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The Hadoop Approach

Hadoop is designed to efficiently process large volumes of information by connecting many commodity computers together to work in parallel. The theoretical 1000-CPU machine described earlier would cost a very large amount of money, far more than 1,000 single-CPU or 250 quad-core machines. Hadoop will tie these smaller and more reasonably priced machines together into a single cost-effective compute cluster.

COMPARISON TO EXISTING TECHNIQUES

Performing computation on large volumes of data has been done before, usually in a distributed setting. What makes Hadoop unique is its simplified programming model which allows the user to quickly write and test distributed systems, and its efficient, automatic distribution of data and work across machines and in turn utilizing the underlying parallelism of the CPU cores.

Grid scheduling of computers can be done with existing systems such as Condor. But Condor does not automatically distribute data: a separate SAN must be managed in addition to the compute cluster. Furthermore, collaboration between multiple compute nodes must be managed with a communication system such as MPI. This programming model is challenging to work with and can lead to the introduction of subtle errors.

DATA DISTRIBUTION

In a Hadoop cluster, data is distributed to all the nodes of the cluster as it is being loaded in. The Hadoop Distributed File System (HDFS) will split large data files into chunks which are managed by different nodes in the cluster. In addition to this each chunk is replicated across several machines, so that a single machine failure does not result in any data being unavailable. An active monitoring system then re-replicates the data in response to system failures which can result in partial storage. Even though the file chunks are replicated and distributed across several machines, they form a single namespace, so their contents are universally accessible.

Data is conceptually record-oriented in the Hadoop programming framework. Individual input files are broken into lines or into other formats specific to the application logic. Each process running on a node in the cluster then processes a subset of these records. The Hadoop framework then schedules these processes in proximity to the location of data/records using knowledge from the distributed file system. Since files are spread across the distributed file system as chunks, each compute process running on a node operates on a subset of the data. Which data operated on by a node is chosen based on its locality to the node: most data is read from the local disk straight into the CPU, alleviating strain on network bandwidth and preventing unnecessary network transfers. This strategy of moving computation to the data, instead of moving the data to the computation allows Hadoop to achieve high data locality which in turn results in high performance.

load-into-dfs

Figure 1.1: Data is distributed across nodes at load time.

MAPREDUCE: ISOLATED PROCESSES

Hadoop limits the amount of communication which can be performed by the processes, as each individual record is processed by a task in isolation from one another. While this sounds like a major limitation at first, it makes the whole framework much more reliable. Hadoop will not run just any program and distribute it across a cluster. Programs must be written to conform to a particular programming model, named “MapReduce.”

In MapReduce, records are processed in isolation by tasks called Mappers. The output from the Mappers is then brought together into a second set of tasks called Reducers, where results from different mappers can be merged together.

mapreduce-process

Figure 1.2: Mapping and reducing tasks run on nodes where individual records of data are already present.

Separate nodes in a Hadoop cluster still communicate with one another. However, in contrast to more conventional distributed systems where application developers explicitly marshal byte streams from node to node over sockets or through MPI buffers, communication in Hadoop is performed implicitly. Pieces of data can be tagged with key names which inform Hadoop how to send related bits of information to a common destination node. Hadoop internally manages all of the data transfer and cluster topology issues.

By restricting the communication between nodes, Hadoop makes the distributed system much more reliable. Individual node failures can be worked around by restarting tasks on other machines. Since user-level tasks do not communicate explicitly with one another, no messages need to be exchanged by user programs, nor do nodes need to roll back to pre-arranged checkpoints to partially restart the computation. The other workers continue to operate as though nothing went wrong, leaving the challenging aspects of partially restarting the program to the underlying Hadoop layer.

FLAT SCALABILITY

One of the major benefits of using Hadoop in contrast to other distributed systems is its flat scalability curve. Executing Hadoop on a limited amount of data on a small number of nodes may not demonstrate particularly stellar performance as the overhead involved in starting Hadoop programs is relatively high. Other parallel/distributed programming paradigms such as MPI (Message Passing Interface) may perform much better on two, four, or perhaps a dozen machines. Though the effort of coordinating work among a small number of machines may be better-performed by such systems, the price paid in performance and engineering effort (when adding more hardware as a result of increasing data volumes) increases non-linearly.

A program written in distributed frameworks other than Hadoop may require large amounts of refactoring when scaling from ten to one hundred or one thousand machines. This may involve having the program be rewritten several times; fundamental elements of its design may also put an upper bound on the scale to which the application can grow.

Hadoop, however, is specifically designed to have a very flat scalability curve. After a Hadoop program is written and functioning on ten nodes, very little–if any–work is required for that same program to run on a much larger amount of hardware. Orders of magnitude of growth can be managed with little re-work required for your applications. The underlying Hadoop platform will manage the data and hardware resources and provide dependable performance growth proportionate to the number of machines available.

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