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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.exec;
import static org.apache.hadoop.hive.ql.optimizer.SortedDynPartitionOptimizer.BUCKET_NUMBER_COL_NAME;
import static org.apache.hadoop.hive.ql.plan.ReduceSinkDesc.ReducerTraits.UNIFORM;
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 java.util.function.BiFunction;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hive.conf.HiveConf;
import org.apache.hadoop.hive.ql.CompilationOpContext;
import org.apache.hadoop.hive.ql.io.AcidUtils;
import org.apache.hadoop.hive.ql.io.HiveKey;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.plan.ExprNodeConstantDesc;
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.ByteStream;
import org.apache.hadoop.hive.serde2.SerDeException;
import org.apache.hadoop.hive.serde2.Serializer;
import org.apache.hadoop.hive.serde2.binarysortable.BinarySortableSerDe;
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.*;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hive.common.util.Murmur3;
/**
* Reduce Sink Operator sends output to the reduce stage.
**/
public class ReduceSinkOperator extends TerminalOperator
implements Serializable, TopNHash.BinaryCollector {
private static final long serialVersionUID = 1L;
private transient ObjectInspector[] partitionObjectInspectors;
private transient ObjectInspector[] bucketObjectInspectors;
private transient int buckColIdxInKey;
/**
* {@link org.apache.hadoop.hive.ql.optimizer.SortedDynPartitionOptimizer}
*/
private transient int buckColIdxInKeyForSdpo = -1;
private boolean firstRow;
private transient int tag;
private boolean skipTag = false;
private transient int[] valueIndex; // index for value(+ from keys, - from values)
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;
protected transient byte[] tagByte = new byte[1];
protected transient int numDistributionKeys;
protected transient int numDistinctExprs;
protected transient String[] inputAliases; // input aliases of this RS for join (used for PPD)
protected transient boolean useUniformHash = false;
// picks topN K:V pairs from input.
protected transient TopNHash reducerHash;
protected transient HiveKey keyWritable = new HiveKey();
protected transient ObjectInspector keyObjectInspector;
protected transient ObjectInspector valueObjectInspector;
protected transient Object[] cachedValues;
protected transient List> distinctColIndices;
protected transient Random random;
protected transient BiFunction hashFunc;
/**
* 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 distinct 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;
protected transient long cntr = 1;
protected transient long logEveryNRows = 0;
/** Kryo ctor. */
protected ReduceSinkOperator() {
super();
}
public ReduceSinkOperator(CompilationOpContext ctx) {
super(ctx);
}
@Override
protected void initializeOp(Configuration hconf) throws HiveException {
super.initializeOp(hconf);
try {
numRows = 0;
cntr = 1;
logEveryNRows = HiveConf.getLongVar(hconf, HiveConf.ConfVars.HIVE_LOG_N_RECORDS);
List keys = conf.getKeyCols();
if (LOG.isDebugEnabled()) {
LOG.debug("keys size is " + keys.size());
for (ExprNodeDesc k : keys) {
LOG.debug("Key exprNodeDesc " + k.getExprString());
}
}
keyEval = new ExprNodeEvaluator[keys.size()];
int i = 0;
for (ExprNodeDesc e : keys) {
if (e instanceof ExprNodeConstantDesc && (BUCKET_NUMBER_COL_NAME).equals(((ExprNodeConstantDesc)e).getValue())) {
buckColIdxInKeyForSdpo = i;
}
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;
skipTag = conf.getSkipTag();
if (LOG.isInfoEnabled()) {
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 = conf.isPTFReduceSink() ? new PTFTopNHash() : new TopNHash();
reducerHash.initialize(limit, memUsage, conf.isMapGroupBy(), this, conf, hconf);
}
useUniformHash = conf.getReducerTraits().contains(UNIFORM);
firstRow = true;
// acidOp flag has to be checked to use JAVA hash which works like
// identity function for integers, necessary to read RecordIdentifier
// incase of ACID updates/deletes.
boolean acidOp = conf.getWriteType() == AcidUtils.Operation.UPDATE ||
conf.getWriteType() == AcidUtils.Operation.DELETE;
hashFunc = bucketingVersion == 2 && !acidOp ?
ObjectInspectorUtils::getBucketHashCode :
ObjectInspectorUtils::getBucketHashCodeOld;
} catch (Exception e) {
String msg = "Error initializing ReduceSinkOperator: " + e.getMessage();
LOG.error(msg, e);
throw new RuntimeException(e);
}
}
/**
* 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> distinctColIndices,
List outputColNames,
int length, ObjectInspector rowInspector)
throws HiveException {
int inspectorLen = evals.length > length ? length + 1 : evals.length;
List sois = new ArrayList(inspectorLen);
// keys
ObjectInspector[] fieldObjectInspectors = initEvaluators(evals, 0, length, rowInspector);
sois.addAll(Arrays.asList(fieldObjectInspectors));
if (outputColNames.size() > length) {
// union keys
assert distinctColIndices != null;
List uois = new ArrayList();
for (List distinctCols : distinctColIndices) {
List names = new ArrayList();
List eois = new ArrayList();
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 process(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.
if (LOG.isInfoEnabled()) {
LOG.info("keys are " + conf.getOutputKeyColumnNames() + " num distributions: " +
conf.getNumDistributionKeys());
}
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);
// replace bucketing columns with hashcode % numBuckets
int bucketNumber = -1;
if (bucketEval != null) {
bucketNumber = computeBucketNumber(row, conf.getNumBuckets());
cachedKeys[0][buckColIdxInKey] = new Text(String.valueOf(bucketNumber));
}
if (buckColIdxInKeyForSdpo != -1) {
cachedKeys[0][buckColIdxInKeyForSdpo] = new Text(String.valueOf(bucketNumber));
}
HiveKey firstKey = toHiveKey(cachedKeys[0], tag, null);
int distKeyLength = firstKey.getDistKeyLength();
if (numDistinctExprs > 0) {
populateCachedDistinctKeys(row, 0);
firstKey = toHiveKey(cachedKeys[0], tag, distKeyLength);
}
final int hashCode;
// distKeyLength doesn't include tag, but includes buckNum in cachedKeys[0]
if (useUniformHash && partitionEval.length > 0) {
hashCode = computeMurmurHash(firstKey);
} else {
hashCode = computeHashCode(row, bucketNumber);
}
firstKey.setHashCode(hashCode);
/*
* in case of TopN for windowing, we need to distinguish between rows with
* null partition keys and rows with value 0 for partition keys.
*/
boolean partKeyNull = conf.isPTFReduceSink() && partitionKeysAreNull(row);
// Try to store the first key.
// if TopNHashes aren't active, always forward
// if TopNHashes are active, proceed if not already excluded (i.e order by limit)
final int firstIndex =
(reducerHash != null) ? reducerHash.tryStoreKey(firstKey, partKeyNull) : TopNHash.FORWARD;
if (firstIndex == TopNHash.EXCLUDE)
{
return; // Nothing to do.
}
// Compute value and hashcode - we'd either store or forward them.
BytesWritable value = makeValueWritable(row);
if (firstIndex == TopNHash.FORWARD) {
collect(firstKey, value);
} else {
// invariant: reducerHash != null
assert firstIndex >= 0;
reducerHash.storeValue(firstIndex, firstKey.hashCode(), value, 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, SerDeException {
Object[] bucketFieldValues = new Object[bucketEval.length];
for (int i = 0; i < bucketEval.length; i++) {
bucketFieldValues[i] = bucketEval[i].evaluate(row);
}
return ObjectInspectorUtils.getBucketNumber(
hashFunc.apply(bucketFieldValues, bucketObjectInspectors), numBuckets);
}
private void populateCachedDistributionKeys(Object row) throws HiveException {
for (int i = 0; i < numDistributionKeys; i++) {
cachedKeys[0][i] = keyEval[i].evaluate(row);
}
if (cachedKeys[0].length > numDistributionKeys) {
cachedKeys[0][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);
}
protected final int computeMurmurHash(HiveKey firstKey) {
return Murmur3.hash32(firstKey.getBytes(), firstKey.getDistKeyLength(), 0);
}
/**
* For Acid Update/Delete case, we expect a single partitionEval of the form
* UDFToInteger(ROW__ID) and buckNum == -1 so that the result of this method
* is to return the bucketId extracted from ROW__ID unless it optimized by
* {@link org.apache.hadoop.hive.ql.optimizer.SortedDynPartitionOptimizer}
*/
private int computeHashCode(Object row, int buckNum) 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 {
Object[] bucketFieldValues = new Object[partitionEval.length];
for(int i = 0; i < partitionEval.length; i++) {
bucketFieldValues[i] = partitionEval[i].evaluate(row);
}
keyHashCode = hashFunc.apply(bucketFieldValues, partitionObjectInspectors);
}
int hashCode = buckNum < 0 ? keyHashCode : keyHashCode * 31 + buckNum;
if (LOG.isTraceEnabled()) {
LOG.trace("Going to return hash code " + hashCode);
}
return hashCode;
}
private boolean partitionKeysAreNull(Object row) throws HiveException {
if ( partitionEval.length != 0 ) {
for (int i = 0; i < partitionEval.length; i++) {
Object o = partitionEval[i].evaluate(row);
if ( o != null ) {
return false;
}
}
return true;
}
return false;
}
// 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 || skipTag) {
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;
}
@Override
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) {
numRows++;
runTimeNumRows++;
if (LOG.isTraceEnabled()) {
if (numRows == cntr) {
cntr = logEveryNRows == 0 ? cntr * 10 : numRows + logEveryNRows;
if (cntr < 0 || numRows < 0) {
cntr = 0;
numRows = 1;
}
LOG.info(toString() + ": records written - " + numRows);
}
}
out.collect(keyWritable, valueWritable);
}
}
private BytesWritable makeValueWritable(Object row) throws Exception {
int length = valueEval.length;
// Evaluate the value
for (int i = 0; i < 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 != null) {
reducerHash.flush();
}
runTimeNumRows = numRows;
super.closeOp(abort);
out = null;
random = null;
reducerHash = null;
if (LOG.isTraceEnabled()) {
LOG.info(toString() + ": records written - " + numRows);
}
}
/**
* @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;
}
public void setSkipTag(boolean value) {
this.skipTag = value;
}
public void setValueIndex(int[] valueIndex) {
this.valueIndex = valueIndex;
}
public int[] getValueIndex() {
return valueIndex;
}
public void setInputAliases(String[] inputAliases) {
this.inputAliases = inputAliases;
}
public String[] getInputAliases() {
return inputAliases;
}
@Override
public boolean getIsReduceSink() {
return true;
}
@Override
public String getReduceOutputName() {
return conf.getOutputName();
}
@Override
public void setOutputCollector(OutputCollector _out) {
this.out = _out;
}
}
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