Hadoop MapReduce 1.0 is the original execution engine for Hadoop that performs resource management and data processing. It has two major components: the JobTracker, which runs on the master node, and TaskTrackers, which run on slave nodes. The JobTracker receives jobs from clients, divides them into tasks, and assigns tasks to TaskTrackers. TaskTrackers perform computations on the data located on their nodes and report results back to the JobTracker. When a job is submitted, the JobTracker consults the NameNode to determine data locations, assigns map and reduce tasks to TaskTrackers, and monitors job completion before reporting results back to the client.
This page provides access to information about how to integrate Apache Hadoop with Lustre. We have made several enhancements to improve the use of Hadoop with Lustre and have conducted performance tests to compare the performance of Lustre vs. HDFS when used with Hadoop.
http://wiki.lustre.org/index.php/Integrating_Hadoop_with_Lustre
This presentation explains the architecture of classic mapreduce or MapReduce 1 in Hadoop, Most of the sides are animated. So please download and read it.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.
This page provides access to information about how to integrate Apache Hadoop with Lustre. We have made several enhancements to improve the use of Hadoop with Lustre and have conducted performance tests to compare the performance of Lustre vs. HDFS when used with Hadoop.
http://wiki.lustre.org/index.php/Integrating_Hadoop_with_Lustre
This presentation explains the architecture of classic mapreduce or MapReduce 1 in Hadoop, Most of the sides are animated. So please download and read it.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.
Hadoop interview questions for freshers and experienced people. This is the best place for all beginners and Experts who are eager to learn Hadoop Tutorial from the scratch.
Read more here http://softwarequery.com/hadoop/
Here is our most popular Hadoop Interview Questions and Answers from our Hadoop Developer Interview Guide. Hadoop Developer Interview Guide has over 100 REAL Hadoop Developer Interview Questions with detailed answers and illustrations asked in REAL interviews. The Hadoop Interview Questions listed in the guide are not "might be" asked interview question, they were asked in interviews at least once.
Hadoop interview questions for freshers and experienced people. This is the best place for all beginners and Experts who are eager to learn Hadoop Tutorial from the scratch.
Read more here http://softwarequery.com/hadoop/
Here is our most popular Hadoop Interview Questions and Answers from our Hadoop Developer Interview Guide. Hadoop Developer Interview Guide has over 100 REAL Hadoop Developer Interview Questions with detailed answers and illustrations asked in REAL interviews. The Hadoop Interview Questions listed in the guide are not "might be" asked interview question, they were asked in interviews at least once.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
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Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
2. So, MapReduce is an execution engine of Hadoop and we are going to briefly describe Hadoop 1.0, its
components, that is MapReduce which runs on HDFS. So, MapReduce is the programming paradigm, of
the Hadoop system, for big data computing and it also performs, in MapReduce version 1.0 the resource
management and the data processing, aspects. Also which runs over HDFS 1.0. So, we are going to cover
this Hadoop or a MapReduce 1.0 version, in a brief.
Refer slide time :( 01:04)
3. So, MapReduce has two different major components, one is called the,’ Job Tracker,’ the other one is
called the,’ Task Tracker’.
Refer slide time :( 01:15)
4. And the scenario of the job tracker is, you can see here, in this particular diagram. So, this is the job
tracker, which is a part of MapReduce version one. Now this particular job tracker runs, on the master or
this is the master node. So, this master node and this name node, is a part of HDFS, HDFS 1.0 version.
So, both of them may resides on the same machine or may not be in the same machine, but for the sake of
simplicity we assume that the name node and the job tracker resides, on the same node which is called a
‘Master’, over here in this particular scenario, since it is having a master and several slaves. So, hence this
is basically a client-server architecture, which is followed in MapReduce, version 1.0 and also in the
HDFS version 1.0. So, in this particular diagram, we can see here, the job tracker resides on a particular
node which is basically the, the master node. And on the same master node, let us assume that as DFS
name node is there. So, we are not going to refer in this part of the discussion why because, we are
focusing only on the MapReduce.
So, hence we are not going to discuss this name node part, of HDFS. Now another component, is called
the, ‘Task Tracker’, the task record may resides, on the same node also resides on other different slave
nodes. So, the job tracker and task tracker they, they run in the form of a client-server model. So, job
tracker is basically, running as a must is a server and the task records is run as its client. So, it's a client-
server model let us understand, more into the functionality, of job tracker. So, job tracker as, I have
already mentioned, is hosted inside the master and receives the job execution requests, from the client.
So, the so, the client or the application, which basically is nothing but a MapReduce, program when it is
submitted by the client, then the job tracker has to deal with that particular program execution. So, its
main duty is to break, down the receive, its main duties are to break down, the received job, that is a that
is the big data computation, specified in the form of MapReduce jobs. And this particular MapReduce, is
divided into the smaller chunks and that is the small parts and these small parts that is called, ‘Chunks’,
are allocated with the map function and map and reduced function and this particular partial,
contradictions are happening at this particular slave nodes with the help of the, task tracker. And this is
5. the, the entire unit of execution, of this particular job. So, let us see the more detail of the task tracker. So,
the task tracker is the MapReduce component, on the slave machine, as there are multiple slave machines,
as we have shown here five of them. So, many task records are available, in the cluster its duties to
perform the computations, which are assigned by the by that the job tracker, on the data which is
available on the slave machines. So, the task tracker will communicate, the progress and report the result
back to the job tracker, the master node contains the job tracker and the name node whereas all the slave
nodes contain the, the task tracker and data nodes. So, in this particular way, the job tracker keeps the
track, of the map and reduce jobs, which are being allocated at different nodes and which are executing
on, the data set which are assigned which are allocated, to these particular nodes, where the data is there
the, the map and reduced function or the configuration will be performed. So, it is a computation engine.
So, MapReduce is a computation engine, in version 1.0. so, not only it allocates, the MapReduce jobs to
different slave nodes, where the data also resides, in the form of a chunks and it will then connect and so,
basically not only it assigns but also it tracks, keep track ,of the progress and the resources, which is being
allocated. Okay? Okay? Discovery hmm physically in order the execution steps.
Refer slide time :( 08:04)
6. So, we are going to now trace all the execution steps, for the life cycle of a MapReduce job, till the
application is submitted, by the client and to the to the MapReduce and it finishes and we are going to
trace the, the execution cycle or the execution steps in the MapReduce, version 1.0. so, the first step is,
the client submits the job, to the job tracker for example here, we have to find out we have to 20,000
records, of the customer and we want to find out all the customers from Mumbai. So, that means the
query is basically to be executed, on these data set and this particular operation to find all the customer,
this is the query, which is to be performed, using the MapReduce program which is being submitted. So,
this particular request is being submitted to the job tracker and job tracker will ask the name note, about
the location of this particular data set. So, the job tracker will consult the name node, which is a part of
HDFS, to find out the location, of the data where it is being installed. So, now I say that this 20,000
records are divided like this, there are five different nodes, which stores all of them this is node number
one, two, three, four, and five. So, the records first four thousand records stored are installed on this
particular node number one and the next 4,000 is stored on the node number two and then next four
thousand node number three and furthermore node number 4 and 5 respectively stores the remaining
twenty thousand records.
Now this is called the chunks or the splits. The entire, two thousand twenty thousand record is splitted
and stored on four different, five different nodes and this information will be given from, this name node
back to the job tracker now the job tracker as per the reply by the name node the job tracker ask the
respective task tracker to execute the tasks on their data. So, therefore, this particular job tracker, now
assigns the, the, the map function or map and reduce function map function, on the stash tracker to
execute, on that data chunk. So, with this particular direction the task tracker will perform this execution,
at all places there in parallel. So, this particular execution of MapReduce program is done in parallel, on
all the chunks. So, after the computation, of a MapReduce so, all the results are stored on the same data
node, so whatever is the result it will be stored on the same data node and the name node is informed,
about these particular results. So, the task tracker informs the job tracker, about the completion and the
progress of this jobs assigned to those job tracker now the job tracker informs this particular completion,
to that particular client and the client contacts the name node and retrieve the result back. So, after
completing it this particular job tracker will inform, to the client about the completion of this particular
job, that means now the result of the query is now ready, which the client will be able to access, with the
help of name node and gets back the result. So, this is the entire execution engine, of which is, there in the
form of, a MapReduce version 1.0, that we have briefly explained. Thank you.