The document provides an overview of Hadoop, including:
- A brief history of Hadoop and its origins from Google and Apache projects
- An explanation of Hadoop's architecture including HDFS, MapReduce, JobTracker, TaskTracker, and DataNodes
- Examples of how large companies like Yahoo, Facebook, and Amazon use Hadoop for applications like log processing, searches, and advertisement targeting
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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.
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A summarized version of a presentation regarding Big Data architecture, covering from Big Data concept to Hadoop and tools like Hive, Pig and Cassandra
We Provide Hadoop training institute in Hyderabad and Bangalore with corporate training by 12+ Experience faculty.
Real-time industry experts from MNCs
Resume Preparation by expert Professionals
Lab exercises
Interview Preparation
Experts advice
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
M. Florence Dayana - Hadoop Foundation for Analytics.pptxDr.Florence Dayana
Hadoop Foundation for Analytics
History of Hadoop
Features of Hadoop
Key Advantages of Hadoop
Why Hadoop
Versions of Hadoop
Eco Projects
Essential of Hadoop ecosystem
RDBMS versus Hadoop
Key Aspects of Hadoop
Components of Hadoop
This slide deck that Mr. Minh Tran - KMS's Software Architect shared at "Java-Trends and Career Opportunities" seminar of Information Technology Center of HCMC University of Science.
Apache Hadoop software library is essentially a framework that
allows for the distributed processing of large data-sets across
clusters of computers using a simple programming model.
http://www.learntek.org/product/big-data-and-hadoop/
http://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses. We are dedicated to designing, developing and implementing training programs for students, corporate employees and business professional.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
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M. Florence Dayana - Hadoop Foundation for Analytics.pptxDr.Florence Dayana
Hadoop Foundation for Analytics
History of Hadoop
Features of Hadoop
Key Advantages of Hadoop
Why Hadoop
Versions of Hadoop
Eco Projects
Essential of Hadoop ecosystem
RDBMS versus Hadoop
Key Aspects of Hadoop
Components of Hadoop
This slide deck that Mr. Minh Tran - KMS's Software Architect shared at "Java-Trends and Career Opportunities" seminar of Information Technology Center of HCMC University of Science.
Apache Hadoop software library is essentially a framework that
allows for the distributed processing of large data-sets across
clusters of computers using a simple programming model.
http://www.learntek.org/product/big-data-and-hadoop/
http://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses. We are dedicated to designing, developing and implementing training programs for students, corporate employees and business professional.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
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Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Understanding Globus Data Transfers with NetSageGlobus
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Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
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Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
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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?”
7. Hadoop’s Developers
Doug Cutting
2005: Doug Cutting and Michael J. Cafarella developed
Hadoop to support distribution for the Nutch search
engine project.
The project was funded by Yahoo.
2006: Yahoo gave the project to Apache
Software Foundation.
9. 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
10. 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
12. 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
14. 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
15. 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
16. 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
19. 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
20. 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
21. 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)
22. 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:
23. 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
24. 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
25. 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
26. 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
27. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
38. 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
39. 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
40. 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
41. 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
42. 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);
43. 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();
46. 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));
47. 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);