作业的减少阶段失败：
失败的Reduce Tasks每项任务失败的原因是：
任务尝试_201301251556_1637_r_000005_0无法报告状态600秒。杀死！
详细问题：
Map阶段接收每个格式的记录：time，rid，data。
数据的格式为：数据元素及其计数。
例如：a，1b，4c，7与记录的数据相对应。
映射器为每个数据元素输出每条记录的数据。例如：
键:(时间，a，），val :(摆脱，数据） key：（time，b，），val :( rid，data） key：（time，c，），val：（rid，data）
每个reduce从所有记录中接收与相同密钥对应的所有数据。 例如： key :( time，a），val：（rid1，data）和 key :( time，a），val：（rid2，data） 达到相同的减少实例。
它在这里进行一些处理并输出类似的rids。
对于像10MB这样的小型数据集，我的程序运行没有问题。但是，由于上述原因，当数据增加到1G时失败。我不知道为什么会这样。请帮忙！
减少代码：
下面有两个类：
VCLReduce0Split CoreSplit 一个。 VCLReduce0SPlit
public class VCLReduce0Split extends MapReduceBase implements Reducer<Text, Text, Text, Text>{
// @SuppressWarnings("unchecked")
public void reduce (Text key, Iterator<Text> values, OutputCollector<Text, Text> output, Reporter reporter) throws IOException {
String key_str = key.toString();
StringTokenizer stk = new StringTokenizer(key_str);
String t = stk.nextToken();
HashMap<String, String> hmap = new HashMap<String, String>();
while(values.hasNext())
{
StringBuffer sbuf1 = new StringBuffer();
String val = values.next().toString();
StringTokenizer st = new StringTokenizer(val);
String uid = st.nextToken();
String data = st.nextToken();
int total_size = 0;
StringTokenizer stx = new StringTokenizer(data,"|");
StringBuffer sbuf = new StringBuffer();
while(stx.hasMoreTokens())
{
String data_part = stx.nextToken();
String data_freq = stx.nextToken();
// System.out.println("data_part:----->"+data_part+" data_freq:----->"+data_freq);
sbuf.append(data_part);
sbuf.append("|");
sbuf.append(data_freq);
sbuf.append("|");
}
/*
for(int i = 0; i<parts.length-1; i++)
{
System.out.println("data:--------------->"+data);
int part_size = Integer.parseInt(parts[i+1]);
sbuf.append(parts[i]);
sbuf.append("|");
sbuf.append(part_size);
sbuf.append("|");
total_size = part_size+total_size;
i++;
}*/
sbuf1.append(String.valueOf(total_size));
sbuf1.append(",");
sbuf1.append(sbuf);
if(uid.equals("203664471")){
// System.out.println("data:--------------------------->"+data+" tot_size:---->"+total_size+" sbuf:------->"+sbuf);
}
hmap.put(uid, sbuf1.toString());
}
float threshold = (float)0.8;
CoreSplit obj = new CoreSplit();
ArrayList<CustomMapSimilarity> al = obj.similarityCalculation(t, hmap, threshold);
for(int i = 0; i<al.size(); i++)
{
CustomMapSimilarity cmaps = al.get(i);
String xy_pair = cmaps.getRIDPair();
String similarity = cmaps.getSimilarity();
output.collect(new Text(xy_pair), new Text(similarity));
}
}
}
湾coreSplit
package com.a;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.Set;
import java.util.StringTokenizer;
import java.util.TreeMap;
import org.apache.commons.collections.map.MultiValueMap;
public class PPJoinPlusCoreOptNewSplit{
public ArrayList<CustomMapSimilarity> similarityCalculation(String time, HashMap<String,String>hmap, float t)
{
ArrayList<CustomMapSimilarity> als = new ArrayList<CustomMapSimilarity>();
ArrayList<CustomMapSimilarity> alsim = new ArrayList<CustomMapSimilarity>();
Iterator<String> iter = hmap.keySet().iterator();
MultiValueMap index = new MultiValueMap();
String RID;
TreeMap<String, Integer> hmap2;
Iterator<String> iter1;
int size;
float prefix_size;
HashMap<String, Float> alpha;
HashMap<String, CustomMapOverlap> hmap_overlap;
String data;
while(iter.hasNext())
{
RID = (String)iter.next();
String data_val = hmap.get(RID);
StringTokenizer st = new StringTokenizer(data_val,",");
// System.out.println("data_val:--**********-->"+data_val+" RID:------------>"+RID+" time::---?"+time);
String RIDsize = st.nextToken();
size = Integer.parseInt(RIDsize);
data = st.nextToken();
StringTokenizer st1 = new StringTokenizer(data,"\\|");
String[] parts = data.split("\\|");
// hmap2 = (TreeMap<String, Integer>)hmap.get(RID);
// iter1 = hmap2.keySet().iterator();
// size = hmap_size.get(RID);
prefix_size = (float)(size-(0.8*size)+1);
if(size==1)
{
prefix_size = 1;
}
alpha = new HashMap<String, Float>();
hmap_overlap = new HashMap<String, CustomMapOverlap>();
// Iterator<String> iter2 = hmap2.keySet().iterator();
int prefix_index = 0;
int pi=0;
for(float j = 0; j<=prefix_size; j++)
{
boolean prefix_chk = false;
prefix_index++;
String ptoken = parts[pi];
// System.out.println("data:---->"+data+" ptoken:---->"+ptoken);
float val = Float.parseFloat(parts[pi+1]);
float temp_j = j;
j = j+val;
boolean j_l = false ;
float prefix_contri = 0;
pi= pi+2;
if(j>prefix_size)
{
// prefix_contri = j-temp_j;
prefix_contri = prefix_size-temp_j;
if(prefix_contri>0)
{
j_l = true;
prefix_chk = false;
}
else
{
prefix_chk = true;
}
}
if(prefix_chk == false){
filters(index, ptoken, RID, hmap,t, size, val, j_l, alpha, hmap_overlap, j, prefix_contri);
CustomMapPrefixTokens cmapt = new CustomMapPrefixTokens(RID,j);
index.put(ptoken, cmapt);
}
}
als = calcSimilarity(time, RID, hmap, alpha, hmap_overlap);
for(int i = 0; i<als.size(); i++)
{
if(als.get(i).getRIDPair()!=null)
{
alsim.add(als.get(i));
}
}
}
return alsim;
}
public void filters(MultiValueMap index, String ptoken, String RID, HashMap<String, String> hmap, float t, int size, float val, boolean j_l, HashMap<String, Float> alpha, HashMap<String, CustomMapOverlap> hmap_overlap, float j, float prefix_contri)
{
@SuppressWarnings("unchecked")
ArrayList<CustomMapPrefixTokens> positions_list = (ArrayList<CustomMapPrefixTokens>) index.get(ptoken);
if((positions_list!=null) &&(positions_list.size()!=0))
{
CustomMapPrefixTokens cmapt ;
String y;
Iterator<String> iter3;
int y_size = 0;
float check_size = 0;
// TreeMap<String, Integer> hmapy;
float RID_val=0;
float y_overlap = 0;
float ubound = 0;
ArrayList<Float> fl = new ArrayList<Float>();
StringTokenizer st;
for(int k = 0; k<positions_list.size(); k++)
{
cmapt = positions_list.get(k);
if(!cmapt.getRID().equals(RID))
{
y = hmap.get(cmapt.getRID());
// iter3 = y.keySet().iterator();
String yRID = cmapt.getRID();
st = new StringTokenizer(y,",");
y_size = Integer.parseInt(st.nextToken());
check_size = (float)0.8*(size);
if(y_size>=check_size)
{
//hmapy = hmap.get(yRID);
String y_data = st.nextToken();
StringTokenizer st1 = new StringTokenizer(y_data,"\\|");
while(st1.hasMoreTokens())
{
String token = st1.nextToken();
if(token.equals(ptoken))
{
String nxt_token = st1.nextToken();
// System.out.println("ydata:--->"+y_data+" nxt_token:--->"+nxt_token);
RID_val = (float)Integer.parseInt(nxt_token);
break;
}
}
// RID_val = (float) hmapy.get(ptoken);
float alpha1 = (float)(0.8/1.8)*(size+y_size);
fl = overlapCalc(alpha1, size, y_size, cmapt, j, alpha, j_l,RID_val,val,prefix_contri);
ubound = fl.get(0);
y_overlap = fl.get(1);
positionFilter(ubound, alpha1, cmapt, y_overlap, hmap_overlap);
}
}
}
}
}
public void positionFilter( float ubound,float alpha1, CustomMapPrefixTokens cmapt, float y_overlap, HashMap<String, CustomMapOverlap> hmap_overlap)
{
float y_overlap_total = 0;
if(null!=hmap_overlap.get(cmapt.getRID()))
{
y_overlap_total = hmap_overlap.get(cmapt.getRID()).getOverlap();
if((y_overlap_total+ubound)>=alpha1)
{
CustomMapOverlap cmap_tmp = hmap_overlap.get(cmapt.getRID());
float y_o_t = y_overlap+y_overlap_total;
cmap_tmp.setOverlap(y_o_t);
hmap_overlap.put(cmapt.getRID(),cmap_tmp);
}
else
{
float n = 0;
hmap_overlap.put(cmapt.getRID(), new CustomMapOverlap(cmapt.getRID(),n));
}
}
else
{
CustomMapOverlap cmap_tmp = new CustomMapOverlap(cmapt.getRID(),y_overlap);
hmap_overlap.put(cmapt.getRID(), cmap_tmp);
}
}
public ArrayList<Float> overlapCalc(float alpha1, int size, int y_size, CustomMapPrefixTokens cmapt, float j, HashMap<String, Float> alpha, boolean j_l, float RID_val, float val, float prefix_contri )
{
alpha.put(cmapt.getRID(), alpha1);
float min1 = y_size-cmapt.getPosition();
float min2 = size-j;
float min = 0;
float y_overlap = 0;
if(min1<min2)
{
min = min1;
}
else
{
min = min2;
}
if(j_l==true)
{
val = prefix_contri;
}
if(RID_val<val)
{
y_overlap = RID_val;
}
else
{
y_overlap = val;
}
float ubound = y_overlap+min;
ArrayList<Float> fl = new ArrayList<Float>();
fl.add(ubound);
fl.add(y_overlap);
return fl;
}
public ArrayList<CustomMapSimilarity> calcSimilarity( String time, String RID, HashMap<String,String> hmap , HashMap<String, Float> alpha, HashMap<String, CustomMapOverlap> hmap_overlap)
{
float jaccard = 0;
CustomMapSimilarity cms = new CustomMapSimilarity(null, null);
ArrayList<CustomMapSimilarity> alsim = new ArrayList<CustomMapSimilarity>();
Iterator<String> iter = hmap_overlap.keySet().iterator();
while(iter.hasNext())
{
String key = (String)iter.next();
CustomMapOverlap val = (CustomMapOverlap)hmap_overlap.get(key);
float overlap = (float)val.getOverlap();
if(overlap>0)
{
String yRID = val.getRID();
String RIDpair = RID+" "+yRID;
jaccard = unionIntersection(hmap, RIDpair);
if(jaccard>0.8)
{
cms = new CustomMapSimilarity(time+" "+RIDpair, String.valueOf(jaccard));
alsim.add(cms);
}
}
}
return alsim;
}
public float unionIntersection( HashMap<String,String> hmap, String RIDpair)
{
StringTokenizer st = new StringTokenizer(RIDpair);
String xRID = st.nextToken();
String yRID = st.nextToken();
String xdata = hmap.get(xRID);
String ydata = hmap.get(yRID);
int total_union = 0;
int xval = 0;
int yval = 0;
int part_union = 0;
int total_intersect = 0;
// System.out.println("xdata:------*************>"+xdata);
StringTokenizer xtokenizer = new StringTokenizer(xdata,",");
StringTokenizer ytokenizer = new StringTokenizer(ydata,",");
// String[] xpart = xdata.split(",");
// String[] ypart = ydata.split(",");
xtokenizer.nextToken();
ytokenizer.nextToken();
String datax = xtokenizer.nextToken();
String datay = ytokenizer.nextToken();
HashMap<String,Integer> x = new HashMap<String, Integer>();
HashMap<String,Integer> y = new HashMap<String, Integer>();
String [] xparts;
xparts = datax.toString().split("\\|");
String [] yparts;
yparts = datay.toString().split("\\|");
for(int i = 0; i<xparts.length-1; i++)
{
int part_size = Integer.parseInt(xparts[i+1]);
x.put(xparts[i], part_size);
i++;
}
for(int i = 0; i<yparts.length-1; i++)
{
int part_size = Integer.parseInt(yparts[i+1]);
y.put(xparts[i], part_size);
i++;
}
Set<String> xset = x.keySet();
Set<String> yset = y.keySet();
for(String elm:xset )
{
yval = 0;
xval = (Integer)x.get(elm);
part_union = 0;
int part_intersect = 0;
if(yset.contains(elm)){
yval = (Integer) y.get(elm);
if(xval>yval)
{
part_union = xval;
part_intersect = yval;
}
else
{
part_union = yval;
part_intersect = xval;
}
total_intersect = total_intersect+part_intersect;
}
else
{
part_union = xval;
}
total_union = total_union+part_union;
}
for(String elm: yset)
{
part_union = 0;
if(!xset.contains(elm))
{
part_union = (Integer) y.get(elm);
total_union = total_union+part_union;
}
}
float jaccard = (float)total_intersect/total_union;
return jaccard;
}
}
答案 0 :(得分：10)
超时的原因可能是您的reducer中长时间运行的计算，而不会将进度报告回Hadoop框架。这可以使用不同的方法解决：
予。增加mapred-site.xml中的超时时间：
<property>
<name>mapred.task.timeout</name>
<value>1200000</value>
</property>
默认值为600000 ms = 600 seconds。
II。报告每个x记录的进度，如Reducer example in javadoc：
public void reduce(K key, Iterator<V> values,
OutputCollector<K, V> output,
Reporter reporter) throws IOException {
// report progress
if ((noValues%10) == 0) {
reporter.progress();
}
// ...
}
您可以选择按example：
中的方式增加自定义计数器reporter.incrCounter(NUM_RECORDS, 1);
答案 1 :(得分：2)
您可能已经消耗了所有Java的堆空间，或者GC过于频繁地发生，没有机会让reducer向master报告状态，因此被杀死。
另一种可能性是减速器中的一个变得太偏斜了数据，即对于特定的摆脱，有很多记录。
尝试通过设置以下配置来增加Java堆：
mapred.child.java.opts
到
-Xmx2048m
此外，尝试通过将以下配置设置为比当前配置更低的值（默认值为2）来减少并行Reducer的数量：
mapred.tasktracker.reduce.tasks.maximum