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

Example 26 with TableDesc

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

the class HiveContextAwareRecordReader method doNext.

public boolean doNext(K key, V value) throws IOException {
    if (this.isSorted) {
        if (this.getIOContext().shouldEndBinarySearch() || (!this.getIOContext().useSorted() && this.wasUsingSortedSearch)) {
            beginLinearSearch();
            this.wasUsingSortedSearch = false;
            this.getIOContext().setEndBinarySearch(false);
        }
        if (this.getIOContext().useSorted()) {
            if (this.genericUDFClassName == null && this.getIOContext().getGenericUDFClassName() != null) {
                setGenericUDFClassName(this.getIOContext().getGenericUDFClassName());
            }
            if (this.getIOContext().isBinarySearching()) {
                // Proceed with a binary search
                if (this.getIOContext().getComparison() != null) {
                    switch(this.getIOContext().getComparison()) {
                        case GREATER:
                        case EQUAL:
                            // Indexes have only one entry per value, could go linear from here, if we want to
                            // use this for any sorted table, we'll need to continue the search
                            rangeEnd = previousPosition;
                            break;
                        case LESS:
                            rangeStart = previousPosition;
                            break;
                        default:
                            break;
                    }
                }
                long position = (rangeStart + rangeEnd) / 2;
                sync(position);
                long newPosition = getSyncedPosition();
                // matching rows must be in the final block, so we can end the binary search.
                if (newPosition == previousPosition || newPosition >= splitEnd) {
                    this.getIOContext().setBinarySearching(false);
                    sync(rangeStart);
                }
                previousPosition = newPosition;
            } else if (foundAllTargets()) {
                // Found all possible rows which will not be filtered
                return false;
            }
        }
    }
    try {
        /**
       * When start reading new file, check header, footer rows.
       * If file contains header, skip header lines before reading the records.
       * If file contains footer, used a FooterBuffer to remove footer lines
       * at the end of the table file.
       **/
        if (this.ioCxtRef.getCurrentBlockStart() == 0) {
            // Check if the table file has header to skip.
            footerBuffer = null;
            Path filePath = this.ioCxtRef.getInputPath();
            PartitionDesc part = null;
            try {
                if (pathToPartitionInfo == null) {
                    pathToPartitionInfo = Utilities.getMapWork(jobConf).getPathToPartitionInfo();
                }
                part = HiveFileFormatUtils.getPartitionDescFromPathRecursively(pathToPartitionInfo, filePath, IOPrepareCache.get().getPartitionDescMap());
            } catch (AssertionError ae) {
                LOG.info("Cannot get partition description from " + this.ioCxtRef.getInputPath() + "because " + ae.getMessage());
                part = null;
            } catch (Exception e) {
                LOG.info("Cannot get partition description from " + this.ioCxtRef.getInputPath() + "because " + e.getMessage());
                part = null;
            }
            TableDesc table = (part == null) ? null : part.getTableDesc();
            if (table != null) {
                headerCount = Utilities.getHeaderCount(table);
                footerCount = Utilities.getFooterCount(table, jobConf);
            }
            // If input contains header, skip header.
            if (!Utilities.skipHeader(recordReader, headerCount, (WritableComparable) key, (Writable) value)) {
                return false;
            }
            if (footerCount > 0) {
                footerBuffer = new FooterBuffer();
                if (!footerBuffer.initializeBuffer(jobConf, recordReader, footerCount, (WritableComparable) key, (Writable) value)) {
                    return false;
                }
            }
        }
        if (footerBuffer == null) {
            // Table files don't have footer rows.
            return recordReader.next(key, value);
        } else {
            return footerBuffer.updateBuffer(jobConf, recordReader, (WritableComparable) key, (Writable) value);
        }
    } catch (Exception e) {
        return HiveIOExceptionHandlerUtil.handleRecordReaderNextException(e, jobConf);
    }
}

Example 27 with TableDesc

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

the class HiveInputFormat method getSplits.

public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException {
    PerfLogger perfLogger = SessionState.getPerfLogger();
    perfLogger.PerfLogBegin(CLASS_NAME, PerfLogger.GET_SPLITS);
    init(job);
    Path[] dirs = getInputPaths(job);
    JobConf newjob = new JobConf(job);
    List<InputSplit> result = new ArrayList<InputSplit>();
    List<Path> currentDirs = new ArrayList<Path>();
    Class<? extends InputFormat> currentInputFormatClass = null;
    TableDesc currentTable = null;
    TableScanOperator currentTableScan = null;
    boolean pushDownProjection = false;
    //Buffers to hold filter pushdown information
    StringBuilder readColumnsBuffer = new StringBuilder(newjob.get(ColumnProjectionUtils.READ_COLUMN_IDS_CONF_STR, ""));
    ;
    StringBuilder readColumnNamesBuffer = new StringBuilder(newjob.get(ColumnProjectionUtils.READ_COLUMN_NAMES_CONF_STR, ""));
    // for each dir, get the InputFormat, and do getSplits.
    for (Path dir : dirs) {
        PartitionDesc part = getPartitionDescFromPath(pathToPartitionInfo, dir);
        Class<? extends InputFormat> inputFormatClass = part.getInputFileFormatClass();
        TableDesc table = part.getTableDesc();
        TableScanOperator tableScan = null;
        List<String> aliases = mrwork.getPathToAliases().get(dir);
        // Make filter pushdown information available to getSplits.
        if ((aliases != null) && (aliases.size() == 1)) {
            Operator op = mrwork.getAliasToWork().get(aliases.get(0));
            if ((op != null) && (op instanceof TableScanOperator)) {
                tableScan = (TableScanOperator) op;
                //Reset buffers to store filter push down columns
                readColumnsBuffer.setLength(0);
                readColumnNamesBuffer.setLength(0);
                // push down projections.
                ColumnProjectionUtils.appendReadColumns(readColumnsBuffer, readColumnNamesBuffer, tableScan.getNeededColumnIDs(), tableScan.getNeededColumns());
                pushDownProjection = true;
                // push down filters
                pushFilters(newjob, tableScan);
            }
        } else {
            if (LOG.isDebugEnabled()) {
                LOG.debug("aliases: {} pathToAliases: {} dir: {}", aliases, mrwork.getPathToAliases(), dir);
            }
        }
        if (!currentDirs.isEmpty() && inputFormatClass.equals(currentInputFormatClass) && table.equals(currentTable) && tableScan == currentTableScan) {
            currentDirs.add(dir);
            continue;
        }
        if (!currentDirs.isEmpty()) {
            if (LOG.isInfoEnabled()) {
                LOG.info("Generating splits as currentDirs is not empty. currentDirs: {}", currentDirs);
            }
            // set columns to read in conf
            if (pushDownProjection) {
                pushProjection(newjob, readColumnsBuffer, readColumnNamesBuffer);
            }
            addSplitsForGroup(currentDirs, currentTableScan, newjob, getInputFormatFromCache(currentInputFormatClass, job), currentInputFormatClass, currentDirs.size() * (numSplits / dirs.length), currentTable, result);
        }
        currentDirs.clear();
        currentDirs.add(dir);
        currentTableScan = tableScan;
        currentTable = table;
        currentInputFormatClass = inputFormatClass;
    }
    // set columns to read in conf
    if (pushDownProjection) {
        pushProjection(newjob, readColumnsBuffer, readColumnNamesBuffer);
    }
    if (dirs.length != 0) {
        if (LOG.isInfoEnabled()) {
            LOG.info("Generating splits for dirs: {}", dirs);
        }
        addSplitsForGroup(currentDirs, currentTableScan, newjob, getInputFormatFromCache(currentInputFormatClass, job), currentInputFormatClass, currentDirs.size() * (numSplits / dirs.length), currentTable, result);
    }
    Utilities.clearWorkMapForConf(job);
    if (LOG.isInfoEnabled()) {
        LOG.info("number of splits " + result.size());
    }
    perfLogger.PerfLogEnd(CLASS_NAME, PerfLogger.GET_SPLITS);
    return result.toArray(new HiveInputSplit[result.size()]);
}

Example 28 with TableDesc

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

the class CombineHiveInputFormat method getCombineSplits.

/**
   * Create Hive splits based on CombineFileSplit.
   */
private InputSplit[] getCombineSplits(JobConf job, int numSplits, Map<Path, PartitionDesc> pathToPartitionInfo) throws IOException {
    init(job);
    Map<Path, ArrayList<String>> pathToAliases = mrwork.getPathToAliases();
    Map<String, Operator<? extends OperatorDesc>> aliasToWork = mrwork.getAliasToWork();
    CombineFileInputFormatShim combine = ShimLoader.getHadoopShims().getCombineFileInputFormat();
    InputSplit[] splits = null;
    if (combine == null) {
        splits = super.getSplits(job, numSplits);
        return splits;
    }
    if (combine.getInputPathsShim(job).length == 0) {
        throw new IOException("No input paths specified in job");
    }
    ArrayList<InputSplit> result = new ArrayList<InputSplit>();
    // combine splits only from same tables and same partitions. Do not combine splits from multiple
    // tables or multiple partitions.
    Path[] paths = StringInternUtils.internUriStringsInPathArray(combine.getInputPathsShim(job));
    List<Path> inpDirs = new ArrayList<Path>();
    List<Path> inpFiles = new ArrayList<Path>();
    Map<CombinePathInputFormat, CombineFilter> poolMap = new HashMap<CombinePathInputFormat, CombineFilter>();
    Set<Path> poolSet = new HashSet<Path>();
    for (Path path : paths) {
        PartitionDesc part = HiveFileFormatUtils.getPartitionDescFromPathRecursively(pathToPartitionInfo, path, IOPrepareCache.get().allocatePartitionDescMap());
        TableDesc tableDesc = part.getTableDesc();
        if ((tableDesc != null) && tableDesc.isNonNative()) {
            return super.getSplits(job, numSplits);
        }
        // Use HiveInputFormat if any of the paths is not splittable
        Class inputFormatClass = part.getInputFileFormatClass();
        String inputFormatClassName = inputFormatClass.getName();
        InputFormat inputFormat = getInputFormatFromCache(inputFormatClass, job);
        String deserializerClassName = null;
        try {
            deserializerClassName = part.getDeserializer(job).getClass().getName();
        } catch (Exception e) {
        // ignore
        }
        FileSystem inpFs = path.getFileSystem(job);
        //don't combine if inputformat is a SymlinkTextInputFormat
        if (inputFormat instanceof SymlinkTextInputFormat) {
            splits = super.getSplits(job, numSplits);
            return splits;
        }
        Path filterPath = path;
        // Does a pool exist for this path already
        CombineFilter f = null;
        List<Operator<? extends OperatorDesc>> opList = null;
        if (!mrwork.isMapperCannotSpanPartns()) {
            //if mapper can span partitions, make sure a splits does not contain multiple
            // opList + inputFormatClassName + deserializerClassName combination
            // This is done using the Map of CombinePathInputFormat to PathFilter
            opList = HiveFileFormatUtils.doGetWorksFromPath(pathToAliases, aliasToWork, filterPath);
            CombinePathInputFormat combinePathInputFormat = new CombinePathInputFormat(opList, inputFormatClassName, deserializerClassName);
            f = poolMap.get(combinePathInputFormat);
            if (f == null) {
                f = new CombineFilter(filterPath);
                LOG.info("CombineHiveInputSplit creating pool for " + path + "; using filter path " + filterPath);
                combine.createPool(job, f);
                poolMap.put(combinePathInputFormat, f);
            } else {
                LOG.info("CombineHiveInputSplit: pool is already created for " + path + "; using filter path " + filterPath);
                f.addPath(filterPath);
            }
        } else {
            // but won't cross multiple partitions if the user has asked so.
            if (!path.getFileSystem(job).getFileStatus(path).isDir()) {
                // path is not directory
                filterPath = path.getParent();
                inpFiles.add(path);
                poolSet.add(filterPath);
            } else {
                inpDirs.add(path);
            }
        }
    }
    // Processing directories
    List<CombineFileSplit> iss = new ArrayList<CombineFileSplit>();
    if (!mrwork.isMapperCannotSpanPartns()) {
        //mapper can span partitions
        //combine into as few as one split, subject to the PathFilters set
        // using combine.createPool.
        iss = Arrays.asList(combine.getSplits(job, 1));
    } else {
        for (Path path : inpDirs) {
            processPaths(job, combine, iss, path);
        }
        if (inpFiles.size() > 0) {
            // Processing files
            for (Path filterPath : poolSet) {
                combine.createPool(job, new CombineFilter(filterPath));
            }
            processPaths(job, combine, iss, inpFiles.toArray(new Path[0]));
        }
    }
    if (mrwork.getNameToSplitSample() != null && !mrwork.getNameToSplitSample().isEmpty()) {
        iss = sampleSplits(iss);
    }
    for (CombineFileSplit is : iss) {
        CombineHiveInputSplit csplit = new CombineHiveInputSplit(job, is, pathToPartitionInfo);
        result.add(csplit);
    }
    LOG.info("number of splits " + result.size());
    return result.toArray(new CombineHiveInputSplit[result.size()]);
}

Example 29 with TableDesc

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

the class GenMapRedUtils method createMRWorkForMergingFiles.

/**
   * @param fsInput The FileSink operator.
   * @param ctx The MR processing context.
   * @param finalName the final destination path the merge job should output.
   * @param dependencyTask
   * @param mvTasks
   * @param conf
   * @param currTask
   * @throws SemanticException

   * create a Map-only merge job using CombineHiveInputFormat for all partitions with
   * following operators:
   *          MR job J0:
   *          ...
   *          |
   *          v
   *          FileSinkOperator_1 (fsInput)
   *          |
   *          v
   *          Merge job J1:
   *          |
   *          v
   *          TableScan (using CombineHiveInputFormat) (tsMerge)
   *          |
   *          v
   *          FileSinkOperator (fsMerge)
   *
   *          Here the pathToPartitionInfo & pathToAlias will remain the same, which means the paths
   *          do
   *          not contain the dynamic partitions (their parent). So after the dynamic partitions are
   *          created (after the first job finished before the moveTask or ConditionalTask start),
   *          we need to change the pathToPartitionInfo & pathToAlias to include the dynamic
   *          partition
   *          directories.
   *
   */
public static void createMRWorkForMergingFiles(FileSinkOperator fsInput, Path finalName, DependencyCollectionTask dependencyTask, List<Task<MoveWork>> mvTasks, HiveConf conf, Task<? extends Serializable> currTask) throws SemanticException {
    //
    // 1. create the operator tree
    //
    FileSinkDesc fsInputDesc = fsInput.getConf();
    // Create a TableScan operator
    RowSchema inputRS = fsInput.getSchema();
    TableScanOperator tsMerge = GenMapRedUtils.createTemporaryTableScanOperator(fsInput.getCompilationOpContext(), inputRS);
    // Create a FileSink operator
    TableDesc ts = (TableDesc) fsInputDesc.getTableInfo().clone();
    FileSinkDesc fsOutputDesc = new FileSinkDesc(finalName, ts, conf.getBoolVar(ConfVars.COMPRESSRESULT));
    FileSinkOperator fsOutput = (FileSinkOperator) OperatorFactory.getAndMakeChild(fsOutputDesc, inputRS, tsMerge);
    // If the input FileSinkOperator is a dynamic partition enabled, the tsMerge input schema
    // needs to include the partition column, and the fsOutput should have
    // a DynamicPartitionCtx to indicate that it needs to dynamically partitioned.
    DynamicPartitionCtx dpCtx = fsInputDesc.getDynPartCtx();
    if (dpCtx != null && dpCtx.getNumDPCols() > 0) {
        // adding DP ColumnInfo to the RowSchema signature
        ArrayList<ColumnInfo> signature = inputRS.getSignature();
        String tblAlias = fsInputDesc.getTableInfo().getTableName();
        for (String dpCol : dpCtx.getDPColNames()) {
            ColumnInfo colInfo = new ColumnInfo(dpCol, // all partition column type should be string
            TypeInfoFactory.stringTypeInfo, tblAlias, // partition column is virtual column
            true);
            signature.add(colInfo);
        }
        inputRS.setSignature(signature);
        // create another DynamicPartitionCtx, which has a different input-to-DP column mapping
        DynamicPartitionCtx dpCtx2 = new DynamicPartitionCtx(dpCtx);
        fsOutputDesc.setDynPartCtx(dpCtx2);
        // update the FileSinkOperator to include partition columns
        usePartitionColumns(fsInputDesc.getTableInfo().getProperties(), dpCtx.getDPColNames());
    } else {
        // non-partitioned table
        fsInputDesc.getTableInfo().getProperties().remove(org.apache.hadoop.hive.metastore.api.hive_metastoreConstants.META_TABLE_PARTITION_COLUMNS);
    }
    //
    // 2. Constructing a conditional task consisting of a move task and a map reduce task
    //
    MoveWork dummyMv = new MoveWork(null, null, null, new LoadFileDesc(fsInputDesc.getFinalDirName(), finalName, true, null, null), false);
    MapWork cplan;
    Serializable work;
    if ((conf.getBoolVar(ConfVars.HIVEMERGERCFILEBLOCKLEVEL) && fsInputDesc.getTableInfo().getInputFileFormatClass().equals(RCFileInputFormat.class)) || (conf.getBoolVar(ConfVars.HIVEMERGEORCFILESTRIPELEVEL) && fsInputDesc.getTableInfo().getInputFileFormatClass().equals(OrcInputFormat.class))) {
        cplan = GenMapRedUtils.createMergeTask(fsInputDesc, finalName, dpCtx != null && dpCtx.getNumDPCols() > 0, fsInput.getCompilationOpContext());
        if (conf.getVar(ConfVars.HIVE_EXECUTION_ENGINE).equals("tez")) {
            work = new TezWork(conf.getVar(HiveConf.ConfVars.HIVEQUERYID), conf);
            cplan.setName("File Merge");
            ((TezWork) work).add(cplan);
        } else if (conf.getVar(ConfVars.HIVE_EXECUTION_ENGINE).equals("spark")) {
            work = new SparkWork(conf.getVar(HiveConf.ConfVars.HIVEQUERYID));
            cplan.setName("Spark Merge File Work");
            ((SparkWork) work).add(cplan);
        } else {
            work = cplan;
        }
    } else {
        cplan = createMRWorkForMergingFiles(conf, tsMerge, fsInputDesc);
        if (conf.getVar(ConfVars.HIVE_EXECUTION_ENGINE).equals("tez")) {
            work = new TezWork(conf.getVar(HiveConf.ConfVars.HIVEQUERYID), conf);
            cplan.setName("File Merge");
            ((TezWork) work).add(cplan);
        } else if (conf.getVar(ConfVars.HIVE_EXECUTION_ENGINE).equals("spark")) {
            work = new SparkWork(conf.getVar(HiveConf.ConfVars.HIVEQUERYID));
            cplan.setName("Spark Merge File Work");
            ((SparkWork) work).add(cplan);
        } else {
            work = new MapredWork();
            ((MapredWork) work).setMapWork(cplan);
        }
    }
    // use CombineHiveInputFormat for map-only merging
    cplan.setInputformat("org.apache.hadoop.hive.ql.io.CombineHiveInputFormat");
    // NOTE: we should gather stats in MR1 rather than MR2 at merge job since we don't
    // know if merge MR2 will be triggered at execution time
    Task<MoveWork> mvTask = GenMapRedUtils.findMoveTask(mvTasks, fsOutput);
    ConditionalTask cndTsk = GenMapRedUtils.createCondTask(conf, currTask, dummyMv, work, fsInputDesc.getFinalDirName(), finalName, mvTask, dependencyTask);
    // keep the dynamic partition context in conditional task resolver context
    ConditionalResolverMergeFilesCtx mrCtx = (ConditionalResolverMergeFilesCtx) cndTsk.getResolverCtx();
    mrCtx.setDPCtx(fsInputDesc.getDynPartCtx());
    mrCtx.setLbCtx(fsInputDesc.getLbCtx());
}

Example 30 with TableDesc

use of org.apache.hadoop.hive.ql.plan.TableDesc 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).setTableDesc(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);
        mapJoinDescriptor.setTagOrder(tags);
        mapJoinDescriptor.setHandleSkewJoin(false);
        mapJoinDescriptor.setNullSafes(joinDescriptor.getNullSafes());
        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, jc);
        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, parseCtx.getConf());
    cndTsk.setListTasks(listTasks);
    cndTsk.setResolver(new ConditionalResolverSkewJoin());
    cndTsk.setResolverCtx(context);
    currTask.setChildTasks(new ArrayList<Task<? extends Serializable>>());
    currTask.addDependentTask(cndTsk);
    return;
}