/**
* 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.parse;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Deque;
import java.util.HashMap;
import java.util.HashSet;
import java.util.LinkedHashMap;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Set;
import java.util.Stack;
import java.util.concurrent.atomic.AtomicInteger;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.conf.HiveConf.ConfVars;
import org.apache.hadoop.hive.ql.Context;
import org.apache.hadoop.hive.ql.exec.AppMasterEventOperator;
import org.apache.hadoop.hive.ql.exec.CommonMergeJoinOperator;
import org.apache.hadoop.hive.ql.exec.ConditionalTask;
import org.apache.hadoop.hive.ql.exec.DummyStoreOperator;
import org.apache.hadoop.hive.ql.exec.FileSinkOperator;
import org.apache.hadoop.hive.ql.exec.FilterOperator;
import org.apache.hadoop.hive.ql.exec.JoinOperator;
import org.apache.hadoop.hive.ql.exec.MapJoinOperator;
import org.apache.hadoop.hive.ql.exec.Operator;
import org.apache.hadoop.hive.ql.exec.ReduceSinkOperator;
import org.apache.hadoop.hive.ql.exec.TableScanOperator;
import org.apache.hadoop.hive.ql.exec.Task;
import org.apache.hadoop.hive.ql.exec.UnionOperator;
import org.apache.hadoop.hive.ql.exec.tez.TezTask;
import org.apache.hadoop.hive.ql.hooks.ReadEntity;
import org.apache.hadoop.hive.ql.hooks.WriteEntity;
import org.apache.hadoop.hive.ql.lib.CompositeProcessor;
import org.apache.hadoop.hive.ql.lib.DefaultRuleDispatcher;
import org.apache.hadoop.hive.ql.lib.Dispatcher;
import org.apache.hadoop.hive.ql.lib.ForwardWalker;
import org.apache.hadoop.hive.ql.lib.GraphWalker;
import org.apache.hadoop.hive.ql.lib.Node;
import org.apache.hadoop.hive.ql.lib.NodeProcessor;
import org.apache.hadoop.hive.ql.lib.Rule;
import org.apache.hadoop.hive.ql.lib.RuleRegExp;
import org.apache.hadoop.hive.ql.metadata.Hive;
import org.apache.hadoop.hive.ql.optimizer.ConstantPropagate;
import org.apache.hadoop.hive.ql.optimizer.ConvertJoinMapJoin;
import org.apache.hadoop.hive.ql.optimizer.DynamicPartitionPruningOptimization;
import org.apache.hadoop.hive.ql.optimizer.MergeJoinProc;
import org.apache.hadoop.hive.ql.optimizer.ReduceSinkMapJoinProc;
import org.apache.hadoop.hive.ql.optimizer.RemoveDynamicPruningBySize;
import org.apache.hadoop.hive.ql.optimizer.SetReducerParallelism;
import org.apache.hadoop.hive.ql.optimizer.metainfo.annotation.AnnotateWithOpTraits;
import org.apache.hadoop.hive.ql.optimizer.physical.CrossProductCheck;
import org.apache.hadoop.hive.ql.optimizer.physical.MetadataOnlyOptimizer;
import org.apache.hadoop.hive.ql.optimizer.physical.NullScanOptimizer;
import org.apache.hadoop.hive.ql.optimizer.physical.PhysicalContext;
import org.apache.hadoop.hive.ql.optimizer.physical.StageIDsRearranger;
import org.apache.hadoop.hive.ql.optimizer.physical.Vectorizer;
import org.apache.hadoop.hive.ql.optimizer.stats.annotation.AnnotateWithStatistics;
import org.apache.hadoop.hive.ql.plan.BaseWork;
import org.apache.hadoop.hive.ql.plan.DynamicPruningEventDesc;
import org.apache.hadoop.hive.ql.plan.MapWork;
import org.apache.hadoop.hive.ql.plan.MoveWork;
import org.apache.hadoop.hive.ql.plan.OperatorDesc;
import org.apache.hadoop.hive.ql.plan.TezWork;
import org.apache.hadoop.hive.ql.session.SessionState.LogHelper;
/**
* TezCompiler translates the operator plan into TezTasks.
*/
public class TezCompiler extends TaskCompiler {
protected final Log LOG = LogFactory.getLog(TezCompiler.class);
public TezCompiler() {
}
@Override
public void init(HiveConf conf, LogHelper console, Hive db) {
super.init(conf, console, db);
// Tez requires us to use RPC for the query plan
HiveConf.setBoolVar(conf, ConfVars.HIVE_RPC_QUERY_PLAN, true);
// We require the use of recursive input dirs for union processing
conf.setBoolean("mapred.input.dir.recursive", true);
HiveConf.setBoolVar(conf, ConfVars.HIVE_HADOOP_SUPPORTS_SUBDIRECTORIES, true);
}
@Override
protected void optimizeOperatorPlan(ParseContext pCtx, Set<ReadEntity> inputs,
Set<WriteEntity> outputs) throws SemanticException {
// Create the context for the walker
OptimizeTezProcContext procCtx = new OptimizeTezProcContext(conf, pCtx, inputs, outputs);
// setup dynamic partition pruning where possible
runDynamicPartitionPruning(procCtx, inputs, outputs);
// setup stats in the operator plan
runStatsAnnotation(procCtx);
// run the optimizations that use stats for optimization
runStatsDependentOptimizations(procCtx, inputs, outputs);
// after the stats phase we might have some cyclic dependencies that we need
// to take care of.
runCycleAnalysisForPartitionPruning(procCtx, inputs, outputs);
}
private void runCycleAnalysisForPartitionPruning(OptimizeTezProcContext procCtx,
Set<ReadEntity> inputs, Set<WriteEntity> outputs) throws SemanticException {
if (!procCtx.conf.getBoolVar(ConfVars.TEZ_DYNAMIC_PARTITION_PRUNING)) {
return;
}
boolean cycleFree = false;
while (!cycleFree) {
cycleFree = true;
Set<Set<Operator<?>>> components = getComponents(procCtx);
for (Set<Operator<?>> component : components) {
if (LOG.isDebugEnabled()) {
LOG.debug("Component: ");
for (Operator<?> co : component) {
LOG.debug("Operator: " + co.getName() + ", " + co.getIdentifier());
}
}
if (component.size() != 1) {
LOG.info("Found cycle in operator plan...");
cycleFree = false;
removeEventOperator(component);
break;
}
}
LOG.info("Cycle free: " + cycleFree);
}
}
private void removeEventOperator(Set<Operator<?>> component) {
AppMasterEventOperator victim = null;
for (Operator<?> o : component) {
if (o instanceof AppMasterEventOperator) {
if (victim == null
|| o.getConf().getStatistics().getDataSize() < victim.getConf().getStatistics()
.getDataSize()) {
victim = (AppMasterEventOperator) o;
}
}
}
Operator<?> child = victim;
Operator<?> curr = victim;
while (curr.getChildOperators().size() <= 1) {
child = curr;
curr = curr.getParentOperators().get(0);
}
// 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) victim.getConf()).getTableScan().toString()
+ ". Needed to break cyclic dependency");
curr.removeChild(child);
}
// Tarjan's algo
private Set<Set<Operator<?>>> getComponents(OptimizeTezProcContext procCtx) {
Deque<Operator<?>> deque = new LinkedList<Operator<?>>();
deque.addAll(procCtx.parseContext.getTopOps().values());
AtomicInteger index = new AtomicInteger();
Map<Operator<?>, Integer> indexes = new HashMap<Operator<?>, Integer>();
Map<Operator<?>, Integer> lowLinks = new HashMap<Operator<?>, Integer>();
Stack<Operator<?>> nodes = new Stack<Operator<?>>();
Set<Set<Operator<?>>> components = new HashSet<Set<Operator<?>>>();
for (Operator<?> o : deque) {
if (!indexes.containsKey(o)) {
connect(o, index, nodes, indexes, lowLinks, components);
}
}
return components;
}
private void connect(Operator<?> o, AtomicInteger index, Stack<Operator<?>> nodes,
Map<Operator<?>, Integer> indexes, Map<Operator<?>, Integer> lowLinks,
Set<Set<Operator<?>>> components) {
indexes.put(o, index.get());
lowLinks.put(o, index.get());
index.incrementAndGet();
nodes.push(o);
List<Operator<?>> children;
if (o instanceof AppMasterEventOperator) {
children = new ArrayList<Operator<?>>();
children.addAll(o.getChildOperators());
TableScanOperator ts = ((DynamicPruningEventDesc) o.getConf()).getTableScan();
LOG.debug("Adding special edge: " + o.getName() + " --> " + ts.toString());
children.add(ts);
} else {
children = o.getChildOperators();
}
for (Operator<?> child : children) {
if (!indexes.containsKey(child)) {
connect(child, index, nodes, indexes, lowLinks, components);
lowLinks.put(o, Math.min(lowLinks.get(o), lowLinks.get(child)));
} else if (nodes.contains(child)) {
lowLinks.put(o, Math.min(lowLinks.get(o), indexes.get(child)));
}
}
if (lowLinks.get(o).equals(indexes.get(o))) {
Set<Operator<?>> component = new HashSet<Operator<?>>();
components.add(component);
Operator<?> current;
do {
current = nodes.pop();
component.add(current);
} while (current != o);
}
}
private void runStatsAnnotation(OptimizeTezProcContext procCtx) throws SemanticException {
new AnnotateWithStatistics().transform(procCtx.parseContext);
new AnnotateWithOpTraits().transform(procCtx.parseContext);
}
private void runStatsDependentOptimizations(OptimizeTezProcContext procCtx,
Set<ReadEntity> inputs, Set<WriteEntity> outputs) throws SemanticException {
// Sequence of TableScan operators to be walked
Deque<Operator<?>> deque = new LinkedList<Operator<?>>();
deque.addAll(procCtx.parseContext.getTopOps().values());
// create a walker which walks the tree in a DFS manner while maintaining
// the operator stack.
Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
opRules.put(new RuleRegExp("Set parallelism - ReduceSink",
ReduceSinkOperator.getOperatorName() + "%"),
new SetReducerParallelism());
opRules.put(new RuleRegExp("Convert Join to Map-join",
JoinOperator.getOperatorName() + "%"), new ConvertJoinMapJoin());
opRules.put(
new RuleRegExp("Remove dynamic pruning by size",
AppMasterEventOperator.getOperatorName() + "%"),
new RemoveDynamicPruningBySize());
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
List<Node> topNodes = new ArrayList<Node>();
topNodes.addAll(procCtx.parseContext.getTopOps().values());
GraphWalker ogw = new ForwardWalker(disp);
ogw.startWalking(topNodes, null);
}
private void runDynamicPartitionPruning(OptimizeTezProcContext procCtx, Set<ReadEntity> inputs,
Set<WriteEntity> outputs) throws SemanticException {
if (!procCtx.conf.getBoolVar(ConfVars.TEZ_DYNAMIC_PARTITION_PRUNING)) {
return;
}
// Sequence of TableScan operators to be walked
Deque<Operator<?>> deque = new LinkedList<Operator<?>>();
deque.addAll(procCtx.parseContext.getTopOps().values());
Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
opRules.put(
new RuleRegExp(new String("Dynamic Partition Pruning"), FilterOperator.getOperatorName()
+ "%"), new DynamicPartitionPruningOptimization());
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
List<Node> topNodes = new ArrayList<Node>();
topNodes.addAll(procCtx.parseContext.getTopOps().values());
GraphWalker ogw = new ForwardWalker(disp);
ogw.startWalking(topNodes, null);
// need a new run of the constant folding because we might have created lots
// of "and true and true" conditions.
if(procCtx.conf.getBoolVar(ConfVars.HIVEOPTCONSTANTPROPAGATION)) {
new ConstantPropagate().transform(procCtx.parseContext);
}
}
@Override
protected void generateTaskTree(List<Task<? extends Serializable>> rootTasks, ParseContext pCtx,
List<Task<MoveWork>> mvTask, Set<ReadEntity> inputs, Set<WriteEntity> outputs)
throws SemanticException {
GenTezUtils.getUtils().resetSequenceNumber();
ParseContext tempParseContext = getParseContext(pCtx, rootTasks);
GenTezWork genTezWork = new GenTezWork(GenTezUtils.getUtils());
GenTezProcContext procCtx = new GenTezProcContext(
conf, tempParseContext, mvTask, rootTasks, inputs, outputs);
// create a walker which walks the tree in a DFS manner while maintaining
// the operator stack.
// The dispatcher generates the plan from the operator tree
Map<Rule, NodeProcessor> opRules = new LinkedHashMap<Rule, NodeProcessor>();
opRules.put(new RuleRegExp("Split Work - ReduceSink",
ReduceSinkOperator.getOperatorName() + "%"),
genTezWork);
opRules.put(new RuleRegExp("No more walking on ReduceSink-MapJoin",
MapJoinOperator.getOperatorName() + "%"), new ReduceSinkMapJoinProc());
opRules.put(new RuleRegExp("Recoginze a Sorted Merge Join operator to setup the right edge and"
+ " stop traversing the DummyStore-MapJoin", CommonMergeJoinOperator.getOperatorName()
+ "%"), new MergeJoinProc());
opRules.put(new RuleRegExp("Split Work + Move/Merge - FileSink",
FileSinkOperator.getOperatorName() + "%"),
new CompositeProcessor(new FileSinkProcessor(), genTezWork));
opRules.put(new RuleRegExp("Split work - DummyStore", DummyStoreOperator.getOperatorName()
+ "%"), genTezWork);
opRules.put(new RuleRegExp("Handle Potential Analyze Command",
TableScanOperator.getOperatorName() + "%"),
new ProcessAnalyzeTable(GenTezUtils.getUtils()));
opRules.put(new RuleRegExp("Remember union",
UnionOperator.getOperatorName() + "%"),
new UnionProcessor());
opRules.put(new RuleRegExp("AppMasterEventOperator",
AppMasterEventOperator.getOperatorName() + "%"),
new AppMasterEventProcessor());
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
List<Node> topNodes = new ArrayList<Node>();
topNodes.addAll(pCtx.getTopOps().values());
GraphWalker ogw = new GenTezWorkWalker(disp, procCtx);
ogw.startWalking(topNodes, null);
// we need to clone some operator plans and remove union operators still
for (BaseWork w: procCtx.workWithUnionOperators) {
GenTezUtils.getUtils().removeUnionOperators(conf, procCtx, w);
}
// then we make sure the file sink operators are set up right
for (FileSinkOperator fileSink: procCtx.fileSinkSet) {
GenTezUtils.getUtils().processFileSink(procCtx, fileSink);
}
// and finally we hook up any events that need to be sent to the tez AM
LOG.debug("There are " + procCtx.eventOperatorSet.size() + " app master events.");
for (AppMasterEventOperator event : procCtx.eventOperatorSet) {
LOG.debug("Handling AppMasterEventOperator: " + event);
GenTezUtils.getUtils().processAppMasterEvent(procCtx, event);
}
}
@Override
protected void setInputFormat(Task<? extends Serializable> task) {
if (task instanceof TezTask) {
TezWork work = ((TezTask)task).getWork();
List<BaseWork> all = work.getAllWork();
for (BaseWork w: all) {
if (w instanceof MapWork) {
MapWork mapWork = (MapWork) w;
HashMap<String, Operator<? extends OperatorDesc>> opMap = mapWork.getAliasToWork();
if (!opMap.isEmpty()) {
for (Operator<? extends OperatorDesc> op : opMap.values()) {
setInputFormat(mapWork, op);
}
}
}
}
} else if (task instanceof ConditionalTask) {
List<Task<? extends Serializable>> listTasks
= ((ConditionalTask) task).getListTasks();
for (Task<? extends Serializable> tsk : listTasks) {
setInputFormat(tsk);
}
}
if (task.getChildTasks() != null) {
for (Task<? extends Serializable> childTask : task.getChildTasks()) {
setInputFormat(childTask);
}
}
}
private void setInputFormat(MapWork work, Operator<? extends OperatorDesc> op) {
if (op == null) {
return;
}
if (op.isUseBucketizedHiveInputFormat()) {
work.setUseBucketizedHiveInputFormat(true);
return;
}
if (op.getChildOperators() != null) {
for (Operator<? extends OperatorDesc> childOp : op.getChildOperators()) {
setInputFormat(work, childOp);
}
}
}
@Override
protected void decideExecMode(List<Task<? extends Serializable>> rootTasks, Context ctx,
GlobalLimitCtx globalLimitCtx)
throws SemanticException {
// currently all Tez work is on the cluster
return;
}
@Override
protected void optimizeTaskPlan(List<Task<? extends Serializable>> rootTasks, ParseContext pCtx,
Context ctx) throws SemanticException {
PhysicalContext physicalCtx = new PhysicalContext(conf, pCtx, pCtx.getContext(), rootTasks,
pCtx.getFetchTask());
if (conf.getBoolVar(HiveConf.ConfVars.HIVENULLSCANOPTIMIZE)) {
physicalCtx = new NullScanOptimizer().resolve(physicalCtx);
} else {
LOG.debug("Skipping null scan query optimization");
}
if (conf.getBoolVar(HiveConf.ConfVars.HIVEMETADATAONLYQUERIES)) {
physicalCtx = new MetadataOnlyOptimizer().resolve(physicalCtx);
} else {
LOG.debug("Skipping metadata only query optimization");
}
if (conf.getBoolVar(HiveConf.ConfVars.HIVE_CHECK_CROSS_PRODUCT)) {
physicalCtx = new CrossProductCheck().resolve(physicalCtx);
} else {
LOG.debug("Skipping cross product analysis");
}
if (conf.getBoolVar(HiveConf.ConfVars.HIVE_VECTORIZATION_ENABLED)) {
physicalCtx = new Vectorizer().resolve(physicalCtx);
} else {
LOG.debug("Skipping vectorization");
}
if (!"none".equalsIgnoreCase(conf.getVar(HiveConf.ConfVars.HIVESTAGEIDREARRANGE))) {
physicalCtx = new StageIDsRearranger().resolve(physicalCtx);
} else {
LOG.debug("Skipping stage id rearranger");
}
return;
}
}