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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.ArrayList;
import java.util.List;
import org.apache.commons.lang.ArrayUtils;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer;
import org.apache.hadoop.hive.ql.exec.persistence.HybridHashTableContainer.HashPartition;
import org.apache.hadoop.hive.ql.exec.persistence.MapJoinBytesTableContainer;
import org.apache.hadoop.hive.ql.exec.persistence.MapJoinTableContainer;
import org.apache.hadoop.hive.ql.exec.vector.ColumnVector;
import org.apache.hadoop.hive.ql.exec.vector.VectorDeserializeRow;
import org.apache.hadoop.hive.ql.exec.vector.VectorSerializeRow;
import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext;
import org.apache.hadoop.hive.ql.exec.vector.VectorizedBatchUtil;
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.exec.vector.mapjoin.hashtable.VectorMapJoinHashMapResult;
import org.apache.hadoop.hive.ql.exec.vector.mapjoin.optimized.VectorMapJoinOptimizedCreateHashTable;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.plan.OperatorDesc;
import org.apache.hadoop.hive.serde2.SerDeException;
import org.apache.hadoop.hive.serde2.WriteBuffers.ByteSegmentRef;
import org.apache.hadoop.hive.serde2.lazybinary.fast.LazyBinaryDeserializeRead;
import org.apache.hadoop.hive.serde2.lazybinary.fast.LazyBinarySerializeWrite;
import org.apache.hadoop.hive.serde2.objectinspector.StructObjectInspector;
import org.apache.hadoop.hive.serde2.typeinfo.PrimitiveTypeInfo;
import org.apache.hadoop.hive.serde2.ByteStream.Output;
/**
* This class has methods for generating vectorized join results and forwarding batchs.
*
* In some cases when can forward the big table batch by setting scratch columns
* with small table results and then making use of our output projection to pick out all the
* output result columns. This can improve performance by avoiding copying big table values.
* So, we will use the big table batch's selected in use to represent those rows.
*
* At the same time, some output results need to be formed in the overflow batch.
* For example, to form N x M cross product output results. In this case, we will copy big
* table values into the overflow batch and set scratch columns in it for small table results.
* The "schema" of the overflow batch is the same as the big table batch so child operators
* only need one definition of their input batch. The overflow batch will be typically be
* forwarded when it gets full, which might not be during a process call.
*
* NOTE: Child operators should not remember a received batch.
*/
public abstract class VectorMapJoinGenerateResultOperator extends VectorMapJoinCommonOperator {
private static final long serialVersionUID = 1L;
private static final Log LOG = LogFactory.getLog(VectorMapJoinGenerateResultOperator.class.getName());
private static final String CLASS_NAME = VectorMapJoinGenerateResultOperator.class.getName();
private transient PrimitiveTypeInfo[] bigTablePrimitiveTypeInfos;
private transient VectorSerializeRow bigTableVectorSerializeRow;
private transient VectorDeserializeRow bigTableVectorDeserializeRow;
// Debug display.
protected transient long batchCounter;
public VectorMapJoinGenerateResultOperator() {
super();
}
public VectorMapJoinGenerateResultOperator(VectorizationContext vContext, OperatorDesc conf)
throws HiveException {
super(vContext, conf);
}
protected void commonSetup(VectorizedRowBatch batch) throws HiveException {
super.commonSetup(batch);
batchCounter = 0;
}
//------------------------------------------------------------------------------------------------
protected void performValueExpressions(VectorizedRowBatch batch,
int[] allMatchs, int allMatchCount) {
/*
* For the moment, pretend all matched are selected so we can evaluate the value
* expressions.
*
* Since we may use the overflow batch when generating results, we will assign the
* selected and real batch size later...
*/
int[] saveSelected = batch.selected;
batch.selected = allMatchs;
boolean saveSelectedInUse = batch.selectedInUse;
batch.selectedInUse = true;
batch.size = allMatchCount;
// Run our value expressions over whole batch.
for(VectorExpression ve: bigTableValueExpressions) {
ve.evaluate(batch);
}
batch.selected = saveSelected;
batch.selectedInUse = saveSelectedInUse;
}
//------------------------------------------------------------------------------------------------
/*
* Common generate join results from hash maps used by Inner and Outer joins.
*/
/**
* Generate join results for a single small table value match.
*
* @param batch
* The big table batch.
* @param hashMapResult
* The hash map results for the matching key.
* @param allMatchs
* The selection array for all matches key.
* @param allMatchesIndex
* Index into allMatches of the matching key we are generating for.
* @param duplicateCount
* Number of equal key rows.
* @param numSel
* Current number of rows that are remaining in the big table for forwarding.
* @return
* The new count of selected rows.
*/
protected int generateHashMapResultSingleValue(VectorizedRowBatch batch,
VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex,
int duplicateCount, int numSel) throws HiveException, IOException {
// Read single value.
ByteSegmentRef byteSegmentRef = hashMapResult.first();
// Generate result within big table batch itself.
for (int i = 0; i < duplicateCount; i++) {
int batchIndex = allMatchs[allMatchesIndex + i];
if (bigTableVectorCopyOuterKeys != null) {
// Copy within row.
bigTableVectorCopyOuterKeys.copyByReference(batch, batchIndex, batch, batchIndex);
}
if (smallTableVectorDeserializeRow != null) {
byte[] bytes = byteSegmentRef.getBytes();
int offset = (int) byteSegmentRef.getOffset();
int length = byteSegmentRef.getLength();
smallTableVectorDeserializeRow.setBytes(bytes, offset, length);
smallTableVectorDeserializeRow.deserializeByValue(batch, batchIndex);
}
// VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, "generateHashMapResultSingleValue big table");
// Use the big table row as output.
batch.selected[numSel++] = batchIndex;
}
return numSel;
}
/**
* Generate results for a N x M cross product.
*
* @param batch
* The big table batch.
* @param hashMapResult
* The hash map results for the matching key.
* @param allMatchs
* The all match selected array that contains (physical) batch indices.
* @param allMatchesIndex
* The index of the match key.
* @param duplicateCount
* Number of equal key rows.
*/
protected void generateHashMapResultMultiValue(VectorizedRowBatch batch,
VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex,
int duplicateCount) throws HiveException, IOException {
if (useOverflowRepeatedThreshold &&
hashMapResult.isCappedCountAvailable() &&
hashMapResult.cappedCount() > overflowRepeatedThreshold) {
// Large cross product: generate the vector optimization using repeating vectorized
// row batch optimization in the overflow batch.
generateHashMapResultLargeMultiValue(
batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount);
return;
}
// We do the cross product of the N big table equal key row's values against the
// small table matching key which has M value rows into overflow batch.
for (int i = 0; i < duplicateCount; i++) {
int batchIndex = allMatchs[allMatchesIndex + i];
ByteSegmentRef byteSegmentRef = hashMapResult.first();
while (byteSegmentRef != null) {
// Copy the BigTable values into the overflow batch. Since the overflow batch may
// not get flushed here, we must copy by value.
if (bigTableRetainedVectorCopy != null) {
bigTableRetainedVectorCopy.copyByValue(batch, batchIndex,
overflowBatch, overflowBatch.size);
}
// Reference the keys we just copied above.
if (bigTableVectorCopyOuterKeys != null) {
bigTableVectorCopyOuterKeys.copyByReference(overflowBatch, overflowBatch.size,
overflowBatch, overflowBatch.size);
}
if (smallTableVectorDeserializeRow != null) {
byte[] bytes = byteSegmentRef.getBytes();
int offset = (int) byteSegmentRef.getOffset();
int length = byteSegmentRef.getLength();
smallTableVectorDeserializeRow.setBytes(bytes, offset, length);
smallTableVectorDeserializeRow.deserializeByValue(overflowBatch, overflowBatch.size);
}
// VectorizedBatchUtil.debugDisplayOneRow(overflowBatch, overflowBatch.size, "generateHashMapResultMultiValue overflow");
overflowBatch.size++;
if (overflowBatch.size == overflowBatch.DEFAULT_SIZE) {
forwardOverflow();
}
byteSegmentRef = hashMapResult.next();
}
}
}
/**
* Generate optimized results for a large N x M cross product using repeated vectorized row
* batch optimization.
*
* @param batch
* The big table batch.
* @param hashMapResult
* The hash map results for the matching key.
* @param allMatchs
* The all match selected array that contains (physical) batch indices.
* @param allMatchesIndex
* The index of the match key.
* @param duplicateCount
* Number of equal key rows.
*/
private void generateHashMapResultLargeMultiValue(VectorizedRowBatch batch,
VectorMapJoinHashMapResult hashMapResult, int[] allMatchs, int allMatchesIndex,
int duplicateCount) throws HiveException, IOException {
// Kick out previous overflow batch results.
if (overflowBatch.size > 0) {
forwardOverflow();
}
ByteSegmentRef byteSegmentRef = hashMapResult.first();
while (true) {
// Fill up as much of the overflow batch as possible with small table values.
while (byteSegmentRef != null) {
if (smallTableVectorDeserializeRow != null) {
byte[] bytes = byteSegmentRef.getBytes();
int offset = (int) byteSegmentRef.getOffset();
int length = byteSegmentRef.getLength();
smallTableVectorDeserializeRow.setBytes(bytes, offset, length);
smallTableVectorDeserializeRow.deserializeByValue(overflowBatch, overflowBatch.DEFAULT_SIZE);
}
overflowBatch.size++;
if (overflowBatch.size == overflowBatch.DEFAULT_SIZE) {
break;
}
byteSegmentRef = hashMapResult.next();
}
// Forward the overflow batch over and over:
//
// Reference a new one big table row's values each time
// cross product
// Current "batch" of small table values.
//
// TODO: This could be further optimized to copy big table (equal) keys once
// and only copy big table values each time...
// And, not set repeating every time...
//
for (int i = 0; i < duplicateCount; i++) {
int batchIndex = allMatchs[allMatchesIndex + i];
if (bigTableRetainedVectorCopy != null) {
// The one big table row's values repeat.
bigTableRetainedVectorCopy.copyByReference(batch, batchIndex, overflowBatch, 0);
for (int column : bigTableRetainedMapping.getOutputColumns()) {
overflowBatch.cols[column].isRepeating = true;
}
}
if (bigTableVectorCopyOuterKeys != null) {
bigTableVectorCopyOuterKeys.copyByReference(batch, batchIndex, overflowBatch, 0);
for (int column : bigTableOuterKeyOutputVectorColumns) {
overflowBatch.cols[column].isRepeating = true;
}
}
// Crucial here that we don't reset the overflow batch, or we will loose the small table
// values we put in above.
forwardOverflowNoReset();
// Hand reset the big table columns.
for (int column : bigTableRetainedMapping.getOutputColumns()) {
ColumnVector colVector = overflowBatch.cols[column];
colVector.reset();
}
if (bigTableVectorCopyOuterKeys != null) {
for (int column : bigTableOuterKeyOutputVectorColumns) {
ColumnVector colVector = overflowBatch.cols[column];
colVector.reset();
}
}
}
if (hashMapResult.isEof()) {
break;
}
byteSegmentRef = hashMapResult.next();
// Get ready for a another round of small table values.
overflowBatch.reset();
}
// Clear away any residue from our optimizations.
overflowBatch.reset();
}
/**
* Generate optimized results when entire batch key is repeated and it matched the hash map.
*
* @param batch
* The big table batch.
* @param hashMapResult
* The hash map results for the repeated key.
*/
protected void generateHashMapResultRepeatedAll(VectorizedRowBatch batch,
VectorMapJoinHashMapResult hashMapResult) throws IOException, HiveException {
int[] selected = batch.selected;
if (batch.selectedInUse) {
// The selected array is already filled in as we want it.
} else {
for (int i = 0; i < batch.size; i++) {
selected[i] = i;
}
batch.selectedInUse = true;
}
int numSel = 0;
if (hashMapResult.isSingleRow()) {
numSel = generateHashMapResultSingleValue(batch, hashMapResult,
batch.selected, 0, batch.size, numSel);
} else {
generateHashMapResultMultiValue(batch, hashMapResult,
batch.selected, 0, batch.size);
}
batch.size = numSel;
}
//-----------------------------------------------------------------------------------------------
/*
* Spill.
*/
private void setupSpillSerDe(VectorizedRowBatch batch) throws HiveException {
PrimitiveTypeInfo[] inputObjInspectorsTypeInfos =
VectorizedBatchUtil.primitiveTypeInfosFromStructObjectInspector(
(StructObjectInspector) inputObjInspectors[posBigTable]);
List projectedColumns = vContext.getProjectedColumns();
int projectionSize = vContext.getProjectedColumns().size();
List typeInfoList = new ArrayList();
List noNullsProjectionList = new ArrayList();
for (int i = 0; i < projectionSize; i++) {
int projectedColumn = projectedColumns.get(i);
if (batch.cols[projectedColumn] != null) {
typeInfoList.add(inputObjInspectorsTypeInfos[i]);
noNullsProjectionList.add(projectedColumn);
}
}
int[] noNullsProjection = ArrayUtils.toPrimitive(noNullsProjectionList.toArray(new Integer[0]));
int noNullsProjectionSize = noNullsProjection.length;
bigTablePrimitiveTypeInfos = typeInfoList.toArray(new PrimitiveTypeInfo[0]);
bigTableVectorSerializeRow =
new VectorSerializeRow(new LazyBinarySerializeWrite(noNullsProjectionSize));
bigTableVectorSerializeRow.init(
bigTablePrimitiveTypeInfos,
noNullsProjectionList);
bigTableVectorDeserializeRow = new VectorDeserializeRow(
new LazyBinaryDeserializeRead(bigTablePrimitiveTypeInfos));
bigTableVectorDeserializeRow.init(noNullsProjection);
}
private void spillSerializeRow(VectorizedRowBatch batch, int batchIndex,
VectorMapJoinHashTableResult hashTableResult) throws IOException {
int partitionId = hashTableResult.spillPartitionId();
HybridHashTableContainer ht = (HybridHashTableContainer) mapJoinTables[posSingleVectorMapJoinSmallTable];
HashPartition hp = ht.getHashPartitions()[partitionId];
VectorMapJoinRowBytesContainer rowBytesContainer = hp.getMatchfileRowBytesContainer();
Output output = rowBytesContainer.getOuputForRowBytes();
// int offset = output.getLength();
bigTableVectorSerializeRow.setOutputAppend(output);
bigTableVectorSerializeRow.serializeWrite(batch, batchIndex);
// int length = output.getLength() - offset;
rowBytesContainer.finishRow();
// LOG.debug("spillSerializeRow spilled batchIndex " + batchIndex + ", length " + length);
}
protected void spillHashMapBatch(VectorizedRowBatch batch,
VectorMapJoinHashTableResult[] hashTableResults,
int[] spills, int[] spillHashTableResultIndices, int spillCount)
throws HiveException, IOException {
if (bigTableVectorSerializeRow == null) {
setupSpillSerDe(batch);
}
for (int i = 0; i < spillCount; i++) {
int batchIndex = spills[i];
int hashTableResultIndex = spillHashTableResultIndices[i];
VectorMapJoinHashTableResult hashTableResult = hashTableResults[hashTableResultIndex];
spillSerializeRow(batch, batchIndex, hashTableResult);
}
}
protected void spillBatchRepeated(VectorizedRowBatch batch,
VectorMapJoinHashTableResult hashTableResult) throws HiveException, IOException {
if (bigTableVectorSerializeRow == null) {
setupSpillSerDe(batch);
}
int[] selected = batch.selected;
boolean selectedInUse = batch.selectedInUse;
for (int logical = 0; logical < batch.size; logical++) {
int batchIndex = (selectedInUse ? selected[logical] : logical);
spillSerializeRow(batch, batchIndex, hashTableResult);
}
}
@Override
protected void reloadHashTable(byte pos, int partitionId)
throws IOException, HiveException, SerDeException, ClassNotFoundException {
// The super method will reload a hash table partition of one of the small tables.
// Currently, for native vector map join it will only be one small table.
super.reloadHashTable(pos, partitionId);
MapJoinTableContainer smallTable = spilledMapJoinTables[pos];
vectorMapJoinHashTable = VectorMapJoinOptimizedCreateHashTable.createHashTable(conf,
smallTable);
needHashTableSetup = true;
if (LOG.isDebugEnabled()) {
LOG.debug(CLASS_NAME + " reloadHashTable!");
}
}
@Override
protected void reProcessBigTable(int partitionId)
throws HiveException {
if (LOG.isDebugEnabled()) {
LOG.debug(CLASS_NAME + " reProcessBigTable enter...");
}
if (spillReplayBatch == null) {
// The process method was not called -- no big table rows.
return;
}
HashPartition partition = firstSmallTable.getHashPartitions()[partitionId];
int rowCount = 0;
int batchCount = 0;
try {
VectorMapJoinRowBytesContainer bigTable = partition.getMatchfileRowBytesContainer();
bigTable.prepareForReading();
while (bigTable.readNext()) {
rowCount++;
byte[] bytes = bigTable.currentBytes();
int offset = bigTable.currentOffset();
int length = bigTable.currentLength();
// LOG.debug(CLASS_NAME + " reProcessBigTable serialized row #" + rowCount + ", offset " + offset + ", length " + length);
bigTableVectorDeserializeRow.setBytes(bytes, offset, length);
bigTableVectorDeserializeRow.deserializeByValue(spillReplayBatch, spillReplayBatch.size);
spillReplayBatch.size++;
if (spillReplayBatch.size == VectorizedRowBatch.DEFAULT_SIZE) {
// LOG.debug("reProcessBigTable going to call process with spillReplayBatch.size " + spillReplayBatch.size + " rows");
process(spillReplayBatch, posBigTable); // call process once we have a full batch
spillReplayBatch.reset();
batchCount++;
}
}
// Process the row batch that has less than DEFAULT_SIZE rows
if (spillReplayBatch.size > 0) {
// LOG.debug("reProcessBigTable going to call process with spillReplayBatch.size " + spillReplayBatch.size + " rows");
process(spillReplayBatch, posBigTable);
spillReplayBatch.reset();
batchCount++;
}
bigTable.clear();
} catch (Exception e) {
LOG.info(CLASS_NAME + " reProcessBigTable exception! " + e);
throw new HiveException(e);
}
if (LOG.isDebugEnabled()) {
LOG.debug(CLASS_NAME + " reProcessBigTable exit! " + rowCount + " row processed and " + batchCount + " batches processed");
}
}
//-----------------------------------------------------------------------------------------------
/*
* Forwarding.
*/
/**
* Forward the big table batch to the children.
*
* @param batch
* The big table batch.
*/
public void forwardBigTableBatch(VectorizedRowBatch batch) throws HiveException {
// Save original projection.
int[] originalProjections = batch.projectedColumns;
int originalProjectionSize = batch.projectionSize;
// Project with the output of our operator.
batch.projectionSize = outputProjection.length;
batch.projectedColumns = outputProjection;
forward(batch, null);
// Revert the projected columns back, because batch can be re-used by our parent operators.
batch.projectionSize = originalProjectionSize;
batch.projectedColumns = originalProjections;
}
/**
* Forward the overflow batch and reset the batch.
*/
protected void forwardOverflow() throws HiveException {
forward(overflowBatch, null);
overflowBatch.reset();
}
/**
* Forward the overflow batch, but do not reset the batch.
*/
private void forwardOverflowNoReset() throws HiveException {
forward(overflowBatch, null);
}
/*
* Close.
*/
/**
* On close, make sure a partially filled overflow batch gets forwarded.
*/
@Override
public void closeOp(boolean aborted) throws HiveException {
super.closeOp(aborted);
if (!aborted && overflowBatch.size > 0) {
forwardOverflow();
}
if (LOG.isDebugEnabled()) {
LOG.debug("VectorMapJoinInnerLongOperator closeOp " + batchCounter + " batches processed");
}
}
//-----------------------------------------------------------------------------------------------
/*
* Debug.
*/
public boolean verifyMonotonicallyIncreasing(int[] selected, int size) {
if (size == 0) {
return true;
}
int prevBatchIndex = selected[0];
for (int i = 1; i < size; i++) {
int batchIndex = selected[i];
if (batchIndex <= prevBatchIndex) {
return false;
}
prevBatchIndex = batchIndex;
}
return true;
}
public static String intArrayToRangesString(int selection[], int size) {
if (size == 0) {
return "[]";
}
StringBuilder sb = new StringBuilder();
// Use ranges and duplicate multipliers to reduce the size of the display.
sb.append("[");
int firstIndex = 0;
int firstValue = selection[0];
boolean duplicates = false;
int i = 1;
for ( ; i < size; i++) {
int newValue = selection[i];
if (newValue == selection[i - 1]) {
// Duplicate.
duplicates = true;
if (newValue == firstValue) {
continue;
} else {
// Prior none, singleton, or range?
int priorRangeLength = i - 1 - firstIndex;
if (priorRangeLength == 0) {
continue;
}
if (firstIndex > 0) {
sb.append(",");
}
sb.append(firstValue);
if (priorRangeLength > 1) {
sb.append(".." + selection[i - 2]);
}
firstIndex = i - 1;
firstValue = newValue;
continue;
}
} else {
if (duplicates) {
int numDuplicates = i - firstIndex;
if (firstIndex > 0) {
sb.append(",");
}
sb.append(numDuplicates + "*" + firstValue);
duplicates = false;
firstIndex = i;
firstValue = newValue;
continue;
} if (newValue == selection[i - 1] + 1) {
// Continue range..
continue;
} else {
// Prior singleton or range?
int priorRangeLength = i - firstIndex;
if (firstIndex > 0) {
sb.append(",");
}
sb.append(firstValue);
if (priorRangeLength > 1) {
sb.append(".." + selection[i - 1]);
}
firstIndex = i;
firstValue = newValue;
continue;
}
}
}
if (duplicates) {
int numDuplicates = i - firstIndex;
if (firstIndex > 0) {
sb.append(",");
}
sb.append(numDuplicates + "*" + firstValue);
} else {
// Last singleton or range?
int priorRangeLength = i - firstIndex;
if (firstIndex > 0) {
sb.append(",");
}
sb.append(firstValue);
if (priorRangeLength > 1) {
sb.append(".." + selection[i - 1]);
}
}
sb.append("]");
return sb.toString();
}
public static String longArrayToRangesString(long selection[], int size) {
if (size == 0) {
return "[]";
}
StringBuilder sb = new StringBuilder();
// Use ranges and duplicate multipliers to reduce the size of the display.
sb.append("[");
int firstIndex = 0;
long firstValue = selection[0];
boolean duplicates = false;
int i = 1;
for ( ; i < size; i++) {
long newValue = selection[i];
if (newValue == selection[i - 1]) {
// Duplicate.
duplicates = true;
if (newValue == firstValue) {
continue;
} else {
// Prior none, singleton, or range?
int priorRangeLength = i - 1 - firstIndex;
if (priorRangeLength == 0) {
continue;
}
if (firstIndex > 0) {
sb.append(",");
}
sb.append(firstValue);
if (priorRangeLength > 1) {
sb.append(".." + selection[i - 2]);
}
firstIndex = i - 1;
firstValue = newValue;
continue;
}
} else {
if (duplicates) {
int numDuplicates = i - firstIndex;
if (firstIndex > 0) {
sb.append(",");
}
sb.append(numDuplicates + "*" + firstValue);
duplicates = false;
firstIndex = i;
firstValue = newValue;
continue;
} if (newValue == selection[i - 1] + 1) {
// Continue range..
continue;
} else {
// Prior singleton or range?
int priorRangeLength = i - firstIndex;
if (firstIndex > 0) {
sb.append(",");
}
sb.append(firstValue);
if (priorRangeLength > 1) {
sb.append(".." + selection[i - 1]);
}
firstIndex = i;
firstValue = newValue;
continue;
}
}
}
if (duplicates) {
int numDuplicates = i - firstIndex;
if (firstIndex > 0) {
sb.append(",");
}
sb.append(numDuplicates + "*" + firstValue);
} else {
// Last singleton or range?
int priorRangeLength = i - firstIndex;
if (firstIndex > 0) {
sb.append(",");
}
sb.append(firstValue);
if (priorRangeLength > 1) {
sb.append(".." + selection[i - 1]);
}
}
sb.append("]");
return sb.toString();
}
}
