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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.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.Stack;
import org.apache.hadoop.hive.common.JavaUtils;
import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.conf.HiveConf.ConfVars;
import org.apache.hadoop.hive.ql.exec.AppMasterEventOperator;
import org.apache.hadoop.hive.ql.exec.CommonJoinOperator;
import org.apache.hadoop.hive.ql.exec.CommonMergeJoinOperator;
import org.apache.hadoop.hive.ql.exec.DummyStoreOperator;
import org.apache.hadoop.hive.ql.exec.FileSinkOperator;
import org.apache.hadoop.hive.ql.exec.GroupByOperator;
import org.apache.hadoop.hive.ql.exec.JoinOperator;
import org.apache.hadoop.hive.ql.exec.MapJoinOperator;
import org.apache.hadoop.hive.ql.exec.MuxOperator;
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.SelectOperator;
import org.apache.hadoop.hive.ql.exec.TableScanOperator;
import org.apache.hadoop.hive.ql.exec.TezDummyStoreOperator;
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.GenTezUtils;
import org.apache.hadoop.hive.ql.parse.OptimizeTezProcContext;
import org.apache.hadoop.hive.ql.parse.ParseContext;
import org.apache.hadoop.hive.ql.parse.SemanticException;
import org.apache.hadoop.hive.ql.plan.ColStatistics;
import org.apache.hadoop.hive.ql.plan.CommonMergeJoinDesc;
import org.apache.hadoop.hive.ql.plan.DynamicPruningEventDesc;
import org.apache.hadoop.hive.ql.plan.ExprNodeColumnDesc;
import org.apache.hadoop.hive.ql.plan.ExprNodeDesc;
import org.apache.hadoop.hive.ql.plan.JoinCondDesc;
import org.apache.hadoop.hive.ql.plan.JoinDesc;
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.Statistics;
import org.apache.hadoop.hive.ql.stats.StatsUtils;
import org.apache.hadoop.util.ReflectionUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* ConvertJoinMapJoin is an optimization that replaces a common join
* (aka shuffle join) with a map join (aka broadcast or fragment replicate
* join when possible. Map joins have restrictions on which joins can be
* converted (e.g.: full outer joins cannot be handled as map joins) as well
* as memory restrictions (one side of the join has to fit into memory).
*/
public class ConvertJoinMapJoin implements NodeProcessor {
private static final Logger LOG = LoggerFactory.getLogger(ConvertJoinMapJoin.class.getName());
@Override
/*
* (non-Javadoc) we should ideally not modify the tree we traverse. However,
* since we need to walk the tree at any time when we modify the operator, we
* might as well do it here.
*/
public Object
process(Node nd, Stack stack, NodeProcessorCtx procCtx, Object... nodeOutputs)
throws SemanticException {
OptimizeTezProcContext context = (OptimizeTezProcContext) procCtx;
JoinOperator joinOp = (JoinOperator) nd;
long maxSize = context.conf.getLongVar(HiveConf.ConfVars.HIVECONVERTJOINNOCONDITIONALTASKTHRESHOLD);
TezBucketJoinProcCtx tezBucketJoinProcCtx = new TezBucketJoinProcCtx(context.conf);
if (!context.conf.getBoolVar(HiveConf.ConfVars.HIVECONVERTJOIN)) {
// we are just converting to a common merge join operator. The shuffle
// join in map-reduce case.
Object retval = checkAndConvertSMBJoin(context, joinOp, tezBucketJoinProcCtx);
if (retval == null) {
return retval;
} else {
fallbackToReduceSideJoin(joinOp, context);
return null;
}
}
// if we have traits, and table info is present in the traits, we know the
// exact number of buckets. Else choose the largest number of estimated
// reducers from the parent operators.
int numBuckets = -1;
if (context.conf.getBoolVar(HiveConf.ConfVars.HIVE_CONVERT_JOIN_BUCKET_MAPJOIN_TEZ)) {
numBuckets = estimateNumBuckets(joinOp, true);
} else {
numBuckets = 1;
}
LOG.info("Estimated number of buckets " + numBuckets);
int mapJoinConversionPos = getMapJoinConversionPos(joinOp, context, numBuckets, false, maxSize, true);
if (mapJoinConversionPos < 0) {
Object retval = checkAndConvertSMBJoin(context, joinOp, tezBucketJoinProcCtx);
if (retval == null) {
return retval;
} else {
// only case is full outer join with SMB enabled which is not possible. Convert to regular
// join.
fallbackToReduceSideJoin(joinOp, context);
return null;
}
}
if (numBuckets > 1) {
if (context.conf.getBoolVar(HiveConf.ConfVars.HIVE_CONVERT_JOIN_BUCKET_MAPJOIN_TEZ)) {
if (convertJoinBucketMapJoin(joinOp, context, mapJoinConversionPos, tezBucketJoinProcCtx)) {
return null;
}
}
}
// check if we can convert to map join no bucket scaling.
LOG.info("Convert to non-bucketed map join");
if (numBuckets != 1) {
mapJoinConversionPos = getMapJoinConversionPos(joinOp, context, 1, false, maxSize, true);
}
if (mapJoinConversionPos < 0) {
// we are just converting to a common merge join operator. The shuffle
// join in map-reduce case.
fallbackToReduceSideJoin(joinOp, context);
return null;
}
MapJoinOperator mapJoinOp = convertJoinMapJoin(joinOp, context, mapJoinConversionPos, true);
// map join operator by default has no bucket cols and num of reduce sinks
// reduced by 1
mapJoinOp.setOpTraits(new OpTraits(null, -1, null, joinOp.getOpTraits().getNumReduceSinks()));
mapJoinOp.setStatistics(joinOp.getStatistics());
// propagate this change till the next RS
for (Operator childOp : mapJoinOp.getChildOperators()) {
setAllChildrenTraits(childOp, mapJoinOp.getOpTraits());
}
return null;
}
@SuppressWarnings("unchecked")
private Object checkAndConvertSMBJoin(OptimizeTezProcContext context, JoinOperator joinOp,
TezBucketJoinProcCtx tezBucketJoinProcCtx) throws SemanticException {
// we cannot convert to bucket map join, we cannot convert to
// map join either based on the size. Check if we can convert to SMB join.
if ((HiveConf.getBoolVar(context.conf, ConfVars.HIVE_AUTO_SORTMERGE_JOIN) == false)
|| ((!HiveConf.getBoolVar(context.conf, ConfVars.HIVE_AUTO_SORTMERGE_JOIN_REDUCE))
&& joinOp.getOpTraits().getNumReduceSinks() >= 2)) {
fallbackToReduceSideJoin(joinOp, context);
return null;
}
Class bigTableMatcherClass = null;
try {
String selector = HiveConf.getVar(context.parseContext.getConf(),
HiveConf.ConfVars.HIVE_AUTO_SORTMERGE_JOIN_BIGTABLE_SELECTOR);
bigTableMatcherClass =
JavaUtils.loadClass(selector);
} catch (ClassNotFoundException e) {
throw new SemanticException(e.getMessage());
}
BigTableSelectorForAutoSMJ bigTableMatcher =
ReflectionUtils.newInstance(bigTableMatcherClass, null);
JoinDesc joinDesc = joinOp.getConf();
JoinCondDesc[] joinCondns = joinDesc.getConds();
Set joinCandidates = MapJoinProcessor.getBigTableCandidates(joinCondns);
if (joinCandidates.isEmpty()) {
// This is a full outer join. This can never be a map-join
// of any type. So return false.
return false;
}
int mapJoinConversionPos =
bigTableMatcher.getBigTablePosition(context.parseContext, joinOp, joinCandidates);
if (mapJoinConversionPos < 0) {
// contains aliases from sub-query
// we are just converting to a common merge join operator. The shuffle
// join in map-reduce case.
fallbackToReduceSideJoin(joinOp, context);
return null;
}
if (checkConvertJoinSMBJoin(joinOp, context, mapJoinConversionPos, tezBucketJoinProcCtx)) {
convertJoinSMBJoin(joinOp, context, mapJoinConversionPos,
tezBucketJoinProcCtx.getNumBuckets(), true);
} else {
// we are just converting to a common merge join operator. The shuffle
// join in map-reduce case.
fallbackToReduceSideJoin(joinOp, context);
}
return null;
}
// replaces the join operator with a new CommonJoinOperator, removes the
// parent reduce sinks
private void convertJoinSMBJoin(JoinOperator joinOp, OptimizeTezProcContext context,
int mapJoinConversionPos, int numBuckets, boolean adjustParentsChildren)
throws SemanticException {
MapJoinDesc mapJoinDesc = null;
if (adjustParentsChildren) {
mapJoinDesc = MapJoinProcessor.getMapJoinDesc(context.conf,
joinOp, joinOp.getConf().isLeftInputJoin(), joinOp.getConf().getBaseSrc(),
joinOp.getConf().getMapAliases(), mapJoinConversionPos, true);
} else {
JoinDesc joinDesc = joinOp.getConf();
// retain the original join desc in the map join.
mapJoinDesc =
new MapJoinDesc(
MapJoinProcessor.getKeys(joinOp.getConf().isLeftInputJoin(),
joinOp.getConf().getBaseSrc(), joinOp).getSecond(),
null, joinDesc.getExprs(), null, null,
joinDesc.getOutputColumnNames(), mapJoinConversionPos, joinDesc.getConds(),
joinDesc.getFilters(), joinDesc.getNoOuterJoin(), null);
mapJoinDesc.setNullSafes(joinDesc.getNullSafes());
mapJoinDesc.setFilterMap(joinDesc.getFilterMap());
mapJoinDesc.setResidualFilterExprs(joinDesc.getResidualFilterExprs());
mapJoinDesc.resetOrder();
}
CommonMergeJoinOperator mergeJoinOp =
(CommonMergeJoinOperator) OperatorFactory.get(joinOp.getCompilationOpContext(),
new CommonMergeJoinDesc(numBuckets, mapJoinConversionPos, mapJoinDesc),
joinOp.getSchema());
int numReduceSinks = joinOp.getOpTraits().getNumReduceSinks();
OpTraits opTraits = new OpTraits(joinOp.getOpTraits().getBucketColNames(), numBuckets,
joinOp.getOpTraits().getSortCols(), numReduceSinks);
mergeJoinOp.setOpTraits(opTraits);
mergeJoinOp.setStatistics(joinOp.getStatistics());
for (Operator parentOp : joinOp.getParentOperators()) {
int pos = parentOp.getChildOperators().indexOf(joinOp);
parentOp.getChildOperators().remove(pos);
parentOp.getChildOperators().add(pos, mergeJoinOp);
}
for (Operator childOp : joinOp.getChildOperators()) {
int pos = childOp.getParentOperators().indexOf(joinOp);
childOp.getParentOperators().remove(pos);
childOp.getParentOperators().add(pos, mergeJoinOp);
}
List> childOperators = mergeJoinOp.getChildOperators();
List> parentOperators = mergeJoinOp.getParentOperators();
childOperators.clear();
parentOperators.clear();
childOperators.addAll(joinOp.getChildOperators());
parentOperators.addAll(joinOp.getParentOperators());
mergeJoinOp.getConf().setGenJoinKeys(false);
if (adjustParentsChildren) {
mergeJoinOp.getConf().setGenJoinKeys(true);
List> newParentOpList = new ArrayList>();
for (Operator parentOp : mergeJoinOp.getParentOperators()) {
for (Operator grandParentOp : parentOp.getParentOperators()) {
grandParentOp.getChildOperators().remove(parentOp);
grandParentOp.getChildOperators().add(mergeJoinOp);
newParentOpList.add(grandParentOp);
}
}
mergeJoinOp.getParentOperators().clear();
mergeJoinOp.getParentOperators().addAll(newParentOpList);
List> parentOps =
new ArrayList>(mergeJoinOp.getParentOperators());
for (Operator parentOp : parentOps) {
int parentIndex = mergeJoinOp.getParentOperators().indexOf(parentOp);
if (parentIndex == mapJoinConversionPos) {
continue;
}
// insert the dummy store operator here
DummyStoreOperator dummyStoreOp = new TezDummyStoreOperator(
mergeJoinOp.getCompilationOpContext());
dummyStoreOp.setParentOperators(new ArrayList>());
dummyStoreOp.setChildOperators(new ArrayList>());
dummyStoreOp.getChildOperators().add(mergeJoinOp);
int index = parentOp.getChildOperators().indexOf(mergeJoinOp);
parentOp.getChildOperators().remove(index);
parentOp.getChildOperators().add(index, dummyStoreOp);
dummyStoreOp.getParentOperators().add(parentOp);
mergeJoinOp.getParentOperators().remove(parentIndex);
mergeJoinOp.getParentOperators().add(parentIndex, dummyStoreOp);
}
}
mergeJoinOp.cloneOriginalParentsList(mergeJoinOp.getParentOperators());
}
private void setAllChildrenTraits(Operator currentOp, OpTraits opTraits) {
if (currentOp instanceof ReduceSinkOperator) {
return;
}
currentOp.setOpTraits(new OpTraits(opTraits.getBucketColNames(),
opTraits.getNumBuckets(), opTraits.getSortCols(), opTraits.getNumReduceSinks()));
for (Operator childOp : currentOp.getChildOperators()) {
if ((childOp instanceof ReduceSinkOperator) || (childOp instanceof GroupByOperator)) {
break;
}
setAllChildrenTraits(childOp, opTraits);
}
}
private boolean convertJoinBucketMapJoin(JoinOperator joinOp, OptimizeTezProcContext context,
int bigTablePosition, TezBucketJoinProcCtx tezBucketJoinProcCtx) throws SemanticException {
if (!checkConvertJoinBucketMapJoin(joinOp, context, bigTablePosition, tezBucketJoinProcCtx)) {
LOG.info("Check conversion to bucket map join failed.");
return false;
}
MapJoinOperator mapJoinOp = convertJoinMapJoin(joinOp, context, bigTablePosition, true);
if (mapJoinOp == null) {
LOG.debug("Conversion to bucket map join failed.");
return false;
}
MapJoinDesc joinDesc = mapJoinOp.getConf();
joinDesc.setBucketMapJoin(true);
// we can set the traits for this join operator
OpTraits opTraits = new OpTraits(joinOp.getOpTraits().getBucketColNames(),
tezBucketJoinProcCtx.getNumBuckets(), null, joinOp.getOpTraits().getNumReduceSinks());
mapJoinOp.setOpTraits(opTraits);
mapJoinOp.setStatistics(joinOp.getStatistics());
setNumberOfBucketsOnChildren(mapJoinOp);
// Once the conversion is done, we can set the partitioner to bucket cols on the small table
Map bigTableBucketNumMapping = new HashMap();
bigTableBucketNumMapping.put(joinDesc.getBigTableAlias(), tezBucketJoinProcCtx.getNumBuckets());
joinDesc.setBigTableBucketNumMapping(bigTableBucketNumMapping);
return true;
}
/*
* This method tries to convert a join to an SMB. This is done based on
* traits. If the sorted by columns are the same as the join columns then, we
* can convert the join to an SMB. Otherwise retain the bucket map join as it
* is still more efficient than a regular join.
*/
private boolean checkConvertJoinSMBJoin(JoinOperator joinOp, OptimizeTezProcContext context,
int bigTablePosition, TezBucketJoinProcCtx tezBucketJoinProcCtx) throws SemanticException {
ReduceSinkOperator bigTableRS =
(ReduceSinkOperator) joinOp.getParentOperators().get(bigTablePosition);
int numBuckets = bigTableRS.getParentOperators().get(0).getOpTraits().getNumBuckets();
int size = -1;
for (Operator parentOp : joinOp.getParentOperators()) {
// each side better have 0 or more RS. if either side is unbalanced, cannot convert.
// This is a workaround for now. Right fix would be to refactor code in the
// MapRecordProcessor and ReduceRecordProcessor with respect to the sources.
@SuppressWarnings({"rawtypes","unchecked"})
Set set =
OperatorUtils.findOperatorsUpstream(parentOp.getParentOperators(),
ReduceSinkOperator.class);
if (size < 0) {
size = set.size();
continue;
}
if (((size > 0) && (set.size() > 0)) || ((size == 0) && (set.size() == 0))) {
continue;
} else {
return false;
}
}
// the sort and bucket cols have to match on both sides for this
// transformation of the join operation
for (Operator parentOp : joinOp.getParentOperators()) {
if (!(parentOp instanceof ReduceSinkOperator)) {
// could be mux/demux operators. Currently not supported
LOG.info("Found correlation optimizer operators. Cannot convert to SMB at this time.");
return false;
}
ReduceSinkOperator rsOp = (ReduceSinkOperator) parentOp;
if (checkColEquality(rsOp.getParentOperators().get(0).getOpTraits().getSortCols(), rsOp
.getOpTraits().getSortCols(), rsOp.getColumnExprMap(), tezBucketJoinProcCtx, false) == false) {
LOG.info("We cannot convert to SMB because the sort column names do not match.");
return false;
}
if (checkColEquality(rsOp.getParentOperators().get(0).getOpTraits().getBucketColNames(), rsOp
.getOpTraits().getBucketColNames(), rsOp.getColumnExprMap(), tezBucketJoinProcCtx, true)
== false) {
LOG.info("We cannot convert to SMB because bucket column names do not match.");
return false;
}
}
if (numBuckets < 0) {
numBuckets = bigTableRS.getConf().getNumReducers();
}
tezBucketJoinProcCtx.setNumBuckets(numBuckets);
LOG.info("We can convert the join to an SMB join.");
return true;
}
private void setNumberOfBucketsOnChildren(Operator currentOp) {
int numBuckets = currentOp.getOpTraits().getNumBuckets();
for (Operatorop : currentOp.getChildOperators()) {
if (!(op instanceof ReduceSinkOperator) && !(op instanceof GroupByOperator)) {
op.getOpTraits().setNumBuckets(numBuckets);
setNumberOfBucketsOnChildren(op);
}
}
}
/*
* If the parent reduce sink of the big table side has the same emit key cols as its parent, we
* can create a bucket map join eliminating the reduce sink.
*/
private boolean checkConvertJoinBucketMapJoin(JoinOperator joinOp,
OptimizeTezProcContext context, int bigTablePosition,
TezBucketJoinProcCtx tezBucketJoinProcCtx) throws SemanticException {
// bail on mux-operator because mux operator masks the emit keys of the
// constituent reduce sinks
if (!(joinOp.getParentOperators().get(0) instanceof ReduceSinkOperator)) {
LOG.info("Operator is " + joinOp.getParentOperators().get(0).getName() +
". Cannot convert to bucket map join");
return false;
}
ReduceSinkOperator rs = (ReduceSinkOperator) joinOp.getParentOperators().get(bigTablePosition);
List> parentColNames = rs.getOpTraits().getBucketColNames();
Operator parentOfParent = rs.getParentOperators().get(0);
List> grandParentColNames = parentOfParent.getOpTraits().getBucketColNames();
int numBuckets = parentOfParent.getOpTraits().getNumBuckets();
// all keys matched.
if (checkColEquality(grandParentColNames, parentColNames, rs.getColumnExprMap(),
tezBucketJoinProcCtx, true) == false) {
LOG.info("No info available to check for bucket map join. Cannot convert");
return false;
}
/*
* this is the case when the big table is a sub-query and is probably already bucketed by the
* join column in say a group by operation
*/
if (numBuckets < 0) {
numBuckets = rs.getConf().getNumReducers();
}
tezBucketJoinProcCtx.setNumBuckets(numBuckets);
return true;
}
private boolean checkColEquality(List> grandParentColNames,
List> parentColNames, Map colExprMap,
TezBucketJoinProcCtx tezBucketJoinProcCtx, boolean strict) {
if ((grandParentColNames == null) || (parentColNames == null)) {
return false;
}
if ((parentColNames != null) && (parentColNames.isEmpty() == false)) {
for (List listBucketCols : grandParentColNames) {
// can happen if this operator does not carry forward the previous bucketing columns
// for e.g. another join operator which does not carry one of the sides' key columns
if (listBucketCols.isEmpty()) {
continue;
}
int colCount = 0;
// parent op is guaranteed to have a single list because it is a reduce sink
for (String colName : parentColNames.get(0)) {
if (listBucketCols.size() <= colCount) {
// can happen with virtual columns. RS would add the column to its output columns
// but it would not exist in the grandparent output columns or exprMap.
return false;
}
// all columns need to be at least a subset of the parentOfParent's bucket cols
ExprNodeDesc exprNodeDesc = colExprMap.get(colName);
if (exprNodeDesc instanceof ExprNodeColumnDesc) {
if (((ExprNodeColumnDesc) exprNodeDesc).getColumn()
.equals(listBucketCols.get(colCount))) {
colCount++;
} else {
break;
}
}
if (colCount == parentColNames.get(0).size()) {
if (strict) {
if (colCount == listBucketCols.size()) {
return true;
} else {
return false;
}
} else {
return true;
}
}
}
}
return false;
}
return false;
}
/**
* Obtain big table position for join.
*
* @param joinOp join operator
* @param context optimization context
* @param buckets bucket count for Bucket Map Join conversion consideration or reduce count
* for Dynamic Hash Join conversion consideration
* @param skipJoinTypeChecks whether to skip join type checking
* @param maxSize size threshold for Map Join conversion
* @param checkHashTableEntries whether to check threshold for distinct keys in hash table for Map Join
* @return returns big table position or -1 if it cannot be determined
* @throws SemanticException
*/
public int getMapJoinConversionPos(JoinOperator joinOp, OptimizeTezProcContext context,
int buckets, boolean skipJoinTypeChecks, long maxSize, boolean checkHashTableEntries)
throws SemanticException {
if (!skipJoinTypeChecks) {
/*
* HIVE-9038: Join tests fail in tez when we have more than 1 join on the same key and there is
* an outer join down the join tree that requires filterTag. We disable this conversion to map
* join here now. We need to emulate the behavior of HashTableSinkOperator as in MR or create a
* new operation to be able to support this. This seems like a corner case enough to special
* case this for now.
*/
if (joinOp.getConf().getConds().length > 1) {
boolean hasOuter = false;
for (JoinCondDesc joinCondDesc : joinOp.getConf().getConds()) {
switch (joinCondDesc.getType()) {
case JoinDesc.INNER_JOIN:
case JoinDesc.LEFT_SEMI_JOIN:
case JoinDesc.UNIQUE_JOIN:
hasOuter = false;
break;
case JoinDesc.FULL_OUTER_JOIN:
case JoinDesc.LEFT_OUTER_JOIN:
case JoinDesc.RIGHT_OUTER_JOIN:
hasOuter = true;
break;
default:
throw new SemanticException("Unknown join type " + joinCondDesc.getType());
}
}
if (hasOuter) {
return -1;
}
}
}
Set bigTableCandidateSet =
MapJoinProcessor.getBigTableCandidates(joinOp.getConf().getConds());
int bigTablePosition = -1;
// big input cumulative row count
long bigInputCumulativeCardinality = -1L;
// stats of the big input
Statistics bigInputStat = null;
// bigTableFound means we've encountered a table that's bigger than the
// max. This table is either the the big table or we cannot convert.
boolean foundInputNotFittingInMemory = false;
// total size of the inputs
long totalSize = 0;
for (int pos = 0; pos < joinOp.getParentOperators().size(); pos++) {
Operator parentOp = joinOp.getParentOperators().get(pos);
Statistics currInputStat = parentOp.getStatistics();
if (currInputStat == null) {
LOG.warn("Couldn't get statistics from: " + parentOp);
return -1;
}
long inputSize = currInputStat.getDataSize();
boolean currentInputNotFittingInMemory = false;
if ((bigInputStat == null)
|| ((bigInputStat != null) && (inputSize > bigInputStat.getDataSize()))) {
if (foundInputNotFittingInMemory) {
// cannot convert to map join; we've already chosen a big table
// on size and there's another one that's bigger.
return -1;
}
if (inputSize/buckets > maxSize) {
if (!bigTableCandidateSet.contains(pos)) {
// can't use the current table as the big table, but it's too
// big for the map side.
return -1;
}
currentInputNotFittingInMemory = true;
foundInputNotFittingInMemory = true;
}
}
long currentInputCumulativeCardinality;
if (foundInputNotFittingInMemory) {
currentInputCumulativeCardinality = -1L;
} else {
Long cardinality = computeCumulativeCardinality(parentOp);
if (cardinality == null) {
// We could not get stats, we cannot convert
return -1;
}
currentInputCumulativeCardinality = cardinality;
}
// This input is the big table if it is contained in the big candidates set, and either:
// 1) we have not chosen a big table yet, or
// 2) it has been chosen as the big table above, or
// 3) the cumulative cardinality for this input is higher, or
// 4) the cumulative cardinality is equal, but the size is bigger,
boolean selectedBigTable = bigTableCandidateSet.contains(pos) &&
(bigInputStat == null || currentInputNotFittingInMemory ||
(!foundInputNotFittingInMemory && (currentInputCumulativeCardinality > bigInputCumulativeCardinality ||
(currentInputCumulativeCardinality == bigInputCumulativeCardinality && inputSize > bigInputStat.getDataSize()))));
if (bigInputStat != null && selectedBigTable) {
// We are replacing the current big table with a new one, thus
// we need to count the current one as a map table then.
totalSize += bigInputStat.getDataSize();
// Check if number of distinct keys is larger than given max
// number of entries for HashMap. If it is, we do not convert.
if (checkHashTableEntries && !checkNumberOfEntriesForHashTable(joinOp, bigTablePosition, context)) {
return -1;
}
} else if (!selectedBigTable) {
// This is not the first table and we are not using it as big table,
// in fact, we're adding this table as a map table
totalSize += inputSize;
// Check if number of distinct keys is larger than given max
// number of entries for HashMap. If it is, we do not convert.
if (checkHashTableEntries && !checkNumberOfEntriesForHashTable(joinOp, pos, context)) {
return -1;
}
}
if (totalSize/buckets > maxSize) {
// sum of small tables size in this join exceeds configured limit
// hence cannot convert.
return -1;
}
if (selectedBigTable) {
bigTablePosition = pos;
bigInputCumulativeCardinality = currentInputCumulativeCardinality;
bigInputStat = currInputStat;
}
}
return bigTablePosition;
}
// This is akin to CBO cumulative cardinality model
private static Long computeCumulativeCardinality(Operator op) {
long cumulativeCardinality = 0L;
if (op instanceof CommonJoinOperator) {
// Choose max
for (Operator inputOp : op.getParentOperators()) {
Long inputCardinality = computeCumulativeCardinality(inputOp);
if (inputCardinality == null) {
return null;
}
if (inputCardinality > cumulativeCardinality) {
cumulativeCardinality = inputCardinality;
}
}
} else {
// Choose cumulative
for (Operator inputOp : op.getParentOperators()) {
Long inputCardinality = computeCumulativeCardinality(inputOp);
if (inputCardinality == null) {
return null;
}
cumulativeCardinality += inputCardinality;
}
}
Statistics currInputStat = op.getStatistics();
if (currInputStat == null) {
LOG.warn("Couldn't get statistics from: " + op);
return null;
}
cumulativeCardinality += currInputStat.getNumRows();
return cumulativeCardinality;
}
/*
* Once we have decided on the map join, the tree would transform from
*
* | |
* Join MapJoin
* / \ / \
* RS RS ---> RS TS (big table)
* / \ /
* TS TS TS (small table)
*
* for tez.
*/
public MapJoinOperator convertJoinMapJoin(JoinOperator joinOp, OptimizeTezProcContext context,
int bigTablePosition, boolean removeReduceSink) throws SemanticException {
// bail on mux operator because currently the mux operator masks the emit keys
// of the constituent reduce sinks.
for (Operator parentOp : joinOp.getParentOperators()) {
if (parentOp instanceof MuxOperator) {
return null;
}
}
// can safely convert the join to a map join.
MapJoinOperator mapJoinOp =
MapJoinProcessor.convertJoinOpMapJoinOp(context.conf, joinOp,
joinOp.getConf().isLeftInputJoin(), joinOp.getConf().getBaseSrc(),
joinOp.getConf().getMapAliases(), bigTablePosition, true, removeReduceSink);
mapJoinOp.getConf().setHybridHashJoin(HiveConf.getBoolVar(context.conf,
HiveConf.ConfVars.HIVEUSEHYBRIDGRACEHASHJOIN));
List joinExprs = mapJoinOp.getConf().getKeys().values().iterator().next();
if (joinExprs.size() == 0) { // In case of cross join, we disable hybrid grace hash join
mapJoinOp.getConf().setHybridHashJoin(false);
}
Operator parentBigTableOp =
mapJoinOp.getParentOperators().get(bigTablePosition);
if (parentBigTableOp instanceof ReduceSinkOperator) {
Operator parentSelectOpOfBigTableOp = parentBigTableOp.getParentOperators().get(0);
if (removeReduceSink) {
for (Operator p : parentBigTableOp.getParentOperators()) {
// we might have generated a dynamic partition operator chain. Since
// we're removing the reduce sink we need do remove that too.
Set> dynamicPartitionOperators = new HashSet>();
Map, AppMasterEventOperator> opEventPairs = new HashMap<>();
for (Operator c : p.getChildOperators()) {
AppMasterEventOperator event = findDynamicPartitionBroadcast(c);
if (event != null) {
dynamicPartitionOperators.add(c);
opEventPairs.put(c, event);
}
}
for (Operator c : dynamicPartitionOperators) {
if (context.pruningOpsRemovedByPriorOpt.isEmpty() ||
!context.pruningOpsRemovedByPriorOpt.contains(opEventPairs.get(c))) {
p.removeChild(c);
// at this point we've found the fork in the op pipeline that has the pruning as a child plan.
LOG.info("Disabling dynamic pruning for: "
+ ((DynamicPruningEventDesc) opEventPairs.get(c).getConf()).getTableScan().getName()
+ ". Need to be removed together with reduce sink");
}
}
for (Operator op : dynamicPartitionOperators) {
context.pruningOpsRemovedByPriorOpt.add(opEventPairs.get(op));
}
}
mapJoinOp.getParentOperators().remove(bigTablePosition);
if (!(mapJoinOp.getParentOperators().contains(parentBigTableOp.getParentOperators().get(0)))) {
mapJoinOp.getParentOperators().add(bigTablePosition,
parentBigTableOp.getParentOperators().get(0));
}
parentBigTableOp.getParentOperators().get(0).removeChild(parentBigTableOp);
}
for (Operatorop : mapJoinOp.getParentOperators()) {
if (!(op.getChildOperators().contains(mapJoinOp))) {
op.getChildOperators().add(mapJoinOp);
}
op.getChildOperators().remove(joinOp);
}
// Remove semijoin Op if there is any.
// The semijoin branch can potentially create a task level cycle
// with the hashjoin except when it is dynamically partitioned hash
// join which takes place in a separate task.
if (context.parseContext.getRsOpToTsOpMap().size() > 0
&& removeReduceSink) {
removeCycleCreatingSemiJoinOps(mapJoinOp, parentSelectOpOfBigTableOp,
context.parseContext);
}
}
return mapJoinOp;
}
// Remove any semijoin branch associated with hashjoin's parent's operator
// pipeline which can cause a cycle after hashjoin optimization.
private void removeCycleCreatingSemiJoinOps(MapJoinOperator mapjoinOp,
Operator parentSelectOpOfBigTable,
ParseContext parseContext) throws SemanticException {
Map semiJoinMap =
new HashMap();
for (Operator op : parentSelectOpOfBigTable.getChildOperators()) {
if (!(op instanceof SelectOperator)) {
continue;
}
while (op.getChildOperators().size() > 0) {
op = op.getChildOperators().get(0);
}
// If not ReduceSink Op, skip
if (!(op instanceof ReduceSinkOperator)) {
continue;
}
ReduceSinkOperator rs = (ReduceSinkOperator) op;
TableScanOperator ts = parseContext.getRsOpToTsOpMap().get(rs);
if (ts == null) {
// skip, no semijoin branch
continue;
}
// Found a semijoin branch.
for (Operator parent : mapjoinOp.getParentOperators()) {
if (!(parent instanceof ReduceSinkOperator)) {
continue;
}
Set tsOps = OperatorUtils.findOperatorsUpstream(parent,
TableScanOperator.class);
for (TableScanOperator parentTS : tsOps) {
// If the parent is same as the ts, then we have a cycle.
if (ts == parentTS) {
semiJoinMap.put(rs, ts);
break;
}
}
}
}
if (semiJoinMap.size() > 0) {
for (ReduceSinkOperator rs : semiJoinMap.keySet()) {
GenTezUtils.removeBranch(rs);
GenTezUtils.removeSemiJoinOperator(parseContext, rs,
semiJoinMap.get(rs));
}
}
}
private AppMasterEventOperator findDynamicPartitionBroadcast(Operator parent) {
for (Operator op : parent.getChildOperators()) {
while (op != null) {
if (op instanceof AppMasterEventOperator && op.getConf() instanceof DynamicPruningEventDesc) {
// found dynamic partition pruning operator
return (AppMasterEventOperator)op;
}
if (op instanceof ReduceSinkOperator || op instanceof FileSinkOperator) {
// crossing reduce sink or file sink means the pruning isn't for this parent.
break;
}
if (op.getChildOperators().size() != 1) {
// dynamic partition pruning pipeline doesn't have multiple children
break;
}
op = op.getChildOperators().get(0);
}
}
return null;
}
/**
* Estimate the number of buckets in the join, using the parent operators' OpTraits and/or
* parent operators' number of reducers
* @param joinOp
* @param useOpTraits Whether OpTraits should be used for the estimate.
* @return
*/
private static int estimateNumBuckets(JoinOperator joinOp, boolean useOpTraits) {
int numBuckets = -1;
int estimatedBuckets = -1;
for (OperatorparentOp : joinOp.getParentOperators()) {
if (parentOp.getOpTraits().getNumBuckets() > 0) {
numBuckets = (numBuckets < parentOp.getOpTraits().getNumBuckets()) ?
parentOp.getOpTraits().getNumBuckets() : numBuckets;
}
if (parentOp instanceof ReduceSinkOperator) {
ReduceSinkOperator rs = (ReduceSinkOperator) parentOp;
estimatedBuckets = (estimatedBuckets < rs.getConf().getNumReducers()) ?
rs.getConf().getNumReducers() : estimatedBuckets;
}
}
if (!useOpTraits) {
// Ignore the value we got from OpTraits.
// The logic below will fall back to the estimate from numReducers
numBuckets = -1;
}
if (numBuckets <= 0) {
numBuckets = estimatedBuckets;
if (numBuckets <= 0) {
numBuckets = 1;
}
}
return numBuckets;
}
private boolean convertJoinDynamicPartitionedHashJoin(JoinOperator joinOp, OptimizeTezProcContext context)
throws SemanticException {
// Attempt dynamic partitioned hash join
// Since we don't have big table index yet, must start with estimate of numReducers
int numReducers = estimateNumBuckets(joinOp, false);
LOG.info("Try dynamic partitioned hash join with estimated " + numReducers + " reducers");
int bigTablePos = getMapJoinConversionPos(joinOp, context, numReducers, false,
context.conf.getLongVar(HiveConf.ConfVars.HIVECONVERTJOINNOCONDITIONALTASKTHRESHOLD),
false);
if (bigTablePos >= 0) {
// Now that we have the big table index, get real numReducers value based on big table RS
ReduceSinkOperator bigTableParentRS =
(ReduceSinkOperator) (joinOp.getParentOperators().get(bigTablePos));
numReducers = bigTableParentRS.getConf().getNumReducers();
LOG.debug("Real big table reducers = " + numReducers);
MapJoinOperator mapJoinOp = convertJoinMapJoin(joinOp, context, bigTablePos, false);
if (mapJoinOp != null) {
LOG.info("Selected dynamic partitioned hash join");
mapJoinOp.getConf().setDynamicPartitionHashJoin(true);
// Set OpTraits for dynamically partitioned hash join:
// bucketColNames: Re-use previous joinOp's bucketColNames. Parent operators should be
// reduce sink, which should have bucket columns based on the join keys.
// numBuckets: set to number of reducers
// sortCols: This is an unsorted join - no sort cols
OpTraits opTraits = new OpTraits(
joinOp.getOpTraits().getBucketColNames(),
numReducers,
null,
joinOp.getOpTraits().getNumReduceSinks());
mapJoinOp.setOpTraits(opTraits);
mapJoinOp.setStatistics(joinOp.getStatistics());
// propagate this change till the next RS
for (Operator childOp : mapJoinOp.getChildOperators()) {
setAllChildrenTraits(childOp, mapJoinOp.getOpTraits());
}
return true;
}
}
return false;
}
private void fallbackToReduceSideJoin(JoinOperator joinOp, OptimizeTezProcContext context)
throws SemanticException {
if (context.conf.getBoolVar(HiveConf.ConfVars.HIVECONVERTJOIN) &&
context.conf.getBoolVar(HiveConf.ConfVars.HIVEDYNAMICPARTITIONHASHJOIN)) {
if (convertJoinDynamicPartitionedHashJoin(joinOp, context)) {
return;
}
}
int pos = getMapJoinConversionPos(joinOp, context, estimateNumBuckets(joinOp, false),
true, Long.MAX_VALUE, false);
if (pos < 0) {
LOG.info("Could not get a valid join position. Defaulting to position 0");
pos = 0;
}
// we are just converting to a common merge join operator. The shuffle
// join in map-reduce case.
LOG.info("Fallback to common merge join operator");
convertJoinSMBJoin(joinOp, context, pos, 0, false);
}
/* Returns true if it passes the test, false otherwise. */
private boolean checkNumberOfEntriesForHashTable(JoinOperator joinOp, int position,
OptimizeTezProcContext context) {
long max = HiveConf.getLongVar(context.parseContext.getConf(),
HiveConf.ConfVars.HIVECONVERTJOINMAXENTRIESHASHTABLE);
if (max < 1) {
// Max is disabled, we can safely return true
return true;
}
// Calculate number of different entries and evaluate
ReduceSinkOperator rsOp = (ReduceSinkOperator) joinOp.getParentOperators().get(position);
List keys = StatsUtils.getQualifedReducerKeyNames(rsOp.getConf().getOutputKeyColumnNames());
Statistics inputStats = rsOp.getStatistics();
List columnStats = new ArrayList<>();
for (String key : keys) {
ColStatistics cs = inputStats.getColumnStatisticsFromColName(key);
if (cs == null) {
LOG.debug("Couldn't get statistics for: {}", key);
return true;
}
columnStats.add(cs);
}
long numRows = inputStats.getNumRows();
long estimation = estimateNDV(numRows, columnStats);
LOG.debug("Estimated NDV for input {}: {}; Max NDV for MapJoin conversion: {}",
position, estimation, max);
if (estimation > max) {
// Estimation larger than max
LOG.debug("Number of different entries for HashTable is greater than the max; "
+ "we do not converting to MapJoin");
return false;
}
// We can proceed with the conversion
return true;
}
private static long estimateNDV(long numRows, List columnStats) {
// If there is a single column, return the number of distinct values
if (columnStats.size() == 1) {
return columnStats.get(0).getCountDistint();
}
// The expected number of distinct values when choosing p values
// with replacement from n integers is n . (1 - ((n - 1) / n) ^ p).
//
// If we have several uniformly distributed attributes A1 ... Am
// with N1 ... Nm distinct values, they behave as one uniformly
// distributed attribute with N1 * ... * Nm distinct values.
long n = 1L;
for (ColStatistics cs : columnStats) {
final long ndv = cs.getCountDistint();
if (ndv > 1) {
n = StatsUtils.safeMult(n, ndv);
}
}
final double nn = (double) n;
final double a = (nn - 1d) / nn;
if (a == 1d) {
// A under-flows if nn is large.
return numRows;
}
final double v = nn * (1d - Math.pow(a, numRows));
// Cap at fact-row-count, because numerical artifacts can cause it
// to go a few % over.
return Math.min(Math.round(v), numRows);
}
}
