroupByDesc.Mode.FINAL && isDistinct), amode));
        String field = getColumnInternalName(groupByKeys.size() + aggregations.size() - 1);
        outputColumnNames.add(field);
        groupByOutputRowResolver.putExpression(value, new ColumnInfo(field, udaf.returnType, "", false));
    }
    float groupByMemoryUsage = HiveConf.getFloatVar(conf, HiveConf.ConfVars.HIVEMAPAGGRHASHMEMORY);
    float memoryThreshold = HiveConf.getFloatVar(conf, HiveConf.ConfVars.HIVEMAPAGGRMEMORYTHRESHOLD);
    // Nothing special needs to be done for grouping sets if
    // this is the final group by operator, and multiple rows corresponding to the
    // grouping sets have been generated upstream.
    // However, if an addition MR job has been created to handle grouping sets,
    // additional rows corresponding to grouping sets need to be created here.
    Operator op = putOpInsertMap(OperatorFactory.getAndMakeChild(new GroupByDesc(mode, outputColumnNames, groupByKeys, aggregations, groupByMemoryUsage, memoryThreshold, groupingSets, groupingSetsPresent && groupingSetsNeedAdditionalMRJob, groupingSetsPosition, containsDistinctAggr), new RowSchema(groupByOutputRowResolver.getColumnInfos()), reduceSinkOperatorInfo), groupByOutputRowResolver);
    op.setColumnExprMap(colExprMap);
    return op;
}

Example 2 with Operator

use of org.apache.hadoop.hive.ql.exec.Operator in project hive by apache.

the class SemanticAnalyzer method genGroupByPlanMapGroupByOperator.

/**
   * Generate the map-side GroupByOperator for the Query Block
   * (qb.getParseInfo().getXXX(dest)). The new GroupByOperator will be a child
   * of the inputOperatorInfo.
   *
   * @param mode
   *          The mode of the aggregation (HASH)
   * @param genericUDAFEvaluators
   *          If not null, this function will store the mapping from Aggregation
   *          StringTree to the genericUDAFEvaluator in this parameter, so it
   *          can be used in the next-stage GroupBy aggregations.
   * @return the new GroupByOperator
   */
@SuppressWarnings("nls")
private Operator genGroupByPlanMapGroupByOperator(QB qb, String dest, List<ASTNode> grpByExprs, Operator inputOperatorInfo, GroupByDesc.Mode mode, Map<String, GenericUDAFEvaluator> genericUDAFEvaluators, List<Integer> groupingSetKeys, boolean groupingSetsPresent) throws SemanticException {
    RowResolver groupByInputRowResolver = opParseCtx.get(inputOperatorInfo).getRowResolver();
    QBParseInfo parseInfo = qb.getParseInfo();
    RowResolver groupByOutputRowResolver = new RowResolver();
    groupByOutputRowResolver.setIsExprResolver(true);
    ArrayList<ExprNodeDesc> groupByKeys = new ArrayList<ExprNodeDesc>();
    ArrayList<String> outputColumnNames = new ArrayList<String>();
    ArrayList<AggregationDesc> aggregations = new ArrayList<AggregationDesc>();
    Map<String, ExprNodeDesc> colExprMap = new HashMap<String, ExprNodeDesc>();
    for (int i = 0; i < grpByExprs.size(); ++i) {
        ASTNode grpbyExpr = grpByExprs.get(i);
        ExprNodeDesc grpByExprNode = genExprNodeDesc(grpbyExpr, groupByInputRowResolver);
        if ((grpByExprNode instanceof ExprNodeColumnDesc) && ExprNodeDescUtils.indexOf(grpByExprNode, groupByKeys) >= 0) {
            // Skip duplicated grouping keys, it happens when define column alias.
            grpByExprs.remove(i--);
            continue;
        }
        groupByKeys.add(grpByExprNode);
        String field = getColumnInternalName(i);
        outputColumnNames.add(field);
        groupByOutputRowResolver.putExpression(grpbyExpr, new ColumnInfo(field, grpByExprNode.getTypeInfo(), "", false));
        colExprMap.put(field, groupByKeys.get(groupByKeys.size() - 1));
    }
    // The grouping set key is present after the grouping keys, before the distinct keys
    int groupingSetsPosition = -1;
    // for the grouping set (corresponding to the rollup).
    if (groupingSetsPresent) {
        groupingSetsPosition = groupByKeys.size();
        createNewGroupingKey(groupByKeys, outputColumnNames, groupByOutputRowResolver, colExprMap);
    }
    // If there is a distinctFuncExp, add all parameters to the reduceKeys.
    if (!parseInfo.getDistinctFuncExprsForClause(dest).isEmpty()) {
        List<ASTNode> list = parseInfo.getDistinctFuncExprsForClause(dest);
        for (ASTNode value : list) {
            // 0 is function name
            for (int i = 1; i < value.getChildCount(); i++) {
                ASTNode parameter = (ASTNode) value.getChild(i);
                if (groupByOutputRowResolver.getExpression(parameter) == null) {
                    ExprNodeDesc distExprNode = genExprNodeDesc(parameter, groupByInputRowResolver);
                    groupByKeys.add(distExprNode);
                    String field = getColumnInternalName(groupByKeys.size() - 1);
                    outputColumnNames.add(field);
                    groupByOutputRowResolver.putExpression(parameter, new ColumnInfo(field, distExprNode.getTypeInfo(), "", false));
                    colExprMap.put(field, groupByKeys.get(groupByKeys.size() - 1));
                }
            }
        }
    }
    // For each aggregation
    HashMap<String, ASTNode> aggregationTrees = parseInfo.getAggregationExprsForClause(dest);
    assert (aggregationTrees != null);
    boolean containsDistinctAggr = false;
    for (Map.Entry<String, ASTNode> entry : aggregationTrees.entrySet()) {
        ASTNode value = entry.getValue();
        String aggName = unescapeIdentifier(value.getChild(0).getText());
        ArrayList<ExprNodeDesc> aggParameters = new ArrayList<ExprNodeDesc>();
        // 0 is the function name
        for (int i = 1; i < value.getChildCount(); i++) {
            ASTNode paraExpr = (ASTNode) value.getChild(i);
            ExprNodeDesc paraExprNode = genExprNodeDesc(paraExpr, groupByInputRowResolver);
            aggParameters.add(paraExprNode);
        }
        boolean isDistinct = value.getType() == HiveParser.TOK_FUNCTIONDI;
        containsDistinctAggr = containsDistinctAggr || isDistinct;
        boolean isAllColumns = value.getType() == HiveParser.TOK_FUNCTIONSTAR;
        Mode amode = groupByDescModeToUDAFMode(mode, isDistinct);
        GenericUDAFEvaluator genericUDAFEvaluator = getGenericUDAFEvaluator(aggName, aggParameters, value, isDistinct, isAllColumns);
        assert (genericUDAFEvaluator != null);
        GenericUDAFInfo udaf = getGenericUDAFInfo(genericUDAFEvaluator, amode, aggParameters);
        aggregations.add(new AggregationDesc(aggName.toLowerCase(), udaf.genericUDAFEvaluator, udaf.convertedParameters, isDistinct, amode));
        String field = getColumnInternalName(groupByKeys.size() + aggregations.size() - 1);
        outputColumnNames.add(field);
        if (groupByOutputRowResolver.getExpression(value) == null) {
            groupByOutputRowResolver.putExpression(value, new ColumnInfo(field, udaf.returnType, "", false));
        }
        // GroupByOperators
        if (genericUDAFEvaluators != null) {
            genericUDAFEvaluators.put(entry.getKey(), genericUDAFEvaluator);
        }
    }
    float groupByMemoryUsage = HiveConf.getFloatVar(conf, HiveConf.ConfVars.HIVEMAPAGGRHASHMEMORY);
    float memoryThreshold = HiveConf.getFloatVar(conf, HiveConf.ConfVars.HIVEMAPAGGRMEMORYTHRESHOLD);
    Operator op = putOpInsertMap(OperatorFactory.getAndMakeChild(new GroupByDesc(mode, outputColumnNames, groupByKeys, aggregations, false, groupByMemoryUsage, memoryThreshold, groupingSetKeys, groupingSetsPresent, groupingSetsPosition, containsDistinctAggr), new RowSchema(groupByOutputRowResolver.getColumnInfos()), inputOperatorInfo), groupByOutputRowResolver);
    op.setColumnExprMap(colExprMap);
    return op;
}

Example 3 with Operator

use of org.apache.hadoop.hive.ql.exec.Operator in project hive by apache.

the class HiveOpConverter method genReduceSink.

@SuppressWarnings({ "rawtypes", "unchecked" })
private static ReduceSinkOperator genReduceSink(Operator<?> input, String tableAlias, ExprNodeDesc[] keys, int tag, ArrayList<ExprNodeDesc> partitionCols, String order, String nullOrder, int numReducers, Operation acidOperation, HiveConf hiveConf) throws SemanticException {
    // dummy for backtracking
    Operator dummy = Operator.createDummy();
    dummy.setParentOperators(Arrays.asList(input));
    ArrayList<ExprNodeDesc> reduceKeys = new ArrayList<ExprNodeDesc>();
    ArrayList<ExprNodeDesc> reduceKeysBack = new ArrayList<ExprNodeDesc>();
    // Compute join keys and store in reduceKeys
    for (ExprNodeDesc key : keys) {
        reduceKeys.add(key);
        reduceKeysBack.add(ExprNodeDescUtils.backtrack(key, dummy, input));
    }
    // Walk over the input schema and copy in the output
    ArrayList<ExprNodeDesc> reduceValues = new ArrayList<ExprNodeDesc>();
    ArrayList<ExprNodeDesc> reduceValuesBack = new ArrayList<ExprNodeDesc>();
    Map<String, ExprNodeDesc> colExprMap = new HashMap<String, ExprNodeDesc>();
    List<ColumnInfo> inputColumns = input.getSchema().getSignature();
    ArrayList<ColumnInfo> outputColumns = new ArrayList<ColumnInfo>();
    List<String> outputColumnNames = new ArrayList<String>();
    int[] index = new int[inputColumns.size()];
    for (int i = 0; i < inputColumns.size(); i++) {
        ColumnInfo colInfo = inputColumns.get(i);
        String outputColName = colInfo.getInternalName();
        ExprNodeColumnDesc expr = new ExprNodeColumnDesc(colInfo);
        // backtrack can be null when input is script operator
        ExprNodeDesc exprBack = ExprNodeDescUtils.backtrack(expr, dummy, input);
        int kindex = exprBack == null ? -1 : ExprNodeDescUtils.indexOf(exprBack, reduceKeysBack);
        if (kindex >= 0) {
            ColumnInfo newColInfo = new ColumnInfo(colInfo);
            newColInfo.setInternalName(Utilities.ReduceField.KEY + ".reducesinkkey" + kindex);
            newColInfo.setAlias(outputColName);
            newColInfo.setTabAlias(tableAlias);
            outputColumns.add(newColInfo);
            index[i] = kindex;
            continue;
        }
        int vindex = exprBack == null ? -1 : ExprNodeDescUtils.indexOf(exprBack, reduceValuesBack);
        if (vindex >= 0) {
            index[i] = -vindex - 1;
            continue;
        }
        index[i] = -reduceValues.size() - 1;
        reduceValues.add(expr);
        reduceValuesBack.add(exprBack);
        ColumnInfo newColInfo = new ColumnInfo(colInfo);
        newColInfo.setInternalName(Utilities.ReduceField.VALUE + "." + outputColName);
        newColInfo.setAlias(outputColName);
        newColInfo.setTabAlias(tableAlias);
        outputColumns.add(newColInfo);
        outputColumnNames.add(outputColName);
    }
    dummy.setParentOperators(null);
    // Use only 1 reducer if no reduce keys
    if (reduceKeys.size() == 0) {
        numReducers = 1;
        // Cartesian product is not supported in strict mode
        String error = StrictChecks.checkCartesian(hiveConf);
        if (error != null)
            throw new SemanticException(error);
    }
    ReduceSinkDesc rsDesc;
    if (order.isEmpty()) {
        rsDesc = PlanUtils.getReduceSinkDesc(reduceKeys, reduceValues, outputColumnNames, false, tag, reduceKeys.size(), numReducers, acidOperation);
    } else {
        rsDesc = PlanUtils.getReduceSinkDesc(reduceKeys, reduceValues, outputColumnNames, false, tag, partitionCols, order, nullOrder, numReducers, acidOperation);
    }
    ReduceSinkOperator rsOp = (ReduceSinkOperator) OperatorFactory.getAndMakeChild(rsDesc, new RowSchema(outputColumns), input);
    List<String> keyColNames = rsDesc.getOutputKeyColumnNames();
    for (int i = 0; i < keyColNames.size(); i++) {
        colExprMap.put(Utilities.ReduceField.KEY + "." + keyColNames.get(i), reduceKeys.get(i));
    }
    List<String> valColNames = rsDesc.getOutputValueColumnNames();
    for (int i = 0; i < valColNames.size(); i++) {
        colExprMap.put(Utilities.ReduceField.VALUE + "." + valColNames.get(i), reduceValues.get(i));
    }
    rsOp.setValueIndex(index);
    rsOp.setColumnExprMap(colExprMap);
    rsOp.setInputAliases(input.getSchema().getTableNames().toArray(new String[input.getSchema().getTableNames().size()]));
    if (LOG.isDebugEnabled()) {
        LOG.debug("Generated " + rsOp + " with row schema: [" + rsOp.getSchema() + "]");
    }
    return rsOp;
}

Example 4 with Operator

use of org.apache.hadoop.hive.ql.exec.Operator in project hive by apache.

the class HiveGBOpConvUtil method genMapSideGB.

@SuppressWarnings("unchecked")
private static OpAttr genMapSideGB(OpAttr inputOpAf, GBInfo gbAttrs) throws SemanticException {
    ArrayList<String> outputColNames = new ArrayList<String>();
    ArrayList<ColumnInfo> colInfoLst = new ArrayList<ColumnInfo>();
    Map<String, ExprNodeDesc> colExprMap = new HashMap<String, ExprNodeDesc>();
    Set<String> gbKeyColsAsNamesFrmIn = new HashSet<String>();
    String colOutputName = null;
    // 1. Build GB Keys, grouping set starting position
    // 1.1 First Add original GB Keys
    ArrayList<ExprNodeDesc> gbKeys = new ArrayList<ExprNodeDesc>();
    for (int i = 0; i < gbAttrs.gbKeys.size(); i++) {
        gbKeys.add(gbAttrs.gbKeys.get(i));
        colOutputName = SemanticAnalyzer.getColumnInternalName(i);
        colInfoLst.add(new ColumnInfo(colOutputName, gbAttrs.gbKeyTypes.get(i), "", false));
        outputColNames.add(colOutputName);
        gbKeyColsAsNamesFrmIn.add(gbAttrs.gbKeyColNamesInInput.get(i));
        colExprMap.put(colOutputName, gbKeys.get(i));
    }
    // 1.2. Adjust GroupingSet Position, GBKeys for GroupingSet Position if
    // needed. NOTE: GroupingID is added to map side GB only if we don't GrpSet
    // doesn't require additional MR Jobs
    int groupingSetsPosition = -1;
    boolean inclGrpID = inclGrpSetInMapSide(gbAttrs);
    if (inclGrpID) {
        groupingSetsPosition = gbKeys.size();
        addGrpSetCol(true, null, false, gbKeys, outputColNames, colInfoLst, colExprMap);
    }
    // gen would have prevented it)
    for (int i = 0; i < gbAttrs.distExprNodes.size(); i++) {
        if (!gbKeyColsAsNamesFrmIn.contains(gbAttrs.distExprNames.get(i))) {
            gbKeys.add(gbAttrs.distExprNodes.get(i));
            colOutputName = SemanticAnalyzer.getColumnInternalName(gbKeys.size() - 1);
            colInfoLst.add(new ColumnInfo(colOutputName, gbAttrs.distExprTypes.get(i), "", false));
            outputColNames.add(colOutputName);
            gbKeyColsAsNamesFrmIn.add(gbAttrs.distExprNames.get(i));
            colExprMap.put(colOutputName, gbKeys.get(gbKeys.size() - 1));
        }
    }
    // 2. Build Aggregations
    ArrayList<AggregationDesc> aggregations = new ArrayList<AggregationDesc>();
    for (UDAFAttrs udafAttr : gbAttrs.udafAttrs) {
        Mode amode = SemanticAnalyzer.groupByDescModeToUDAFMode(GroupByDesc.Mode.HASH, udafAttr.isDistinctUDAF);
        aggregations.add(new AggregationDesc(udafAttr.udafName.toLowerCase(), udafAttr.udafEvaluator, udafAttr.udafParams, udafAttr.isDistinctUDAF, amode));
        GenericUDAFInfo udafInfo;
        try {
            udafInfo = SemanticAnalyzer.getGenericUDAFInfo(udafAttr.udafEvaluator, amode, udafAttr.udafParams);
        } catch (SemanticException e) {
            throw new RuntimeException(e);
        }
        colOutputName = SemanticAnalyzer.getColumnInternalName(gbKeys.size() + aggregations.size() - 1);
        colInfoLst.add(new ColumnInfo(colOutputName, udafInfo.returnType, "", false));
        outputColNames.add(colOutputName);
    }
    // 3. Create GB
    @SuppressWarnings("rawtypes") Operator gbOp = OperatorFactory.getAndMakeChild(new GroupByDesc(GroupByDesc.Mode.HASH, outputColNames, gbKeys, aggregations, false, gbAttrs.groupByMemoryUsage, gbAttrs.memoryThreshold, gbAttrs.grpSets, inclGrpID, groupingSetsPosition, gbAttrs.containsDistinctAggr), new RowSchema(colInfoLst), inputOpAf.inputs.get(0));
    // 5. Setup Expr Col Map
    // NOTE: UDAF is not included in ExprColMap
    gbOp.setColumnExprMap(colExprMap);
    return new OpAttr("", new HashSet<Integer>(), gbOp);
}

Example 5 with Operator

use of org.apache.hadoop.hive.ql.exec.Operator in project hive by apache.

the class MapReduceCompiler method generateTaskTree.

@Override
protected void generateTaskTree(List<Task<? extends Serializable>> rootTasks, ParseContext pCtx, List<Task<MoveWork>> mvTask, Set<ReadEntity> inputs, Set<WriteEntity> outputs) throws SemanticException {
    // generate map reduce plans
    ParseContext tempParseContext = getParseContext(pCtx, rootTasks);
    GenMRProcContext procCtx = new GenMRProcContext(conf, // Must be deterministic order map for consistent q-test output across Java versions
    new LinkedHashMap<Operator<? extends OperatorDesc>, Task<? extends Serializable>>(), tempParseContext, mvTask, rootTasks, new LinkedHashMap<Operator<? extends OperatorDesc>, GenMapRedCtx>(), inputs, outputs);
    // 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(new String("R1"), TableScanOperator.getOperatorName() + "%"), new GenMRTableScan1());
    opRules.put(new RuleRegExp(new String("R2"), TableScanOperator.getOperatorName() + "%.*" + ReduceSinkOperator.getOperatorName() + "%"), new GenMRRedSink1());
    opRules.put(new RuleRegExp(new String("R3"), ReduceSinkOperator.getOperatorName() + "%.*" + ReduceSinkOperator.getOperatorName() + "%"), new GenMRRedSink2());
    opRules.put(new RuleRegExp(new String("R4"), FileSinkOperator.getOperatorName() + "%"), new GenMRFileSink1());
    opRules.put(new RuleRegExp(new String("R5"), UnionOperator.getOperatorName() + "%"), new GenMRUnion1());
    opRules.put(new RuleRegExp(new String("R6"), UnionOperator.getOperatorName() + "%.*" + ReduceSinkOperator.getOperatorName() + "%"), new GenMRRedSink3());
    opRules.put(new RuleRegExp(new String("R7"), MapJoinOperator.getOperatorName() + "%"), MapJoinFactory.getTableScanMapJoin());
    // The dispatcher fires the processor corresponding to the closest matching
    // rule and passes the context along
    Dispatcher disp = new DefaultRuleDispatcher(new GenMROperator(), opRules, procCtx);
    GraphWalker ogw = new GenMapRedWalker(disp);
    ArrayList<Node> topNodes = new ArrayList<Node>();
    topNodes.addAll(pCtx.getTopOps().values());
    ogw.startWalking(topNodes, null);
}