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

Example 1 with HashTableDummyOperator

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

the class GenTezWork method process.

@Override
public Object process(Node nd, Stack<Node> stack, NodeProcessorCtx procContext, Object... nodeOutputs) throws SemanticException {
    GenTezProcContext context = (GenTezProcContext) procContext;
    assert context != null && context.currentTask != null && context.currentRootOperator != null;
    // Operator is a file sink or reduce sink. Something that forces
    // a new vertex.
    Operator<?> operator = (Operator<?>) 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);
    if (context.clonedReduceSinks.contains(operator)) {
        // just skip and keep going
        return null;
    }
    TezWork tezWork = context.currentTask.getWork();
    // 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)) {
        // will result into a vertex with multiple FS or RS operators.
        if (context.childToWorkMap.containsKey(operator)) {
            // if we've seen both root and child, we can bail.
            // clear out the mapjoin set. we don't need it anymore.
            context.currentMapJoinOperators.clear();
            // clear out the union set. we don't need it anymore.
            context.currentUnionOperators.clear();
            return null;
        } else {
            // At this point we don't have to do anything special. 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) {
            work = utils.createMapWork(context, root, tezWork, null);
        } else {
            work = GenTezUtils.createReduceWork(context, root, tezWork);
        }
        context.rootToWorkMap.put(root, work);
    }
    // this is where we set the sort columns that we will be using for KeyValueInputMerge
    if (operator instanceof DummyStoreOperator) {
        work.addSortCols(root.getOpTraits().getSortCols().get(0));
    }
    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);
    }
    // which can affect the working of all downstream transformations.
    if (context.currentMergeJoinOperator != null) {
        // we are currently walking the big table side of the merge join. we need to create or hook up
        // merge join work.
        MergeJoinWork mergeJoinWork = null;
        if (context.opMergeJoinWorkMap.containsKey(context.currentMergeJoinOperator)) {
            // we have found a merge work corresponding to this closing operator. Hook up this work.
            mergeJoinWork = context.opMergeJoinWorkMap.get(context.currentMergeJoinOperator);
        } else {
            // we need to create the merge join work
            mergeJoinWork = new MergeJoinWork();
            mergeJoinWork.setMergeJoinOperator(context.currentMergeJoinOperator);
            tezWork.add(mergeJoinWork);
            context.opMergeJoinWorkMap.put(context.currentMergeJoinOperator, mergeJoinWork);
        }
        // connect the work correctly.
        work.addSortCols(root.getOpTraits().getSortCols().get(0));
        mergeJoinWork.addMergedWork(work, null, context.leafOperatorToFollowingWork);
        Operator<? extends OperatorDesc> parentOp = getParentFromStack(context.currentMergeJoinOperator, stack);
        // Set the big table position. Both the reduce work and merge join operator
        // should be set with the same value.
        //      int pos = context.currentMergeJoinOperator.getTagForOperator(parentOp);
        int pos = context.currentMergeJoinOperator.getConf().getBigTablePosition();
        work.setTag(pos);
        context.currentMergeJoinOperator.getConf().setBigTablePosition(pos);
        tezWork.setVertexType(work, VertexType.MULTI_INPUT_UNINITIALIZED_EDGES);
        for (BaseWork parentWork : tezWork.getParents(work)) {
            TezEdgeProperty edgeProp = tezWork.getEdgeProperty(parentWork, work);
            tezWork.disconnect(parentWork, work);
            tezWork.connect(parentWork, mergeJoinWork, edgeProp);
        }
        for (BaseWork childWork : tezWork.getChildren(work)) {
            TezEdgeProperty edgeProp = tezWork.getEdgeProperty(work, childWork);
            tezWork.disconnect(work, childWork);
            tezWork.connect(mergeJoinWork, childWork, edgeProp);
        }
        tezWork.remove(work);
        context.rootToWorkMap.put(root, mergeJoinWork);
        context.childToWorkMap.get(operator).remove(work);
        context.childToWorkMap.get(operator).add(mergeJoinWork);
        work = mergeJoinWork;
        context.currentMergeJoinOperator = null;
    }
    // remember which mapjoin operator links with which work
    if (!context.currentMapJoinOperators.isEmpty()) {
        for (MapJoinOperator mj : context.currentMapJoinOperators) {
            // so we can later run the same logic that is run in ReduceSinkMapJoinProc.
            if (mj.getConf().isDynamicPartitionHashJoin()) {
                // Since this is a dynamic partitioned hash join, the work for this join should be a ReduceWork
                ReduceWork reduceWork = (ReduceWork) work;
                int bigTablePosition = mj.getConf().getPosBigTable();
                reduceWork.setTag(bigTablePosition);
                // Use context.mapJoinParentMap to get the original RS parents, because
                // the MapJoin's parents may have been replaced by dummy operator.
                List<Operator<?>> mapJoinOriginalParents = context.mapJoinParentMap.get(mj);
                if (mapJoinOriginalParents == null) {
                    throw new SemanticException("Unexpected error - context.mapJoinParentMap did not have an entry for " + mj);
                }
                for (int pos = 0; pos < mapJoinOriginalParents.size(); ++pos) {
                    // This processing only needs to happen for the small tables
                    if (pos == bigTablePosition) {
                        continue;
                    }
                    Operator<?> parentOp = mapJoinOriginalParents.get(pos);
                    context.smallTableParentToMapJoinMap.put(parentOp, mj);
                    ReduceSinkOperator parentRS = (ReduceSinkOperator) parentOp;
                    // TableDesc needed for dynamic partitioned hash join
                    GenMapRedUtils.setKeyAndValueDesc(reduceWork, parentRS);
                    // has its ReduceSink parent removed.
                    if (!context.mapJoinToUnprocessedSmallTableReduceSinks.get(mj).contains(parentRS)) {
                        // This reduce sink has been processed already, so the work for the parentRS exists
                        BaseWork parentWork = ReduceSinkMapJoinProc.getMapJoinParentWork(context, parentRS);
                        int tag = parentRS.getConf().getTag();
                        tag = (tag == -1 ? 0 : tag);
                        reduceWork.getTagToInput().put(tag, parentWork.getName());
                    }
                }
            }
            LOG.debug("Processing map join: " + mj);
            // 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, TezEdgeProperty> linkWorkMap = context.linkOpWithWorkMap.get(mj);
                if (linkWorkMap != null) {
                    // Note: it's not quite clear why this is done inside this if. Seems like it should be on the top level.
                    if (context.linkChildOpWithDummyOp.containsKey(mj)) {
                        if (LOG.isDebugEnabled()) {
                            LOG.debug("Adding dummy ops to work: " + work.getName() + ": " + context.linkChildOpWithDummyOp.get(mj));
                        }
                        for (Operator<?> dummy : context.linkChildOpWithDummyOp.get(mj)) {
                            work.addDummyOp((HashTableDummyOperator) dummy);
                        }
                    }
                    for (Entry<BaseWork, TezEdgeProperty> parentWorkMap : linkWorkMap.entrySet()) {
                        BaseWork parentWork = parentWorkMap.getKey();
                        LOG.debug("connecting " + parentWork.getName() + " with " + work.getName());
                        TezEdgeProperty edgeProp = parentWorkMap.getValue();
                        tezWork.connect(parentWork, work, edgeProp);
                        if (edgeProp.getEdgeType() == EdgeType.CUSTOM_EDGE) {
                            tezWork.setVertexType(work, VertexType.INITIALIZED_EDGES);
                        }
                        // 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(), new RowSchema(r.getSchema()), r.getParentOperators());
                                context.clonedReduceSinks.add(r);
                            }
                            r.getConf().setOutputName(work.getName());
                            context.connectedReduceSinks.add(r);
                        }
                    }
                }
            }
        }
        // clear out the set. we don't need it anymore.
        context.currentMapJoinOperators.clear();
    }
    // we might have to connect parent work with this work later.
    for (Operator<?> parent : new ArrayList<Operator<?>>(root.getParentOperators())) {
        if (LOG.isDebugEnabled()) {
            LOG.debug("Removing " + parent + " as parent from " + root);
        }
        context.leafOperatorToFollowingWork.remove(parent);
        context.leafOperatorToFollowingWork.put(parent, work);
        root.removeParent(parent);
    }
    if (!context.currentUnionOperators.isEmpty()) {
        // if there are union all operators, it means that the walking context contains union all operators.
        // please see more details of context.currentUnionOperator in GenTezWorkWalker
        UnionWork unionWork;
        if (context.unionWorkMap.containsKey(operator)) {
            // since we've passed this operator before.
            assert operator.getChildOperators().isEmpty();
            unionWork = (UnionWork) context.unionWorkMap.get(operator);
            // finally connect the union work with work
            connectUnionWorkWithWork(unionWork, work, tezWork, context);
        } else {
            // we've not seen this terminal before. we need to check
            // rootUnionWorkMap which contains the information of mapping the root
            // operator of a union work to a union work
            unionWork = context.rootUnionWorkMap.get(root);
            if (unionWork == null) {
                // if unionWork is null, it means it is the first time. we need to
                // create a union work object and add this work to it. Subsequent
                // work should reference the union and not the actual work.
                unionWork = GenTezUtils.createUnionWork(context, root, operator, tezWork);
                // finally connect the union work with work
                connectUnionWorkWithWork(unionWork, work, tezWork, context);
            }
        }
        context.currentUnionOperators.clear();
        work = unionWork;
    }
    // reasons. Roots are data sources, leaves are data sinks. I know.
    if (context.leafOperatorToFollowingWork.containsKey(operator)) {
        BaseWork followingWork = context.leafOperatorToFollowingWork.get(operator);
        long bytesPerReducer = context.conf.getLongVar(HiveConf.ConfVars.BYTESPERREDUCER);
        LOG.debug("Second pass. Leaf operator: " + operator + " has common downstream work:" + followingWork);
        if (operator instanceof DummyStoreOperator) {
            // this is the small table side.
            assert (followingWork instanceof MergeJoinWork);
            MergeJoinWork mergeJoinWork = (MergeJoinWork) followingWork;
            CommonMergeJoinOperator mergeJoinOp = mergeJoinWork.getMergeJoinOperator();
            work.setTag(mergeJoinOp.getTagForOperator(operator));
            mergeJoinWork.addMergedWork(null, work, context.leafOperatorToFollowingWork);
            tezWork.setVertexType(mergeJoinWork, VertexType.MULTI_INPUT_UNINITIALIZED_EDGES);
            for (BaseWork parentWork : tezWork.getParents(work)) {
                TezEdgeProperty edgeProp = tezWork.getEdgeProperty(parentWork, work);
                tezWork.disconnect(parentWork, work);
                tezWork.connect(parentWork, mergeJoinWork, edgeProp);
            }
            work = mergeJoinWork;
        } else {
            // need to add this branch to the key + value info
            assert operator instanceof ReduceSinkOperator && ((followingWork instanceof ReduceWork) || (followingWork instanceof MergeJoinWork) || followingWork instanceof UnionWork);
            ReduceSinkOperator rs = (ReduceSinkOperator) operator;
            ReduceWork rWork = null;
            if (followingWork instanceof MergeJoinWork) {
                MergeJoinWork mergeJoinWork = (MergeJoinWork) followingWork;
                rWork = (ReduceWork) mergeJoinWork.getMainWork();
            } else if (followingWork instanceof UnionWork) {
                // this can only be possible if there is merge work followed by the union
                UnionWork unionWork = (UnionWork) followingWork;
                int index = getFollowingWorkIndex(tezWork, unionWork, rs);
                BaseWork baseWork = tezWork.getChildren(unionWork).get(index);
                if (baseWork instanceof MergeJoinWork) {
                    MergeJoinWork mergeJoinWork = (MergeJoinWork) baseWork;
                    // disconnect the connection to union work and connect to merge work
                    followingWork = mergeJoinWork;
                    rWork = (ReduceWork) mergeJoinWork.getMainWork();
                } else {
                    rWork = (ReduceWork) baseWork;
                }
            } else {
                rWork = (ReduceWork) followingWork;
            }
            GenMapRedUtils.setKeyAndValueDesc(rWork, rs);
            // remember which parent belongs to which tag
            int tag = rs.getConf().getTag();
            rWork.getTagToInput().put(tag == -1 ? 0 : tag, work.getName());
            // remember the output name of the reduce sink
            rs.getConf().setOutputName(rWork.getName());
            // For dynamic partitioned hash join, run the ReduceSinkMapJoinProc logic for any
            // ReduceSink parents that we missed.
            MapJoinOperator mj = context.smallTableParentToMapJoinMap.get(rs);
            if (mj != null) {
                // Only need to run the logic for tables we missed
                if (context.mapJoinToUnprocessedSmallTableReduceSinks.get(mj).contains(rs)) {
                    // ReduceSinkMapJoinProc logic does not work unless the ReduceSink is connected as
                    // a parent of the MapJoin, but at this point we have already removed all of the
                    // parents from the MapJoin.
                    // Try temporarily adding the RS as a parent
                    ArrayList<Operator<?>> tempMJParents = new ArrayList<Operator<?>>();
                    tempMJParents.add(rs);
                    mj.setParentOperators(tempMJParents);
                    // ReduceSink also needs MapJoin as child
                    List<Operator<?>> rsChildren = rs.getChildOperators();
                    rsChildren.add(mj);
                    // Since the MapJoin has had all of its other parents removed at this point,
                    // it would be bad here if processReduceSinkToHashJoin() tries to do anything
                    // with the RS parent based on its position in the list of parents.
                    ReduceSinkMapJoinProc.processReduceSinkToHashJoin(rs, mj, context);
                    // Remove any parents from MapJoin again
                    mj.removeParents();
                // TODO: do we also need to remove the MapJoin from the list of RS's children?
                }
            }
            if (!context.connectedReduceSinks.contains(rs)) {
                // add dependency between the two work items
                TezEdgeProperty edgeProp;
                EdgeType edgeType = GenTezUtils.determineEdgeType(work, followingWork, rs);
                if (rWork.isAutoReduceParallelism()) {
                    edgeProp = new TezEdgeProperty(context.conf, edgeType, true, rWork.getMinReduceTasks(), rWork.getMaxReduceTasks(), bytesPerReducer);
                } else {
                    edgeProp = new TezEdgeProperty(edgeType);
                }
                tezWork.connect(work, followingWork, edgeProp);
                context.connectedReduceSinks.add(rs);
            }
        }
    } else {
        LOG.debug("First pass. Leaf operator: " + operator);
    }
    // the next item will be a new root.
    if (!operator.getChildOperators().isEmpty()) {
        assert operator.getChildOperators().size() == 1;
        context.parentOfRoot = operator;
        context.currentRootOperator = operator.getChildOperators().get(0);
        context.preceedingWork = work;
    }
    return null;
}

Example 2 with HashTableDummyOperator

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

the class ReduceRecordProcessor method init.

@Override
void init(MRTaskReporter mrReporter, Map<String, LogicalInput> inputs, Map<String, LogicalOutput> outputs) throws Exception {
    perfLogger.PerfLogBegin(CLASS_NAME, PerfLogger.TEZ_INIT_OPERATORS);
    super.init(mrReporter, inputs, outputs);
    MapredContext.init(false, new JobConf(jconf));
    List<LogicalInput> shuffleInputs = getShuffleInputs(inputs);
    // TODO HIVE-14042. Move to using a loop and a timed wait once TEZ-3302 is fixed.
    checkAbortCondition();
    if (shuffleInputs != null) {
        l4j.info("Waiting for ShuffleInputs to become ready");
        processorContext.waitForAllInputsReady(new ArrayList<Input>(shuffleInputs));
    }
    connectOps.clear();
    ReduceWork redWork = reduceWork;
    l4j.info("Main work is " + reduceWork.getName());
    List<HashTableDummyOperator> workOps = reduceWork.getDummyOps();
    HashSet<HashTableDummyOperator> dummyOps = workOps == null ? null : new HashSet<>(workOps);
    tagToReducerMap.put(redWork.getTag(), redWork);
    if (mergeWorkList != null) {
        for (BaseWork mergeWork : mergeWorkList) {
            if (l4j.isDebugEnabled()) {
                l4j.debug("Additional work " + mergeWork.getName());
            }
            workOps = mergeWork.getDummyOps();
            if (workOps != null) {
                if (dummyOps == null) {
                    dummyOps = new HashSet<>(workOps);
                } else {
                    dummyOps.addAll(workOps);
                }
            }
            ReduceWork mergeReduceWork = (ReduceWork) mergeWork;
            reducer = mergeReduceWork.getReducer();
            // Check immediately after reducer is assigned, in cae the abort came in during
            checkAbortCondition();
            DummyStoreOperator dummyStoreOp = getJoinParentOp(reducer);
            connectOps.put(mergeReduceWork.getTag(), dummyStoreOp);
            tagToReducerMap.put(mergeReduceWork.getTag(), mergeReduceWork);
        }
        ((TezContext) MapredContext.get()).setDummyOpsMap(connectOps);
    }
    checkAbortCondition();
    bigTablePosition = (byte) reduceWork.getTag();
    ObjectInspector[] mainWorkOIs = null;
    ((TezContext) MapredContext.get()).setInputs(inputs);
    ((TezContext) MapredContext.get()).setTezProcessorContext(processorContext);
    int numTags = reduceWork.getTagToValueDesc().size();
    reducer = reduceWork.getReducer();
    // Check immediately after reducer is assigned, in cae the abort came in during
    checkAbortCondition();
    // set memory available for operators
    long memoryAvailableToTask = processorContext.getTotalMemoryAvailableToTask();
    if (reducer.getConf() != null) {
        reducer.getConf().setMaxMemoryAvailable(memoryAvailableToTask);
        l4j.info("Memory available for operators set to {}", LlapUtil.humanReadableByteCount(memoryAvailableToTask));
    }
    OperatorUtils.setMemoryAvailable(reducer.getChildOperators(), memoryAvailableToTask);
    // Setup values registry
    String valueRegistryKey = DynamicValue.DYNAMIC_VALUE_REGISTRY_CACHE_KEY;
    DynamicValueRegistryTez registryTez = dynamicValueCache.retrieve(valueRegistryKey, new Callable<DynamicValueRegistryTez>() {

        @Override
        public DynamicValueRegistryTez call() {
            return new DynamicValueRegistryTez();
        }
    });
    dynamicValueCacheKeys.add(valueRegistryKey);
    RegistryConfTez registryConf = new RegistryConfTez(jconf, reduceWork, processorContext, inputs);
    registryTez.init(registryConf);
    checkAbortCondition();
    if (numTags > 1) {
        sources = new ReduceRecordSource[numTags];
        mainWorkOIs = new ObjectInspector[numTags];
        initializeMultipleSources(reduceWork, numTags, mainWorkOIs, sources);
        ((TezContext) MapredContext.get()).setRecordSources(sources);
        reducer.initialize(jconf, mainWorkOIs);
    } else {
        numTags = tagToReducerMap.keySet().size();
        sources = new ReduceRecordSource[numTags];
        mainWorkOIs = new ObjectInspector[numTags];
        for (int i : tagToReducerMap.keySet()) {
            redWork = tagToReducerMap.get(i);
            reducer = redWork.getReducer();
            // Check immediately after reducer is assigned, in cae the abort came in during
            checkAbortCondition();
            initializeSourceForTag(redWork, i, mainWorkOIs, sources, redWork.getTagToValueDesc().get(0), redWork.getTagToInput().get(0));
            reducer.initializeLocalWork(jconf);
        }
        reducer = reduceWork.getReducer();
        // Check immediately after reducer is assigned, in cae the abort came in during
        checkAbortCondition();
        ((TezContext) MapredContext.get()).setRecordSources(sources);
        reducer.initialize(jconf, new ObjectInspector[] { mainWorkOIs[bigTablePosition] });
        for (int i : tagToReducerMap.keySet()) {
            if (i == bigTablePosition) {
                continue;
            }
            redWork = tagToReducerMap.get(i);
            reducer = redWork.getReducer();
            // Check immediately after reducer is assigned, in cae the abort came in during
            checkAbortCondition();
            reducer.initialize(jconf, new ObjectInspector[] { mainWorkOIs[i] });
        }
    }
    checkAbortCondition();
    reducer = reduceWork.getReducer();
    // initialize reduce operator tree
    try {
        l4j.info(reducer.dump(0));
        // dummy parent operators as well.
        if (dummyOps != null) {
            for (HashTableDummyOperator dummyOp : dummyOps) {
                // TODO HIVE-14042. Propagating abort to dummyOps.
                dummyOp.initialize(jconf, null);
                checkAbortCondition();
            }
        }
        // set output collector for any reduce sink operators in the pipeline.
        List<Operator<?>> children = new LinkedList<Operator<?>>();
        children.add(reducer);
        if (dummyOps != null) {
            children.addAll(dummyOps);
        }
        createOutputMap();
        OperatorUtils.setChildrenCollector(children, outMap);
        checkAbortCondition();
        reducer.setReporter(reporter);
        MapredContext.get().setReporter(reporter);
    } catch (Throwable e) {
        super.setAborted(true);
        if (e instanceof OutOfMemoryError) {
            // Don't create a new object if we are already out of memory
            throw (OutOfMemoryError) e;
        } else if (e instanceof InterruptedException) {
            l4j.info("Hit an interrupt while initializing ReduceRecordProcessor. Message={}", e.getMessage());
            throw (InterruptedException) e;
        } else {
            throw new RuntimeException("Reduce operator initialization failed", e);
        }
    }
    perfLogger.PerfLogEnd(CLASS_NAME, PerfLogger.TEZ_INIT_OPERATORS);
}

Example 3 with HashTableDummyOperator

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

the class ReduceRecordProcessor method close.

@Override
void close() {
    if (cache != null && cacheKeys != null) {
        for (String key : cacheKeys) {
            cache.release(key);
        }
    }
    if (dynamicValueCache != null && dynamicValueCacheKeys != null) {
        for (String k : dynamicValueCacheKeys) {
            dynamicValueCache.release(k);
        }
    }
    try {
        if (isAborted()) {
            for (ReduceRecordSource rs : sources) {
                if (!rs.close()) {
                    // Preserving the old logic. Hmm...
                    setAborted(false);
                    break;
                }
            }
        }
        boolean abort = isAborted();
        reducer.close(abort);
        if (mergeWorkList != null) {
            for (BaseWork redWork : mergeWorkList) {
                ((ReduceWork) redWork).getReducer().close(abort);
            }
        }
        // Need to close the dummyOps as well. The operator pipeline
        // is not considered "closed/done" unless all operators are
        // done. For broadcast joins that includes the dummy parents.
        List<HashTableDummyOperator> dummyOps = reduceWork.getDummyOps();
        if (dummyOps != null) {
            for (Operator<?> dummyOp : dummyOps) {
                dummyOp.close(abort);
            }
        }
        ReportStats rps = new ReportStats(reporter, jconf);
        reducer.preorderMap(rps);
    } catch (Exception e) {
        if (!isAborted()) {
            // signal new failure to map-reduce
            l4j.error("Hit error while closing operators - failing tree");
            throw new RuntimeException("Hive Runtime Error while closing operators: " + e.getMessage(), e);
        }
    } finally {
        Utilities.clearWorkMap(jconf);
        MapredContext.close();
    }
}

Example 4 with HashTableDummyOperator

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

the class SparkReduceSinkMapJoinProc method process.

/* (non-Javadoc)
   * This processor addresses the RS-MJ case that occurs in spark on the small/hash
   * table side of things. The work that RS will be a part of must be connected
   * to the MJ work via be a broadcast edge.
   * We should not walk down the tree when we encounter this pattern because:
   * the type of work (map work or reduce work) needs to be determined
   * on the basis of the big table side because it may be a mapwork (no need for shuffle)
   * or reduce work.
   */
@SuppressWarnings("unchecked")
@Override
public Object process(Node nd, Stack<Node> stack, NodeProcessorCtx procContext, Object... nodeOutputs) throws SemanticException {
    GenSparkProcContext context = (GenSparkProcContext) procContext;
    if (!nd.getClass().equals(MapJoinOperator.class)) {
        return null;
    }
    MapJoinOperator mapJoinOp = (MapJoinOperator) nd;
    if (stack.size() < 2 || !(stack.get(stack.size() - 2) instanceof ReduceSinkOperator)) {
        context.currentMapJoinOperators.add(mapJoinOp);
        return null;
    }
    context.preceedingWork = null;
    context.currentRootOperator = null;
    ReduceSinkOperator parentRS = (ReduceSinkOperator) stack.get(stack.size() - 2);
    // remove the tag for in-memory side of mapjoin
    parentRS.getConf().setSkipTag(true);
    parentRS.setSkipTag(true);
    // remember the original parent list before we start modifying it.
    if (!context.mapJoinParentMap.containsKey(mapJoinOp)) {
        List<Operator<?>> parents = new ArrayList<Operator<?>>(mapJoinOp.getParentOperators());
        context.mapJoinParentMap.put(mapJoinOp, parents);
    }
    List<BaseWork> mapJoinWork;
    /*
     *  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);
    int workMapSize = context.childToWorkMap.get(parentRS).size();
    Preconditions.checkArgument(workMapSize == 1, "AssertionError: expected context.childToWorkMap.get(parentRS).size() to be 1, but was " + workMapSize);
    BaseWork parentWork = context.childToWorkMap.get(parentRS).get(0);
    // 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");
    }
    LOG.debug("Mapjoin " + mapJoinOp + ", pos: " + pos + " --> " + parentWork.getName());
    mapJoinOp.getConf().getParentToInput().put(pos, parentWork.getName());
    SparkEdgeProperty edgeProp = new SparkEdgeProperty(SparkEdgeProperty.SHUFFLE_NONE);
    if (mapJoinWork != null) {
        for (BaseWork myWork : mapJoinWork) {
            // link the work with the work associated with the reduce sink that triggered this rule
            SparkWork sparkWork = context.currentTask.getWork();
            LOG.debug("connecting " + parentWork.getName() + " with " + myWork.getName());
            sparkWork.connect(parentWork, myWork, edgeProp);
        }
    }
    // remember in case we need to connect additional work later
    Map<BaseWork, SparkEdgeProperty> linkWorkMap = null;
    if (context.linkOpWithWorkMap.containsKey(mapJoinOp)) {
        linkWorkMap = context.linkOpWithWorkMap.get(mapJoinOp);
    } else {
        linkWorkMap = new HashMap<BaseWork, SparkEdgeProperty>();
    }
    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();
    HashTableDummyOperator dummyOp = (HashTableDummyOperator) OperatorFactory.get(mapJoinOp.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 (int i = 0; i < keyCols.size(); i++) {
        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) {
            myWork.addDummyOp(dummyOp);
        }
    }
    if (context.linkChildOpWithDummyOp.containsKey(mapJoinOp)) {
        for (Operator<?> op : context.linkChildOpWithDummyOp.get(mapJoinOp)) {
            dummyOperators.add(op);
        }
    }
    context.linkChildOpWithDummyOp.put(mapJoinOp, dummyOperators);
    // replace ReduceSinkOp with HashTableSinkOp for the RSops which are parents of MJop
    MapJoinDesc mjDesc = mapJoinOp.getConf();
    HiveConf conf = context.conf;
    // Unlike in MR, we may call this method multiple times, for each
    // small table HTS. But, since it's idempotent, it should be OK.
    mjDesc.resetOrder();
    float hashtableMemoryUsage;
    if (hasGroupBy(mapJoinOp, context)) {
        hashtableMemoryUsage = conf.getFloatVar(HiveConf.ConfVars.HIVEHASHTABLEFOLLOWBYGBYMAXMEMORYUSAGE);
    } else {
        hashtableMemoryUsage = conf.getFloatVar(HiveConf.ConfVars.HIVEHASHTABLEMAXMEMORYUSAGE);
    }
    mjDesc.setHashTableMemoryUsage(hashtableMemoryUsage);
    SparkHashTableSinkDesc hashTableSinkDesc = new SparkHashTableSinkDesc(mjDesc);
    SparkHashTableSinkOperator hashTableSinkOp = (SparkHashTableSinkOperator) OperatorFactory.get(mapJoinOp.getCompilationOpContext(), hashTableSinkDesc);
    byte tag = (byte) pos;
    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);
    }
    //get all parents of reduce sink
    List<Operator<? extends OperatorDesc>> rsParentOps = parentRS.getParentOperators();
    for (Operator<? extends OperatorDesc> parent : rsParentOps) {
        parent.replaceChild(parentRS, hashTableSinkOp);
    }
    hashTableSinkOp.setParentOperators(rsParentOps);
    hashTableSinkOp.getConf().setTag(tag);
    return true;
}

Example 5 with HashTableDummyOperator

use of org.apache.hadoop.hive.ql.exec.HashTableDummyOperator 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()) {
        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 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;
}