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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.parse.spark;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Deque;
import java.util.HashMap;
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 org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.ql.Context;
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.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.SMBMapJoinOperator;
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.spark.SparkTask;
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.DefaultGraphWalker;
import org.apache.hadoop.hive.ql.lib.DefaultRuleDispatcher;
import org.apache.hadoop.hive.ql.lib.Dispatcher;
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.NodeProcessorCtx;
import org.apache.hadoop.hive.ql.lib.Rule;
import org.apache.hadoop.hive.ql.lib.RuleRegExp;
import org.apache.hadoop.hive.ql.lib.TypeRule;
import org.apache.hadoop.hive.ql.log.PerfLogger;
import org.apache.hadoop.hive.ql.metadata.Hive;
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.SparkCrossProductCheck;
import org.apache.hadoop.hive.ql.optimizer.physical.SparkMapJoinResolver;
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.spark.SetSparkReducerParallelism;
import org.apache.hadoop.hive.ql.optimizer.spark.SparkJoinHintOptimizer;
import org.apache.hadoop.hive.ql.optimizer.spark.SparkJoinOptimizer;
import org.apache.hadoop.hive.ql.optimizer.spark.SparkReduceSinkMapJoinProc;
import org.apache.hadoop.hive.ql.optimizer.spark.SparkSkewJoinResolver;
import org.apache.hadoop.hive.ql.optimizer.spark.SparkSortMergeJoinFactory;
import org.apache.hadoop.hive.ql.optimizer.spark.SplitSparkWorkResolver;
import org.apache.hadoop.hive.ql.parse.GlobalLimitCtx;
import org.apache.hadoop.hive.ql.parse.ParseContext;
import org.apache.hadoop.hive.ql.parse.SemanticException;
import org.apache.hadoop.hive.ql.parse.TaskCompiler;
import org.apache.hadoop.hive.ql.plan.BaseWork;
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.SparkWork;
import org.apache.hadoop.hive.ql.session.SessionState.LogHelper;
/**
* SparkCompiler translates the operator plan into SparkTasks.
*
* Cloned from TezCompiler.
*/
public class SparkCompiler extends TaskCompiler {
private static final String CLASS_NAME = SparkCompiler.class.getName();
private static final PerfLogger PERF_LOGGER = PerfLogger.getPerfLogger();
private static final Log LOGGER = LogFactory.getLog(SparkCompiler.class);
public SparkCompiler() {
}
@Override
protected void optimizeOperatorPlan(ParseContext pCtx, Set inputs,
Set outputs) throws SemanticException {
PERF_LOGGER.PerfLogBegin(CLASS_NAME, PerfLogger.SPARK_OPTIMIZE_OPERATOR_TREE);
// Sequence of TableScan operators to be walked
Deque> deque = new LinkedList>();
deque.addAll(pCtx.getTopOps().values());
OptimizeSparkProcContext procCtx = new OptimizeSparkProcContext(conf, pCtx, inputs, outputs, deque);
// create a walker which walks the tree in a DFS manner while maintaining
// the operator stack.
Map opRules = new LinkedHashMap();
opRules.put(new RuleRegExp("Set parallelism - ReduceSink",
ReduceSinkOperator.getOperatorName() + "%"),
new SetSparkReducerParallelism());
opRules.put(new TypeRule(JoinOperator.class), new SparkJoinOptimizer(pCtx));
opRules.put(new TypeRule(MapJoinOperator.class), new SparkJoinHintOptimizer(pCtx));
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
GraphWalker ogw = new DefaultGraphWalker(disp);
// Create a list of topop nodes
ArrayList topNodes = new ArrayList();
topNodes.addAll(pCtx.getTopOps().values());
ogw.startWalking(topNodes, null);
PERF_LOGGER.PerfLogEnd(CLASS_NAME, PerfLogger.SPARK_OPTIMIZE_OPERATOR_TREE);
}
/**
* TODO: need to turn on rules that's commented out and add more if necessary.
*/
@Override
protected void generateTaskTree(List> rootTasks, ParseContext pCtx,
List> mvTask, Set inputs, Set outputs)
throws SemanticException {
PERF_LOGGER.PerfLogBegin(CLASS_NAME, PerfLogger.SPARK_GENERATE_TASK_TREE);
GenSparkUtils.getUtils().resetSequenceNumber();
ParseContext tempParseContext = getParseContext(pCtx, rootTasks);
GenSparkWork genSparkWork = new GenSparkWork(GenSparkUtils.getUtils());
GenSparkProcContext procCtx = new GenSparkProcContext(
conf, tempParseContext, mvTask, rootTasks, inputs, outputs, pCtx.getTopOps());
// 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 opRules = new LinkedHashMap();
opRules.put(new RuleRegExp("Split Work - ReduceSink",
ReduceSinkOperator.getOperatorName() + "%"), genSparkWork);
opRules.put(new TypeRule(MapJoinOperator.class), new SparkReduceSinkMapJoinProc());
opRules.put(new RuleRegExp("Split Work + Move/Merge - FileSink",
FileSinkOperator.getOperatorName() + "%"),
new CompositeProcessor(new SparkFileSinkProcessor(), genSparkWork));
opRules.put(new RuleRegExp("Handle Analyze Command",
TableScanOperator.getOperatorName() + "%"),
new SparkProcessAnalyzeTable(GenSparkUtils.getUtils()));
opRules.put(new RuleRegExp("Remember union", UnionOperator.getOperatorName() + "%"),
new NodeProcessor() {
@Override
public Object process(Node n, Stack s,
NodeProcessorCtx procCtx, Object... os) throws SemanticException {
GenSparkProcContext context = (GenSparkProcContext) procCtx;
UnionOperator union = (UnionOperator) n;
// simply need to remember that we've seen a union.
context.currentUnionOperators.add(union);
return null;
}
}
);
/**
* SMB join case: (Big) (Small) (Small)
* TS TS TS
* \ | /
* \ DS DS
* \ | /
* SMBJoinOP
*
* Some of the other processors are expecting only one traversal beyond SMBJoinOp.
* We need to traverse from the big-table path only, and stop traversing on the small-table path once we reach SMBJoinOp.
* Also add some SMB join information to the context, so we can properly annotate the MapWork later on.
*/
opRules.put(new TypeRule(SMBMapJoinOperator.class),
new NodeProcessor() {
@Override
public Object process(Node currNode, Stack stack,
NodeProcessorCtx procCtx, Object... os) throws SemanticException {
GenSparkProcContext context = (GenSparkProcContext) procCtx;
SMBMapJoinOperator currSmbNode = (SMBMapJoinOperator) currNode;
SparkSMBMapJoinInfo smbMapJoinCtx = context.smbMapJoinCtxMap.get(currSmbNode);
if (smbMapJoinCtx == null) {
smbMapJoinCtx = new SparkSMBMapJoinInfo();
context.smbMapJoinCtxMap.put(currSmbNode, smbMapJoinCtx);
}
for (Node stackNode : stack) {
if (stackNode instanceof DummyStoreOperator) {
//If coming from small-table side, do some book-keeping, and skip traversal.
smbMapJoinCtx.smallTableRootOps.add(context.currentRootOperator);
return true;
}
}
//If coming from big-table side, do some book-keeping, and continue traversal
smbMapJoinCtx.bigTableRootOp = context.currentRootOperator;
return false;
}
}
);
// The dispatcher fires the processor corresponding to the closest matching
// rule and passes the context along
Dispatcher disp = new DefaultRuleDispatcher(null, opRules, procCtx);
List topNodes = new ArrayList();
topNodes.addAll(pCtx.getTopOps().values());
GraphWalker ogw = new GenSparkWorkWalker(disp, procCtx);
ogw.startWalking(topNodes, null);
// we need to clone some operator plans and remove union operators still
for (BaseWork w: procCtx.workWithUnionOperators) {
GenSparkUtils.getUtils().removeUnionOperators(conf, procCtx, w);
}
// we need to fill MapWork with 'local' work and bucket information for SMB Join.
GenSparkUtils.getUtils().annotateMapWork(procCtx);
// finally make sure the file sink operators are set up right
for (FileSinkOperator fileSink: procCtx.fileSinkSet) {
GenSparkUtils.getUtils().processFileSink(procCtx, fileSink);
}
PERF_LOGGER.PerfLogEnd(CLASS_NAME, PerfLogger.SPARK_GENERATE_TASK_TREE);
}
@Override
protected void setInputFormat(Task task) {
if (task instanceof SparkTask) {
SparkWork work = ((SparkTask)task).getWork();
List all = work.getAllWork();
for (BaseWork w: all) {
if (w instanceof MapWork) {
MapWork mapWork = (MapWork) w;
HashMap> opMap = mapWork.getAliasToWork();
if (!opMap.isEmpty()) {
for (Operator op : opMap.values()) {
setInputFormat(mapWork, op);
}
}
}
}
} else if (task instanceof ConditionalTask) {
List> listTasks
= ((ConditionalTask) task).getListTasks();
for (Task tsk : listTasks) {
setInputFormat(tsk);
}
}
if (task.getChildTasks() != null) {
for (Task childTask : task.getChildTasks()) {
setInputFormat(childTask);
}
}
}
private void setInputFormat(MapWork work, Operator op) {
if (op.isUseBucketizedHiveInputFormat()) {
work.setUseBucketizedHiveInputFormat(true);
return;
}
if (op.getChildOperators() != null) {
for (Operator childOp : op.getChildOperators()) {
setInputFormat(work, childOp);
}
}
}
@Override
protected void decideExecMode(List> rootTasks, Context ctx,
GlobalLimitCtx globalLimitCtx) throws SemanticException {
// currently all Spark work is on the cluster
return;
}
@Override
protected void optimizeTaskPlan(List> rootTasks, ParseContext pCtx,
Context ctx) throws SemanticException {
PERF_LOGGER.PerfLogBegin(CLASS_NAME, PerfLogger.SPARK_OPTIMIZE_TASK_TREE);
PhysicalContext physicalCtx = new PhysicalContext(conf, pCtx, pCtx.getContext(), rootTasks,
pCtx.getFetchTask());
physicalCtx = new SplitSparkWorkResolver().resolve(physicalCtx);
if (conf.getBoolVar(HiveConf.ConfVars.HIVESKEWJOIN)) {
(new SparkSkewJoinResolver()).resolve(physicalCtx);
} else {
LOG.debug("Skipping runtime skew join optimization");
}
physicalCtx = new SparkMapJoinResolver().resolve(physicalCtx);
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 SparkCrossProductCheck().resolve(physicalCtx);
} else {
LOG.debug("Skipping cross product analysis");
}
if (conf.getBoolVar(HiveConf.ConfVars.HIVE_VECTORIZATION_ENABLED)) {
(new Vectorizer()).resolve(physicalCtx);
} else {
LOG.debug("Skipping vectorization");
}
if (!"none".equalsIgnoreCase(conf.getVar(HiveConf.ConfVars.HIVESTAGEIDREARRANGE))) {
(new StageIDsRearranger()).resolve(physicalCtx);
} else {
LOG.debug("Skipping stage id rearranger");
}
PERF_LOGGER.PerfLogEnd(CLASS_NAME, PerfLogger.SPARK_OPTIMIZE_TASK_TREE);
return;
}
}
