blob: 8921cb119d69e0393a05249af03101e65ccdc30f [file] [log] [blame]
/*
* 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.backend.hadoop.executionengine.physicalLayer.relationalOperators;
import org.apache.pig.backend.executionengine.ExecException;
import org.apache.pig.backend.hadoop.executionengine.physicalLayer.POStatus;
import org.apache.pig.backend.hadoop.executionengine.physicalLayer.PhysicalOperator;
import org.apache.pig.backend.hadoop.executionengine.physicalLayer.Result;
import org.apache.pig.backend.hadoop.executionengine.physicalLayer.plans.PhyPlanVisitor;
import org.apache.pig.data.Tuple;
import org.apache.pig.impl.builtin.PoissonSampleLoader;
import org.apache.pig.impl.plan.OperatorKey;
import org.apache.pig.impl.plan.VisitorException;
public class POPoissonSample extends PhysicalOperator {
protected static final long serialVersionUID = 1L;
// 17 is not a magic number. It can be obtained by using a poisson
// cumulative distribution function with the mean set to 10 (empirically,
// minimum number of samples) and the confidence set to 95%
public static final int DEFAULT_SAMPLE_RATE = 17;
protected int sampleRate = 0;
protected float heapPerc = 0f;
protected Long totalMemory;
protected transient boolean initialized;
// num of rows skipped so far
protected transient int numSkipped;
// num of rows sampled so far
protected transient int numRowsSampled;
// average size of tuple in memory, for tuples sampled
protected transient long avgTupleMemSz;
// current row number
protected transient long rowNum;
// number of tuples to skip after each sample
protected transient long skipInterval;
// bytes in input to skip after every sample.
// divide this by avgTupleMemSize to get skipInterval
protected transient long memToSkipPerSample;
// has the special row with row number information been returned
protected transient boolean numRowSplTupleReturned;
// new Sample result
protected transient Result newSample;
public POPoissonSample(OperatorKey k, int rp, int sr, float hp, long tm) {
super(k, rp, null);
sampleRate = sr;
heapPerc = hp;
if (tm != -1) {
totalMemory = tm;
}
}
@Override
public Tuple illustratorMarkup(Object in, Object out, int eqClassIndex) {
// TODO Auto-generated method stub
return null;
}
@Override
public void visit(PhyPlanVisitor v) throws VisitorException {
v.visitPoissonSample(this);
}
@Override
public Result getNextTuple() throws ExecException {
if (!initialized) {
numSkipped = 0;
numRowsSampled = 0;
avgTupleMemSz = 0;
rowNum = 0;
skipInterval = -1;
memToSkipPerSample = 0;
if (totalMemory == null) {
// Initialize in backend to get memory of task
totalMemory = Runtime.getRuntime().maxMemory();
}
initialized = true;
}
if (numRowSplTupleReturned) {
// row num special row has been returned after all inputs
// were read, nothing more to read
return RESULT_EOP;
}
Result res = null;
if (skipInterval == -1) {
// select first tuple as sample and calculate
// number of tuples to be skipped
while (true) {
res = processInput();
if (res.returnStatus == POStatus.STATUS_NULL) {
continue;
} else if (res.returnStatus == POStatus.STATUS_EOP) {
return res;
} else if (res.returnStatus == POStatus.STATUS_ERR) {
return res;
}
if (res.result == null) {
continue;
}
long availRedMem = (long) (totalMemory * heapPerc);
memToSkipPerSample = availRedMem/sampleRate;
updateSkipInterval((Tuple)res.result);
rowNum++;
newSample = res;
break;
}
}
// skip tuples
while (numSkipped < skipInterval) {
res = processInput();
if (res.returnStatus == POStatus.STATUS_NULL) {
continue;
} else if (res.returnStatus == POStatus.STATUS_EOP) {
if (this.parentPlan.endOfAllInput) {
return createNumRowTuple((Tuple)newSample.result);
} else {
return res;
}
} else if (res.returnStatus == POStatus.STATUS_ERR){
return res;
}
rowNum++;
numSkipped++;
}
// skipped enough, get new sample
while (true) {
res = processInput();
if (res.returnStatus == POStatus.STATUS_NULL) {
continue;
} else if (res.returnStatus == POStatus.STATUS_EOP) {
if (this.parentPlan.endOfAllInput) {
return createNumRowTuple((Tuple)newSample.result);
} else {
return res;
}
} else if (res.returnStatus == POStatus.STATUS_ERR){
return res;
}
if (res.result == null) {
continue;
}
updateSkipInterval((Tuple)res.result);
Result currentSample = newSample;
rowNum++;
newSample = res;
// reset skipped
numSkipped = 0;
return currentSample;
}
}
@Override
public boolean supportsMultipleInputs() {
return false;
}
@Override
public boolean supportsMultipleOutputs() {
return false;
}
@Override
public String name() {
return getAliasString() + "PoissonSample - " + mKey.toString();
}
/**
* Update the average tuple size base on newly sampled tuple t
* and recalculate skipInterval
* @param t - tuple
*/
protected void updateSkipInterval(Tuple t) {
avgTupleMemSz =
((avgTupleMemSz*numRowsSampled) + t.getMemorySize())/(numRowsSampled + 1);
skipInterval = memToSkipPerSample/avgTupleMemSz;
// skipping fewer number of rows the first few times, to reduce the
// probability of first tuples size (if much smaller than rest)
// resulting in very few samples being sampled. Sampling a little extra
// is OK
if(numRowsSampled < 5) {
skipInterval = skipInterval/(10-numRowsSampled);
}
++numRowsSampled;
}
/**
* @param sample - sample tuple
* @return - Tuple appended with special marker string column, num-rows column
* @throws ExecException
*/
protected Result createNumRowTuple(Tuple sample) throws ExecException {
int sz = (sample == null) ? 0 : sample.size();
Tuple t = mTupleFactory.newTuple(sz + 2);
if (sample != null) {
for (int i=0; i<sample.size(); i++){
t.set(i, sample.get(i));
}
}
t.set(sz, PoissonSampleLoader.NUMROWS_TUPLE_MARKER);
t.set(sz + 1, rowNum);
numRowSplTupleReturned = true;
return new Result(POStatus.STATUS_OK, t);
}
}