I have a set of servers with a total of 56 CPU cores available for this - these are mostly dual core and quad core, but also a large DL with 16 cores. The system needs to be designed for online queries, I'm ideally looking to implement a web-service which returns JSON output on demand from a front-end.
Overview[ edit ] MapReduce is a framework for processing parallelizable problems across large datasets using a large number of computers nodescollectively referred to as a cluster if all nodes are on the same local network and use similar hardware or a grid if the nodes are shared across geographically and administratively distributed systems, and use more heterogenous hardware.
Processing can occur on data stored either in a filesystem unstructured or in a database structured. MapReduce can take advantage of the locality of data, processing it near the place it is stored in order to minimize communication overhead.
A MapReduce framework or system is usually composed of three operations or steps: A master node ensures that only one copy of redundant input data is processed. MapReduce allows for distributed processing of the map and reduction operations. Similarly, a set of 'reducers' can perform the reduction phase, provided that all outputs of the map operation that share the same key are presented to the same reducer at the same time, or that the reduction function is associative.
Another way to look at MapReduce is as a 5-step parallel and distributed computation: Prepare the Map input — the "MapReduce system" designates Map processors, assigns the input key value K1 that each processor would work on, and provides that processor with all the input data associated with that key value.
Run the user-provided Map code — Map is run exactly once for each K1 key value, generating output organized by key values K2. Run the user-provided Reduce code — Reduce is run exactly once for each K2 key value produced by the Map step.
Produce the final output — the MapReduce system collects all the Reduce output, and sorts it by K2 to produce the final outcome. These five steps can be logically thought of as running in sequence — each step starts only after the previous step is completed — although in practice they can be interleaved as long as the final result is not affected.
In many situations, the input data might already be distributed "sharded" among many different servers, in which case step 1 could sometimes be greatly simplified by assigning Map servers that would process the locally present input data.
Similarly, step 3 could sometimes be sped up by assigning Reduce processors that are as close as possible to the Map-generated data they need to process. Logical view[ edit ] The Map and Reduce functions of MapReduce are both defined with respect to data structured in key, value pairs.
Map takes one pair of data with a type in one data domainand returns a list of pairs in a different domain: This produces a list of pairs keyed by k2 for each call. After that, the MapReduce framework collects all pairs with the same key k2 from all lists and groups them together, creating one group for each key.
The Reduce function is then applied in parallel to each group, which in turn produces a collection of values in the same domain: The returns of all calls are collected as the desired result list.
Thus the MapReduce framework transforms a list of key, value pairs into a list of values. This behavior is different from the typical functional programming map and reduce combination, which accepts a list of arbitrary values and returns one single value that combines all the values returned by map.
It is necessary but not sufficient to have implementations of the map and reduce abstractions in order to implement MapReduce.
Distributed implementations of MapReduce require a means of connecting the processes performing the Map and Reduce phases. This may be a distributed file system. Other options are possible, such as direct streaming from mappers to reducers, or for the mapping processors to serve up their results to reducers that query them.MapReduce: Distributed Computing for Machine Learning Dan Gillick, Arlo Faria, John DeNero December 18, Abstract We use Hadoop, an open-source implementation of Google’s distributed ﬁle system and the.
MapReduce and the Hadoop framework for implementing distributed computing provide an approach for working with extremely large datasets distributed across a .
5 Hadoop & MapReduce • Hadoop: A software framework that supports distributed computing using MapReduce – Distributed, redundant f ile system (HDFS) – Job distribution, balancing, recovery, scheduler, etc.
Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies – all centered around distributed systems.
Learn about such fundamental distributed computing "concepts" for cloud computing. Some of. MapReduce: Distributed Computing for Machine Learning Dan Gillick, Arlo Faria, John DeNero December 18, Abstract We use Hadoop, an open-source implementation of Google’s distributed ﬁle system and the.
CouchDB looks nice as a document store and knows about key: value style documents and versioning and MapReduce, but I can't find anything about how it can be used as a distributed MapReduce system.