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/**
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.hadoop.hive.ql.optimizer;
import java.util.ArrayList;
import java.util.EnumSet;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Stack;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.hive.ql.exec.HashTableDummyOperator;
import org.apache.hadoop.hive.ql.exec.MapJoinOperator;
import org.apache.hadoop.hive.ql.exec.Operator;
import org.apache.hadoop.hive.ql.exec.OperatorFactory;
import org.apache.hadoop.hive.ql.exec.OperatorUtils;
import org.apache.hadoop.hive.ql.exec.ReduceSinkOperator;
import org.apache.hadoop.hive.ql.exec.RowSchema;
import org.apache.hadoop.hive.ql.exec.TableScanOperator;
import org.apache.hadoop.hive.ql.exec.Utilities;
import org.apache.hadoop.hive.ql.lib.Node;
import org.apache.hadoop.hive.ql.lib.NodeProcessor;
import org.apache.hadoop.hive.ql.lib.NodeProcessorCtx;
import org.apache.hadoop.hive.ql.parse.GenTezProcContext;
import org.apache.hadoop.hive.ql.parse.SemanticException;
import org.apache.hadoop.hive.ql.plan.BaseWork;
import org.apache.hadoop.hive.ql.plan.ColStatistics;
import org.apache.hadoop.hive.ql.plan.ExprNodeDesc;
import org.apache.hadoop.hive.ql.plan.HashTableDummyDesc;
import org.apache.hadoop.hive.ql.plan.MapJoinDesc;
import org.apache.hadoop.hive.ql.plan.OpTraits;
import org.apache.hadoop.hive.ql.plan.OperatorDesc;
import org.apache.hadoop.hive.ql.plan.PlanUtils;
import org.apache.hadoop.hive.ql.plan.ReduceSinkDesc;
import org.apache.hadoop.hive.ql.plan.Statistics;
import org.apache.hadoop.hive.ql.plan.TableDesc;
import org.apache.hadoop.hive.ql.plan.TezEdgeProperty;
import org.apache.hadoop.hive.ql.plan.TezEdgeProperty.EdgeType;
import org.apache.hadoop.hive.ql.plan.TezWork;
import org.apache.hadoop.hive.ql.plan.TezWork.VertexType;
import org.apache.hadoop.hive.ql.stats.StatsUtils;
import static org.apache.hadoop.hive.ql.plan.ReduceSinkDesc.ReducerTraits.FIXED;
public class ReduceSinkMapJoinProc implements NodeProcessor {
protected transient Log LOG = LogFactory.getLog(this.getClass().getName());
/* (non-Javadoc)
* This processor addresses the RS-MJ case that occurs in tez 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.
*/
@Override
public Object process(Node nd, Stack stack, NodeProcessorCtx procContext, Object... nodeOutputs)
throws SemanticException {
GenTezProcContext context = (GenTezProcContext) procContext;
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> parents = new ArrayList>(mapJoinOp.getParentOperators());
context.mapJoinParentMap.put(mapJoinOp, parents);
}
List 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;
if (context.unionWorkMap.containsKey(parentRS)) {
parentWork = context.unionWorkMap.get(parentRS);
} else {
assert context.childToWorkMap.get(parentRS).size() == 1;
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");
}
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 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;
}
}
}
LOG.info("Mapjoin " + mapJoinOp + ", 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()) {
// disable auto parallelism for bucket map joins
parentRS.getConf().setReducerTraits(EnumSet.of(FIXED));
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.findSingleOperatorUpstream(
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.findSingleOperatorUpstream(
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;
}
}
}
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 (parentRS.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(
(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 linkWorkMap = null;
if (context.linkOpWithWorkMap.containsKey(mapJoinOp)) {
linkWorkMap = context.linkOpWithWorkMap.get(mapJoinOp);
} else {
linkWorkMap = new HashMap();
}
linkWorkMap.put(parentWork, edgeProp);
context.linkOpWithWorkMap.put(mapJoinOp, linkWorkMap);
List reduceSinks
= context.linkWorkWithReduceSinkMap.get(parentWork);
if (reduceSinks == null) {
reduceSinks = new ArrayList();
}
reduceSinks.add(parentRS);
context.linkWorkWithReduceSinkMap.put(parentWork, reduceSinks);
// create the dummy operators
List> dummyOperators = new ArrayList>();
// create an new operator: HashTableDummyOperator, which share the table desc
HashTableDummyDesc desc = new HashTableDummyDesc();
@SuppressWarnings("unchecked")
HashTableDummyOperator dummyOp = (HashTableDummyOperator) OperatorFactory.get(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> keyExprMap = mapJoinOp.getConf().getKeys();
List keyCols = keyExprMap.get(Byte.valueOf((byte) 0));
StringBuffer keyOrder = new StringBuffer();
for (ExprNodeDesc k: keyCols) {
keyOrder.append("+");
}
TableDesc keyTableDesc = PlanUtils.getReduceKeyTableDesc(PlanUtils
.getFieldSchemasFromColumnList(keyCols, "mapjoinkey"), keyOrder.toString());
mapJoinOp.getConf().setKeyTableDesc(keyTableDesc);
// let the dummy op be the parent of mapjoin op
mapJoinOp.replaceParent(parentRS, dummyOp);
List> dummyChildren =
new ArrayList>();
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> childOperators = parentRS.getChildOperators();
int childIndex = childOperators.indexOf(mapJoinOp);
childOperators.remove(childIndex);
// the "work" needs to know about the dummy operators. They have to be separately initialized
// 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);
return true;
}
}
