Examples with MapWork org.apache.hadoop.hive.ql.plan.MapWork used on opensource projects

Example 21 with MapWork

use of org.apache.hadoop.hive.ql.plan.MapWork in project hive by apache.

the class GenSparkUtils method removeUnionOperators.

// removes any union operator and clones the plan
public void removeUnionOperators(GenSparkProcContext context, BaseWork work) throws SemanticException {
    List<Operator<?>> roots = new ArrayList<Operator<?>>();
    // replacing getAliasToWork, so should use that information instead.
    if (work instanceof MapWork) {
        roots.addAll(((MapWork) work).getAliasToWork().values());
    } else {
        roots.addAll(work.getAllRootOperators());
    }
    if (work.getDummyOps() != null) {
        roots.addAll(work.getDummyOps());
    }
    // need to clone the plan.
    List<Operator<?>> newRoots = SerializationUtilities.cloneOperatorTree(roots);
    // Build a map to map the original FileSinkOperator and the cloned FileSinkOperators
    // This map is used for set the stats flag for the cloned FileSinkOperators in later process
    Iterator<Operator<?>> newRootsIt = newRoots.iterator();
    for (Operator<?> root : roots) {
        Operator<?> newRoot = newRootsIt.next();
        List<Operator<?>> newOpQueue = new LinkedList<Operator<?>>();
        collectOperators(newRoot, newOpQueue);
        List<Operator<?>> opQueue = new LinkedList<Operator<?>>();
        collectOperators(root, opQueue);
        Iterator<Operator<?>> newOpQueueIt = newOpQueue.iterator();
        for (Operator<?> op : opQueue) {
            Operator<?> newOp = newOpQueueIt.next();
            // though we clone the op tree, we're still using the same MapWork/ReduceWork.
            if (context.rootToWorkMap.containsKey(op)) {
                context.rootToWorkMap.put(newOp, context.rootToWorkMap.get(op));
            }
            if (op instanceof FileSinkOperator) {
                List<FileSinkOperator> fileSinkList = context.fileSinkMap.get(op);
                if (fileSinkList == null) {
                    fileSinkList = new LinkedList<FileSinkOperator>();
                }
                fileSinkList.add((FileSinkOperator) newOp);
                context.fileSinkMap.put((FileSinkOperator) op, fileSinkList);
            } else if (op instanceof SparkPartitionPruningSinkOperator) {
                SparkPartitionPruningSinkOperator oldPruningSink = (SparkPartitionPruningSinkOperator) op;
                SparkPartitionPruningSinkOperator newPruningSink = (SparkPartitionPruningSinkOperator) newOp;
                for (int i = 0; i < oldPruningSink.getConf().getTargetInfos().size(); i++) {
                    newPruningSink.getConf().getTargetInfos().get(i).tableScan = oldPruningSink.getConf().getTargetInfos().get(i).tableScan;
                }
                context.pruningSinkSet.add(newPruningSink);
                context.pruningSinkSet.remove(oldPruningSink);
            }
        }
    }
    // we're cloning the operator plan but we're retaining the original work. That means
    // that root operators have to be replaced with the cloned ops. The replacement map
    // tells you what that mapping is.
    Map<Operator<?>, Operator<?>> replacementMap = new HashMap<Operator<?>, Operator<?>>();
    // there's some special handling for dummyOps required. Mapjoins won't be properly
    // initialized if their dummy parents aren't initialized. Since we cloned the plan
    // we need to replace the dummy operators in the work with the cloned ones.
    List<HashTableDummyOperator> dummyOps = new LinkedList<HashTableDummyOperator>();
    Iterator<Operator<?>> it = newRoots.iterator();
    for (Operator<?> orig : roots) {
        Set<FileSinkOperator> fsOpSet = OperatorUtils.findOperators(orig, FileSinkOperator.class);
        for (FileSinkOperator fsOp : fsOpSet) {
            context.fileSinkSet.remove(fsOp);
        }
        Operator<?> newRoot = it.next();
        if (newRoot instanceof HashTableDummyOperator) {
            dummyOps.add((HashTableDummyOperator) newRoot);
            it.remove();
        } else {
            replacementMap.put(orig, newRoot);
        }
    }
    // now we remove all the unions. we throw away any branch that's not reachable from
    // the current set of roots. The reason is that those branches will be handled in
    // different tasks.
    Deque<Operator<?>> operators = new LinkedList<Operator<?>>();
    operators.addAll(newRoots);
    Set<Operator<?>> seen = new HashSet<Operator<?>>();
    while (!operators.isEmpty()) {
        Operator<?> current = operators.pop();
        seen.add(current);
        if (current instanceof FileSinkOperator) {
            FileSinkOperator fileSink = (FileSinkOperator) current;
            // remember it for additional processing later
            context.fileSinkSet.add(fileSink);
            FileSinkDesc desc = fileSink.getConf();
            Path path = desc.getDirName();
            List<FileSinkDesc> linked;
            if (!context.linkedFileSinks.containsKey(path)) {
                linked = new ArrayList<FileSinkDesc>();
                context.linkedFileSinks.put(path, linked);
            }
            linked = context.linkedFileSinks.get(path);
            linked.add(desc);
            desc.setLinkedFileSinkDesc(linked);
        }
        if (current instanceof UnionOperator) {
            Operator<?> parent = null;
            int count = 0;
            for (Operator<?> op : current.getParentOperators()) {
                if (seen.contains(op)) {
                    ++count;
                    parent = op;
                }
            }
            // we should have been able to reach the union from only one side.
            Preconditions.checkArgument(count <= 1, "AssertionError: expected count to be <= 1, but was " + count);
            if (parent == null) {
                // root operator is union (can happen in reducers)
                replacementMap.put(current, current.getChildOperators().get(0));
            } else {
                parent.removeChildAndAdoptItsChildren(current);
            }
        }
        if (current instanceof FileSinkOperator || current instanceof ReduceSinkOperator) {
            current.setChildOperators(null);
        } else {
            operators.addAll(current.getChildOperators());
        }
    }
    work.setDummyOps(dummyOps);
    work.replaceRoots(replacementMap);
}

Example 22 with MapWork

use of org.apache.hadoop.hive.ql.plan.MapWork in project hive by apache.

the class GenSparkUtils method annotateMapWork.

/**
 * Fill MapWork with 'local' work and bucket information for SMB Join.
 * @param context context, containing references to MapWorks and their SMB information.
 * @throws SemanticException
 */
public void annotateMapWork(GenSparkProcContext context) throws SemanticException {
    for (SMBMapJoinOperator smbMapJoinOp : context.smbMapJoinCtxMap.keySet()) {
        // initialize mapwork with smbMapJoin information.
        SparkSMBMapJoinInfo smbMapJoinInfo = context.smbMapJoinCtxMap.get(smbMapJoinOp);
        MapWork work = smbMapJoinInfo.mapWork;
        SparkSortMergeJoinFactory.annotateMapWork(context, work, smbMapJoinOp, (TableScanOperator) smbMapJoinInfo.bigTableRootOp, false);
        for (Operator<?> smallTableRootOp : smbMapJoinInfo.smallTableRootOps) {
            SparkSortMergeJoinFactory.annotateMapWork(context, work, smbMapJoinOp, (TableScanOperator) smallTableRootOp, true);
        }
    }
}

Example 23 with MapWork

use of org.apache.hadoop.hive.ql.plan.MapWork in project hive by apache.

the class SparkProcessAnalyzeTable method process.

@SuppressWarnings("unchecked")
@Override
public Object process(Node nd, Stack<Node> stack, NodeProcessorCtx procContext, Object... nodeOutputs) throws SemanticException {
    GenSparkProcContext context = (GenSparkProcContext) procContext;
    TableScanOperator tableScan = (TableScanOperator) nd;
    ParseContext parseContext = context.parseContext;
    Table table = tableScan.getConf().getTableMetadata();
    @SuppressWarnings("rawtypes") Class<? extends InputFormat> inputFormat = table.getInputFormatClass();
    if (parseContext.getQueryProperties().isAnalyzeCommand()) {
        Preconditions.checkArgument(tableScan.getChildOperators() == null || tableScan.getChildOperators().size() == 0, "AssertionError: expected tableScan.getChildOperators() to be null, " + "or tableScan.getChildOperators().size() to be 0");
        String alias = null;
        for (String a : parseContext.getTopOps().keySet()) {
            if (tableScan == parseContext.getTopOps().get(a)) {
                alias = a;
            }
        }
        Preconditions.checkArgument(alias != null, "AssertionError: expected alias to be not null");
        SparkWork sparkWork = context.currentTask.getWork();
        if (OrcInputFormat.class.isAssignableFrom(inputFormat) || MapredParquetInputFormat.class.isAssignableFrom(inputFormat)) {
            // For ORC & Parquet, all the following statements are the same
            // ANALYZE TABLE T [PARTITION (...)] COMPUTE STATISTICS
            // ANALYZE TABLE T [PARTITION (...)] COMPUTE STATISTICS noscan;
            // There will not be any Spark job above this task
            StatsWork statWork = new StatsWork(table, parseContext.getConf());
            statWork.setFooterScan();
            // If partition is specified, get pruned partition list
            Set<Partition> confirmedParts = GenMapRedUtils.getConfirmedPartitionsForScan(tableScan);
            if (confirmedParts.size() > 0) {
                List<String> partCols = GenMapRedUtils.getPartitionColumns(tableScan);
                PrunedPartitionList partList = new PrunedPartitionList(table, confirmedParts, partCols, false);
                statWork.addInputPartitions(partList.getPartitions());
            }
            Task<StatsWork> snjTask = TaskFactory.get(statWork);
            snjTask.setParentTasks(null);
            context.rootTasks.remove(context.currentTask);
            context.rootTasks.add(snjTask);
            return true;
        } else {
            // ANALYZE TABLE T [PARTITION (...)] COMPUTE STATISTICS;
            // The plan consists of a simple SparkTask followed by a StatsTask.
            // The Spark task is just a simple TableScanOperator
            BasicStatsWork basicStatsWork = new BasicStatsWork(table.getTableSpec());
            basicStatsWork.setNoScanAnalyzeCommand(parseContext.getQueryProperties().isNoScanAnalyzeCommand());
            StatsWork columnStatsWork = new StatsWork(table, basicStatsWork, parseContext.getConf());
            columnStatsWork.collectStatsFromAggregator(tableScan.getConf());
            columnStatsWork.setSourceTask(context.currentTask);
            Task<StatsWork> statsTask = TaskFactory.get(columnStatsWork);
            context.currentTask.addDependentTask(statsTask);
            // The plan consists of a StatsTask only.
            if (parseContext.getQueryProperties().isNoScanAnalyzeCommand()) {
                statsTask.setParentTasks(null);
                context.rootTasks.remove(context.currentTask);
                context.rootTasks.add(statsTask);
            }
            // NOTE: here we should use the new partition predicate pushdown API to get a list of pruned list,
            // and pass it to setTaskPlan as the last parameter
            Set<Partition> confirmedPartns = GenMapRedUtils.getConfirmedPartitionsForScan(tableScan);
            PrunedPartitionList partitions = null;
            if (confirmedPartns.size() > 0) {
                List<String> partCols = GenMapRedUtils.getPartitionColumns(tableScan);
                partitions = new PrunedPartitionList(table, confirmedPartns, partCols, false);
            }
            MapWork w = utils.createMapWork(context, tableScan, sparkWork, partitions);
            w.setGatheringStats(true);
            return true;
        }
    }
    return null;
}

Example 24 with MapWork

use of org.apache.hadoop.hive.ql.plan.MapWork in project hive by apache.

the class GenMRSkewJoinProcessor method processSkewJoin.

/**
 * Create tasks for processing skew joins. The idea is (HIVE-964) to use
 * separated jobs and map-joins to handle skew joins.
 * <p>
 * <ul>
 * <li>
 * Number of mr jobs to handle skew keys is the number of table minus 1 (we
 * can stream the last table, so big keys in the last table will not be a
 * problem).
 * <li>
 * At runtime in Join, we output big keys in one table into one corresponding
 * directories, and all same keys in other tables into different dirs(one for
 * each table). The directories will look like:
 * <ul>
 * <li>
 * dir-T1-bigkeys(containing big keys in T1), dir-T2-keys(containing keys
 * which is big in T1),dir-T3-keys(containing keys which is big in T1), ...
 * <li>
 * dir-T1-keys(containing keys which is big in T2), dir-T2-bigkeys(containing
 * big keys in T2),dir-T3-keys(containing keys which is big in T2), ...
 * <li>
 * dir-T1-keys(containing keys which is big in T3), dir-T2-keys(containing big
 * keys in T3),dir-T3-bigkeys(containing keys which is big in T3), ... .....
 * </ul>
 * </ul>
 * For each table, we launch one mapjoin job, taking the directory containing
 * big keys in this table and corresponding dirs in other tables as input.
 * (Actally one job for one row in the above.)
 *
 * <p>
 * For more discussions, please check
 * https://issues.apache.org/jira/browse/HIVE-964.
 */
@SuppressWarnings("unchecked")
public static void processSkewJoin(JoinOperator joinOp, Task<? extends Serializable> currTask, ParseContext parseCtx) throws SemanticException {
    // now does not work with outer joins
    if (!GenMRSkewJoinProcessor.skewJoinEnabled(parseCtx.getConf(), joinOp)) {
        return;
    }
    List<Task<? extends Serializable>> children = currTask.getChildTasks();
    Path baseTmpDir = parseCtx.getContext().getMRTmpPath();
    JoinDesc joinDescriptor = joinOp.getConf();
    Map<Byte, List<ExprNodeDesc>> joinValues = joinDescriptor.getExprs();
    int numAliases = joinValues.size();
    Map<Byte, Path> bigKeysDirMap = new HashMap<Byte, Path>();
    Map<Byte, Map<Byte, Path>> smallKeysDirMap = new HashMap<Byte, Map<Byte, Path>>();
    Map<Byte, Path> skewJoinJobResultsDir = new HashMap<Byte, Path>();
    Byte[] tags = joinDescriptor.getTagOrder();
    for (int i = 0; i < numAliases; i++) {
        Byte alias = tags[i];
        bigKeysDirMap.put(alias, getBigKeysDir(baseTmpDir, alias));
        Map<Byte, Path> smallKeysMap = new HashMap<Byte, Path>();
        smallKeysDirMap.put(alias, smallKeysMap);
        for (Byte src2 : tags) {
            if (!src2.equals(alias)) {
                smallKeysMap.put(src2, getSmallKeysDir(baseTmpDir, alias, src2));
            }
        }
        skewJoinJobResultsDir.put(alias, getBigKeysSkewJoinResultDir(baseTmpDir, alias));
    }
    joinDescriptor.setHandleSkewJoin(true);
    joinDescriptor.setBigKeysDirMap(bigKeysDirMap);
    joinDescriptor.setSmallKeysDirMap(smallKeysDirMap);
    joinDescriptor.setSkewKeyDefinition(HiveConf.getIntVar(parseCtx.getConf(), HiveConf.ConfVars.HIVESKEWJOINKEY));
    HashMap<Path, Task<? extends Serializable>> bigKeysDirToTaskMap = new HashMap<Path, Task<? extends Serializable>>();
    List<Serializable> listWorks = new ArrayList<Serializable>();
    List<Task<? extends Serializable>> listTasks = new ArrayList<Task<? extends Serializable>>();
    MapredWork currPlan = (MapredWork) currTask.getWork();
    TableDesc keyTblDesc = (TableDesc) currPlan.getReduceWork().getKeyDesc().clone();
    List<String> joinKeys = Utilities.getColumnNames(keyTblDesc.getProperties());
    List<String> joinKeyTypes = Utilities.getColumnTypes(keyTblDesc.getProperties());
    Map<Byte, TableDesc> tableDescList = new HashMap<Byte, TableDesc>();
    Map<Byte, RowSchema> rowSchemaList = new HashMap<Byte, RowSchema>();
    Map<Byte, List<ExprNodeDesc>> newJoinValues = new HashMap<Byte, List<ExprNodeDesc>>();
    Map<Byte, List<ExprNodeDesc>> newJoinKeys = new HashMap<Byte, List<ExprNodeDesc>>();
    // used for create mapJoinDesc, should be in order
    List<TableDesc> newJoinValueTblDesc = new ArrayList<TableDesc>();
    for (Byte tag : tags) {
        newJoinValueTblDesc.add(null);
    }
    for (int i = 0; i < numAliases; i++) {
        Byte alias = tags[i];
        List<ExprNodeDesc> valueCols = joinValues.get(alias);
        String colNames = "";
        String colTypes = "";
        int columnSize = valueCols.size();
        List<ExprNodeDesc> newValueExpr = new ArrayList<ExprNodeDesc>();
        List<ExprNodeDesc> newKeyExpr = new ArrayList<ExprNodeDesc>();
        ArrayList<ColumnInfo> columnInfos = new ArrayList<ColumnInfo>();
        boolean first = true;
        for (int k = 0; k < columnSize; k++) {
            TypeInfo type = valueCols.get(k).getTypeInfo();
            // any name, it does not matter.
            String newColName = i + "_VALUE_" + k;
            ColumnInfo columnInfo = new ColumnInfo(newColName, type, alias.toString(), false);
            columnInfos.add(columnInfo);
            newValueExpr.add(new ExprNodeColumnDesc(columnInfo));
            if (!first) {
                colNames = colNames + ",";
                colTypes = colTypes + ",";
            }
            first = false;
            colNames = colNames + newColName;
            colTypes = colTypes + valueCols.get(k).getTypeString();
        }
        // we are putting join keys at last part of the spilled table
        for (int k = 0; k < joinKeys.size(); k++) {
            if (!first) {
                colNames = colNames + ",";
                colTypes = colTypes + ",";
            }
            first = false;
            colNames = colNames + joinKeys.get(k);
            colTypes = colTypes + joinKeyTypes.get(k);
            ColumnInfo columnInfo = new ColumnInfo(joinKeys.get(k), TypeInfoFactory.getPrimitiveTypeInfo(joinKeyTypes.get(k)), alias.toString(), false);
            columnInfos.add(columnInfo);
            newKeyExpr.add(new ExprNodeColumnDesc(columnInfo));
        }
        newJoinValues.put(alias, newValueExpr);
        newJoinKeys.put(alias, newKeyExpr);
        tableDescList.put(alias, Utilities.getTableDesc(colNames, colTypes));
        rowSchemaList.put(alias, new RowSchema(columnInfos));
        // construct value table Desc
        String valueColNames = "";
        String valueColTypes = "";
        first = true;
        for (int k = 0; k < columnSize; k++) {
            // any name, it does not matter.
            String newColName = i + "_VALUE_" + k;
            if (!first) {
                valueColNames = valueColNames + ",";
                valueColTypes = valueColTypes + ",";
            }
            valueColNames = valueColNames + newColName;
            valueColTypes = valueColTypes + valueCols.get(k).getTypeString();
            first = false;
        }
        newJoinValueTblDesc.set(Byte.valueOf((byte) i), Utilities.getTableDesc(valueColNames, valueColTypes));
    }
    joinDescriptor.setSkewKeysValuesTables(tableDescList);
    joinDescriptor.setKeyTableDesc(keyTblDesc);
    for (int i = 0; i < numAliases - 1; i++) {
        Byte src = tags[i];
        MapWork newPlan = PlanUtils.getMapRedWork().getMapWork();
        // This code has been only added for testing
        boolean mapperCannotSpanPartns = parseCtx.getConf().getBoolVar(HiveConf.ConfVars.HIVE_MAPPER_CANNOT_SPAN_MULTIPLE_PARTITIONS);
        newPlan.setMapperCannotSpanPartns(mapperCannotSpanPartns);
        MapredWork clonePlan = SerializationUtilities.clonePlan(currPlan);
        Operator<? extends OperatorDesc>[] parentOps = new TableScanOperator[tags.length];
        for (int k = 0; k < tags.length; k++) {
            Operator<? extends OperatorDesc> ts = GenMapRedUtils.createTemporaryTableScanOperator(joinOp.getCompilationOpContext(), rowSchemaList.get((byte) k));
            ((TableScanOperator) ts).setTableDescSkewJoin(tableDescList.get((byte) k));
            parentOps[k] = ts;
        }
        Operator<? extends OperatorDesc> tblScan_op = parentOps[i];
        ArrayList<String> aliases = new ArrayList<String>();
        String alias = src.toString().intern();
        aliases.add(alias);
        Path bigKeyDirPath = bigKeysDirMap.get(src);
        newPlan.addPathToAlias(bigKeyDirPath, aliases);
        newPlan.getAliasToWork().put(alias, tblScan_op);
        PartitionDesc part = new PartitionDesc(tableDescList.get(src), null);
        newPlan.addPathToPartitionInfo(bigKeyDirPath, part);
        newPlan.getAliasToPartnInfo().put(alias, part);
        Operator<? extends OperatorDesc> reducer = clonePlan.getReduceWork().getReducer();
        assert reducer instanceof JoinOperator;
        JoinOperator cloneJoinOp = (JoinOperator) reducer;
        String dumpFilePrefix = "mapfile" + PlanUtils.getCountForMapJoinDumpFilePrefix();
        MapJoinDesc mapJoinDescriptor = new MapJoinDesc(newJoinKeys, keyTblDesc, newJoinValues, newJoinValueTblDesc, newJoinValueTblDesc, joinDescriptor.getOutputColumnNames(), i, joinDescriptor.getConds(), joinDescriptor.getFilters(), joinDescriptor.getNoOuterJoin(), dumpFilePrefix, joinDescriptor.getMemoryMonitorInfo(), joinDescriptor.getInMemoryDataSize());
        mapJoinDescriptor.setTagOrder(tags);
        mapJoinDescriptor.setHandleSkewJoin(false);
        mapJoinDescriptor.setNullSafes(joinDescriptor.getNullSafes());
        mapJoinDescriptor.setColumnExprMap(joinDescriptor.getColumnExprMap());
        MapredLocalWork localPlan = new MapredLocalWork(new LinkedHashMap<String, Operator<? extends OperatorDesc>>(), new LinkedHashMap<String, FetchWork>());
        Map<Byte, Path> smallTblDirs = smallKeysDirMap.get(src);
        for (int j = 0; j < numAliases; j++) {
            if (j == i) {
                continue;
            }
            Byte small_alias = tags[j];
            Operator<? extends OperatorDesc> tblScan_op2 = parentOps[j];
            localPlan.getAliasToWork().put(small_alias.toString(), tblScan_op2);
            Path tblDir = smallTblDirs.get(small_alias);
            localPlan.getAliasToFetchWork().put(small_alias.toString(), new FetchWork(tblDir, tableDescList.get(small_alias)));
        }
        newPlan.setMapRedLocalWork(localPlan);
        // construct a map join and set it as the child operator of tblScan_op
        MapJoinOperator mapJoinOp = (MapJoinOperator) OperatorFactory.getAndMakeChild(joinOp.getCompilationOpContext(), mapJoinDescriptor, (RowSchema) null, parentOps);
        // change the children of the original join operator to point to the map
        // join operator
        List<Operator<? extends OperatorDesc>> childOps = cloneJoinOp.getChildOperators();
        for (Operator<? extends OperatorDesc> childOp : childOps) {
            childOp.replaceParent(cloneJoinOp, mapJoinOp);
        }
        mapJoinOp.setChildOperators(childOps);
        HiveConf jc = new HiveConf(parseCtx.getConf(), GenMRSkewJoinProcessor.class);
        newPlan.setNumMapTasks(HiveConf.getIntVar(jc, HiveConf.ConfVars.HIVESKEWJOINMAPJOINNUMMAPTASK));
        newPlan.setMinSplitSize(HiveConf.getLongVar(jc, HiveConf.ConfVars.HIVESKEWJOINMAPJOINMINSPLIT));
        newPlan.setInputformat(HiveInputFormat.class.getName());
        MapredWork w = new MapredWork();
        w.setMapWork(newPlan);
        Task<? extends Serializable> skewJoinMapJoinTask = TaskFactory.get(w);
        skewJoinMapJoinTask.setFetchSource(currTask.isFetchSource());
        bigKeysDirToTaskMap.put(bigKeyDirPath, skewJoinMapJoinTask);
        listWorks.add(skewJoinMapJoinTask.getWork());
        listTasks.add(skewJoinMapJoinTask);
    }
    if (children != null) {
        for (Task<? extends Serializable> tsk : listTasks) {
            for (Task<? extends Serializable> oldChild : children) {
                tsk.addDependentTask(oldChild);
            }
        }
        currTask.setChildTasks(new ArrayList<Task<? extends Serializable>>());
        for (Task<? extends Serializable> oldChild : children) {
            oldChild.getParentTasks().remove(currTask);
        }
        listTasks.addAll(children);
    }
    ConditionalResolverSkewJoinCtx context = new ConditionalResolverSkewJoinCtx(bigKeysDirToTaskMap, children);
    ConditionalWork cndWork = new ConditionalWork(listWorks);
    ConditionalTask cndTsk = (ConditionalTask) TaskFactory.get(cndWork);
    cndTsk.setListTasks(listTasks);
    cndTsk.setResolver(new ConditionalResolverSkewJoin());
    cndTsk.setResolverCtx(context);
    currTask.setChildTasks(new ArrayList<Task<? extends Serializable>>());
    currTask.addDependentTask(cndTsk);
    return;
}

Example 25 with MapWork

use of org.apache.hadoop.hive.ql.plan.MapWork in project hive by apache.

the class GenMapRedUtils method getMapRedWorkFromConf.

/**
 * create a new plan and return. The pan won't contain the name to split
 * sample information in parse context.
 *
 * @return the new plan
 */
public static MapredWork getMapRedWorkFromConf(HiveConf conf) {
    MapredWork mrWork = new MapredWork();
    MapWork work = mrWork.getMapWork();
    boolean mapperCannotSpanPartns = conf.getBoolVar(HiveConf.ConfVars.HIVE_MAPPER_CANNOT_SPAN_MULTIPLE_PARTITIONS);
    work.setMapperCannotSpanPartns(mapperCannotSpanPartns);
    work.setPathToAliases(new LinkedHashMap<Path, ArrayList<String>>());
    work.setPathToPartitionInfo(new LinkedHashMap<Path, PartitionDesc>());
    work.setAliasToWork(new LinkedHashMap<String, Operator<? extends OperatorDesc>>());
    return mrWork;
}