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

Example 36 with MapJoinOperator

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

the class ReduceSinkMapJoinProc method processReduceSinkToHashJoin.

public static Object processReduceSinkToHashJoin(ReduceSinkOperator parentRS, MapJoinOperator mapJoinOp, GenTezProcContext context) throws SemanticException {
    // remove the tag for in-memory side of mapjoin
    parentRS.getConf().setSkipTag(true);
    parentRS.setSkipTag(true);
    // Mark this small table as being processed
    if (mapJoinOp.getConf().isDynamicPartitionHashJoin()) {
        context.mapJoinToUnprocessedSmallTableReduceSinks.get(mapJoinOp).remove(parentRS);
    }
    List<BaseWork> mapJoinWork = null;
    /*
     *  if there was a pre-existing work generated for the big-table mapjoin side,
     *  we need to hook the work generated for the RS (associated with the RS-MJ pattern)
     *  with the pre-existing work.
     *
     *  Otherwise, we need to associate that the mapjoin op
     *  to be linked to the RS work (associated with the RS-MJ pattern).
     *
     */
    mapJoinWork = context.mapJoinWorkMap.get(mapJoinOp);
    BaseWork parentWork = getMapJoinParentWork(context, parentRS);
    // set the link between mapjoin and parent vertex
    int pos = context.mapJoinParentMap.get(mapJoinOp).indexOf(parentRS);
    if (pos == -1) {
        throw new SemanticException("Cannot find position of parent in mapjoin");
    }
    MapJoinDesc joinConf = mapJoinOp.getConf();
    long keyCount = Long.MAX_VALUE, rowCount = Long.MAX_VALUE, bucketCount = 1;
    long tableSize = Long.MAX_VALUE;
    Statistics stats = parentRS.getStatistics();
    if (stats != null) {
        keyCount = rowCount = stats.getNumRows();
        if (keyCount <= 0) {
            keyCount = rowCount = Long.MAX_VALUE;
        }
        tableSize = stats.getDataSize();
        ArrayList<String> keyCols = parentRS.getConf().getOutputKeyColumnNames();
        if (keyCols != null && !keyCols.isEmpty()) {
            // See if we can arrive at a smaller number using distinct stats from key columns.
            long maxKeyCount = 1;
            String prefix = Utilities.ReduceField.KEY.toString();
            for (String keyCol : keyCols) {
                ExprNodeDesc realCol = parentRS.getColumnExprMap().get(prefix + "." + keyCol);
                ColStatistics cs = StatsUtils.getColStatisticsFromExpression(context.conf, stats, realCol);
                if (cs == null || cs.getCountDistint() <= 0) {
                    maxKeyCount = Long.MAX_VALUE;
                    break;
                }
                maxKeyCount *= cs.getCountDistint();
                if (maxKeyCount >= keyCount) {
                    break;
                }
            }
            keyCount = Math.min(maxKeyCount, keyCount);
        }
        if (joinConf.isBucketMapJoin()) {
            OpTraits opTraits = mapJoinOp.getOpTraits();
            bucketCount = (opTraits == null) ? -1 : opTraits.getNumBuckets();
            if (bucketCount > 0) {
                // We cannot obtain a better estimate without CustomPartitionVertex providing it
                // to us somehow; in which case using statistics would be completely unnecessary.
                keyCount /= bucketCount;
                tableSize /= bucketCount;
            }
        } else if (joinConf.isDynamicPartitionHashJoin()) {
            // For dynamic partitioned hash join, assuming table is split evenly among the reduce tasks.
            bucketCount = parentRS.getConf().getNumReducers();
            keyCount /= bucketCount;
            tableSize /= bucketCount;
        }
    }
    if (keyCount == 0) {
        keyCount = 1;
    }
    if (tableSize == 0) {
        tableSize = 1;
    }
    LOG.info("Mapjoin " + mapJoinOp + "(bucket map join = " + joinConf.isBucketMapJoin() + "), pos: " + pos + " --> " + parentWork.getName() + " (" + keyCount + " keys estimated from " + rowCount + " rows, " + bucketCount + " buckets)");
    joinConf.getParentToInput().put(pos, parentWork.getName());
    if (keyCount != Long.MAX_VALUE) {
        joinConf.getParentKeyCounts().put(pos, keyCount);
    }
    joinConf.getParentDataSizes().put(pos, tableSize);
    int numBuckets = -1;
    EdgeType edgeType = EdgeType.BROADCAST_EDGE;
    if (joinConf.isBucketMapJoin()) {
        numBuckets = (Integer) joinConf.getBigTableBucketNumMapping().values().toArray()[0];
        /*
       * Here, we can be in one of 4 states.
       *
       * 1. If map join work is null implies that we have not yet traversed the big table side. We
       * just need to see if we can find a reduce sink operator in the big table side. This would
       * imply a reduce side operation.
       *
       * 2. If we don't find a reducesink in 1 it has to be the case that it is a map side operation.
       *
       * 3. If we have already created a work item for the big table side, we need to see if we can
       * find a table scan operator in the big table side. This would imply a map side operation.
       *
       * 4. If we don't find a table scan operator, it has to be a reduce side operation.
       */
        if (mapJoinWork == null) {
            Operator<?> rootOp = OperatorUtils.findSingleOperatorUpstreamJoinAccounted(mapJoinOp.getParentOperators().get(joinConf.getPosBigTable()), ReduceSinkOperator.class);
            if (rootOp == null) {
                // likely we found a table scan operator
                edgeType = EdgeType.CUSTOM_EDGE;
            } else {
                // we have found a reduce sink
                edgeType = EdgeType.CUSTOM_SIMPLE_EDGE;
            }
        } else {
            Operator<?> rootOp = OperatorUtils.findSingleOperatorUpstreamJoinAccounted(mapJoinOp.getParentOperators().get(joinConf.getPosBigTable()), TableScanOperator.class);
            if (rootOp != null) {
                // likely we found a table scan operator
                edgeType = EdgeType.CUSTOM_EDGE;
            } else {
                // we have found a reduce sink
                edgeType = EdgeType.CUSTOM_SIMPLE_EDGE;
            }
        }
    } else if (mapJoinOp.getConf().isDynamicPartitionHashJoin()) {
        if (parentRS.getConf().isForwarding()) {
            edgeType = EdgeType.ONE_TO_ONE_EDGE;
        } else {
            edgeType = EdgeType.CUSTOM_SIMPLE_EDGE;
        }
    }
    if (edgeType == EdgeType.CUSTOM_EDGE) {
        // disable auto parallelism for bucket map joins
        parentRS.getConf().setReducerTraits(EnumSet.of(FIXED));
    }
    TezEdgeProperty edgeProp = new TezEdgeProperty(null, edgeType, numBuckets);
    if (mapJoinWork != null) {
        for (BaseWork myWork : mapJoinWork) {
            // link the work with the work associated with the reduce sink that triggered this rule
            TezWork tezWork = context.currentTask.getWork();
            LOG.debug("connecting " + parentWork.getName() + " with " + myWork.getName());
            tezWork.connect(parentWork, myWork, edgeProp);
            if (edgeType == EdgeType.CUSTOM_EDGE) {
                tezWork.setVertexType(myWork, VertexType.INITIALIZED_EDGES);
            }
            ReduceSinkOperator r = null;
            if (context.connectedReduceSinks.contains(parentRS)) {
                LOG.debug("Cloning reduce sink " + parentRS + " for multi-child broadcast edge");
                // we've already set this one up. Need to clone for the next work.
                r = (ReduceSinkOperator) OperatorFactory.getAndMakeChild(parentRS.getCompilationOpContext(), (ReduceSinkDesc) parentRS.getConf().clone(), new RowSchema(parentRS.getSchema()), parentRS.getParentOperators());
                context.clonedReduceSinks.add(r);
            } else {
                r = parentRS;
            }
            // remember the output name of the reduce sink
            r.getConf().setOutputName(myWork.getName());
            context.connectedReduceSinks.add(r);
        }
    }
    // remember in case we need to connect additional work later
    Map<BaseWork, TezEdgeProperty> linkWorkMap = null;
    if (context.linkOpWithWorkMap.containsKey(mapJoinOp)) {
        linkWorkMap = context.linkOpWithWorkMap.get(mapJoinOp);
    } else {
        linkWorkMap = new HashMap<BaseWork, TezEdgeProperty>();
    }
    linkWorkMap.put(parentWork, edgeProp);
    context.linkOpWithWorkMap.put(mapJoinOp, linkWorkMap);
    List<ReduceSinkOperator> reduceSinks = context.linkWorkWithReduceSinkMap.get(parentWork);
    if (reduceSinks == null) {
        reduceSinks = new ArrayList<ReduceSinkOperator>();
    }
    reduceSinks.add(parentRS);
    context.linkWorkWithReduceSinkMap.put(parentWork, reduceSinks);
    // create the dummy operators
    List<Operator<?>> dummyOperators = new ArrayList<Operator<?>>();
    // create an new operator: HashTableDummyOperator, which share the table desc
    HashTableDummyDesc desc = new HashTableDummyDesc();
    @SuppressWarnings("unchecked") HashTableDummyOperator dummyOp = (HashTableDummyOperator) OperatorFactory.get(parentRS.getCompilationOpContext(), desc);
    TableDesc tbl;
    // need to create the correct table descriptor for key/value
    RowSchema rowSchema = parentRS.getParentOperators().get(0).getSchema();
    tbl = PlanUtils.getReduceValueTableDesc(PlanUtils.getFieldSchemasFromRowSchema(rowSchema, ""));
    dummyOp.getConf().setTbl(tbl);
    Map<Byte, List<ExprNodeDesc>> keyExprMap = mapJoinOp.getConf().getKeys();
    List<ExprNodeDesc> keyCols = keyExprMap.get(Byte.valueOf((byte) 0));
    StringBuilder keyOrder = new StringBuilder();
    StringBuilder keyNullOrder = new StringBuilder();
    for (ExprNodeDesc k : keyCols) {
        keyOrder.append("+");
        keyNullOrder.append("a");
    }
    TableDesc keyTableDesc = PlanUtils.getReduceKeyTableDesc(PlanUtils.getFieldSchemasFromColumnList(keyCols, "mapjoinkey"), keyOrder.toString(), keyNullOrder.toString());
    mapJoinOp.getConf().setKeyTableDesc(keyTableDesc);
    // let the dummy op be the parent of mapjoin op
    mapJoinOp.replaceParent(parentRS, dummyOp);
    List<Operator<? extends OperatorDesc>> dummyChildren = new ArrayList<Operator<? extends OperatorDesc>>();
    dummyChildren.add(mapJoinOp);
    dummyOp.setChildOperators(dummyChildren);
    dummyOperators.add(dummyOp);
    // cut the operator tree so as to not retain connections from the parent RS downstream
    List<Operator<? extends OperatorDesc>> childOperators = parentRS.getChildOperators();
    int childIndex = childOperators.indexOf(mapJoinOp);
    childOperators.remove(childIndex);
    // at task startup
    if (mapJoinWork != null) {
        for (BaseWork myWork : mapJoinWork) {
            LOG.debug("adding dummy op to work " + myWork.getName() + " from MJ work: " + dummyOp);
            myWork.addDummyOp(dummyOp);
        }
    }
    if (context.linkChildOpWithDummyOp.containsKey(mapJoinOp)) {
        for (Operator<?> op : context.linkChildOpWithDummyOp.get(mapJoinOp)) {
            dummyOperators.add(op);
        }
    }
    context.linkChildOpWithDummyOp.put(mapJoinOp, dummyOperators);
    return true;
}

Example 37 with MapJoinOperator

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

the class SparkSMBJoinHintOptimizer method removeSmallTableReduceSink.

/**
 * In bucket mapjoin, there are ReduceSinks that mark a small table parent (Reduce Sink are removed from big-table).
 * In SMB join these are not expected for any parents, either from small or big tables.
 * @param mapJoinOp
 */
@SuppressWarnings("unchecked")
private void removeSmallTableReduceSink(MapJoinOperator mapJoinOp) {
    SMBJoinDesc smbJoinDesc = new SMBJoinDesc(mapJoinOp.getConf());
    List<Operator<? extends OperatorDesc>> parentOperators = mapJoinOp.getParentOperators();
    for (int i = 0; i < parentOperators.size(); i++) {
        Operator<? extends OperatorDesc> par = parentOperators.get(i);
        if (i != smbJoinDesc.getPosBigTable()) {
            if (par instanceof ReduceSinkOperator) {
                List<Operator<? extends OperatorDesc>> grandParents = par.getParentOperators();
                Preconditions.checkArgument(grandParents.size() == 1, "AssertionError: expect # of parents to be 1, but was " + grandParents.size());
                Operator<? extends OperatorDesc> grandParent = grandParents.get(0);
                grandParent.removeChild(par);
                grandParent.setChildOperators(Utilities.makeList(mapJoinOp));
                mapJoinOp.getParentOperators().set(i, grandParent);
            }
        }
    }
}

Example 38 with MapJoinOperator

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

the class GenSparkSkewJoinProcessor method processSkewJoin.

@SuppressWarnings("unchecked")
public static void processSkewJoin(JoinOperator joinOp, Task<? extends Serializable> currTask, ReduceWork reduceWork, ParseContext parseCtx) throws SemanticException {
    SparkWork currentWork = ((SparkTask) currTask).getWork();
    if (currentWork.getChildren(reduceWork).size() > 0) {
        LOG.warn("Skip runtime skew join as the ReduceWork has child work and hasn't been split.");
        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 each joining table, set dir for big key and small keys properly
    for (int i = 0; i < numAliases; i++) {
        Byte alias = tags[i];
        bigKeysDirMap.put(alias, GenMRSkewJoinProcessor.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, GenMRSkewJoinProcessor.getSmallKeysDir(baseTmpDir, alias, src2));
            }
        }
        skewJoinJobResultsDir.put(alias, GenMRSkewJoinProcessor.getBigKeysSkewJoinResultDir(baseTmpDir, alias));
    }
    joinDescriptor.setHandleSkewJoin(true);
    joinDescriptor.setBigKeysDirMap(bigKeysDirMap);
    joinDescriptor.setSmallKeysDirMap(smallKeysDirMap);
    joinDescriptor.setSkewKeyDefinition(HiveConf.getIntVar(parseCtx.getConf(), HiveConf.ConfVars.HIVESKEWJOINKEY));
    // create proper table/column desc for spilled tables
    TableDesc keyTblDesc = (TableDesc) reduceWork.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 (int i = 0; i < tags.length; i++) {
        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.getType(), columnInfo.getInternalName(), columnInfo.getTabAlias(), false));
            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.getType(), columnInfo.getInternalName(), columnInfo.getTabAlias(), false));
        }
        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) i, Utilities.getTableDesc(valueColNames, valueColTypes));
    }
    joinDescriptor.setSkewKeysValuesTables(tableDescList);
    joinDescriptor.setKeyTableDesc(keyTblDesc);
    // create N-1 map join tasks
    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>>();
    for (int i = 0; i < numAliases - 1; i++) {
        Byte src = tags[i];
        HiveConf hiveConf = new HiveConf(parseCtx.getConf(), GenSparkSkewJoinProcessor.class);
        SparkWork sparkWork = new SparkWork(parseCtx.getConf().getVar(HiveConf.ConfVars.HIVEQUERYID));
        Task<? extends Serializable> skewJoinMapJoinTask = TaskFactory.get(sparkWork);
        skewJoinMapJoinTask.setFetchSource(currTask.isFetchSource());
        // create N TableScans
        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;
        }
        // create the MapJoinOperator
        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());
        // temporarily, mark it as child of all the TS
        MapJoinOperator mapJoinOp = (MapJoinOperator) OperatorFactory.getAndMakeChild(joinOp.getCompilationOpContext(), mapJoinDescriptor, null, parentOps);
        // clone the original join operator, and replace it with the MJ
        // this makes sure MJ has the same downstream operator plan as the original join
        List<Operator<?>> reducerList = new ArrayList<Operator<?>>();
        reducerList.add(reduceWork.getReducer());
        Operator<? extends OperatorDesc> reducer = SerializationUtilities.cloneOperatorTree(reducerList).get(0);
        Preconditions.checkArgument(reducer instanceof JoinOperator, "Reducer should be join operator, but actually is " + reducer.getName());
        JoinOperator cloneJoinOp = (JoinOperator) reducer;
        List<Operator<? extends OperatorDesc>> childOps = cloneJoinOp.getChildOperators();
        for (Operator<? extends OperatorDesc> childOp : childOps) {
            childOp.replaceParent(cloneJoinOp, mapJoinOp);
        }
        mapJoinOp.setChildOperators(childOps);
        // set memory usage for the MJ operator
        setMemUsage(mapJoinOp, skewJoinMapJoinTask, parseCtx);
        // create N MapWorks and add them to the SparkWork
        MapWork bigMapWork = null;
        Map<Byte, Path> smallTblDirs = smallKeysDirMap.get(src);
        for (int j = 0; j < tags.length; j++) {
            MapWork mapWork = PlanUtils.getMapRedWork().getMapWork();
            sparkWork.add(mapWork);
            // This code has been only added for testing
            boolean mapperCannotSpanPartns = parseCtx.getConf().getBoolVar(HiveConf.ConfVars.HIVE_MAPPER_CANNOT_SPAN_MULTIPLE_PARTITIONS);
            mapWork.setMapperCannotSpanPartns(mapperCannotSpanPartns);
            Operator<? extends OperatorDesc> tableScan = parentOps[j];
            String alias = tags[j].toString();
            ArrayList<String> aliases = new ArrayList<String>();
            aliases.add(alias);
            Path path;
            if (j == i) {
                path = bigKeysDirMap.get(tags[j]);
                bigKeysDirToTaskMap.put(path, skewJoinMapJoinTask);
                bigMapWork = mapWork;
            } else {
                path = smallTblDirs.get(tags[j]);
            }
            mapWork.addPathToAlias(path, aliases);
            mapWork.getAliasToWork().put(alias, tableScan);
            PartitionDesc partitionDesc = new PartitionDesc(tableDescList.get(tags[j]), null);
            mapWork.addPathToPartitionInfo(path, partitionDesc);
            mapWork.getAliasToPartnInfo().put(alias, partitionDesc);
            mapWork.setName("Map " + GenSparkUtils.getUtils().getNextSeqNumber());
        }
        // connect all small dir map work to the big dir map work
        Preconditions.checkArgument(bigMapWork != null, "Haven't identified big dir MapWork");
        // these 2 flags are intended only for the big-key map work
        bigMapWork.setNumMapTasks(HiveConf.getIntVar(hiveConf, HiveConf.ConfVars.HIVESKEWJOINMAPJOINNUMMAPTASK));
        bigMapWork.setMinSplitSize(HiveConf.getLongVar(hiveConf, HiveConf.ConfVars.HIVESKEWJOINMAPJOINMINSPLIT));
        // use HiveInputFormat so that we can control the number of map tasks
        bigMapWork.setInputformat(HiveInputFormat.class.getName());
        for (BaseWork work : sparkWork.getRoots()) {
            Preconditions.checkArgument(work instanceof MapWork, "All root work should be MapWork, but got " + work.getClass().getSimpleName());
            if (work != bigMapWork) {
                sparkWork.connect(work, bigMapWork, new SparkEdgeProperty(SparkEdgeProperty.SHUFFLE_NONE));
            }
        }
        // insert SparkHashTableSink and Dummy operators
        for (int j = 0; j < tags.length; j++) {
            if (j != i) {
                insertSHTS(tags[j], (TableScanOperator) parentOps[j], bigMapWork);
            }
        }
        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);
        for (Task<? extends Serializable> oldChild : children) {
            listWorks.add(oldChild.getWork());
        }
    }
    ConditionalResolverSkewJoin.ConditionalResolverSkewJoinCtx context = new ConditionalResolverSkewJoin.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);
}

Example 39 with MapJoinOperator

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

the class GenSparkSkewJoinProcessor method insertSHTS.

/**
 * Insert SparkHashTableSink and HashTableDummy between small dir TS and MJ.
 */
@SuppressWarnings("unchecked")
private static void insertSHTS(byte tag, TableScanOperator tableScan, MapWork bigMapWork) {
    Preconditions.checkArgument(tableScan.getChildOperators().size() == 1 && tableScan.getChildOperators().get(0) instanceof MapJoinOperator);
    HashTableDummyDesc desc = new HashTableDummyDesc();
    HashTableDummyOperator dummyOp = (HashTableDummyOperator) OperatorFactory.get(tableScan.getCompilationOpContext(), desc);
    dummyOp.getConf().setTbl(tableScan.getTableDescSkewJoin());
    MapJoinOperator mapJoinOp = (MapJoinOperator) tableScan.getChildOperators().get(0);
    mapJoinOp.replaceParent(tableScan, dummyOp);
    List<Operator<? extends OperatorDesc>> mapJoinChildren = new ArrayList<Operator<? extends OperatorDesc>>();
    mapJoinChildren.add(mapJoinOp);
    dummyOp.setChildOperators(mapJoinChildren);
    bigMapWork.addDummyOp(dummyOp);
    MapJoinDesc mjDesc = mapJoinOp.getConf();
    // mapjoin should not be affected by join reordering
    mjDesc.resetOrder();
    SparkHashTableSinkDesc hashTableSinkDesc = new SparkHashTableSinkDesc(mjDesc);
    SparkHashTableSinkOperator hashTableSinkOp = (SparkHashTableSinkOperator) OperatorFactory.get(tableScan.getCompilationOpContext(), hashTableSinkDesc);
    int[] valueIndex = mjDesc.getValueIndex(tag);
    if (valueIndex != null) {
        List<ExprNodeDesc> newValues = new ArrayList<ExprNodeDesc>();
        List<ExprNodeDesc> values = hashTableSinkDesc.getExprs().get(tag);
        for (int index = 0; index < values.size(); index++) {
            if (valueIndex[index] < 0) {
                newValues.add(values.get(index));
            }
        }
        hashTableSinkDesc.getExprs().put(tag, newValues);
    }
    tableScan.replaceChild(mapJoinOp, hashTableSinkOp);
    List<Operator<? extends OperatorDesc>> tableScanParents = new ArrayList<Operator<? extends OperatorDesc>>();
    tableScanParents.add(tableScan);
    hashTableSinkOp.setParentOperators(tableScanParents);
    hashTableSinkOp.getConf().setTag(tag);
}

Example 40 with MapJoinOperator

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

the class GenSparkWork method process.

@Override
public Object process(Node nd, Stack<Node> stack, NodeProcessorCtx procContext, Object... nodeOutputs) throws SemanticException {
    GenSparkProcContext context = (GenSparkProcContext) procContext;
    Preconditions.checkArgument(context != null, "AssertionError: expected context to be not null");
    Preconditions.checkArgument(context.currentTask != null, "AssertionError: expected context.currentTask to be not null");
    Preconditions.checkArgument(context.currentRootOperator != null, "AssertionError: expected context.currentRootOperator to be not null");
    // Operator is a file sink or reduce sink. Something that forces a new vertex.
    @SuppressWarnings("unchecked") Operator<? extends OperatorDesc> operator = (Operator<? extends OperatorDesc>) nd;
    // root is the start of the operator pipeline we're currently
    // packing into a vertex, typically a table scan, union or join
    Operator<?> root = context.currentRootOperator;
    LOG.debug("Root operator: " + root);
    LOG.debug("Leaf operator: " + operator);
    SparkWork sparkWork = context.currentTask.getWork();
    SMBMapJoinOperator smbOp = GenSparkUtils.getChildOperator(root, SMBMapJoinOperator.class);
    // Right now the work graph is pretty simple. If there is no
    // Preceding work we have a root and will generate a map
    // vertex. If there is a preceding work we will generate
    // a reduce vertex
    BaseWork work;
    if (context.rootToWorkMap.containsKey(root)) {
        // having seen the root operator before means there was a branch in the
        // operator graph. There's typically two reasons for that: a) mux/demux
        // b) multi insert. Mux/Demux will hit the same leaf again, multi insert
        // will result into a vertex with multiple FS or RS operators.
        // At this point we don't have to do anything special in this case. Just
        // run through the regular paces w/o creating a new task.
        work = context.rootToWorkMap.get(root);
    } else {
        // create a new vertex
        if (context.preceedingWork == null) {
            if (smbOp == null) {
                work = utils.createMapWork(context, root, sparkWork, null);
            } else {
                // save work to be initialized later with SMB information.
                work = utils.createMapWork(context, root, sparkWork, null, true);
                context.smbMapJoinCtxMap.get(smbOp).mapWork = (MapWork) work;
            }
        } else {
            work = utils.createReduceWork(context, root, sparkWork);
        }
        context.rootToWorkMap.put(root, work);
    }
    if (!context.childToWorkMap.containsKey(operator)) {
        List<BaseWork> workItems = new LinkedList<BaseWork>();
        workItems.add(work);
        context.childToWorkMap.put(operator, workItems);
    } else {
        context.childToWorkMap.get(operator).add(work);
    }
    // remember which mapjoin operator links with which work
    if (!context.currentMapJoinOperators.isEmpty()) {
        for (MapJoinOperator mj : context.currentMapJoinOperators) {
            LOG.debug("Processing map join: " + mj);
            // remember the mapping in case we scan another branch of the mapjoin later
            if (!context.mapJoinWorkMap.containsKey(mj)) {
                List<BaseWork> workItems = new LinkedList<BaseWork>();
                workItems.add(work);
                context.mapJoinWorkMap.put(mj, workItems);
            } else {
                context.mapJoinWorkMap.get(mj).add(work);
            }
            /*
         * this happens in case of map join operations.
         * The tree looks like this:
         *
         *        RS <--- we are here perhaps
         *        |
         *     MapJoin
         *     /     \
         *   RS       TS
         *  /
         * TS
         *
         * If we are at the RS pointed above, and we may have already visited the
         * RS following the TS, we have already generated work for the TS-RS.
         * We need to hook the current work to this generated work.
         */
            if (context.linkOpWithWorkMap.containsKey(mj)) {
                Map<BaseWork, SparkEdgeProperty> linkWorkMap = context.linkOpWithWorkMap.get(mj);
                if (linkWorkMap != null) {
                    if (context.linkChildOpWithDummyOp.containsKey(mj)) {
                        for (Operator<?> dummy : context.linkChildOpWithDummyOp.get(mj)) {
                            work.addDummyOp((HashTableDummyOperator) dummy);
                        }
                    }
                    for (Entry<BaseWork, SparkEdgeProperty> parentWorkMap : linkWorkMap.entrySet()) {
                        BaseWork parentWork = parentWorkMap.getKey();
                        LOG.debug("connecting " + parentWork.getName() + " with " + work.getName());
                        SparkEdgeProperty edgeProp = parentWorkMap.getValue();
                        sparkWork.connect(parentWork, work, edgeProp);
                        // of the downstream work
                        for (ReduceSinkOperator r : context.linkWorkWithReduceSinkMap.get(parentWork)) {
                            if (r.getConf().getOutputName() != null) {
                                LOG.debug("Cloning reduce sink for multi-child broadcast edge");
                                // we've already set this one up. Need to clone for the next work.
                                r = (ReduceSinkOperator) OperatorFactory.getAndMakeChild(r.getCompilationOpContext(), (ReduceSinkDesc) r.getConf().clone(), r.getParentOperators());
                            }
                            r.getConf().setOutputName(work.getName());
                        }
                    }
                }
            }
        }
        // clear out the set. we don't need it anymore.
        context.currentMapJoinOperators.clear();
    }
    // with this root operator.
    if (root.getNumParent() > 0) {
        Preconditions.checkArgument(work instanceof ReduceWork, "AssertionError: expected work to be a ReduceWork, but was " + work.getClass().getName());
        ReduceWork reduceWork = (ReduceWork) work;
        for (Operator<?> parent : new ArrayList<Operator<?>>(root.getParentOperators())) {
            Preconditions.checkArgument(parent instanceof ReduceSinkOperator, "AssertionError: expected operator to be a ReduceSinkOperator, but was " + parent.getClass().getName());
            ReduceSinkOperator rsOp = (ReduceSinkOperator) parent;
            SparkEdgeProperty edgeProp = GenSparkUtils.getEdgeProperty(context.conf, rsOp, reduceWork);
            rsOp.getConf().setOutputName(reduceWork.getName());
            GenMapRedUtils.setKeyAndValueDesc(reduceWork, rsOp);
            context.leafOpToFollowingWorkInfo.put(rsOp, ObjectPair.create(edgeProp, reduceWork));
            LOG.debug("Removing " + parent + " as parent from " + root);
            root.removeParent(parent);
        }
    }
    // the union operators from the operator tree later.
    if (!context.currentUnionOperators.isEmpty()) {
        context.currentUnionOperators.clear();
        context.workWithUnionOperators.add(work);
    }
    // reasons. Roots are data sources, leaves are data sinks. I know.
    if (context.leafOpToFollowingWorkInfo.containsKey(operator)) {
        ObjectPair<SparkEdgeProperty, ReduceWork> childWorkInfo = context.leafOpToFollowingWorkInfo.get(operator);
        SparkEdgeProperty edgeProp = childWorkInfo.getFirst();
        ReduceWork childWork = childWorkInfo.getSecond();
        LOG.debug("Second pass. Leaf operator: " + operator + " has common downstream work:" + childWork);
        // we don't want to connect them with the work associated with TS more than once.
        if (sparkWork.getEdgeProperty(work, childWork) == null) {
            sparkWork.connect(work, childWork, edgeProp);
        } else {
            LOG.debug("work " + work.getName() + " is already connected to " + childWork.getName() + " before");
        }
    } else {
        LOG.debug("First pass. Leaf operator: " + operator);
    }
    // the next item will be a new root.
    if (!operator.getChildOperators().isEmpty()) {
        Preconditions.checkArgument(operator.getChildOperators().size() == 1, "AssertionError: expected operator.getChildOperators().size() to be 1, but was " + operator.getChildOperators().size());
        context.parentOfRoot = operator;
        context.currentRootOperator = operator.getChildOperators().get(0);
        context.preceedingWork = work;
    }
    return null;
}