They are separated out because both of these packages represent 2 different APIs. org.apache.hadoop.mapred is the older API and org.apache.hadoop.mapreduce is the new one. And it was done to allow programmers write MapReduce jobs in a more convenient, easier and sophisticated fashion. You might find this presentation useful, which talks about the differences in detail. … Read more
Functionality wise there is not much difference between the old (o.a.h.mapred) and the new (o.a.h.mapreduce) API. The only significant difference is that records are pushed to the mapper/reducer in the old API. While the new API supports both pull/push mechanism. You can get more information about the pull mechanism here. Also, the old API has … Read more
It represents a resource (memory) on a single node at a given cluster. A container is supervised by the node manager scheduled by the resource manager One MR task runs in such container(s).
Have you check out Akka? While akka is really a distributed Actor model based concurrency framework, you can implement a lot of things simply with little code. It’s just so easy to divide work into pieces with it, and it automatically takes full advantage of a multi-core machine, as well as being able to use … Read more
From the Spark Scala Programming guide’s section on “Hadoop Datasets”: Spark can create distributed datasets from any file stored in the Hadoop distributed file system (HDFS) or other storage systems supported by Hadoop (including your local file system, Amazon S3, Hypertable, HBase, etc). Spark supports text files, SequenceFiles, and any other Hadoop InputFormat. Support for … Read more
The query you want is this: db.collection.find({“users”:{“$not”:{“$elemMatch”:{“user”:{$nin:[1,5,7]}}}}}) This says find me all documents that don’t have elements that are outside of the list 1,5,7.
One key difference is that F-J seems to be designed to work on a single Java VM, while M-R is explicitly designed to work on a large cluster of machines. These are very different scenarios. F-J offers facilities to partition a task into several subtasks, in a recursive-looking fashion; more tiers, possibility of ‘inter-fork’ communication … Read more
Map and ReduceByKey Input type and output type of reduce must be the same, therefore if you want to aggregate a list, you have to map the input to lists. Afterwards you combine the lists into one list. Combining lists You’ll need a method to combine lists into one list. Python provides some methods to … Read more
Actually stdout only shows the System.out.println() of the non-map reduce classes. The System.out.println() for map and reduce phases can be seen in the logs. Easy way to access the logs is http://localhost:50030/jobtracker.jsp->click on the completed job->click on map or reduce task->click on tasknumber->task logs->stdout logs. Hope this helps
Every test I have personally run (including using your own data) shows aggregation framework being a multiple faster than map reduce, and usually being an order of magnitude faster. Just taking 1/10th of the data you posted (but rather than clearing OS cache, warming the cache first – because I want to measure performance of … Read more