[
https://issues.apache.org/jira/browse/HIVE-21196?focusedWorklogId=467253&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-467253
]
ASF GitHub Bot logged work on HIVE-21196:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 06/Aug/20 12:10
Start Date: 06/Aug/20 12:10
Worklog Time Spent: 10m
Work Description: zabetak commented on a change in pull request #1325:
URL: https://github.com/apache/hive/pull/1325#discussion_r466366197
##########
File path:
ql/src/java/org/apache/hadoop/hive/ql/optimizer/SemiJoinReductionMerge.java
##########
@@ -0,0 +1,399 @@
+/*
+ * 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 org.apache.hadoop.hive.conf.HiveConf;
+import org.apache.hadoop.hive.ql.exec.ColumnInfo;
+import org.apache.hadoop.hive.ql.exec.FilterOperator;
+import org.apache.hadoop.hive.ql.exec.GroupByOperator;
+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.SelectOperator;
+import org.apache.hadoop.hive.ql.exec.TableScanOperator;
+import org.apache.hadoop.hive.ql.exec.Utilities;
+import org.apache.hadoop.hive.ql.io.AcidUtils;
+import org.apache.hadoop.hive.ql.parse.GenTezUtils;
+import org.apache.hadoop.hive.ql.parse.ParseContext;
+import org.apache.hadoop.hive.ql.parse.RuntimeValuesInfo;
+import org.apache.hadoop.hive.ql.parse.SemanticAnalyzer;
+import org.apache.hadoop.hive.ql.parse.SemanticException;
+import org.apache.hadoop.hive.ql.parse.SemiJoinBranchInfo;
+import org.apache.hadoop.hive.ql.plan.AggregationDesc;
+import org.apache.hadoop.hive.ql.plan.DynamicValue;
+import org.apache.hadoop.hive.ql.plan.ExprNodeColumnDesc;
+import org.apache.hadoop.hive.ql.plan.ExprNodeConstantDesc;
+import org.apache.hadoop.hive.ql.plan.ExprNodeDesc;
+import org.apache.hadoop.hive.ql.plan.ExprNodeDynamicValueDesc;
+import org.apache.hadoop.hive.ql.plan.ExprNodeGenericFuncDesc;
+import org.apache.hadoop.hive.ql.plan.FilterDesc;
+import org.apache.hadoop.hive.ql.plan.GroupByDesc;
+import org.apache.hadoop.hive.ql.plan.PlanUtils;
+import org.apache.hadoop.hive.ql.plan.ReduceSinkDesc;
+import org.apache.hadoop.hive.ql.plan.SelectDesc;
+import org.apache.hadoop.hive.ql.plan.TableDesc;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFBloomFilter;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMin;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFBetween;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFInBloomFilter;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFMurmurHash;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFOPAnd;
+import org.apache.hadoop.hive.ql.util.NullOrdering;
+import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
+import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory;
+
+import java.util.ArrayDeque;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.Deque;
+import java.util.EnumSet;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.SortedMap;
+import java.util.TreeMap;
+
+public class SemiJoinReductionMerge extends Transform {
+
+ public ParseContext transform(ParseContext parseContext) throws
SemanticException {
+ Map<ReduceSinkOperator, SemiJoinBranchInfo> map =
parseContext.getRsToSemiJoinBranchInfo();
+ if (map.isEmpty()) {
+ return parseContext;
+ }
+ HiveConf hiveConf = parseContext.getConf();
+
+ // Order does not really matter but it is necessary to keep plans stable
+ SortedMap<SJSourceTarget, List<ReduceSinkOperator>> sameTableSJ =
+ new TreeMap<>(Comparator.comparing(SJSourceTarget::toString));
+ for (Map.Entry<ReduceSinkOperator, SemiJoinBranchInfo> smjEntry :
map.entrySet()) {
+ TableScanOperator ts = smjEntry.getValue().getTsOp();
+ // Semijoin optimization branch should look like
<Parent>-SEL-GB1-RS1-GB2-RS2
+ SelectOperator selOp = OperatorUtils.ancestor(smjEntry.getKey(),
SelectOperator.class, 0, 0, 0, 0);
+ assert selOp != null;
+ assert selOp.getParentOperators().size() == 1;
+ Operator<?> source = selOp.getParentOperators().get(0);
+ SJSourceTarget sjKey = new SJSourceTarget(source, ts);
+ List<ReduceSinkOperator> ops = sameTableSJ.computeIfAbsent(sjKey,
tableScanOperator -> new ArrayList<>());
+ ops.add(smjEntry.getKey());
+ }
+ for (Map.Entry<SJSourceTarget, List<ReduceSinkOperator>> sjMergeCandidate
: sameTableSJ.entrySet()) {
+ final List<ReduceSinkOperator> sjBrances = sjMergeCandidate.getValue();
+ if (sjBrances.size() < 2) {
+ continue;
+ }
+ // Order does not really matter but it is necessary to keep plans stable
+ sjBrances.sort(Comparator.comparing(Operator::getIdentifier));
+
+ List<SelectOperator> selOps = new ArrayList<>(sjBrances.size());
+ for (ReduceSinkOperator rs : sjBrances) {
+ selOps.add(OperatorUtils.ancestor(rs, SelectOperator.class, 0, 0, 0,
0));
+ }
+ SelectOperator selectOp =
mergeSelectOps(sjMergeCandidate.getKey().source, selOps);
+
+ GroupByOperator gbPartialOp = createGroupBy(selectOp, selectOp,
GroupByDesc.Mode.HASH, hiveConf);
+
+ ReduceSinkOperator rsPartialOp = createReduceSink(gbPartialOp,
NullOrdering.defaultNullOrder(hiveConf));
+
rsPartialOp.getConf().setReducerTraits(EnumSet.of(ReduceSinkDesc.ReducerTraits.QUICKSTART));
+
+ GroupByOperator gbCompleteOp = createGroupBy(selectOp, rsPartialOp,
GroupByDesc.Mode.FINAL, hiveConf);
+
+ ReduceSinkOperator rsCompleteOp = createReduceSink(gbCompleteOp,
NullOrdering.defaultNullOrder(hiveConf));
+
+ final TableScanOperator sjTargetTable = sjMergeCandidate.getKey().target;
+ SemiJoinBranchInfo sjInfo = new SemiJoinBranchInfo(sjTargetTable, false);
+ parseContext.getRsToSemiJoinBranchInfo().put(rsCompleteOp, sjInfo);
+
+ // Save the info that is required at query time to resolve
dynamic/runtime values.
+ RuntimeValuesInfo valuesInfo = createRuntimeValuesInfo(rsCompleteOp,
sjBrances, parseContext);
+ parseContext.getRsToRuntimeValuesInfoMap().put(rsCompleteOp, valuesInfo);
+
+ ExprNodeGenericFuncDesc sjPredicate = createSemiJoinPredicate(sjBrances,
valuesInfo, parseContext);
+
+ // Update filter operators with the new semi-join predicate
+ for (Operator<?> op : sjTargetTable.getChildOperators()) {
+ if (op instanceof FilterOperator) {
+ FilterDesc filter = ((FilterOperator) op).getConf();
+ filter.setPredicate(and(Arrays.asList(filter.getPredicate(),
sjPredicate)));
+ }
+ }
+ // Update tableScan with the new semi-join predicate
+
sjTargetTable.getConf().setFilterExpr(and(Arrays.asList(sjTargetTable.getConf().getFilterExpr(),
sjPredicate)));
+
+ for (ReduceSinkOperator rs : sjBrances) {
+ GenTezUtils.removeSemiJoinOperator(parseContext, rs, sjTargetTable);
+ GenTezUtils.removeBranch(rs);
+ }
+
+ // TODO How to associate multi-cols with gb ?
+ // parseContext.getColExprToGBMap().put(key, gb);
+ }
+ return parseContext;
+ }
+
+ private static ExprNodeGenericFuncDesc
createSemiJoinPredicate(List<ReduceSinkOperator> sjBranches,
+ RuntimeValuesInfo sjValueInfo, ParseContext context) {
+ // Performance note: To speed-up evaluation 'BETWEEN' predicates should
come before the 'IN_BLOOM_FILTER'
+ Deque<String> dynamicIds = new
ArrayDeque<>(sjValueInfo.getDynamicValueIDs());
+ List<ExprNodeDesc> sjPredicates = new ArrayList<>();
+ List<ExprNodeDesc> hashArgs = new ArrayList<>();
+ for (ReduceSinkOperator rs : sjBranches) {
+ RuntimeValuesInfo info = context.getRsToRuntimeValuesInfoMap().get(rs);
+ assert info.getTargetColumns().size() == 1;
+ final ExprNodeDesc targetColumn = info.getTargetColumns().get(0);
+ TypeInfo typeInfo = targetColumn.getTypeInfo();
+ DynamicValue minDynamic = new DynamicValue(dynamicIds.poll(), typeInfo);
+ DynamicValue maxDynamic = new DynamicValue(dynamicIds.poll(), typeInfo);
+
+ List<ExprNodeDesc> betweenArgs = Arrays.asList(
+ // Use false to not invert between result
+ new ExprNodeConstantDesc(Boolean.FALSE),
+ targetColumn,
+ new ExprNodeDynamicValueDesc(minDynamic),
+ new ExprNodeDynamicValueDesc(maxDynamic));
+ ExprNodeDesc betweenExp =
+ new ExprNodeGenericFuncDesc(TypeInfoFactory.booleanTypeInfo, new
GenericUDFBetween(), "between", betweenArgs);
+ sjPredicates.add(betweenExp);
+ hashArgs.add(targetColumn);
+ }
+
+ ExprNodeDesc hashExp =
+ new ExprNodeGenericFuncDesc(TypeInfoFactory.intTypeInfo, new
GenericUDFMurmurHash(), "hash", hashArgs);
+
+ assert dynamicIds.size() == 1 : "There should be one column left untreated
the one with the bloom filter";
+ DynamicValue bloomDynamic = new DynamicValue(dynamicIds.poll(),
TypeInfoFactory.binaryTypeInfo);
+ sjPredicates.add(
+ new ExprNodeGenericFuncDesc(TypeInfoFactory.booleanTypeInfo, new
GenericUDFInBloomFilter(), "in_bloom_filter",
+ Arrays.asList(hashExp, new
ExprNodeDynamicValueDesc(bloomDynamic))));
+ return and(sjPredicates);
+ }
+
+ private static RuntimeValuesInfo createRuntimeValuesInfo(ReduceSinkOperator
rs, List<ReduceSinkOperator> sjBranches,
+ ParseContext parseContext) {
+ List<ExprNodeDesc> valueCols = rs.getConf().getValueCols();
+ RuntimeValuesInfo info = new RuntimeValuesInfo();
+ TableDesc rsFinalTableDesc =
+
PlanUtils.getReduceValueTableDesc(PlanUtils.getFieldSchemasFromColumnList(valueCols,
"_col"));
+ List<String> dynamicValueIDs = new ArrayList<>();
+ for (ExprNodeDesc rsCol : valueCols) {
+ dynamicValueIDs.add(rs.toString() + rsCol.getExprString());
+ }
+
+ info.setTableDesc(rsFinalTableDesc);
+ info.setDynamicValueIDs(dynamicValueIDs);
+ info.setColExprs(valueCols);
+ List<ExprNodeDesc> targetTableExpressions = new ArrayList<>();
+ for (ReduceSinkOperator sjBranch : sjBranches) {
+ RuntimeValuesInfo sjInfo =
parseContext.getRsToRuntimeValuesInfoMap().get(sjBranch);
+ assert sjInfo.getTargetColumns().size() == 1;
+ targetTableExpressions.add(sjInfo.getTargetColumns().get(0));
+ }
+ info.setTargetColumns(targetTableExpressions);
+ return info;
+ }
+
+ private static SelectOperator mergeSelectOps(Operator<?> parent,
List<SelectOperator> selectOperators) {
+ List<String> colNames = new ArrayList<>();
+ List<ExprNodeDesc> colDescs = new ArrayList<>();
+ List<ColumnInfo> columnInfos = new ArrayList<>();
+ Map<String, ExprNodeDesc> selectColumnExprMap = new HashMap<>();
+ for (SelectOperator sel : selectOperators) {
+ for (ExprNodeDesc col : sel.getConf().getColList()) {
Review comment:
Nope, at this point we should have only single column selections. I
added some doc and precondition checks.
##########
File path:
ql/src/java/org/apache/hadoop/hive/ql/optimizer/SemiJoinReductionMerge.java
##########
@@ -0,0 +1,399 @@
+/*
+ * 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 org.apache.hadoop.hive.conf.HiveConf;
+import org.apache.hadoop.hive.ql.exec.ColumnInfo;
+import org.apache.hadoop.hive.ql.exec.FilterOperator;
+import org.apache.hadoop.hive.ql.exec.GroupByOperator;
+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.SelectOperator;
+import org.apache.hadoop.hive.ql.exec.TableScanOperator;
+import org.apache.hadoop.hive.ql.exec.Utilities;
+import org.apache.hadoop.hive.ql.io.AcidUtils;
+import org.apache.hadoop.hive.ql.parse.GenTezUtils;
+import org.apache.hadoop.hive.ql.parse.ParseContext;
+import org.apache.hadoop.hive.ql.parse.RuntimeValuesInfo;
+import org.apache.hadoop.hive.ql.parse.SemanticAnalyzer;
+import org.apache.hadoop.hive.ql.parse.SemanticException;
+import org.apache.hadoop.hive.ql.parse.SemiJoinBranchInfo;
+import org.apache.hadoop.hive.ql.plan.AggregationDesc;
+import org.apache.hadoop.hive.ql.plan.DynamicValue;
+import org.apache.hadoop.hive.ql.plan.ExprNodeColumnDesc;
+import org.apache.hadoop.hive.ql.plan.ExprNodeConstantDesc;
+import org.apache.hadoop.hive.ql.plan.ExprNodeDesc;
+import org.apache.hadoop.hive.ql.plan.ExprNodeDynamicValueDesc;
+import org.apache.hadoop.hive.ql.plan.ExprNodeGenericFuncDesc;
+import org.apache.hadoop.hive.ql.plan.FilterDesc;
+import org.apache.hadoop.hive.ql.plan.GroupByDesc;
+import org.apache.hadoop.hive.ql.plan.PlanUtils;
+import org.apache.hadoop.hive.ql.plan.ReduceSinkDesc;
+import org.apache.hadoop.hive.ql.plan.SelectDesc;
+import org.apache.hadoop.hive.ql.plan.TableDesc;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFBloomFilter;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFEvaluator;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMax;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDAFMin;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFBetween;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFInBloomFilter;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFMurmurHash;
+import org.apache.hadoop.hive.ql.udf.generic.GenericUDFOPAnd;
+import org.apache.hadoop.hive.ql.util.NullOrdering;
+import org.apache.hadoop.hive.serde2.typeinfo.TypeInfo;
+import org.apache.hadoop.hive.serde2.typeinfo.TypeInfoFactory;
+
+import java.util.ArrayDeque;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.Deque;
+import java.util.EnumSet;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.SortedMap;
+import java.util.TreeMap;
+
+public class SemiJoinReductionMerge extends Transform {
+
+ public ParseContext transform(ParseContext parseContext) throws
SemanticException {
+ Map<ReduceSinkOperator, SemiJoinBranchInfo> map =
parseContext.getRsToSemiJoinBranchInfo();
+ if (map.isEmpty()) {
+ return parseContext;
+ }
+ HiveConf hiveConf = parseContext.getConf();
+
+ // Order does not really matter but it is necessary to keep plans stable
+ SortedMap<SJSourceTarget, List<ReduceSinkOperator>> sameTableSJ =
+ new TreeMap<>(Comparator.comparing(SJSourceTarget::toString));
+ for (Map.Entry<ReduceSinkOperator, SemiJoinBranchInfo> smjEntry :
map.entrySet()) {
+ TableScanOperator ts = smjEntry.getValue().getTsOp();
+ // Semijoin optimization branch should look like
<Parent>-SEL-GB1-RS1-GB2-RS2
+ SelectOperator selOp = OperatorUtils.ancestor(smjEntry.getKey(),
SelectOperator.class, 0, 0, 0, 0);
+ assert selOp != null;
+ assert selOp.getParentOperators().size() == 1;
+ Operator<?> source = selOp.getParentOperators().get(0);
+ SJSourceTarget sjKey = new SJSourceTarget(source, ts);
+ List<ReduceSinkOperator> ops = sameTableSJ.computeIfAbsent(sjKey,
tableScanOperator -> new ArrayList<>());
+ ops.add(smjEntry.getKey());
+ }
+ for (Map.Entry<SJSourceTarget, List<ReduceSinkOperator>> sjMergeCandidate
: sameTableSJ.entrySet()) {
+ final List<ReduceSinkOperator> sjBrances = sjMergeCandidate.getValue();
+ if (sjBrances.size() < 2) {
+ continue;
+ }
+ // Order does not really matter but it is necessary to keep plans stable
+ sjBrances.sort(Comparator.comparing(Operator::getIdentifier));
+
+ List<SelectOperator> selOps = new ArrayList<>(sjBrances.size());
+ for (ReduceSinkOperator rs : sjBrances) {
+ selOps.add(OperatorUtils.ancestor(rs, SelectOperator.class, 0, 0, 0,
0));
+ }
+ SelectOperator selectOp =
mergeSelectOps(sjMergeCandidate.getKey().source, selOps);
+
+ GroupByOperator gbPartialOp = createGroupBy(selectOp, selectOp,
GroupByDesc.Mode.HASH, hiveConf);
+
+ ReduceSinkOperator rsPartialOp = createReduceSink(gbPartialOp,
NullOrdering.defaultNullOrder(hiveConf));
+
rsPartialOp.getConf().setReducerTraits(EnumSet.of(ReduceSinkDesc.ReducerTraits.QUICKSTART));
+
+ GroupByOperator gbCompleteOp = createGroupBy(selectOp, rsPartialOp,
GroupByDesc.Mode.FINAL, hiveConf);
+
+ ReduceSinkOperator rsCompleteOp = createReduceSink(gbCompleteOp,
NullOrdering.defaultNullOrder(hiveConf));
+
+ final TableScanOperator sjTargetTable = sjMergeCandidate.getKey().target;
+ SemiJoinBranchInfo sjInfo = new SemiJoinBranchInfo(sjTargetTable, false);
+ parseContext.getRsToSemiJoinBranchInfo().put(rsCompleteOp, sjInfo);
+
+ // Save the info that is required at query time to resolve
dynamic/runtime values.
+ RuntimeValuesInfo valuesInfo = createRuntimeValuesInfo(rsCompleteOp,
sjBrances, parseContext);
+ parseContext.getRsToRuntimeValuesInfoMap().put(rsCompleteOp, valuesInfo);
+
+ ExprNodeGenericFuncDesc sjPredicate = createSemiJoinPredicate(sjBrances,
valuesInfo, parseContext);
+
+ // Update filter operators with the new semi-join predicate
+ for (Operator<?> op : sjTargetTable.getChildOperators()) {
+ if (op instanceof FilterOperator) {
+ FilterDesc filter = ((FilterOperator) op).getConf();
+ filter.setPredicate(and(Arrays.asList(filter.getPredicate(),
sjPredicate)));
+ }
+ }
+ // Update tableScan with the new semi-join predicate
+
sjTargetTable.getConf().setFilterExpr(and(Arrays.asList(sjTargetTable.getConf().getFilterExpr(),
sjPredicate)));
+
+ for (ReduceSinkOperator rs : sjBrances) {
+ GenTezUtils.removeSemiJoinOperator(parseContext, rs, sjTargetTable);
+ GenTezUtils.removeBranch(rs);
+ }
+
+ // TODO How to associate multi-cols with gb ?
+ // parseContext.getColExprToGBMap().put(key, gb);
+ }
+ return parseContext;
+ }
+
+ private static ExprNodeGenericFuncDesc
createSemiJoinPredicate(List<ReduceSinkOperator> sjBranches,
+ RuntimeValuesInfo sjValueInfo, ParseContext context) {
+ // Performance note: To speed-up evaluation 'BETWEEN' predicates should
come before the 'IN_BLOOM_FILTER'
+ Deque<String> dynamicIds = new
ArrayDeque<>(sjValueInfo.getDynamicValueIDs());
+ List<ExprNodeDesc> sjPredicates = new ArrayList<>();
+ List<ExprNodeDesc> hashArgs = new ArrayList<>();
+ for (ReduceSinkOperator rs : sjBranches) {
+ RuntimeValuesInfo info = context.getRsToRuntimeValuesInfoMap().get(rs);
+ assert info.getTargetColumns().size() == 1;
+ final ExprNodeDesc targetColumn = info.getTargetColumns().get(0);
+ TypeInfo typeInfo = targetColumn.getTypeInfo();
+ DynamicValue minDynamic = new DynamicValue(dynamicIds.poll(), typeInfo);
+ DynamicValue maxDynamic = new DynamicValue(dynamicIds.poll(), typeInfo);
+
+ List<ExprNodeDesc> betweenArgs = Arrays.asList(
+ // Use false to not invert between result
+ new ExprNodeConstantDesc(Boolean.FALSE),
+ targetColumn,
+ new ExprNodeDynamicValueDesc(minDynamic),
+ new ExprNodeDynamicValueDesc(maxDynamic));
+ ExprNodeDesc betweenExp =
+ new ExprNodeGenericFuncDesc(TypeInfoFactory.booleanTypeInfo, new
GenericUDFBetween(), "between", betweenArgs);
+ sjPredicates.add(betweenExp);
+ hashArgs.add(targetColumn);
+ }
+
+ ExprNodeDesc hashExp =
+ new ExprNodeGenericFuncDesc(TypeInfoFactory.intTypeInfo, new
GenericUDFMurmurHash(), "hash", hashArgs);
+
+ assert dynamicIds.size() == 1 : "There should be one column left untreated
the one with the bloom filter";
+ DynamicValue bloomDynamic = new DynamicValue(dynamicIds.poll(),
TypeInfoFactory.binaryTypeInfo);
+ sjPredicates.add(
+ new ExprNodeGenericFuncDesc(TypeInfoFactory.booleanTypeInfo, new
GenericUDFInBloomFilter(), "in_bloom_filter",
+ Arrays.asList(hashExp, new
ExprNodeDynamicValueDesc(bloomDynamic))));
+ return and(sjPredicates);
+ }
+
+ private static RuntimeValuesInfo createRuntimeValuesInfo(ReduceSinkOperator
rs, List<ReduceSinkOperator> sjBranches,
+ ParseContext parseContext) {
+ List<ExprNodeDesc> valueCols = rs.getConf().getValueCols();
+ RuntimeValuesInfo info = new RuntimeValuesInfo();
+ TableDesc rsFinalTableDesc =
+
PlanUtils.getReduceValueTableDesc(PlanUtils.getFieldSchemasFromColumnList(valueCols,
"_col"));
+ List<String> dynamicValueIDs = new ArrayList<>();
+ for (ExprNodeDesc rsCol : valueCols) {
+ dynamicValueIDs.add(rs.toString() + rsCol.getExprString());
+ }
+
+ info.setTableDesc(rsFinalTableDesc);
+ info.setDynamicValueIDs(dynamicValueIDs);
+ info.setColExprs(valueCols);
+ List<ExprNodeDesc> targetTableExpressions = new ArrayList<>();
+ for (ReduceSinkOperator sjBranch : sjBranches) {
+ RuntimeValuesInfo sjInfo =
parseContext.getRsToRuntimeValuesInfoMap().get(sjBranch);
+ assert sjInfo.getTargetColumns().size() == 1;
+ targetTableExpressions.add(sjInfo.getTargetColumns().get(0));
+ }
+ info.setTargetColumns(targetTableExpressions);
+ return info;
+ }
+
+ private static SelectOperator mergeSelectOps(Operator<?> parent,
List<SelectOperator> selectOperators) {
+ List<String> colNames = new ArrayList<>();
+ List<ExprNodeDesc> colDescs = new ArrayList<>();
+ List<ColumnInfo> columnInfos = new ArrayList<>();
+ Map<String, ExprNodeDesc> selectColumnExprMap = new HashMap<>();
+ for (SelectOperator sel : selectOperators) {
+ for (ExprNodeDesc col : sel.getConf().getColList()) {
+ String colName = HiveConf.getColumnInternalName(colDescs.size());
+ colNames.add(colName);
+ columnInfos.add(new ColumnInfo(colName, col.getTypeInfo(), "", false));
+ colDescs.add(col);
+ selectColumnExprMap.put(colName, col);
+ }
+ }
+ ExprNodeDesc hashExp =
+ new ExprNodeGenericFuncDesc(TypeInfoFactory.intTypeInfo, new
GenericUDFMurmurHash(), "hash", colDescs);
+ String hashName = HiveConf.getColumnInternalName(colDescs.size() + 1);
+ colNames.add(hashName);
+ columnInfos.add(new ColumnInfo(hashName, hashExp.getTypeInfo(), "",
false));
+
+ List<ExprNodeDesc> selDescs = new ArrayList<>(colDescs);
Review comment:
Done
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Issue Time Tracking
-------------------
Worklog Id: (was: 467253)
Time Spent: 1h 40m (was: 1.5h)
> Support semijoin reduction on multiple column join
> --------------------------------------------------
>
> Key: HIVE-21196
> URL: https://issues.apache.org/jira/browse/HIVE-21196
> Project: Hive
> Issue Type: Bug
> Reporter: Deepak Jaiswal
> Assignee: Stamatis Zampetakis
> Priority: Major
> Labels: pull-request-available
> Time Spent: 1h 40m
> Remaining Estimate: 0h
>
> Currently for a query involving join on multiple columns creates separate
> semi join edges for each key which in turn create a bloom filter for each of
> them, like below,
> EXPLAIN select count(*) from srcpart_date_n7 join srcpart_small_n3 on
> (srcpart_date_n7.key = srcpart_small_n3.key1 and srcpart_date_n7.value =
> srcpart_small_n3.value1)
> {code:java}
> Map 1 <- Reducer 5 (BROADCAST_EDGE)
> Reducer 2 <- Map 1 (SIMPLE_EDGE), Map 4 (SIMPLE_EDGE)
> Reducer 3 <- Reducer 2 (CUSTOM_SIMPLE_EDGE)
> Reducer 5 <- Map 4 (CUSTOM_SIMPLE_EDGE)
> #### A masked pattern was here ####
> Vertices:
> Map 1
> Map Operator Tree:
> TableScan
> alias: srcpart_date_n7
> filterExpr: (key is not null and value is not null and (key
> BETWEEN DynamicValue(RS_7_srcpart_small_n3_key1_min) AND
> DynamicValue(RS_7_srcpart_small_n3_key1_max) and in_bloom_filter(key,
> DynamicValue(RS_7_srcpart_small_n3_key1_bloom_filter)))) (type: boolean)
> Statistics: Num rows: 2000 Data size: 356000 Basic stats:
> COMPLETE Column stats: COMPLETE
> Filter Operator
> predicate: ((key BETWEEN
> DynamicValue(RS_7_srcpart_small_n3_key1_min) AND
> DynamicValue(RS_7_srcpart_small_n3_key1_max) and in_bloom_filter(key,
> DynamicValue(RS_7_srcpart_small_n3_key1_bloom_filter))) and key is not null
> and value is not null) (type: boolean)
> Statistics: Num rows: 2000 Data size: 356000 Basic stats:
> COMPLETE Column stats: COMPLETE
> Select Operator
> expressions: key (type: string), value (type: string)
> outputColumnNames: _col0, _col1
> Statistics: Num rows: 2000 Data size: 356000 Basic
> stats: COMPLETE Column stats: COMPLETE
> Reduce Output Operator
> key expressions: _col0 (type: string), _col1 (type:
> string)
> sort order: ++
> Map-reduce partition columns: _col0 (type: string),
> _col1 (type: string)
> Statistics: Num rows: 2000 Data size: 356000 Basic
> stats: COMPLETE Column stats: COMPLETE
> Execution mode: vectorized, llap
> LLAP IO: all inputs
> Map 4
> Map Operator Tree:
> TableScan
> alias: srcpart_small_n3
> filterExpr: (key1 is not null and value1 is not null)
> (type: boolean)
> Statistics: Num rows: 20 Data size: 3560 Basic stats:
> PARTIAL Column stats: PARTIAL
> Filter Operator
> predicate: (key1 is not null and value1 is not null)
> (type: boolean)
> Statistics: Num rows: 20 Data size: 3560 Basic stats:
> PARTIAL Column stats: PARTIAL
> Select Operator
> expressions: key1 (type: string), value1 (type: string)
> outputColumnNames: _col0, _col1
> Statistics: Num rows: 20 Data size: 3560 Basic stats:
> PARTIAL Column stats: PARTIAL
> Reduce Output Operator
> key expressions: _col0 (type: string), _col1 (type:
> string)
> sort order: ++
> Map-reduce partition columns: _col0 (type: string),
> _col1 (type: string)
> Statistics: Num rows: 20 Data size: 3560 Basic stats:
> PARTIAL Column stats: PARTIAL
> Select Operator
> expressions: _col0 (type: string)
> outputColumnNames: _col0
> Statistics: Num rows: 20 Data size: 3560 Basic stats:
> PARTIAL Column stats: PARTIAL
> Group By Operator
> aggregations: min(_col0), max(_col0),
> bloom_filter(_col0, expectedEntries=20)
> mode: hash
> outputColumnNames: _col0, _col1, _col2
> Statistics: Num rows: 1 Data size: 730 Basic stats:
> PARTIAL Column stats: PARTIAL
> Reduce Output Operator
> sort order:
> Statistics: Num rows: 1 Data size: 730 Basic
> stats: PARTIAL Column stats: PARTIAL
> value expressions: _col0 (type: string), _col1
> (type: string), _col2 (type: binary)
> Execution mode: vectorized, llap
> LLAP IO: all inputs
> Reducer 2
> Execution mode: llap
> Reduce Operator Tree:
> Merge Join Operator
> condition map:
> Inner Join 0 to 1
> keys:
> 0 _col0 (type: string), _col1 (type: string)
> 1 _col0 (type: string), _col1 (type: string)
> Statistics: Num rows: 2200 Data size: 391600 Basic stats:
> PARTIAL Column stats: NONE
> Group By Operator
> aggregations: count()
> mode: hash
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 8 Basic stats: PARTIAL
> Column stats: NONE
> Reduce Output Operator
> sort order:
> Statistics: Num rows: 1 Data size: 8 Basic stats: PARTIAL
> Column stats: NONE
> value expressions: _col0 (type: bigint)
> Reducer 3
> Execution mode: vectorized, llap
> Reduce Operator Tree:
> Group By Operator
> aggregations: count(VALUE._col0)
> mode: mergepartial
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 8 Basic stats: PARTIAL
> Column stats: NONE
> File Output Operator
> compressed: false
> Statistics: Num rows: 1 Data size: 8 Basic stats: PARTIAL
> Column stats: NONE
> table:
> input format:
> org.apache.hadoop.mapred.SequenceFileInputFormat
> output format:
> org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
> serde:
> org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
> Reducer 5
> Execution mode: vectorized, llap
> Reduce Operator Tree:
> Group By Operator
> aggregations: min(VALUE._col0), max(VALUE._col1),
> bloom_filter(VALUE._col2, expectedEntries=20)
> mode: final
> outputColumnNames: _col0, _col1, _col2
> Statistics: Num rows: 1 Data size: 730 Basic stats: PARTIAL
> Column stats: PARTIAL
> Reduce Output Operator
> sort order:
> Statistics: Num rows: 1 Data size: 730 Basic stats: PARTIAL
> Column stats: PARTIAL
> value expressions: _col0 (type: string), _col1 (type:
> string), _col2 (type: binary)
> {code}
> Instead it should create one branch for a join with one bloom filter.
>
> The implementation for bloom filter requires getting a hash out of all the
> key columns and converting it to a long and feeding it to bloom filter as
> input. This requires a new UDF which does this. It will be called at both
> bloom filter generation and lookup phases.
> The min and max will stay independent as they are today for each columns.
> A vectorized implementation of such UDF is also required.
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