/*
* 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.pig.data;
import java.io.BufferedInputStream;
import java.io.DataInputStream;
import java.io.DataOutputStream;
import java.io.EOFException;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashSet;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.ListIterator;
import java.util.TreeSet;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.pig.PigConfiguration;
import org.apache.pig.backend.hadoop.executionengine.mapReduceLayer.PigMapReduce;
import org.apache.pig.classification.InterfaceAudience;
import org.apache.pig.classification.InterfaceStability;
/**
* An unordered collection of Tuples with no multiples. Data is
* stored without duplicates as it comes in. When it is time to spill,
* that data is sorted and written to disk. The data is
* stored in a HashSet. When it is time to sort it is placed in an
* ArrayList and then sorted. Dispite all these machinations, this was
* found to be faster than storing it in a TreeSet.
*
* This bag spills pro-actively when the number of tuples in memory
* reaches a limit
*/
@InterfaceAudience.Private
@InterfaceStability.Evolving
public class InternalDistinctBag extends SortedSpillBag {
/**
*
*/
private static final long serialVersionUID = 2L;
private static final Log log = LogFactory.getLog(InternalDistinctBag.class);
private static TupleFactory gTupleFactory = TupleFactory.getInstance();
private transient boolean mReadStarted = false;
public InternalDistinctBag() {
this(1, -1.0f);
}
public InternalDistinctBag(int bagCount) {
this(bagCount, -1.0f);
}
public InternalDistinctBag(int bagCount, float percent) {
super(bagCount, percent);
if (percent < 0) {
percent = 0.2F;
if (PigMapReduce.sJobConfInternal.get() != null) {
String usage = PigMapReduce.sJobConfInternal.get().get(PigConfiguration.PROP_CACHEDBAG_MEMUSAGE);
if (usage != null) {
percent = Float.parseFloat(usage);
}
}
}
init(bagCount, percent);
}
private void init(int bagCount, double percent) {
mContents = new HashSet<Tuple>();
}
@Override
public boolean isSorted() {
return false;
}
@Override
public boolean isDistinct() {
return true;
}
@Override
public long size() {
if (mSpillFiles != null && mSpillFiles.size() > 0){
//We need to racalculate size to guarantee a count of unique
//entries including those on disk
Iterator<Tuple> iter = iterator();
int newSize = 0;
while (iter.hasNext()) {
newSize++;
iter.next();
}
mSize = newSize;
}
return mSize;
}
@Override
public Iterator<Tuple> iterator() {
return new DistinctDataBagIterator();
}
@Override
public void add(Tuple t) {
synchronized(mContents) {
if(mReadStarted) {
throw new IllegalStateException("InternalDistinctBag is closed for adding new tuples");
}
if (mContents.size() > memLimit.getCacheLimit()) {
proactive_spill(null);
}
if (mContents.add(t)) {
mSize ++;
// check how many tuples memory can hold by getting average
// size of first 100 tuples
if(mSize < 100 && (mSpillFiles == null || mSpillFiles.isEmpty())) {
memLimit.addNewObjSize(t.getMemorySize());
}
}
markSpillableIfNecessary();
}
}
/**
* An iterator that handles getting the next tuple from the bag.
* Data can be stored in a combination of in memory and on disk.
*/
private class DistinctDataBagIterator implements Iterator<Tuple> {
private class TContainer implements Comparable<TContainer> {
public Tuple tuple;
public int fileNum;
@Override
@SuppressWarnings("unchecked")
public int compareTo(TContainer other) {
return tuple.compareTo(other.tuple);
}
@Override
public boolean equals(Object obj) {
if (obj instanceof TContainer) {
return compareTo((TContainer)obj) == 0;
}
return false;
}
@Override
public int hashCode() {
return tuple.hashCode();
}
}
// We have to buffer a tuple because there's no easy way for next
// to tell whether or not there's another tuple available, other
// than to read it.
private Tuple mBuf = null;
private int mMemoryPtr = 0;
private TreeSet<TContainer> mMergeTree = null;
private ArrayList<DataInputStream> mStreams = null;
private int mCntr = 0;
@SuppressWarnings("unchecked")
DistinctDataBagIterator() {
// If this is the first read, we need to sort the data.
synchronized(mContents) {
if (!mReadStarted) {
preMerge();
// We're the first reader, we need to sort the data.
// This is in case it gets dumped under us.
ArrayList<Tuple> l = new ArrayList<Tuple>(mContents);
Collections.sort(l);
mContents = l;
mReadStarted = true;
}
}
}
@Override
public boolean hasNext() {
// See if we can find a tuple. If so, buffer it.
mBuf = next();
return mBuf != null;
}
@Override
public Tuple next() {
// This will report progress every 1024 times through next.
// This should be much faster than using mod.
if ((mCntr++ & 0x3ff) == 0) reportProgress();
// If there's one in the buffer, use that one.
if (mBuf != null) {
Tuple t = mBuf;
mBuf = null;
return t;
}
// Check to see if we just need to read from memory.
if (mSpillFiles == null || mSpillFiles.size() == 0) {
return readFromMemory();
}
// We have spill files, so we need to read the next tuple from
// one of those files or from memory.
return readFromTree();
}
/**
* Not implemented.
*/
@Override
public void remove() {}
private Tuple readFromTree() {
if (mMergeTree == null) {
// First read, we need to set up the queue and the array of
// file streams
mMergeTree = new TreeSet<TContainer>();
// Add one to the size in case we spill later.
mStreams =
new ArrayList<DataInputStream>(mSpillFiles.size() + 1);
Iterator<File> i = mSpillFiles.iterator();
while (i.hasNext()) {
try {
DataInputStream in =
new DataInputStream(new BufferedInputStream(
new FileInputStream(i.next())));
mStreams.add(in);
// Add the first tuple from this file into the
// merge queue.
addToQueue(null, mStreams.size() - 1);
} catch (FileNotFoundException fnfe) {
// We can't find our own spill file? That should
// never happen.
String msg = "Unable to find our spill file.";
log.fatal(msg, fnfe);
throw new RuntimeException(msg, fnfe);
}
}
// Prime one from memory too
if (mContents.size() > 0) {
addToQueue(null, -1);
}
}
if (mMergeTree.size() == 0) return null;
// Pop the top one off the queue
TContainer c = mMergeTree.first();
mMergeTree.remove(c);
// Add the next tuple from whereever we read from into the
// queue. Buffer the tuple we're returning, as we'll be
// reusing c.
Tuple t = c.tuple;
addToQueue(c, c.fileNum);
return t;
}
private void addToQueue(TContainer c, int fileNum) {
if (c == null) {
c = new TContainer();
}
c.fileNum = fileNum;
if (fileNum == -1) {
// Need to read from memory.
do {
c.tuple = readFromMemory();
if (c.tuple != null) {
// If we find a unique entry, then add it to the queue.
// Otherwise ignore it and keep reading.
if (mMergeTree.add(c)) {
return;
}
}
} while (c.tuple != null);
return;
}
// Read the next tuple from the indicated file
DataInputStream in = mStreams.get(fileNum);
if (in != null) {
// There's still data in this file
c.tuple = gTupleFactory.newTuple();
do {
try {
c.tuple.readFields(in);
// If we find a unique entry, then add it to the queue.
// Otherwise ignore it and keep reading. If we run out
// of tuples to read that's fine, we just won't add a
// new one from this file.
if (mMergeTree.add(c)) {
return;
}
} catch (EOFException eof) {
// Out of tuples in this file. Set our slot in the
// array to null so we don't keep trying to read from
// this file.
try {
in.close();
}catch(IOException e) {
log.warn("Failed to close spill file.", e);
}
mStreams.set(fileNum, null);
return;
} catch (IOException ioe) {
String msg = "Unable to find our spill file.";
log.fatal(msg, ioe);
throw new RuntimeException(msg, ioe);
}
} while (true);
}
}
// Function assumes that the reader lock is already held before we enter
// this function.
private Tuple readFromMemory() {
if (mContents.size() == 0) return null;
if (mMemoryPtr < mContents.size()) {
return ((ArrayList<Tuple>)mContents).get(mMemoryPtr++);
} else {
return null;
}
}
/**
* Pre-merge if there are too many spill files. This avoids the issue
* of having too large a fan out in our merge. Experimentation by
* the hadoop team has shown that 100 is about the optimal number
* of spill files. This function modifies the mSpillFiles array
* and assumes the write lock is already held. It will not unlock it.
*
* Tuples are reconstituted as tuples, evaluated, and rewritten as
* tuples. This is expensive, but I don't know how to read tuples
* from the file otherwise.
*
* This function is slightly different than the one in
* SortedDataBag, as it uses a TreeSet instead of a PriorityQ.
*/
private void preMerge() {
if (mSpillFiles == null ||
mSpillFiles.size() <= MAX_SPILL_FILES) {
return;
}
// While there are more than max spill files, gather max spill
// files together and merge them into one file. Then remove the others
// from mSpillFiles. The new spill files are attached at the
// end of the list, so I can just keep going until I get a
// small enough number without too much concern over uneven
// size merges. Convert mSpillFiles to a linked list since
// we'll be removing pieces from the middle and we want to do
// it efficiently.
try {
LinkedList<File> ll = new LinkedList<File>(mSpillFiles);
LinkedList<File> filesToDelete = new LinkedList<File>();
while (ll.size() > MAX_SPILL_FILES) {
ListIterator<File> i = ll.listIterator();
mStreams =
new ArrayList<DataInputStream>(MAX_SPILL_FILES);
mMergeTree = new TreeSet<TContainer>();
for (int j = 0; j < MAX_SPILL_FILES; j++) {
try {
File f = i.next();
DataInputStream in =
new DataInputStream(new BufferedInputStream(
new FileInputStream(f)));
mStreams.add(in);
addToQueue(null, mStreams.size() - 1);
i.remove();
filesToDelete.add(f);
} catch (FileNotFoundException fnfe) {
// We can't find our own spill file? That should
// neer happen.
String msg = "Unable to find our spill file.";
log.fatal(msg, fnfe);
throw new RuntimeException(msg, fnfe);
}
}
// Get a new spill file. This adds one to the end of
// the spill files list. So I need to append it to my
// linked list as well so that it's still there when I
// move my linked list back to the spill files.
try {
DataOutputStream out = getSpillFile();
ll.add(mSpillFiles.get(mSpillFiles.size() - 1));
Tuple t;
while ((t = readFromTree()) != null) {
t.write(out);
}
out.flush();
out.close();
} catch (IOException ioe) {
String msg = "Unable to find our spill file.";
log.fatal(msg, ioe);
throw new RuntimeException(msg, ioe);
}
}
// delete files that have been merged into new files
for(File f : filesToDelete){
if( f.delete() == false){
log.warn("Failed to delete spill file: " + f.getPath());
}
}
// clear the list, so that finalize does not delete any files,
// when mSpillFiles is assigned a new value
mSpillFiles.clear();
// Now, move our new list back to the spill files array.
mSpillFiles = new FileList(ll);
} finally {
// Reset mStreams and mMerge so that they'll be allocated
// properly for regular merging.
mStreams = null;
mMergeTree = null;
}
}
}
@Override
public long spill(){
synchronized(mContents) {
if (this.mReadStarted) {
return 0L;
}
return proactive_spill(null);
}
}
}