Hadoop how does it work

In fact, how to secure and govern data lakes is a huge topic for IT. They may rely on data federation techniques to create a logical data structures. We're now seeing Hadoop beginning to sit beside data warehouse environments, as well as certain data sets being offloaded from the data warehouse into Hadoop or new types of data going directly to Hadoop.

The end goal for every organization is to have a right platform for storing and processing data of different schema, formats, etc. Things in the IoT need to know what to communicate and when to act. At the core of the IoT is a streaming, always on torrent of data. Hadoop is often used as the data store for millions or billions of transactions. Massive storage and processing capabilities also allow you to use Hadoop as a sandbox for discovery and definition of patterns to be monitored for prescriptive instruction.

One of the most popular analytical uses by some of Hadoop's largest adopters is for web-based recommendation systems. Facebook — people you may know. LinkedIn — jobs you may be interested in. Netflix, eBay, Hulu — items you may want. These systems analyze huge amounts of data in real time to quickly predict preferences before customers leave the web page. SAS provides a number of techniques and algorithms for creating a recommendation system, ranging from basic distance measures to matrix factorization and collaborative filtering — all of which can be done within Hadoop.

Read how to create recommendation systems in Hadoop and more. MapReduce — a parallel processing software framework. It is comprised of two steps. Map step is a master node that takes inputs and partitions them into smaller subproblems and then distributes them to worker nodes. After the map step has taken place, the master node takes the answers to all of the subproblems and combines them to produce output.

Other software components that can run on top of or alongside Hadoop and have achieved top-level Apache project status include:. Open-source software is created and maintained by a network of developers from around the world. It's free to download, use and contribute to, though more and more commercial versions of Hadoop are becoming available these are often called "distros.

SAS support for big data implementations, including Hadoop, centers on a singular goal — helping you know more, faster, so you can make better decisions. Regardless of how you use the technology, every project should go through an iterative and continuous improvement cycle.

And that includes data preparation and management, data visualization and exploration, analytical model development, model deployment and monitoring. So you can derive insights and quickly turn your big Hadoop data into bigger opportunities. Because SAS is focused on analytics, not storage, we offer a flexible approach to choosing hardware and database vendors.

We can help you deploy the right mix of technologies, including Hadoop and other data warehouse technologies. And remember, the success of any project is determined by the value it brings. So metrics built around revenue generation, margins, risk reduction and process improvements will help pilot projects gain wider acceptance and garner more interest from other departments. We've found that many organizations are looking at how they can implement a project or two in Hadoop, with plans to add more in the future.

More on SAS and Hadoop. History Today's world How it's used How it works. Best Practices. Hadoop What it is and why it matters. Hadoop History As the World Wide Web grew in the late s and early s, search engines and indexes were created to help locate relevant information amid the text-based content. Why is Hadoop important? Ability to store and process huge amounts of any kind of data, quickly. With data volumes and varieties constantly increasing, especially from social media and the Internet of Things IoT , that's a key consideration.

Computing power. Hadoop's distributed computing model processes big data fast. The more computing nodes you use, the more processing power you have. Fault tolerance. Data and application processing are protected against hardware failure. If a node goes down, jobs are automatically redirected to other nodes to make sure the distributed computing does not fail. Multiple copies of all data are stored automatically.

You can store as much data as you want and decide how to use it later. That includes unstructured data like text, images and videos. Low cost. Hardware failure is the norm rather than the exception.

The fact that there are a huge number of components and that each component has a non-trivial probability of failure means that some component of HDFS is always non-functional. Therefore, detection of faults and quick, automatic recovery from them is a core architectural goal of HDFS. Applications that run on HDFS need streaming access to their data sets. They are not general purpose applications that typically run on general purpose file systems.

HDFS is designed more for batch processing rather than interactive use by users. The emphasis is on high throughput of data access rather than low latency of data access. POSIX semantics in a few key areas has been traded to increase data throughput rates.

Applications that run on HDFS have large data sets. A typical file in HDFS is gigabytes to terabytes in size. Thus, HDFS is tuned to support large files. It should provide high aggregate data bandwidth and scale to hundreds of nodes in a single cluster.

It should support tens of millions of files in a single instance. HDFS applications need a write-once-read-many access model for files. A file once created, written, and closed need not be changed. This assumption simplifies data coherency issues and enables high throughput data access. A MapReduce application or a web crawler application fits perfectly with this model. There is a plan to support appending-writes to files in the future. A computation requested by an application is much more efficient if it is executed near the data it operates on.

This is especially true when the size of the data set is huge. This minimizes network congestion and increases the overall throughput of the system. The assumption is that it is often better to migrate the computation closer to where the data is located rather than moving the data to where the application is running. HDFS provides interfaces for applications to move themselves closer to where the data is located.

HDFS has been designed to be easily portable from one platform to another. This facilitates widespread adoption of HDFS as a platform of choice for a large set of applications. An HDFS cluster consists of a single NameNode, a master server that manages the file system namespace and regulates access to files by clients.

In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on.

HDFS exposes a file system namespace and allows user data to be stored in files. Internally, a file is split into one or more blocks and these blocks are stored in a set of DataNodes. The NameNode executes file system namespace operations like opening, closing, and renaming files and directories.

It also determines the mapping of blocks to DataNodes. The DataNodes also perform block creation, deletion, and replication upon instruction from the NameNode. The NameNode and DataNode are pieces of software designed to run on commodity machines. Usage of the highly portable Java language means that HDFS can be deployed on a wide range of machines. A typical deployment has a dedicated machine that runs only the NameNode software. Each of the other machines in the cluster runs one instance of the DataNode software.

The architecture does not preclude running multiple DataNodes on the same machine but in a real deployment that is rarely the case. The existence of a single NameNode in a cluster greatly simplifies the architecture of the system. The system is designed in such a way that user data never flows through the NameNode. HDFS supports a traditional hierarchical file organization. A user or an application can create directories and store files inside these directories.

The file system namespace hierarchy is similar to most other existing file systems; one can create and remove files, move a file from one directory to another, or rename a file. HDFS does not yet implement user quotas. HDFS does not support hard links or soft links. However, the HDFS architecture does not preclude implementing these features. The NameNode maintains the file system namespace. Any change to the file system namespace or its properties is recorded by the NameNode.

An application can specify the number of replicas of a file that should be maintained by HDFS. The number of copies of a file is called the replication factor of that file.

This information is stored by the NameNode. HDFS is designed to reliably store very large files across machines in a large cluster.

It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later.

Files in HDFS are write-once and have strictly one writer at any time. The NameNode makes all decisions regarding replication of blocks. It periodically receives a Heartbeat and a Blockreport from each of the DataNodes in the cluster.

Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. The placement of replicas is critical to HDFS reliability and performance. Optimizing replica placement distinguishes HDFS from most other distributed file systems.

Blocks are also replicated across nodes to reduce the likelihood of failure. The NameNode is the «smart» node in the cluster. It knows exactly which data node contains which blocks and where the data nodes are located within the machine cluster.

The NameNode also manages access to the files, including reads, writes, creates, deletes and replication of data blocks across different data nodes. This means the elements of the cluster can dynamically adapt to the real-time demand of server capacity by adding or subtracting nodes as the system sees fit.

The data nodes constantly communicate with the NameNode to see if they need complete a certain task. Data nodes also communicate with each other so they can cooperate during normal file operations.

Clearly the NameNode is critical to the whole system and should be replicated to prevent system failure. Again, data blocks are replicated across multiple data nodes and access is managed by the NameNode. When this data node comes back to life or a different new data node is detected, that new data node is re- added to the system. That is what makes HDFS resilient and self-healing.

Since data blocks are replicated across several data nodes, the failure of one server will not corrupt a file. The degree of replication and the number of data nodes are adjusted when the cluster is implemented and they can be dynamically adjusted while the cluster is operating. HDFS uses transaction logs and validations to ensure integrity across the cluster. Usually there is one NameNode and possibly a data node running on a physical server in the rack, while all other servers run data nodes only.

Hadoop MapReduce is an implementation of the MapReduce algorithm developed and maintained by the Apache Hadoop project.

The general idea of the MapReduce algorithm is to break down the data into smaller manageable pieces, process the data in parallel on your distributed cluster, and subsequently combine it into the desired result or output. Hadoop MapReduce includes several stages, each with an important set of operations designed to handle big data. The first step is for the program to locate and read the « input file » containing the raw data.

Since the file format is arbitrary, the data must be converted to something the program can process. InputFormat decides how to split the file into smaller pieces using a function called InputSplit. Then the RecordReader transforms the raw data for processing by the map. The result is a sequence of « key » and « value » pairs.

Once the data is in a form acceptable to map, each key-value pair of data is processed by the mapping function.