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Taming Big Data [A Big Data Infographic]

Taming Big Data [A Big Data Infographic]

Big Data can be a beast. Data volumes are growing exponentially.The types of data being created are likewise proliferating. And the speed at which data is being created – and the need to analyze it in near real-time to derive value from it – is increasing with each passing hour.

But Big Data can be tamed. We’ve got living proof. Thanks to new approaches for processing, storing and analyzing massive volumes of multi-structured data – such as Hadoop and MPP analytic databases — enterprises of all types are uncovering new and valuable insights from Big Data everyday.

Leading the way are Web giants like Facebook, LinkedIn and Amazon. Following close behind are early adopters in financial services, healthcare and media. And now it’s your turn. From marketing campaign analysis and social graph analysis to network monitoring, fraud detection and risk modeling, there’s unquestionably a Big Data use case out there with your company’s name on it.

We here at Wikibon are excited to present this compelling Big Data infographic, which we hope will help you better understand how your peers are applying Big Data today and inspire you tame the Big Data beast yourself. Check out videos, market forecasts and deep research at on our curated page about Big Data.


Blogged from: http://wikibon.org/blog/taming-big-data/

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HDFS Architecture

HDFS Architecture

HDFS is a block-structured file system: individual files are broken into blocks of a fixed size. These blocks are stored across a cluster of one or more machines with data storage capacity. Individual machines in the cluster are referred to as DataNodes. A file can be made of several blocks, and they are not necessarily stored on the same machine; the target machines which hold each block are chosen randomly on a block-by-block basis. Thus access to a file may require the cooperation of multiple machines, but supports file sizes far larger than a single-machine DFS; individual files can require more space than a single hard drive could hold.

If several machines must be involved in the serving of a file, then a file could be rendered unavailable by the loss of any one of those machines. HDFS combats this problem by replicating each block across a number of machines (3, by default).

Most block-structured file systems use a block size on the order of 4 or 8 KB. By contrast, the default block size in HDFS is 64MB — orders of magnitude larger. This allows HDFS to decrease the amount of metadata storage required per file (the list of blocks per file will be smaller as the size of individual blocks increases).

Furthermore, it allows for fast streaming reads of data, by keeping large amounts of data sequentially laid out on the disk. The consequence of this decision is that HDFS expects to have very large files, and expects them to be read sequentially. Unlike a file system such as NTFS or EXT, which see many very small files, HDFS expects to store a modest number of very large files: hundreds of megabytes, or gigabytes each. After all, a 100 MB file is not even two full blocks. Files on your computer may also frequently be accessed “randomly,” with applications cherry-picking small amounts of information from several different locations in a file which are not sequentially laid out. By contrast, HDFS expects to read a block start-to-finish for a program.

This makes it particularly useful to the MapReduce style of programming described in Module 4. That having been said, attempting to use HDFS as a general-purpose distributed file system for a diverse set of applications will be suboptimal.

Because HDFS stores files as a set of large blocks across several machines, these files are not part of the ordinary file system. Typing ls on a machine running a DataNode daemon will display the contents of the ordinary Linux file system being used to host the Hadoop services — but it will not include any of the files stored inside the HDFS. This is because HDFS runs in a separate namespace, isolated from the contents of your local files. The files inside HDFS (or more accurately: the blocks that make them up) are stored in a particular directory managed by the DataNode service, but the files will named only with block ids. You cannot interact with HDFS-stored files using ordinary Linux file modification tools (e.g., ls, cp, mv, etc). However, HDFS does come with its own utilities for file management, which act very similar to these familiar tools. A later section in this tutorial will introduce you to these commands and their operation.

It is important for this file system to store its metadata reliably. Furthermore, while the file data is accessed in a write once and read many model, the metadata structures (e.g., the names of files and directories) can be modified by a large number of clients concurrently. It is important that this information is never desynchronized. Therefore, it is all handled by a single machine, called the NameNode. The NameNode stores all the metadata for the file system. Because of the relatively low amount of metadata per file (it only tracks file names, permissions, and the locations of each block of each file), all of this information can be stored in the main memory of the NameNode machine, allowing fast access to the metadata.

To open a file, a client contacts the NameNode and retrieves a list of locations for the blocks that comprise the file. These locations identify the DataNodes which hold each block. Clients then read file data directly from the DataNode servers, possibly in parallel. The NameNode is not directly involved in this bulk data transfer, keeping its overhead to a minimum.

Of course, NameNode information must be preserved even if the NameNode machine fails; there are multiple redundant systems that allow the NameNode to preserve the file system’s metadata even if the NameNode itself crashes irrecoverably. NameNode failure is more severe for the cluster than DataNode failure. While individual DataNodes may crash and the entire cluster will continue to operate, the loss of the NameNode will render the cluster inaccessible until it is manually restored. Fortunately, as the NameNode’s involvement is relatively minimal, the odds of it failing are considerably lower than the odds of an arbitrary DataNode failing at any given point in time.

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HDFS Introduction

HDFS Introduction

HDFS, the Hadoop Distributed File System, is a distributed file system designed to hold very large amounts of data (terabytes or even petabytes), and provide high-throughput access to this information. Files are stored in a redundant fashion across multiple machines to ensure their durability to failure and high availability to very parallel applications. This module introduces the design of this distributed file system and instructions on how to operate it.
A distributed file system is designed to hold a large amount of data and provide access to this data to many clients distributed across a network. There are a number of distributed file systems that solve this problem in different ways.
NFS, the Network File System, is the most ubiquitous distributed file system. It is one of the oldest still in use. While its design is straightforward, it is also very constrained. NFS provides remote access to a single logical volume stored on a single machine. An NFS server makes a portion of its local file system visible to external clients. The clients can then mount this remote file system directly into their own Linux file system, and interact with it as though it were part of the local drive.
One of the primary advantages of this model is its transparency. Clients do not need to be particularly aware that they are working on files stored remotely. The existing standard library methods like open(), close(), fread(), etc. will work on files hosted over NFS.

But as a distributed file system, it is limited in its power. The files in an NFS volume all reside on a single machine. This means that it will only store as much information as can be stored in one machine, and does not provide any reliability guarantees if that machine goes down (e.g., by replicating the files to other servers). Finally, as all the data is stored on a single machine, all the clients must go to this machine to retrieve their data. This can overload the server if a large number of clients must be handled. Clients must also always copy the data to their local machines before they can operate on it.
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HDFS is designed to be robust to a number of the problems that other DFS’s such as NFS are vulnerable to. In particular:

* HDFS is designed to store a very large amount of information (terabytes or petabytes). This requires spreading the data across a large number of machines. It also supports much larger file sizes than NFS.

* HDFS should store data reliably. If individual machines in the cluster malfunction, data should still be available.

* HDFS should provide fast, scalable access to this information. It should be possible to serve a larger number of clients by simply adding more machines to the cluster.

* HDFS should integrate well with Hadoop MapReduce, allowing data to be read and computed upon locally when possible.
But while HDFS is very scalable, its high performance design also restricts it to a particular class of applications; it is not as general-purpose as NFS. There are a large number of additional decisions and trade-offs that were made with HDFS. In particular:
* Applications that use HDFS are assumed to perform long sequential streaming reads from files. HDFS is optimized to provide streaming read performance; this comes at the expense of random seek times to arbitrary positions in files.

* Data will be written to the HDFS once and then read several times; updates to files after they have already been closed are not supported. (An extension to Hadoop will provide support for appending new data to the ends of files; it is scheduled to be included in Hadoop 0.19 but is not available yet.)

* Due to the large size of files, and the sequential nature of reads, the system does not provide a mechanism for local caching of data. The overhead of caching is great enough that data should simply be re-read from HDFS source.

* Individual machines are assumed to fail on a frequent basis, both permanently and intermittently. The cluster must be able to withstand the complete failure of several machines, possibly many happening at the same time (e.g., if a rack fails all together). While performance may degrade proportional to the number of machines lost, the system as a whole should not become overly slow, nor should information be lost. Data replication strategies combat this problem.

The design of HDFS is based on the design of GFS, the Google File System. Its design was described in a paper published by Google.
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