Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/**
* 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.util.ArrayList;
import java.util.LinkedList;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import java.util.Stack;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.hive.common.ObjectPair;
import org.apache.hadoop.hive.ql.exec.HashTableDummyOperator;
import org.apache.hadoop.hive.ql.exec.MapJoinOperator;
import org.apache.hadoop.hive.ql.exec.Operator;
import org.apache.hadoop.hive.ql.exec.OperatorFactory;
import org.apache.hadoop.hive.ql.exec.ReduceSinkOperator;
import org.apache.hadoop.hive.ql.exec.SMBMapJoinOperator;
import org.apache.hadoop.hive.ql.exec.TableScanOperator;
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.optimizer.GenMapRedUtils;
import org.apache.hadoop.hive.ql.optimizer.spark.SparkSortMergeJoinFactory;
import org.apache.hadoop.hive.ql.parse.SemanticException;
import org.apache.hadoop.hive.ql.plan.BaseWork;
import org.apache.hadoop.hive.ql.plan.MapWork;
import org.apache.hadoop.hive.ql.plan.OperatorDesc;
import org.apache.hadoop.hive.ql.plan.ReduceSinkDesc;
import org.apache.hadoop.hive.ql.plan.ReduceWork;
import org.apache.hadoop.hive.ql.plan.SparkEdgeProperty;
import org.apache.hadoop.hive.ql.plan.SparkWork;
import com.google.common.base.Preconditions;
/**
* GenSparkWork separates the operator tree into spark tasks.
* It is called once per leaf operator (operator that forces a new execution unit.)
* and break the operators into work and tasks along the way.
*
* Cloned from GenTezWork.
*/
public class GenSparkWork implements NodeProcessor {
static final private Log LOG = LogFactory.getLog(GenSparkWork.class.getName());
// instance of shared utils
private GenSparkUtils utils = null;
/**
* Constructor takes utils as parameter to facilitate testing
*/
public GenSparkWork(GenSparkUtils utils) {
this.utils = utils;
}
@Override
public Object process(Node nd, Stack stack,
NodeProcessorCtx procContext, Object... nodeOutputs) throws SemanticException {
GenSparkProcContext context = (GenSparkProcContext) procContext;
Preconditions.checkArgument(context != null,
"AssertionError: expected context to be not null");
Preconditions.checkArgument(context.currentTask != null,
"AssertionError: expected context.currentTask to be not null");
Preconditions.checkArgument(context.currentRootOperator != null,
"AssertionError: expected context.currentRootOperator to be not null");
// Operator is a file sink or reduce sink. Something that forces a new vertex.
@SuppressWarnings("unchecked")
Operator operator = (Operator) nd;
// root is the start of the operator pipeline we're currently
// packing into a vertex, typically a table scan, union or join
Operator root = context.currentRootOperator;
LOG.debug("Root operator: " + root);
LOG.debug("Leaf operator: " + operator);
if (context.clonedReduceSinks.contains(operator)) {
// if we're visiting a terminal we've created ourselves,
// just skip and keep going
return null;
}
SparkWork sparkWork = context.currentTask.getWork();
SMBMapJoinOperator smbOp = GenSparkUtils.getChildOperator(root, SMBMapJoinOperator.class);
// Right now the work graph is pretty simple. If there is no
// Preceding work we have a root and will generate a map
// vertex. If there is a preceding work we will generate
// a reduce vertex
BaseWork work;
if (context.rootToWorkMap.containsKey(root)) {
// having seen the root operator before means there was a branch in the
// operator graph. There's typically two reasons for that: a) mux/demux
// b) multi insert. Mux/Demux will hit the same leaf again, multi insert
// will result into a vertex with multiple FS or RS operators.
// At this point we don't have to do anything special in this case. Just
// run through the regular paces w/o creating a new task.
work = context.rootToWorkMap.get(root);
} else {
// create a new vertex
if (context.preceedingWork == null) {
if (smbOp == null) {
work = utils.createMapWork(context, root, sparkWork, null);
} else {
//save work to be initialized later with SMB information.
work = utils.createMapWork(context, root, sparkWork, null, true);
context.smbMapJoinCtxMap.get(smbOp).mapWork = (MapWork) work;
}
} else {
work = utils.createReduceWork(context, root, sparkWork);
}
context.rootToWorkMap.put(root, work);
}
if (!context.childToWorkMap.containsKey(operator)) {
List workItems = new LinkedList();
workItems.add(work);
context.childToWorkMap.put(operator, workItems);
} else {
context.childToWorkMap.get(operator).add(work);
}
// remember which mapjoin operator links with which work
if (!context.currentMapJoinOperators.isEmpty()) {
for (MapJoinOperator mj: context.currentMapJoinOperators) {
LOG.debug("Processing map join: " + mj);
// remember the mapping in case we scan another branch of the mapjoin later
if (!context.mapJoinWorkMap.containsKey(mj)) {
List workItems = new LinkedList();
workItems.add(work);
context.mapJoinWorkMap.put(mj, workItems);
} else {
context.mapJoinWorkMap.get(mj).add(work);
}
/*
* this happens in case of map join operations.
* The tree looks like this:
*
* RS <--- we are here perhaps
* |
* MapJoin
* / \
* RS TS
* /
* TS
*
* If we are at the RS pointed above, and we may have already visited the
* RS following the TS, we have already generated work for the TS-RS.
* We need to hook the current work to this generated work.
*/
if (context.linkOpWithWorkMap.containsKey(mj)) {
Map linkWorkMap = context.linkOpWithWorkMap.get(mj);
if (linkWorkMap != null) {
if (context.linkChildOpWithDummyOp.containsKey(mj)) {
for (Operator dummy: context.linkChildOpWithDummyOp.get(mj)) {
work.addDummyOp((HashTableDummyOperator) dummy);
}
}
for (Entry parentWorkMap : linkWorkMap.entrySet()) {
BaseWork parentWork = parentWorkMap.getKey();
LOG.debug("connecting " + parentWork.getName() + " with " + work.getName());
SparkEdgeProperty edgeProp = parentWorkMap.getValue();
sparkWork.connect(parentWork, work, edgeProp);
// need to set up output name for reduce sink now that we know the name
// of the downstream work
for (ReduceSinkOperator r : context.linkWorkWithReduceSinkMap.get(parentWork)) {
if (r.getConf().getOutputName() != null) {
LOG.debug("Cloning reduce sink for multi-child broadcast edge");
// we've already set this one up. Need to clone for the next work.
r = (ReduceSinkOperator) OperatorFactory.getAndMakeChild(
(ReduceSinkDesc)r.getConf().clone(), r.getParentOperators());
context.clonedReduceSinks.add(r);
}
r.getConf().setOutputName(work.getName());
}
}
}
}
}
// clear out the set. we don't need it anymore.
context.currentMapJoinOperators.clear();
}
// Here we are disconnecting root with its parents. However, we need to save
// a few information, since in future we may reach the parent operators via a
// different path, and we may need to connect parent works with the work associated
// with this root operator.
if (root.getNumParent() > 0) {
Preconditions.checkArgument(work instanceof ReduceWork,
"AssertionError: expected work to be a ReduceWork, but was " + work.getClass().getName());
ReduceWork reduceWork = (ReduceWork) work;
for (Operator parent : new ArrayList>(root.getParentOperators())) {
Preconditions.checkArgument(parent instanceof ReduceSinkOperator,
"AssertionError: expected operator to be a ReduceSinkOperator, but was "
+ parent.getClass().getName());
ReduceSinkOperator rsOp = (ReduceSinkOperator) parent;
SparkEdgeProperty edgeProp = GenSparkUtils.getEdgeProperty(rsOp, reduceWork);
rsOp.getConf().setOutputName(reduceWork.getName());
GenMapRedUtils.setKeyAndValueDesc(reduceWork, rsOp);
context.leafOpToFollowingWorkInfo.put(rsOp, ObjectPair.create(edgeProp, reduceWork));
LOG.debug("Removing " + parent + " as parent from " + root);
root.removeParent(parent);
}
}
// If `currentUnionOperators` is not empty, it means we are creating BaseWork whose operator tree
// contains union operators. In this case, we need to save these BaseWorks, and remove
// the union operators from the operator tree later.
if (!context.currentUnionOperators.isEmpty()) {
context.currentUnionOperators.clear();
context.workWithUnionOperators.add(work);
}
// We're scanning a tree from roots to leaf (this is not technically
// correct, demux and mux operators might form a diamond shape, but
// we will only scan one path and ignore the others, because the
// diamond shape is always contained in a single vertex). The scan
// is depth first and because we remove parents when we pack a pipeline
// into a vertex we will never visit any node twice. But because of that
// we might have a situation where we need to connect 'work' that comes after
// the 'work' we're currently looking at.
//
// Also note: the concept of leaf and root is reversed in hive for historical
// reasons. Roots are data sources, leaves are data sinks. I know.
if (context.leafOpToFollowingWorkInfo.containsKey(operator)) {
ObjectPair childWorkInfo = context.
leafOpToFollowingWorkInfo.get(operator);
SparkEdgeProperty edgeProp = childWorkInfo.getFirst();
ReduceWork childWork = childWorkInfo.getSecond();
LOG.debug("Second pass. Leaf operator: " + operator + " has common downstream work:" + childWork);
// We may have already connected `work` with `childWork`, in case, for example, lateral view:
// TS
// |
// ...
// |
// LVF
// | \
// SEL SEL
// | |
// LVJ-UDTF
// |
// SEL
// |
// RS
// Here, RS can be reached from TS via two different paths. If there is any child work after RS,
// we don't want to connect them with the work associated with TS more than once.
if (sparkWork.getEdgeProperty(work, childWork) == null) {
sparkWork.connect(work, childWork, edgeProp);
} else {
LOG.debug("work " + work.getName() + " is already connected to " + childWork.getName() + " before");
}
} else {
LOG.debug("First pass. Leaf operator: " + operator);
}
// No children means we're at the bottom. If there are more operators to scan
// the next item will be a new root.
if (!operator.getChildOperators().isEmpty()) {
Preconditions.checkArgument(operator.getChildOperators().size() == 1,
"AssertionError: expected operator.getChildOperators().size() to be 1, but was "
+ operator.getChildOperators().size());
context.parentOfRoot = operator;
context.currentRootOperator = operator.getChildOperators().get(0);
context.preceedingWork = work;
}
return null;
}
}
