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If you are search Best Engineering college in India, Then you can trust RCE (Roorkee College of Engineering) services and facilities. They provide the best education facility, highly educated and experienced faculty, well furnished hostels for both boys and girls, top computerized Library, great placement opportunity and more at affordable fee.
Hoodie (Hadoop Upsert Delete and Incremental) is an analytical, scan-optimized data storage abstraction which enables applying mutations to data in HDFS on the order of few minutes and chaining of incremental processing in hadoop
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
Big Data projects are a struggle, not only on the technical side but also on the organizational side. In this talk the author shares his experience and opinions from almost 5 years of Big Data projects and develops an Agile Big Data Model which reflects his ideas on how Big Data projects can be successful, even in large companies.
Talk held at the crossover meetup of the "Agile Stammtisch Rhein-Main" and the "Hadoop & Spark User Group Rhein-Main" at codecentric AG on 31.01.2017.
Talk held at a combined meeting of the Web Performance Karlsruhe (http://www.meetup.com/Karlsruhe-Web-Performance-Group/events/153207062) & Big Data Karlsruhe/Stuttgart (http://www.meetup.com/Big-Data-User-Group-Karlsruhe-Stuttgart/events/162836152) user groups.
Agenda:
- Why Hadoop 2?
- HDFS 2
- YARN
- YARN Apps
- Write your own YARN App
- Tez, Hive & Stinger Initiative
Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q2tcloudcomputing-tw
The presentation is designed for those interested in Hadoop technology, and can enhance your knowledge in Hadoop, such as community history, current development status, features of services, distributed computing framework and scenario of big data development in Enterprise.
From Hadoop, through HIVE, Spark, YARN, searching for the holy grail for low latency SQL dialect like big data query. I talk about OLTP and OLAP, row stores and column stores, and finally arrive to BigQuery. Demo on the web console.
If you are search Best Engineering college in India, Then you can trust RCE (Roorkee College of Engineering) services and facilities. They provide the best education facility, highly educated and experienced faculty, well furnished hostels for both boys and girls, top computerized Library, great placement opportunity and more at affordable fee.
Hoodie (Hadoop Upsert Delete and Incremental) is an analytical, scan-optimized data storage abstraction which enables applying mutations to data in HDFS on the order of few minutes and chaining of incremental processing in hadoop
Hadoop meets Agile! - An Agile Big Data ModelUwe Printz
Big Data projects are a struggle, not only on the technical side but also on the organizational side. In this talk the author shares his experience and opinions from almost 5 years of Big Data projects and develops an Agile Big Data Model which reflects his ideas on how Big Data projects can be successful, even in large companies.
Talk held at the crossover meetup of the "Agile Stammtisch Rhein-Main" and the "Hadoop & Spark User Group Rhein-Main" at codecentric AG on 31.01.2017.
Talk held at a combined meeting of the Web Performance Karlsruhe (http://www.meetup.com/Karlsruhe-Web-Performance-Group/events/153207062) & Big Data Karlsruhe/Stuttgart (http://www.meetup.com/Big-Data-User-Group-Karlsruhe-Stuttgart/events/162836152) user groups.
Agenda:
- Why Hadoop 2?
- HDFS 2
- YARN
- YARN Apps
- Write your own YARN App
- Tez, Hive & Stinger Initiative
Tcloud Computing Hadoop Family and Ecosystem Service 2013.Q2tcloudcomputing-tw
The presentation is designed for those interested in Hadoop technology, and can enhance your knowledge in Hadoop, such as community history, current development status, features of services, distributed computing framework and scenario of big data development in Enterprise.
From Hadoop, through HIVE, Spark, YARN, searching for the holy grail for low latency SQL dialect like big data query. I talk about OLTP and OLAP, row stores and column stores, and finally arrive to BigQuery. Demo on the web console.
Data Pipelines in Hadoop - SAP Meetup in Tel Aviv larsgeorge
This talk is about showing the complexity in building a data pipeline in Hadoop, starting with the technology aspect, and the correlating to the skillsets of current Hadoop adopters.
Top Hadoop Big Data Interview Questions and Answers for FresherJanBask Training
Top Hadoop Big Data Interview Questions and Answers for Fresher , Hadoop, Hadoop Big Data, Hadoop Training, Hadoop Interview Question, Hadoop Interview Answers, Hadoop Big Data Interview Question
This talk takes you on a rollercoaster ride through Hadoop 2 and explains the most significant changes and components.
The talk has been held on the JavaLand conference in Brühl, Germany on 25.03.2014.
Agenda:
- Welcome Office
- YARN Land
- HDFS 2 Land
- YARN App Land
- Enterprise Land
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Introduction to Hadoop Ecosystem was presented to Lansing Java User Group on 2/17/2015 by Vijay Mandava and Lan Jiang. The demo was built on top of HDP 2.2 and AWS cloud.
Modern applications, often called “big-data” analysis, require us to manage immense amounts of data quickly. To deal with applications such as these, a new software stack has evolved.
Hadoop is emerging as the preferred solution for big data analytics across unstructured data. Using real world examples learn how to achieve a competitive advantage by finding effective ways of analyzing new sources of unstructured and machine-generated data.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Data Pipelines in Hadoop - SAP Meetup in Tel Aviv larsgeorge
This talk is about showing the complexity in building a data pipeline in Hadoop, starting with the technology aspect, and the correlating to the skillsets of current Hadoop adopters.
Top Hadoop Big Data Interview Questions and Answers for FresherJanBask Training
Top Hadoop Big Data Interview Questions and Answers for Fresher , Hadoop, Hadoop Big Data, Hadoop Training, Hadoop Interview Question, Hadoop Interview Answers, Hadoop Big Data Interview Question
This talk takes you on a rollercoaster ride through Hadoop 2 and explains the most significant changes and components.
The talk has been held on the JavaLand conference in Brühl, Germany on 25.03.2014.
Agenda:
- Welcome Office
- YARN Land
- HDFS 2 Land
- YARN App Land
- Enterprise Land
I have studied on Big Data analysis and found Hadoop is the best technology and most popular as well for it's distributed data processing approaches. I have gathered all possible information about various Hadoop distributions available in the market and tried to describe most important tools and their functionality in the Hadoop echosystems in this slide show. I have also tried to discuss about connectivity with language R interm of data analysis and visualization perspective. Hope you will be enjoying the whole!
Introduction to Hadoop Ecosystem was presented to Lansing Java User Group on 2/17/2015 by Vijay Mandava and Lan Jiang. The demo was built on top of HDP 2.2 and AWS cloud.
Modern applications, often called “big-data” analysis, require us to manage immense amounts of data quickly. To deal with applications such as these, a new software stack has evolved.
Hadoop is emerging as the preferred solution for big data analytics across unstructured data. Using real world examples learn how to achieve a competitive advantage by finding effective ways of analyzing new sources of unstructured and machine-generated data.
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Invola система автоматических напоминаний по неоплаченным счетамАндрей Ворожев
Система Invola увеличивает процент оплаченных счетов и коммерческих предложений за счет отправки автоматических напоминаний. Возможность отслеживать интенсивность работы менеджеров, результаты сделок. Строить отчеты работы. Автоматически считать причины отказов
StresStimulus Load Testing Tool User Interface Referencestresstimulus
StresStimulus is a performance testing tool which provides a simple way to test websites of any complexity, mobile apps and enterprise applications. This is the User Interface Reference for StresStimulus.
1043: Applications and porting to OpenPOWERNVIDIA Japan
John Ashley
Senior IBM Developer Relationship Manager
NVIDIA Corporation
This session gives you twice the content by incorporating two talks! First, we cover the current state of applications on OpenPOWER with an emphasis on GPU applications. Second, we cover the process of porting applications to OpenPOWER and the motivation to do so.
Trane: Pasos para automatizar un hogar y oficina sin cables Trane México
Beneficios de la automatización en los hogares y oficinas. 7 pasos para automatizar sin la necesidad de utilizar cables y de una manera fácil y eficiente.
Hadoop Administrator Online training course by (Knowledgebee Trainings) with mastering Hadoop Cluster: Planning & Deployment, Monitoring, Performance tuning, Security using Kerberos, HDFS High Availability using Quorum Journal Manager (QJM) and Oozie, Hcatalog/Hive Administration.
Contact : knowledgebee@beenovo.com
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
A summarized version of a presentation regarding Big Data architecture, covering from Big Data concept to Hadoop and tools like Hive, Pig and Cassandra
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
1. Hadoop Training
Institute in Hyderabad
ORIENIT
Orien IT – www.OrienIT.com
Flat No 204, Annapurna Block,Aaditya
Enclave
Ameerpet, Hyderabad – 500038,
Telangana,India.
3. What is Hadoop?
• Apache top level project, open-source
implementation of frameworks for reliable,
scalable, distributed computing and data
storage.
• It is a flexible and highly-available
architecture for large scale computation
and data processing on a network of
commodity hardware.
4. Brief History of Hadoop
• Designed to answer the question:
“How to process big data with
reasonable cost and time?”
8. Some Hadoop Milestones
• 2008 - Hadoop Wins Terabyte Sort Benchmark (sorted 1 terabyte
of data in 209 seconds, compared to previous record of 297 seconds)
• 2009 - Avro and Chukwa became new members of Hadoop
Framework family
• 2010 - Hadoop's Hbase, Hive and Pig subprojects completed, adding
more computational power to Hadoop framework
• 2011 - ZooKeeper Completed
• 2013 - Hadoop 1.1.2 and Hadoop 2.0.3 alpha.
- Ambari, Cassandra, Mahout have been added
9. What is Hadoop?
• Hadoop:
• an open-source software framework that supports data-
intensive distributed applications, licensed under the
Apache v2 license.
• Goals / Requirements:
• Abstract and facilitate the storage and processing of
large and/or rapidly growing data sets
• Structured and non-structured data
• Simple programming models
• High scalability and availability
• Use commodity (cheap!) hardware with little redundancy
• Fault-tolerance
• Move computation rather than data
11. Hadoop’s Architecture
• Distributed, with some centralization
• Main nodes of cluster are where most of the computational power
and storage of the system lies
• Main nodes run TaskTracker to accept and reply to MapReduce
tasks, and also DataNode to store needed blocks closely as
possible
• Central control node runs NameNode to keep track of HDFS
directories & files, and JobTracker to dispatch compute tasks to
TaskTracker
• Written in Java, also supports Python and Ruby
13. Hadoop’s Architecture
• Hadoop Distributed Filesystem
• Tailored to needs of MapReduce
• Targeted towards many reads of filestreams
• Writes are more costly
• High degree of data replication (3x by default)
• No need for RAID on normal nodes
• Large blocksize (64MB)
• Location awareness of DataNodes in network
14. Hadoop’s Architecture
NameNode:
• Stores metadata for the files, like the directory structure of a
typical FS.
• The server holding the NameNode instance is quite crucial,
as there is only one.
• Transaction log for file deletes/adds, etc. Does not use
transactions for whole blocks or file-streams, only metadata.
• Handles creation of more replica blocks when necessary
after a DataNode failure
15. Hadoop’s Architecture
DataNode:
• Stores the actual data in HDFS
• Can run on any underlying filesystem (ext3/4, NTFS, etc)
• Notifies NameNode of what blocks it has
• NameNode replicates blocks 2x in local rack, 1x elsewhere
18. Hadoop’s Architecture
MapReduce Engine:
• JobTracker & TaskTracker
• JobTracker splits up data into smaller tasks(“Map”) and
sends it to the TaskTracker process in each node
• TaskTracker reports back to the JobTracker node and
reports on job progress, sends data (“Reduce”) or requests
new jobs
19. Hadoop’s Architecture
• None of these components are necessarily limited to using
HDFS
• Many other distributed file-systems with quite different
architectures work
• Many other software packages besides Hadoop's
MapReduce platform make use of HDFS
20. Hadoop in the Wild
• Hadoop is in use at most organizations that handle big data:
o Yahoo!
o Facebook
o Amazon
o Netflix
o Etc…
• Some examples of scale:
o Yahoo!’s Search Webmap runs on 10,000 core Linux
cluster and powers Yahoo! Web search
o FB’s Hadoop cluster hosts 100+ PB of data (July, 2012)
& growing at ½ PB/day (Nov, 2012)
21. Hadoop in the Wild
• Advertisement (Mining user behavior to generate
recommendations)
• Searches (group related documents)
• Security (search for uncommon patterns)
Three main applications of Hadoop:
22. Hadoop in the Wild
• Non-realtime large dataset computing:
o NY Times was dynamically generating PDFs of articles
from 1851-1922
o Wanted to pre-generate & statically serve articles to
improve performance
o Using Hadoop + MapReduce running on EC2 / S3,
converted 4TB of TIFFs into 11 million PDF articles in
24 hrs
23. Hadoop in the Wild: Facebook Messages
• Design requirements:
o Integrate display of email, SMS and
chat messages between pairs and
groups of users
o Strong control over who users
receive messages from
o Suited for production use between
500 million people immediately after
launch
o Stringent latency & uptime
requirements
24. Hadoop in the Wild
• System requirements
o High write throughput
o Cheap, elastic storage
o Low latency
o High consistency (within a
single data center good
enough)
o Disk-efficient sequential
and random read
performance
25. Hadoop in the Wild
• Classic alternatives
o These requirements typically met using large MySQL cluster &
caching tiers using Memcached
o Content on HDFS could be loaded into MySQL or Memcached
if needed by web tier
• Problems with previous solutions
o MySQL has low random write throughput… BIG problem for
messaging!
o Difficult to scale MySQL clusters rapidly while maintaining
performance
o MySQL clusters have high management overhead, require
more expensive hardware
26. Hadoop in the Wild
• Facebook’s solution
o Hadoop + HBase as foundations
o Improve & adapt HDFS and HBase to scale to FB’s workload
and operational considerations
Major concern was availability: NameNode is SPOF &
failover times are at least 20 minutes
Proprietary “AvatarNode”: eliminates SPOF, makes HDFS
safe to deploy even with 24/7 uptime requirement
Performance improvements for realtime workload: RPC
timeout. Rather fail fast and try a different DataNode
28. Why use Hadoop?
• Need to process Multi Petabyte Datasets
• Data may not have strict schema
• Expensive to build reliability in each application
• Nodes fails everyday
• Need common infrastructure
• Very Large Distributed File System
• Assumes Commodity Hardware
• Optimized for Batch Processing
• Runs on heterogeneous OS
29. DataNode
• A Block Sever
– Stores data in local file system
– Stores meta-data of a block - checksum
– Serves data and meta-data to clients
• Block Report
– Periodically sends a report of all existing blocks
to NameNode
• Facilitate Pipelining of Data
– Forwards data to other specified DataNodes
30. Block Placement
• Replication Strategy
– One replica on local node
– Second replica on a remote rack
– Third replica on same remote rack
– Additional replicas are randomly placed
• Clients read from nearest replica
31. Data Correctness
• Use Checksums to validate data – CRC32
• File Creation
– Client computes checksum per 512 byte
– DataNode stores the checksum
• File Access
– Client retrieves the data and checksum from
DataNode
– If validation fails, client tries other replicas
32. Data Pipelining
• Client retrieves a list of DataNodes on
which to place replicas of a block
• Client writes block to the first DataNode
• The first DataNode forwards the data to
the next DataNode in the Pipeline
• When all replicas are written, the client
moves on to write the next block in file
33. Hadoop MapReduce
• MapReduce programming model
– Framework for distributed processing of large
data sets
– Pluggable user code runs in generic
framework
• Common design pattern in data
processing
– cat * | grep | sort | uniq -c | cat > file
– input | map | shuffle | reduce | output
36. MapReduce Process
(org.apache.hadoop.mapred)
• JobClient
– Submit job
• JobTracker
– Manage and schedule job, split job into tasks
• TaskTracker
– Start and monitor the task execution
• Child
– The process that really execute the task
37. Inter Process Communication
IPC/RPC (org.apache.hadoop.ipc)
• Protocol
– JobClient <-------------> JobTracker
– TaskTracker <------------> JobTracker
– TaskTracker <-------------> Child
• JobTracker impliments both protocol and works as server
in both IPC
• TaskTracker implements the TaskUmbilicalProtocol; Child
gets task information and reports task status through it.
JobSubmissionProtocol
InterTrackerProtocol
TaskUmbilicalProtocol
38. JobClient.submitJob - 1
• Check input and output, e.g. check if the output
directory is already existing
– job.getInputFormat().validateInput(job);
– job.getOutputFormat().checkOutputSpecs(fs, job);
• Get InputSplits, sort, and write output to HDFS
– InputSplit[] splits = job.getInputFormat().
getSplits(job, job.getNumMapTasks());
– writeSplitsFile(splits, out); // out is
$SYSTEMDIR/$JOBID/job.split
39. JobClient.submitJob - 2
• The jar file and configuration file will be
uploaded to HDFS system directory
– job.write(out); // out is $SYSTEMDIR/$JOBID/job.xml
• JobStatus status =
jobSubmitClient.submitJob(jobId);
– This is an RPC invocation, jobSubmitClient is
a proxy created in the initialization
40. Job initialization on JobTracker - 1
• JobTracker.submitJob(jobID) <-- receive
RPC invocation request
• JobInProgress job = new
JobInProgress(jobId, this, this.conf)
• Add the job into Job Queue
– jobs.put(job.getProfile().getJobId(), job);
– jobsByPriority.add(job);
– jobInitQueue.add(job);
41. Job initialization on JobTracker - 2
• Sort by priority
– resortPriority();
– compare the JobPrioity first, then compare the
JobSubmissionTime
• Wake JobInitThread
– jobInitQueue.notifyall();
– job = jobInitQueue.remove(0);
– job.initTasks();
44. JobTracker Task Scheduling - 1
• Task getNewTaskForTaskTracker(String
taskTracker)
• Compute the maximum tasks that can be
running on taskTracker
– int maxCurrentMap Tasks =
tts.getMaxMapTasks();
– int maxMapLoad =
Math.min(maxCurrentMapTasks,
(int)Math.ceil(double)
remainingMapLoad/numTaskTrackers));
45. JobTracker Task Scheduling - 2
• int numMaps = tts.countMapTasks(); //
running tasks number
• If numMaps < maxMapLoad, then more
tasks can be allocated, then based on
priority, pick the first job from the
jobsByPriority Queue, create a task, and
return to TaskTracker
– Task t = job.obtainNewMapTask(tts,
numTaskTrackers);