More about Hadoop
www.beinghadoop.com
https://www.facebook.com/hadoopinfo
This PPT Gives information about
Complete Hadoop Architecture and
information about
how user request is processed in Hadoop?
About Namenode
Datanode
jobtracker
tasktracker
Hadoop installation Post Configurations
Technical introduction into Apache Spark - the Swiss Army Knife of Big Data analytics tools.
The talk was held at the Big Data User Group Mannheim, Germany at 24.11.2014.
More about Hadoop
www.beinghadoop.com
https://www.facebook.com/hadoopinfo
This PPT Gives information about
Complete Hadoop Architecture and
information about
how user request is processed in Hadoop?
About Namenode
Datanode
jobtracker
tasktracker
Hadoop installation Post Configurations
Technical introduction into Apache Spark - the Swiss Army Knife of Big Data analytics tools.
The talk was held at the Big Data User Group Mannheim, Germany at 24.11.2014.
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
Presentation on 2013-06-27, Workshop on the future of Big Data management, discussing hadoop for a science audience that are either HPC/grid users or people suddenly discovering that their data is accruing towards PB.
The other talks were on GPFS, LustreFS and Ceph, so rather than just do beauty-contest slides, I decided to raise the question of "what is a filesystem?", whether the constraints imposed by the Unix metaphor and API are becoming limits on scale and parallelism (both technically and, for GPFS and Lustre Enterprise in cost).
Then: HDFS as the foundation for the Hadoop stack.
All the other FS talks did emphasise their Hadoop integration, with the Intel talk doing the most to assert performance improvements of LustreFS over HDFSv1 in dfsIO and Terasort (no gridmix?), which showed something important: Hadoop is the application that add DFS developers have to have a story for
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
In these slides is given an overview of the different parts of Apache Spark.
We analyze spark shell both in scala and python. Then we consider Spark SQL with an introduction to Data Frame API. Finally we describe Spark Streaming and we make some code examples.
Topics:spark-shell, pyspark, HDFS, how to copy file to HDFS, spark transformations, spark actions, Spark SQL (Shark),
spark streaming, streaming transformation stateless vs stateful, sliding windows, examples
Hadoop World 2011: Hadoop Troubleshooting 101 - Kate Ting - ClouderaCloudera, Inc.
Attend this session and walk away armed with solutions to the most common customer problems. Learn proactive configuration tweaks and best practices to keep your cluster free of fetch failures, job tracker hangs, and the like.
From docker to kubernetes: running Apache Hadoop in a cloud native wayDataWorks Summit
Creating containers for an application is easy (even if it’s a goold old distributed application like Apache Hadoop), just a few steps of packaging.
The hard part isn't packaging: it's deploying
How can we run the containers together? How to configure them? How do the services in the containers find and talk to each other? How do you deploy and manage clusters with hundred of nodes?
Modern cloud native tools like Kubernetes or Consul/Nomad could help a lot but they could be used in different way.
It this presentation I will demonstrate multiple solutions to manage containerized clusters with different cloud-native tools including kubernetes, and docker-swarm/compose.
No matter which tools you use, the same questions of service discovery and configuration management arise. This talk will show the key elements needed to make that containerized cluster work.
Tools:
kubernetes, docker-swam, docker-compose, consul, consul-template, nomad
together with: Hadoop, Yarn, Spark, Kafka, Zookeeper, Storm….
References:
https://github.com/flokkr
Speaker
Marton Elek, Lead Software Engineer, Hortonworks
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
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tools that can be used to analyse data;
data analysis consulting;
basic data analytics;
data analysis programs;
examples of data analysis tools;
big data analysis tools;
data analytics tools and techniques;
statistics for data analytics;
data analytics tools;
data analytics and big data;
data analytics big data;
data analysis software;
data analytics with excel;
website data analysis;
data analytics companies;
data analysis qualifications;
tools for data analytics;
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qualitative data analysis software;
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data analysis website;
tools for analyzing data;
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it data analytics;
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unstructured data analytics;
data analytics using excel;
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data analytics;
tools of data analysis;
analytical tools for data analysis;
statistical tools to analyse data;
data analysis help;
data analysis education;
statistical technique for data analysis;
tools for data analysis;
how to learn data analysis;
data analytics tutorial;
excel data analytics;
data mining course;
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big data and data analytics;
statistical analysis software;
tools to analyse data;
online data analysis;
data mining software;
data analytics statistics;
how to do data analytics;
statistical data analysis tools;
data analyst tools;
business data analysis;
tools and techniques of data analysis;
education data analysis;
advanced data analytics;
study data analysis;
spreadsheet data analysis;
learn data analysis in excel;
software for data analysis;
shared data warehouse;
what are data analysis tools;
data analytics and statistics;
data analyse;
analysis courses;
data analysis tools for research;
research data analysis tools;
big data analysis;
data mining programs;
applications of data analytics;
data analysis tools and techniques;
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. While developed by Facebook.
Presentation on 2013-06-27, Workshop on the future of Big Data management, discussing hadoop for a science audience that are either HPC/grid users or people suddenly discovering that their data is accruing towards PB.
The other talks were on GPFS, LustreFS and Ceph, so rather than just do beauty-contest slides, I decided to raise the question of "what is a filesystem?", whether the constraints imposed by the Unix metaphor and API are becoming limits on scale and parallelism (both technically and, for GPFS and Lustre Enterprise in cost).
Then: HDFS as the foundation for the Hadoop stack.
All the other FS talks did emphasise their Hadoop integration, with the Intel talk doing the most to assert performance improvements of LustreFS over HDFSv1 in dfsIO and Terasort (no gridmix?), which showed something important: Hadoop is the application that add DFS developers have to have a story for
Apache Sqoop efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Sqoop helps offload certain tasks (such as ETL processing) from the EDW to Hadoop for efficient execution at a much lower cost. Sqoop can also be used to extract data from Hadoop and export it into external structured datastores. Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB
In these slides is given an overview of the different parts of Apache Spark.
We analyze spark shell both in scala and python. Then we consider Spark SQL with an introduction to Data Frame API. Finally we describe Spark Streaming and we make some code examples.
Topics:spark-shell, pyspark, HDFS, how to copy file to HDFS, spark transformations, spark actions, Spark SQL (Shark),
spark streaming, streaming transformation stateless vs stateful, sliding windows, examples
Hadoop World 2011: Hadoop Troubleshooting 101 - Kate Ting - ClouderaCloudera, Inc.
Attend this session and walk away armed with solutions to the most common customer problems. Learn proactive configuration tweaks and best practices to keep your cluster free of fetch failures, job tracker hangs, and the like.
From docker to kubernetes: running Apache Hadoop in a cloud native wayDataWorks Summit
Creating containers for an application is easy (even if it’s a goold old distributed application like Apache Hadoop), just a few steps of packaging.
The hard part isn't packaging: it's deploying
How can we run the containers together? How to configure them? How do the services in the containers find and talk to each other? How do you deploy and manage clusters with hundred of nodes?
Modern cloud native tools like Kubernetes or Consul/Nomad could help a lot but they could be used in different way.
It this presentation I will demonstrate multiple solutions to manage containerized clusters with different cloud-native tools including kubernetes, and docker-swarm/compose.
No matter which tools you use, the same questions of service discovery and configuration management arise. This talk will show the key elements needed to make that containerized cluster work.
Tools:
kubernetes, docker-swam, docker-compose, consul, consul-template, nomad
together with: Hadoop, Yarn, Spark, Kafka, Zookeeper, Storm….
References:
https://github.com/flokkr
Speaker
Marton Elek, Lead Software Engineer, Hortonworks
These slides cover the very basics of Hadoop architecture, in particular HDFS. This was my presentation in the first Delhi Hadoop User Group (DHUG) meetup held at Gurgaon on 10th September 2011. Loved the positive feedback. I'll also upload a more elaborate version covering Hadoop mapreduce architecture as well soon. Most of the stuff covered in these slides can be found in Tom White's book as well (See the last slide)
r packagesdata analytics study material;
learn data analytics online;
data analytics courses;
courses for data analysis;
courses for data analytics;
online data analysis courses;
courses on data analysis;
data analytics classes;
data analysis training courses online;
courses in data analysis;
data analysis courses online;
data analytics training;
courses for data analyst;
data analysis online course;
data analysis certification;
data analysis courses;
data analysis classes;
online course data analysis;
learn data analysis online;
data analysis training;
python for data analysis course;
learn data analytics;
study data analytics;
how to learn data analytics;
data analysis course free;
statistical methods and data analysis;
big data analytics;
data analysis companies;
python data analysis course;
tools that can be used to analyse data;
data analysis consulting;
basic data analytics;
data analysis programs;
examples of data analysis tools;
big data analysis tools;
data analytics tools and techniques;
statistics for data analytics;
data analytics tools;
data analytics and big data;
data analytics big data;
data analysis software;
data analytics with excel;
website data analysis;
data analytics companies;
data analysis qualifications;
tools for data analytics;
data analysis tools;
qualitative data analysis software;
free data analytics;
data analysis website;
tools for analyzing data;
data analytics software;
free data analysis software;
tools for analysing data;
data mining book;
learn data analysis;
about data analytics;
statistical data analysis software;
it data analytics;
data analytics tutorial for beginners;
unstructured data analytics;
data analytics using excel;
dissertation data analysis;
sample of data analysis;
data analysis online;
data analytics;
tools of data analysis;
analytical tools for data analysis;
statistical tools to analyse data;
data analysis help;
data analysis education;
statistical technique for data analysis;
tools for data analysis;
how to learn data analysis;
data analytics tutorial;
excel data analytics;
data mining course;
data analysis software free;
big data and data analytics;
statistical analysis software;
tools to analyse data;
online data analysis;
data mining software;
data analytics statistics;
how to do data analytics;
statistical data analysis tools;
data analyst tools;
business data analysis;
tools and techniques of data analysis;
education data analysis;
advanced data analytics;
study data analysis;
spreadsheet data analysis;
learn data analysis in excel;
software for data analysis;
shared data warehouse;
what are data analysis tools;
data analytics and statistics;
data analyse;
analysis courses;
data analysis tools for research;
research data analysis tools;
big data analysis;
data mining programs;
applications of data analytics;
data analysis tools and techniques;
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Hadoop has proven to be an invaluable tool for many companies over the past few years. Yet it has it's ways and knowing them up front can safe valuable time. This session is a run down of the ever recurring lessons learned from running various Hadoop clusters in production since version 0.15.
What to expect from Hadoop - and what not? How to integrate Hadoop into existing infrastructure? Which data formats to use? What compression? Small files vs big files? Append or not? Essential configuration and operations tips. What about querying all the data? The project, the community and pointers to interesting projects that complement the Hadoop experience.
Hadoop Distributed File System (HDFS) evolves from a MapReduce-centric storage system to a generic, cost-effective storage infrastructure where HDFS stores all data of inside the organizations. The new use case presents a new sets of challenges to the original HDFS architecture. One challenge is to scale the storage management of HDFS - the centralized scheme within NameNode becomes a main bottleneck which limits the total number of files stored. Although a typical large HDFS cluster is able to store several hundred petabytes of data, it is inefficient to handle large amounts of small files under the current architecture.
In this talk, we introduce our new design and in-progress work that re-architects HDFS to attack this limitation. The storage management is enhanced to a distributed scheme. A new concept of storage container is introduced for storing objects. HDFS blocks are stored and managed as objects in the storage containers instead of being tracked only by NameNode. Storage containers are replicated across DataNodes using a newly-developed high-throughput protocol based on the Raft consensus algorithm. Our current prototype shows that under the new architecture the storage management of HDFS scales 10x better, demonstrating that HDFS is capable of storing billions of files.
My presentation for the first user group meeting of our lab's Big Data IWT TETRA project [*]. In the presentation, I gave a demo of Cloudera Manager, discussed 4 micro benchmarks and finalized the presentation with an overview of the Big Bench benchmark.
[*] For more information on what IWT TETRA funding exactly is, see http://www.iwt.be/english/funding/subsidy/tetra
Real time Analytics with Apache Kafka and Apache SparkRahul Jain
A presentation cum workshop on Real time Analytics with Apache Kafka and Apache Spark. Apache Kafka is a distributed publish-subscribe messaging while other side Spark Streaming brings Spark's language-integrated API to stream processing, allows to write streaming applications very quickly and easily. It supports both Java and Scala. In this workshop we are going to explore Apache Kafka, Zookeeper and Spark with a Web click streaming example using Spark Streaming. A clickstream is the recording of the parts of the screen a computer user clicks on while web browsing.
Apache Kafka 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
The title "Big Data using Hadoop.pdf" suggests that the document is likely a PDF file that focuses on the utilization of Hadoop technology in the context of Big Data. Hadoop is a popular open-source framework for distributed storage and processing of large datasets. The document is expected to cover various aspects of working with big data, emphasizing the role of Hadoop in managing and analyzing vast amounts of information.
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.
Design and Research of Hadoop Distributed Cluster Based on RaspberryIJRESJOURNAL
ABSTRACT : Based on the cost saving, this Hadoop distributed cluster based on raspberry is designed for the storage and processing of massive data. This paper expounds the two core technologies in the Hadoop software framework - HDFS distributed file system architecture and MapReduce distributed processing mechanism. The construction method of the cluster is described in detail, and the Hadoop distributed cluster platform is successfully constructed based on the two raspberry factions. The technical knowledge about Hadoop is well understood in theory and practice.
Hadoop Interview Questions and Answers by rohit kapakapa rohit
Hadoop Interview Questions and Answers - More than 130 real time questions and answers covering hadoop hdfs,mapreduce and administrative concepts by rohit kapa
Emerging technologies /frameworks in Big DataRahul Jain
A short overview presentation on Emerging technologies /frameworks in Big Data covering Apache Parquet, Apache Flink, Apache Drill with basic concepts of Columnar Storage and Dremel.
Building a Large Scale SEO/SEM Application with Apache SolrRahul Jain
Slides from my talk on "Building a Large Scale SEO/SEM Application with Apache Solr" in Lucene/Solr Revolution 2014 where I talk how we handle Indexing/Search of 40 billion records (documents)/month in Apache Solr with 4.6 TB compressed index data.
Abstract: We are working on building a SEO/SEM application where an end user search for a "keyword" or a "domain" and gets all the insights about these including Search engine ranking, CPC/CPM, search volume, No. of Ads, competitors details etc. in a couple of seconds. To have this intelligence, we get huge web data from various sources and after intensive processing it is 40 billion records/month in MySQL database with 4.6 TB compressed index data in Apache Solr.
Due to large volume, we faced several challenges while improving indexing performance, search latency and scaling the overall system. In this session, I will talk about our several design approaches to import data faster from MySQL, tricks & techniques to improve the indexing performance, Distributed Search, DocValues(life saver), Redis and the overall system architecture.
Apache Spark is a In Memory Data Processing Solution that can work with existing data source like HDFS and can make use of your existing computation infrastructure like YARN/Mesos etc. This talk will cover a basic introduction of Apache Spark with its various components like MLib, Shark, GrpahX and with few examples.
A short presentation for beginners on Introduction of Machine Learning, What it is, how it works, what all are the popular Machine Learning techniques and learning models (supervised, unsupervised, semi-supervised, reinforcement learning) and how they works with various Industry use-cases and popular examples.
A hibernate tutorial for beginners. It describe the hibernate concepts in a lucid manner and and test project(User application with database) to get hands on over the same.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
3. Introduction
An open source software framework
Supports Data intensive Distributed Applications.
Enables Application to work thousand of computational
independent computers and petabytes of data.
Derived from Google’s Map-Reduce and Google File System
papers.
Written in the Java Programming Language.
Started by Doug Cutting, who named it after his son’s toy
elephant to support distribution for the Nutch ( A sub-project
of Lucene)
3
4. Hadoop (Why)
• Need to process huge datasets on large no. of computers.
• It is expensive to build reliability into each application.
• Nodes fails everyday
- Failure is expected, rather than exceptional.
- Need common infrastructure
- Efficient, reliable, easy to use.
- Open sourced , Apache License
4
5. Hadoop History
• Dec 2004 – Google GFS paper published
• July 2005 – Nutch uses Map-Reduce
• Jan 2006 – Doug Cutting joins Yahoo!
• Feb 2006 – Become Lucene Subproject
• Apr 2007 – Yahoo! On 1000 node cluster
• Jan 2008 – An Apache Top Level Project
• Feb 2008 – Yahoo Production search index
5
6. What is Hadoop Used for ?
• Searching (Yahoo)
• Log Processing
• Recommendation Systems (Facebook, LinkedIn, eBay, Amazon)
• Analytics(Facebook, LinkedIn)
• Video and Image Analysis (NASA)
• Data Retention
6
8. Map-Reduce
Framework for processing parallel
problems across huge datasets using a
large numbers of computers(nodes),
collectively referred as
Cluster : If all nodes are on same local network and
uses similar network.
Or
Grid: If the nodes are shared across geographically
and uses more heterogeneous hardware.
Consists Two Step :
1.Map Step- The master node takes the input,
divides it into smaller sub-problems, and distributes
them to worker nodes. A worker node may do this
again in turn, leading to a multi-level tree structure.
The worker node processes the smaller problem,
and passes the answer back to its master node.
2.Reduce Step -The master node then collects
the answers to all the sub-problems and combines
them in some way to form the output – the answer
to the problem it was originally trying to solve.
Multiple Map-Reduce phases
8
13. Goals of HDFS
1. Very Large Distributed File System
- 10K nodes, 100 million files, 10 PB
2. Assumes Commodity Hardware
- Files are replicated to handle hardware failure
- Detect failures and recovers from them
3. Optimized for Batch Processing
- Data locations exposed so that computation can move to
where data resides.
13
15. Installation/ Configuration
[rjain@10.1.110.12 hadoop-1.0.3]$ vi conf/hdfs-site.xml [rjain@10.1.110.12 hadoop-1.0.3]$ pwd
<configuration> /home/rjain/hadoop-1.0.3
<property>
<name>dfs.replication</name>
[rjain@10.1.110.12 hadoop-1.0.3]$ bin/start-all.sh
<value>1</value>
</property> [rjain@10.1.110.12 hadoop-1.0.3]$ bin/start-mapred.sh
<property>
<name>dfs.permissions</name>
[rjain@10.1.110.12 hadoop-1.0.3]$ bin/start-dfs.sh
<value>true</value>
</property> [rjain@10.1.110.12 hadoop-1.0.3]$ bin/hadoop fs
<property> Usage: java FsShell
<name>dfs.data.dir</name> [-ls <path>]
<value>/home/rjain/rahul/hdfs/data</value> [-lsr <path>] : Recursive version of ls. Similar to Unix ls -R.
</property> [-du <path>] : Displays aggregate length of files contained in the directory or the length of a file.
<property> [-dus <path>] : Displays a summary of file lengths.
<name>dfs.name.dir</name> [-count[-q] <path>]
<value>/home/rjain/rahul/hdfs/name</value> [-mv <src> <dst>]
</property> [-cp <src> <dst>]
</configuration> [-rm [-skipTrash] <path>]
[-rmr [-skipTrash] <path>] : Recursive version of delete(rm).
[rjain@10.1.110.12 hadoop-1.0.3]$ vi conf/mapred-site.xml [-expunge] : Empty the Trash
<configuration> [-put <localsrc> ... <dst>] : Copy single src, or multiple srcs from local file system to the
<property> destination filesystem
<name>mapred.job.tracker</name> [-copyFromLocal <localsrc> ... <dst>]
<value>localhost:9001</value> [-moveFromLocal <localsrc> ... <dst>]
</property> [-get [-ignoreCrc] [-crc] <src> <localdst>]
</configuration> [-getmerge <src> <localdst> [addnl]]
[-cat <src>]
[rjain@10.1.110.12 hadoop-1.0.3]$ vi conf/core-site.xml [-text <src>] : Takes a source file and outputs the file in text format. The allowed formats are zip
<configuration> and TextRecordInputStream.
<property> [-copyToLocal [-ignoreCrc] [-crc] <src> <localdst>]
<name>fs.default.name</name> [-moveToLocal [-crc] <src> <localdst>]
<value>hdfs://localhost:9000</value> [-mkdir <path>]
</property> [-setrep [-R] [-w] <rep> <path/file>] : Changes the replication factor of a file
</configuration> [-touchz <path>] : Create a file of zero length.
[-test -[ezd] <path>] : -e check to see if the file exists. Return 0 if true. -z check to see if the file is
[rjain@10.1.110.12 hadoop-1.0.3]$ jps zero length. Return 0 if true. -d check to see if the path is directory. Return 0 if true.
29756 SecondaryNameNode [-stat [format] <path>] : Returns the stat information on the path like created time of dir
19847 TaskTracker [-tail [-f] <file>] : Displays last kilobyte of the file to stdout
18756 Jps [-chmod [-R] <MODE[,MODE]... | OCTALMODE> PATH...]
29483 NameNode [-chown [-R] [OWNER][:[GROUP]] PATH...]
29619 DataNode [-chgrp [-R] GROUP PATH...] 15
19711 JobTracker [-help [cmd]]
16. HDFS- Read/Write Example
Configuration conf = new Configuration();
FileSystem fs = FileSystem.get(conf);
Given an input/output file name as string, we construct inFile/outFile Path objects.
Most of the FileSystem APIs accepts Path objects.
Path inFile = new Path(argv[0]);
Path outFile = new Path(argv[1]);
Validate the input/output paths before reading/writing.
if (!fs.exists(inFile))
printAndExit("Input file not found");
if (!fs.isFile(inFile))
printAndExit("Input should be a file");
if (fs.exists(outFile))
printAndExit("Output already exists");
Open inFile for reading.
FSDataInputStream in = fs.open(inFile);
Open outFile for writing.
FSDataOutputStream out = fs.create(outFile);
Read from input stream and write to output stream until EOF.
while ((bytesRead = in.read(buffer)) > 0) {
out.write(buffer, 0, bytesRead);
}
Close the streams when done.
in.close();
out.close();
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17. Hadoop Sub-Projects
• Hadoop Common: The common utilities that support the other Hadoop subprojects.
• Hadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput
access to application data.
• Hadoop MapReduce: A software framework for distributed processing of large data sets on
compute clusters.
Other Hadoop-related projects at Apache include:
• Avro™: A data serialization system.
• Cassandra™: A scalable multi-master database with no single points of failure.
• Chukwa™: A data collection system for managing large distributed systems.
• HBase™: A scalable, distributed database that supports structured data storage for large tables.
• Hive™: A data warehouse infrastructure that provides data summarization and ad hoc querying.
• Mahout™: A Scalable machine learning and data mining library.
• Pig™: A high-level data-flow language and execution framework for parallel computation.
• ZooKeeper™: A high-performance coordination service for distributed applications.
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