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:java8...@hotmail.com] Gesendet: Montag, 29. September 2014 15:43 An: user@hadoop.apache.org 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: blanca.hernan...@willhaben.at<mailto:blanca.hernan...@willhaben.at> To: user@hadoop.apache.org<mailto:user@hadoop.apache.org> 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
