/**
* 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.exec;
import java.io.IOException;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.ql.io.HiveKey;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.plan.ExprNodeDesc;
import org.apache.hadoop.hive.ql.plan.ExprNodeDescUtils;
import org.apache.hadoop.hive.ql.plan.ReduceSinkDesc;
import org.apache.hadoop.hive.ql.plan.TableDesc;
import org.apache.hadoop.hive.ql.plan.api.OperatorType;
import org.apache.hadoop.hive.serde2.SerDeException;
import org.apache.hadoop.hive.serde2.Serializer;
import org.apache.hadoop.hive.serde2.objectinspector.InspectableObject;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorFactory;
import org.apache.hadoop.hive.serde2.objectinspector.ObjectInspectorUtils;
import org.apache.hadoop.hive.serde2.objectinspector.StandardUnionObjectInspector.StandardUnion;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.objectinspector.UnionObjectInspector;
import org.apache.hadoop.io.BinaryComparable;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.OutputCollector;
/**
* Reduce Sink Operator sends output to the reduce stage.
**/
public class ReduceSinkOperator extends TerminalOperator<ReduceSinkDesc>
implements Serializable, TopNHash.BinaryCollector {
static {
PTFUtils.makeTransient(ReduceSinkOperator.class, "inputAliases");
}
private static final long serialVersionUID = 1L;
protected transient OutputCollector out;
/**
* The evaluators for the key columns. Key columns decide the sort order on
* the reducer side. Key columns are passed to the reducer in the "key".
*/
protected transient ExprNodeEvaluator[] keyEval;
/**
* The evaluators for the value columns. Value columns are passed to reducer
* in the "value".
*/
protected transient ExprNodeEvaluator[] valueEval;
/**
* The evaluators for the partition columns (CLUSTER BY or DISTRIBUTE BY in
* Hive language). Partition columns decide the reducer that the current row
* goes to. Partition columns are not passed to reducer.
*/
protected transient ExprNodeEvaluator[] partitionEval;
/**
* Evaluators for bucketing columns. This is used to compute bucket number.
*/
protected transient ExprNodeEvaluator[] bucketEval = null;
// TODO: we use MetadataTypedColumnsetSerDe for now, till DynamicSerDe is
// ready
protected transient Serializer keySerializer;
protected transient boolean keyIsText;
protected transient Serializer valueSerializer;
transient int tag;
protected transient byte[] tagByte = new byte[1];
transient protected int numDistributionKeys;
transient protected int numDistinctExprs;
transient String[] inputAliases; // input aliases of this RS for join (used for PPD)
public void setInputAliases(String[] inputAliases) {
this.inputAliases = inputAliases;
}
public String[] getInputAliases() {
return inputAliases;
}
public void setOutputCollector(OutputCollector _out) {
this.out = _out;
}
// picks topN K:V pairs from input.
protected transient TopNHash reducerHash = new TopNHash();
@Override
protected void initializeOp(Configuration hconf) throws HiveException {
try {
List<ExprNodeDesc> keys = conf.getKeyCols();
keyEval = new ExprNodeEvaluator[keys.size()];
int i = 0;
for (ExprNodeDesc e : keys) {
keyEval[i++] = ExprNodeEvaluatorFactory.get(e);
}
numDistributionKeys = conf.getNumDistributionKeys();
distinctColIndices = conf.getDistinctColumnIndices();
numDistinctExprs = distinctColIndices.size();
valueEval = new ExprNodeEvaluator[conf.getValueCols().size()];
i = 0;
for (ExprNodeDesc e : conf.getValueCols()) {
valueEval[i++] = ExprNodeEvaluatorFactory.get(e);
}
partitionEval = new ExprNodeEvaluator[conf.getPartitionCols().size()];
i = 0;
for (ExprNodeDesc e : conf.getPartitionCols()) {
int index = ExprNodeDescUtils.indexOf(e, keys);
partitionEval[i++] = index < 0 ? ExprNodeEvaluatorFactory.get(e): keyEval[index];
}
if (conf.getBucketCols() != null && !conf.getBucketCols().isEmpty()) {
bucketEval = new ExprNodeEvaluator[conf.getBucketCols().size()];
i = 0;
for (ExprNodeDesc e : conf.getBucketCols()) {
int index = ExprNodeDescUtils.indexOf(e, keys);
bucketEval[i++] = index < 0 ? ExprNodeEvaluatorFactory.get(e) : keyEval[index];
}
buckColIdxInKey = conf.getPartitionCols().size();
}
tag = conf.getTag();
tagByte[0] = (byte) tag;
LOG.info("Using tag = " + tag);
TableDesc keyTableDesc = conf.getKeySerializeInfo();
keySerializer = (Serializer) keyTableDesc.getDeserializerClass()
.newInstance();
keySerializer.initialize(null, keyTableDesc.getProperties());
keyIsText = keySerializer.getSerializedClass().equals(Text.class);
TableDesc valueTableDesc = conf.getValueSerializeInfo();
valueSerializer = (Serializer) valueTableDesc.getDeserializerClass()
.newInstance();
valueSerializer.initialize(null, valueTableDesc.getProperties());
int limit = conf.getTopN();
float memUsage = conf.getTopNMemoryUsage();
if (limit >= 0 && memUsage > 0) {
reducerHash.initialize(limit, memUsage, conf.isMapGroupBy(), this);
}
firstRow = true;
initializeChildren(hconf);
} catch (Exception e) {
e.printStackTrace();
throw new RuntimeException(e);
}
}
transient InspectableObject tempInspectableObject = new InspectableObject();
protected transient HiveKey keyWritable = new HiveKey();
protected transient ObjectInspector keyObjectInspector;
protected transient ObjectInspector valueObjectInspector;
transient ObjectInspector[] partitionObjectInspectors;
transient ObjectInspector[] bucketObjectInspectors = null;
transient int buckColIdxInKey;
protected transient Object[] cachedValues;
protected transient List<List<Integer>> distinctColIndices;
/**
* This two dimensional array holds key data and a corresponding Union object
* which contains the tag identifying the aggregate expression for distinct columns.
*
* If there is no distict expression, cachedKeys is simply like this.
* cachedKeys[0] = [col0][col1]
*
* with two distict expression, union(tag:key) is attatched for each distinct expression
* cachedKeys[0] = [col0][col1][0:dist1]
* cachedKeys[1] = [col0][col1][1:dist2]
*
* in this case, child GBY evaluates distict values with expression like KEY.col2:0.dist1
* see {@link ExprNodeColumnEvaluator}
*/
// TODO: we only ever use one row of these at a time. Why do we need to cache multiple?
protected transient Object[][] cachedKeys;
boolean firstRow;
protected transient Random random;
/**
* Initializes array of ExprNodeEvaluator. Adds Union field for distinct
* column indices for group by.
* Puts the return values into a StructObjectInspector with output column
* names.
*
* If distinctColIndices is empty, the object inspector is same as
* {@link Operator#initEvaluatorsAndReturnStruct(ExprNodeEvaluator[], List, ObjectInspector)}
*/
protected static StructObjectInspector initEvaluatorsAndReturnStruct(
ExprNodeEvaluator[] evals, List<List<Integer>> distinctColIndices,
List<String> outputColNames,
int length, ObjectInspector rowInspector)
throws HiveException {
int inspectorLen = evals.length > length ? length + 1 : evals.length;
List<ObjectInspector> sois = new ArrayList<ObjectInspector>(inspectorLen);
// keys
ObjectInspector[] fieldObjectInspectors = initEvaluators(evals, 0, length, rowInspector);
sois.addAll(Arrays.asList(fieldObjectInspectors));
if (outputColNames.size() > length) {
// union keys
assert distinctColIndices != null;
List<ObjectInspector> uois = new ArrayList<ObjectInspector>();
for (List<Integer> distinctCols : distinctColIndices) {
List<String> names = new ArrayList<String>();
List<ObjectInspector> eois = new ArrayList<ObjectInspector>();
int numExprs = 0;
for (int i : distinctCols) {
names.add(HiveConf.getColumnInternalName(numExprs));
eois.add(evals[i].initialize(rowInspector));
numExprs++;
}
uois.add(ObjectInspectorFactory.getStandardStructObjectInspector(names, eois));
}
UnionObjectInspector uoi =
ObjectInspectorFactory.getStandardUnionObjectInspector(uois);
sois.add(uoi);
}
return ObjectInspectorFactory.getStandardStructObjectInspector(outputColNames, sois );
}
@Override
@SuppressWarnings("unchecked")
public void processOp(Object row, int tag) throws HiveException {
try {
ObjectInspector rowInspector = inputObjInspectors[tag];
if (firstRow) {
firstRow = false;
// TODO: this is fishy - we init object inspectors based on first tag. We
// should either init for each tag, or if rowInspector doesn't really
// matter, then we can create this in ctor and get rid of firstRow.
keyObjectInspector = initEvaluatorsAndReturnStruct(keyEval,
distinctColIndices,
conf.getOutputKeyColumnNames(), numDistributionKeys, rowInspector);
valueObjectInspector = initEvaluatorsAndReturnStruct(valueEval,
conf.getOutputValueColumnNames(), rowInspector);
partitionObjectInspectors = initEvaluators(partitionEval, rowInspector);
if (bucketEval != null) {
bucketObjectInspectors = initEvaluators(bucketEval, rowInspector);
}
int numKeys = numDistinctExprs > 0 ? numDistinctExprs : 1;
int keyLen = numDistinctExprs > 0 ? numDistributionKeys + 1 : numDistributionKeys;
cachedKeys = new Object[numKeys][keyLen];
cachedValues = new Object[valueEval.length];
}
// Determine distKeyLength (w/o distincts), and then add the first if present.
populateCachedDistributionKeys(row, 0);
// replace bucketing columns with hashcode % numBuckets
int buckNum = 0;
if (bucketEval != null) {
buckNum = computeBucketNumber(row, conf.getNumBuckets());
cachedKeys[0][buckColIdxInKey] = new IntWritable(buckNum);
}
HiveKey firstKey = toHiveKey(cachedKeys[0], tag, null);
int distKeyLength = firstKey.getDistKeyLength();
if (numDistinctExprs > 0) {
populateCachedDistinctKeys(row, 0);
firstKey = toHiveKey(cachedKeys[0], tag, distKeyLength);
}
// Try to store the first key. If it's not excluded, we will proceed.
int firstIndex = reducerHash.tryStoreKey(firstKey);
if (firstIndex == TopNHash.EXCLUDE) return; // Nothing to do.
// Compute value and hashcode - we'd either store or forward them.
BytesWritable value = makeValueWritable(row);
int hashCode = 0;
if (bucketEval == null) {
hashCode = computeHashCode(row);
} else {
hashCode = computeHashCode(row, buckNum);
}
if (firstIndex == TopNHash.FORWARD) {
firstKey.setHashCode(hashCode);
collect(firstKey, value);
} else {
assert firstIndex >= 0;
reducerHash.storeValue(firstIndex, value, hashCode, false);
}
// All other distinct keys will just be forwarded. This could be optimized...
for (int i = 1; i < numDistinctExprs; i++) {
System.arraycopy(cachedKeys[0], 0, cachedKeys[i], 0, numDistributionKeys);
populateCachedDistinctKeys(row, i);
HiveKey hiveKey = toHiveKey(cachedKeys[i], tag, distKeyLength);
hiveKey.setHashCode(hashCode);
collect(hiveKey, value);
}
} catch (HiveException e) {
throw e;
} catch (Exception e) {
throw new HiveException(e);
}
}
private int computeBucketNumber(Object row, int numBuckets) throws HiveException {
int buckNum = 0;
for (int i = 0; i < bucketEval.length; i++) {
Object o = bucketEval[i].evaluate(row);
buckNum = buckNum * 31 + ObjectInspectorUtils.hashCode(o, bucketObjectInspectors[i]);
}
if (buckNum < 0) {
buckNum = -1 * buckNum;
}
return buckNum % numBuckets;
}
private void populateCachedDistributionKeys(Object row, int index) throws HiveException {
for (int i = 0; i < numDistributionKeys; i++) {
cachedKeys[index][i] = keyEval[i].evaluate(row);
}
if (cachedKeys[0].length > numDistributionKeys) {
cachedKeys[index][numDistributionKeys] = null;
}
}
/**
* Populate distinct keys part of cachedKeys for a particular row.
* @param row the row
* @param index the cachedKeys index to write to
*/
private void populateCachedDistinctKeys(Object row, int index) throws HiveException {
StandardUnion union;
cachedKeys[index][numDistributionKeys] = union = new StandardUnion(
(byte)index, new Object[distinctColIndices.get(index).size()]);
Object[] distinctParameters = (Object[]) union.getObject();
for (int distinctParamI = 0; distinctParamI < distinctParameters.length; distinctParamI++) {
distinctParameters[distinctParamI] =
keyEval[distinctColIndices.get(index).get(distinctParamI)].evaluate(row);
}
union.setTag((byte) index);
}
private int computeHashCode(Object row) throws HiveException {
// Evaluate the HashCode
int keyHashCode = 0;
if (partitionEval.length == 0) {
// If no partition cols, just distribute the data uniformly to provide better
// load balance. If the requirement is to have a single reducer, we should set
// the number of reducers to 1.
// Use a constant seed to make the code deterministic.
if (random == null) {
random = new Random(12345);
}
keyHashCode = random.nextInt();
} else {
for (int i = 0; i < partitionEval.length; i++) {
Object o = partitionEval[i].evaluate(row);
keyHashCode = keyHashCode * 31
+ ObjectInspectorUtils.hashCode(o, partitionObjectInspectors[i]);
}
}
return keyHashCode;
}
private int computeHashCode(Object row, int buckNum) throws HiveException {
int keyHashCode = computeHashCode(row);
keyHashCode = keyHashCode * 31 + buckNum;
return keyHashCode;
}
// Serialize the keys and append the tag
protected HiveKey toHiveKey(Object obj, int tag, Integer distLength) throws SerDeException {
BinaryComparable key = (BinaryComparable)keySerializer.serialize(obj, keyObjectInspector);
int keyLength = key.getLength();
if (tag == -1) {
keyWritable.set(key.getBytes(), 0, keyLength);
} else {
keyWritable.setSize(keyLength + 1);
System.arraycopy(key.getBytes(), 0, keyWritable.get(), 0, keyLength);
keyWritable.get()[keyLength] = tagByte[0];
}
keyWritable.setDistKeyLength((distLength == null) ? keyLength : distLength);
return keyWritable;
}
public void collect(byte[] key, byte[] value, int hash) throws IOException {
HiveKey keyWritable = new HiveKey(key, hash);
BytesWritable valueWritable = new BytesWritable(value);
collect(keyWritable, valueWritable);
}
protected void collect(BytesWritable keyWritable, Writable valueWritable) throws IOException {
// Since this is a terminal operator, update counters explicitly -
// forward is not called
if (null != out) {
out.collect(keyWritable, valueWritable);
}
}
private BytesWritable makeValueWritable(Object row) throws Exception {
// Evaluate the value
for (int i = 0; i < valueEval.length; i++) {
cachedValues[i] = valueEval[i].evaluate(row);
}
// Serialize the value
return (BytesWritable) valueSerializer.serialize(cachedValues, valueObjectInspector);
}
@Override
protected void closeOp(boolean abort) throws HiveException {
if (!abort) {
reducerHash.flush();
}
super.closeOp(abort);
out = null;
}
/**
* @return the name of the operator
*/
@Override
public String getName() {
return getOperatorName();
}
static public String getOperatorName() {
return "RS";
}
@Override
public OperatorType getType() {
return OperatorType.REDUCESINK;
}
@Override
public boolean opAllowedBeforeMapJoin() {
return false;
}
}