Here are my suggestions originally aims to improve the efficient:
1) In your case, you could use "StringBuilder", which has the append method,
should be more efficient to concatenate your string data in this case.2) What I
mean to reuse the Text object is as following: public class mapper extends
Mapper<> () { private Text data = new Text(); @Override
public void map(final Object key, final BasicDBObject val, final Context
context) throws IOException, InterruptedException { //
instead of do "new Text(id)" // you can always use the
following way data.set(id);
context.write(data, bsonWritable); }As you can see, you avoid to
create lots, lots of Text object in the map method. This method could be
invoked a lot of times. In this way, you avoid asking GC to clean a lot of Text
object, by reusing the same Text object per map. I believe you can do the same
for BSONWritable. Check the javadoc for that class.3) 9G is a lot of heap for a
map task. How many map tasks your job generates? Are your source splitable? For
one block data (I assume it is 128M or 256M), I cannot image you need 9G heap
for mapper. Your OOM maybe caused by that your job runs out of physical memory
of all the concurrent running mapper tasks.
1) How many total mapper tasks being generated in your job?2) How many
data/task nodes you have in your cluster? On the OOM node, how many mapper
tasks being kicked off? You can find all these information in the JobTracker in
MR1, or AM in MR2.3) If each mapper assigned 9G memory, and there are multi
mappers running in the OOM node, how much real physical memory you have? 4) You
can see the input source for each mapper task in JobTracker or AM. If failed
mapper is always for the same block, then research that source data file. You
need to have real good reason to allocate 9G heap for a mapper task. Did you
originally start from 1G?
Yong
From: [email protected]
To: [email protected]
Subject: AW: Extremely amount of memory and DB connections by MR Job
Date: Mon, 29 Sep 2014 14:16:24 +0000
Thanks for your answer.
To your questions:
1.
When you claim 96G ram, I am not sure what do you mean?
It is not 96 Gb RAM, it is 9 Gb that our test server has available (is it too
small?).
2.
Your code is not efficient, as using the "+=" on String
I need (or at least I don´t have a better idea) the concatenation of strings
for the emited ID, since I want
to group my objects by, e.g. Audi_A3_2010, another group Audi_A3_2011…. And so
on. These values are fields in the objects I get from the DB (BasicDBObject is
a MongoDB class).
3.
could have reused the Text object in your mapper
I am not sure if I understand your point. I create a new BSONWritable
bsonWritable = new BSONWritable(val); out of my
data base object, since the one given by MongoDB is not mutable, hence not
accepted by haddop api as an outpu.
Now your other questions:
1) Are there any mappers successful?
Yes, but after a while, the job seems to need more memory, it runs very slow
until it crashes.
2) The OOM mapper, is it always on the same block? If so, you need to dig into
the source data for that block, to think why it will cause OOM.
I am not sure about this. Is there a hint in the logs to figure it out?
3) Did you give reasonable heap size for the mapper? What it is?
9 Gb (too small??)
Best regards,
Blanca
Von: java8964 [mailto:[email protected]]
Gesendet: Montag, 29. September 2014 15:43
An: [email protected]
Betreff: RE: Extremely amount of memory and DB connections by MR Job
I don't have any experience with MongoDB, but just gave my 2 cents here.
Your code is not efficient, as using the "+=" on String, and you could have
reused the Text object in your mapper, as it is a mutable class, to be reused
and avoid creating it again and again
like "new Text()" in the mapper. My guess that BSONWritable should be a
similar mutable class, if it aims to be used like the rest Writable Hadoop
class.
But even like that, it should just make your mapper run slower, as a lot of
objects need to be GC, instead of OOM.
When you claim 96G ram, I am not sure what do you mean? From what you said, it
failed in mapper stage, so let's focus on mapper. What max heap size you gave
to the mapper task? I don't think
96G is the setting you mean to give to each mapper task. Otherwise, the only
place I can think is that there are millions of Strings to be appended in one
record by "+=" and cause the OOM.
You need to answer the following questions by yourself:
1) Are there any mappers successful?
2) The OOM mapper, is it always on the same block? If so, you need to dig into
the source data for that block, to think why it will cause OOM.
3) Did you give reasonable heap size for the mapper? What it is?
Yong
From:
[email protected]
To: [email protected]
Subject: Extremely amount of memory and DB connections by MR Job
Date: Mon, 29 Sep 2014 12:57:41 +0000
Hi,
I am using a hadoop map reduce job + mongoDb.
It goes against a data base 252Gb big. During the job the amount of conexions
is over 8000 and we gave already 9Gb RAM. The job is still crashing because of
a OutOfMemory with
only a 8% of the mapping done.
Are this numbers normal? Or did we miss something regarding configuration?
I attach my code, just in case the problem is with it.
Mapper:
public class AveragePriceMapper extends Mapper<Object, BasicDBObject, Text,
BSONWritable> {
@Override
public void map(final Object key, final BasicDBObject val, final Context
context) throws IOException, InterruptedException {
String id = "";
for(String propertyId : currentId.split(AveragePriceGlobal.SEPARATOR)){
id += val.get(propertyId) + AveragePriceGlobal.SEPARATOR;
}
BSONWritable bsonWritable = new BSONWritable(val);
context.write(new Text(id), bsonWritable);
}
}
Reducer:
public class AveragePriceReducer extends Reducer<Text, BSONWritable, Text,
Text> {
public void reduce(final Text pKey, final Iterable<BSONWritable> pValues,
final Context pContext) throws IOException, InterruptedException {
while(pValues.iterator().hasNext() && continueLoop){
BSONWritable next = pValues.iterator().next();
//Make some calculations
} pContext.write(new Text(currentId), new Text(new
MyClass(currentId, AveragePriceGlobal.COMMENT, 0, 0).toString()));
}
}
The configuration includes a query which filters the number of objects to
analyze (not the 252Gb will be analyzed).
Many thanks. Best regards,
Blanca
