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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.vector.mapjoin;
import java.io.IOException;
import java.util.Arrays;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.hive.ql.exec.JoinUtil;
import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.plan.OperatorDesc;
// Multi-Key hash table import.
import org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashMultiSet;
// Multi-Key specific imports.
import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRowNoNulls;
import org.apache.hadoop.hive.serde2.ByteStream.Output;
import org.apache.hadoop.hive.serde2.binarysortable.fast.BinarySortableSerializeWrite;
/*
* Specialized class for doing a vectorized map join that is an inner join on Multi-Key
* and only big table columns appear in the join result so a hash multi-set is used.
*/
public class VectorMapJoinInnerBigOnlyMultiKeyOperator extends VectorMapJoinInnerBigOnlyGenerateResultOperator {
private static final long serialVersionUID = 1L;
private static final Log LOG = LogFactory.getLog(VectorMapJoinInnerBigOnlyMultiKeyOperator.class.getName());
private static final String CLASS_NAME = VectorMapJoinInnerBigOnlyMultiKeyOperator.class.getName();
// (none)
// The above members are initialized by the constructor and must not be
// transient.
//---------------------------------------------------------------------------
// The hash map for this specialized class.
private transient VectorMapJoinBytesHashMultiSet hashMultiSet;
//---------------------------------------------------------------------------
// Multi-Key specific members.
//
// Object that can take a set of columns in row in a vectorized row batch and serialized it.
// Known to not have any nulls.
private transient VectorSerializeRowNoNulls keyVectorSerializeWriteNoNulls;
// The BinarySortable serialization of the current key.
private transient Output currentKeyOutput;
// The BinarySortable serialization of the saved key for a possible series of equal keys.
private transient Output saveKeyOutput;
//---------------------------------------------------------------------------
// Pass-thru constructors.
//
public VectorMapJoinInnerBigOnlyMultiKeyOperator() {
super();
}
public VectorMapJoinInnerBigOnlyMultiKeyOperator(VectorizationContext vContext, OperatorDesc conf) throws HiveException {
super(vContext, conf);
}
//---------------------------------------------------------------------------
// Process Multi-Key Inner Big-Only Join on a vectorized row batch.
//
@Override
public void process(Object row, int tag) throws HiveException {
try {
VectorizedRowBatch batch = (VectorizedRowBatch) row;
alias = (byte) tag;
if (needCommonSetup) {
// Our one time process method initialization.
commonSetup(batch);
/*
* Initialize Multi-Key members for this specialized class.
*/
keyVectorSerializeWriteNoNulls = new VectorSerializeRowNoNulls(
new BinarySortableSerializeWrite(bigTableKeyColumnMap.length));
keyVectorSerializeWriteNoNulls.init(bigTableKeyTypeNames, bigTableKeyColumnMap);
currentKeyOutput = new Output();
saveKeyOutput = new Output();
needCommonSetup = false;
}
if (needHashTableSetup) {
// Setup our hash table specialization. It will be the first time the process
// method is called, or after a Hybrid Grace reload.
/*
* Get our Multi-Key hash multi-set information for this specialized class.
*/
hashMultiSet = (VectorMapJoinBytesHashMultiSet) vectorMapJoinHashTable;
needHashTableSetup = false;
}
batchCounter++;
// Do the per-batch setup for an inner big-only join.
// (Currently none)
// innerBigOnlyPerBatchSetup(batch);
// For inner joins, we may apply the filter(s) now.
for(VectorExpression ve : bigTableFilterExpressions) {
ve.evaluate(batch);
}
final int inputLogicalSize = batch.size;
if (inputLogicalSize == 0) {
if (LOG.isDebugEnabled()) {
LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty");
}
return;
}
// Perform any key expressions. Results will go into scratch columns.
if (bigTableKeyExpressions != null) {
for (VectorExpression ve : bigTableKeyExpressions) {
ve.evaluate(batch);
}
}
/*
* Multi-Key specific declarations.
*/
// None.
/*
* Multi-Key check for repeating.
*/
// If all BigTable input columns to key expressions are isRepeating, then
// calculate key once; lookup once.
boolean allKeyInputColumnsRepeating;
if (bigTableKeyColumnMap.length == 0) {
allKeyInputColumnsRepeating = false;
} else {
allKeyInputColumnsRepeating = true;
for (int i = 0; i < bigTableKeyColumnMap.length; i++) {
if (!batch.cols[bigTableKeyColumnMap[i]].isRepeating) {
allKeyInputColumnsRepeating = false;
break;
}
}
}
if (allKeyInputColumnsRepeating) {
/*
* Repeating.
*/
// All key input columns are repeating. Generate key once. Lookup once.
// Since the key is repeated, we must use entry 0 regardless of selectedInUse.
/*
* Multi-Key specific repeated lookup.
*/
keyVectorSerializeWriteNoNulls.setOutput(currentKeyOutput);
keyVectorSerializeWriteNoNulls.serializeWriteNoNulls(batch, 0);
byte[] keyBytes = currentKeyOutput.getData();
int keyLength = currentKeyOutput.getLength();
JoinUtil.JoinResult joinResult = hashMultiSet.contains(keyBytes, 0, keyLength, hashMultiSetResults[0]);
/*
* Common repeated join result processing.
*/
if (LOG.isDebugEnabled()) {
LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name());
}
finishInnerBigOnlyRepeated(batch, joinResult, hashMultiSetResults[0]);
} else {
/*
* NOT Repeating.
*/
if (LOG.isDebugEnabled()) {
LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated");
}
// We remember any matching rows in matchs / matchSize. At the end of the loop,
// selected / batch.size will represent both matching and non-matching rows for outer join.
// Only deferred rows will have been removed from selected.
int selected[] = batch.selected;
boolean selectedInUse = batch.selectedInUse;
int hashMultiSetResultCount = 0;
int allMatchCount = 0;
int equalKeySeriesCount = 0;
int spillCount = 0;
/*
* Multi-Key specific variables.
*/
Output temp;
// We optimize performance by only looking up the first key in a series of equal keys.
boolean haveSaveKey = false;
JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH;
// Logical loop over the rows in the batch since the batch may have selected in use.
for (int logical = 0; logical < inputLogicalSize; logical++) {
int batchIndex = (selectedInUse ? selected[logical] : logical);
/*
* Multi-Key get key.
*/
// Generate binary sortable key for current row in vectorized row batch.
keyVectorSerializeWriteNoNulls.setOutput(currentKeyOutput);
keyVectorSerializeWriteNoNulls.serializeWriteNoNulls(batch, batchIndex);
/*
* Equal key series checking.
*/
if (!haveSaveKey || !saveKeyOutput.arraysEquals(currentKeyOutput)) {
// New key.
if (haveSaveKey) {
// Move on with our counts.
switch (saveJoinResult) {
case MATCH:
// We have extracted the count from the hash multi-set result, so we don't keep it.
equalKeySeriesCount++;
break;
case SPILL:
// We keep the hash multi-set result for its spill information.
hashMultiSetResultCount++;
break;
case NOMATCH:
break;
}
}
// Regardless of our matching result, we keep that information to make multiple use
// of it for a possible series of equal keys.
haveSaveKey = true;
/*
* Multi-Key specific save key.
*/
temp = saveKeyOutput;
saveKeyOutput = currentKeyOutput;
currentKeyOutput = temp;
/*
* Single-Column Long specific lookup key.
*/
byte[] keyBytes = saveKeyOutput.getData();
int keyLength = saveKeyOutput.getLength();
saveJoinResult = hashMultiSet.contains(keyBytes, 0, keyLength, hashMultiSetResults[hashMultiSetResultCount]);
/*
* Common inner big-only join result processing.
*/
switch (saveJoinResult) {
case MATCH:
equalKeySeriesValueCounts[equalKeySeriesCount] = hashMultiSetResults[hashMultiSetResultCount].count();
equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount;
equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1;
allMatchs[allMatchCount++] = batchIndex;
// VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey);
break;
case SPILL:
spills[spillCount] = batchIndex;
spillHashMapResultIndices[spillCount] = hashMultiSetResultCount;
spillCount++;
break;
case NOMATCH:
// VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey);
break;
}
} else {
// Series of equal keys.
switch (saveJoinResult) {
case MATCH:
equalKeySeriesDuplicateCounts[equalKeySeriesCount]++;
allMatchs[allMatchCount++] = batchIndex;
// VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate");
break;
case SPILL:
spills[spillCount] = batchIndex;
spillHashMapResultIndices[spillCount] = hashMultiSetResultCount;
spillCount++;
break;
case NOMATCH:
// VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate");
break;
}
}
}
if (haveSaveKey) {
// Update our counts for the last key.
switch (saveJoinResult) {
case MATCH:
// We have extracted the count from the hash multi-set result, so we don't keep it.
equalKeySeriesCount++;
break;
case SPILL:
// We keep the hash multi-set result for its spill information.
hashMultiSetResultCount++;
break;
case NOMATCH:
break;
}
}
if (LOG.isDebugEnabled()) {
LOG.debug(CLASS_NAME +
" allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) +
" equalKeySeriesValueCounts " + longArrayToRangesString(equalKeySeriesValueCounts, equalKeySeriesCount) +
" equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) +
" equalKeySeriesDuplicateCounts " + intArrayToRangesString(equalKeySeriesDuplicateCounts, equalKeySeriesCount) +
" spills " + intArrayToRangesString(spills, spillCount) +
" spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) +
" hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMultiSetResults, 0, hashMultiSetResultCount)));
}
finishInnerBigOnly(batch,
allMatchCount, equalKeySeriesCount, spillCount,
(VectorMapJoinHashTableResult[]) hashMultiSetResults, hashMultiSetResultCount);
}
if (batch.size > 0) {
// Forward any remaining selected rows.
forwardBigTableBatch(batch);
}
} catch (IOException e) {
throw new HiveException(e);
} catch (Exception e) {
throw new HiveException(e);
}
}
}
