Hadoop is an open-source software framework for distributed storage and processing of large datasets. It has three core components: HDFS for storage, MapReduce for processing, and YARN for resource management. HDFS stores data as blocks across clusters of commodity servers. MapReduce allows distributed processing of large datasets in parallel. YARN improves on MapReduce and provides a general framework for distributed applications beyond batch processing.
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
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 Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
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
This presentation about Hadoop architecture will help you understand the architecture of Apache Hadoop in detail. In this video, you will learn what is Hadoop, components of Hadoop, what is HDFS, HDFS architecture, Hadoop MapReduce, Hadoop MapReduce example, Hadoop YARN and finally, a demo on MapReduce. Apache Hadoop offers a versatile, adaptable and reliable distributed computing big data framework for a group of systems with capacity limit and local computing power. After watching this video, you will also understand the Hadoop Distributed File System and its features along with the practical implementation.
Below are the topics covered in this Hadoop Architecture presentation:
1. What is Hadoop?
2. Components of Hadoop
3. What is HDFS?
4. HDFS Architecture
5. Hadoop MapReduce
6. Hadoop MapReduce Example
7. Hadoop YARN
8. Demo on MapReduce
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Who should take up this Big Data and Hadoop Certification Training Course?
Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
1. Software Developers and Architects
2. Analytics Professionals
3. Senior IT professionals
4. Testing and Mainframe professionals
5. Data Management Professionals
6. Business Intelligence Professionals
7. Project Managers
8. Aspiring Data Scientists
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
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 Institutes : kelly technologies is the best Hadoop Training Institutes in Hyderabad. Providing Hadoop training by real time faculty in Hyderabad.
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
Hadoop is a well-known framework used for big data processing now-a-days. It implements MapReduce for processing and utilizes distributed file system known as Hadoop Distributed File System (HDFS) to store data. HDFS provides fault tolerant, distributed and scalable storage for big data so that MapReduce can easily perform jobs on this data. Knowledge and understanding of data storage over HDFS is very important for a researcher working on Hadoop for big data storage and processing optimization. The aim of this presentation is to describe the architecture and process flow of HDFS. This presentation highlights prominent features of this file system implemented by Hadoop to execute MapReduce jobs. Moreover the presentation provides the description of process flow for achieving the design objectives of HDFS. Future research directions to explore and improve HDFS performance are also elaborated on.
There's a big shift in both at the architecture and api level from Hadoop 1 vs Hadoop 2, particularly YARN and we had our first meetup to talk about this (http://www.meetup.com/Atlanta-YARN-User-Group/) on 10/13/2013.
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
Hadoop is a well-known framework used for big data processing now-a-days. It implements MapReduce for processing and utilizes distributed file system known as Hadoop Distributed File System (HDFS) to store data. HDFS provides fault tolerant, distributed and scalable storage for big data so that MapReduce can easily perform jobs on this data. Knowledge and understanding of data storage over HDFS is very important for a researcher working on Hadoop for big data storage and processing optimization. The aim of this presentation is to describe the architecture and process flow of HDFS. This presentation highlights prominent features of this file system implemented by Hadoop to execute MapReduce jobs. Moreover the presentation provides the description of process flow for achieving the design objectives of HDFS. Future research directions to explore and improve HDFS performance are also elaborated on.
There's a big shift in both at the architecture and api level from Hadoop 1 vs Hadoop 2, particularly YARN and we had our first meetup to talk about this (http://www.meetup.com/Atlanta-YARN-User-Group/) on 10/13/2013.
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
A brief introduction to Hadoop distributed file system. How a file is broken into blocks, written and replicated on HDFS. How missing replicas are taken care of. How a job is launched and its status is checked. Some advantages and disadvantages of HDFS-1.x
Hadoop Interview Questions And Answers Part-1 | Big Data Interview Questions ...Simplilearn
This video on Hadoop interview questions part-1 will take you through the general Hadoop questions and questions on HDFS, MapReduce and YARN, which are very likely to be asked in any Hadoop interview. It covers all the topics on the major components of Hadoop. This Hadoop tutorial will give you an idea about the different scenario-based questions you could face and some multiple-choice questions as well. Now, let us dive into this Hadoop interview questions video and gear up for youe next Hadoop Interview.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
Presented at a guest lecture at the Rijksuniversiteit Groningen as part of the web and cloud computing master course.
I presented a architecture for and working implementation of doing Hadoop based typeahead style search suggestions. There is a companion github repo with the code and config at: https://github.com/friso/rug (there's no documentation, though).
In this talk, we will present a new distribution of Hadoop, Hops, that can scale the Hadoop Filesystem (HDFS) by 16X, from 70K ops/s to 1.2 million ops/s on Spotiy's industrial Hadoop workload. Hops is an open-source distribution of Apache Hadoop that supports distributed metadata for HSFS (HopsFS) and the ResourceManager in Apache YARN. HopsFS is the first production-grade distributed hierarchical filesystem to store its metadata normalized in an in-memory, shared nothing database. For YARN, we will discuss optimizations that enable 2X throughput increases for the Capacity scheduler, enabling scalability to clusters with >20K nodes. We will discuss the journey of how we reached this milestone, discussing some of the challenges involved in efficiently and safely mapping hierarchical filesystem metadata state and operations onto a shared-nothing, in-memory database. We will also discuss the key database features needed for extreme scaling, such as multi-partition transactions, partition-pruned index scans, distribution-aware transactions, and the streaming changelog API. Hops (www.hops.io) is Apache-licensed open-source and supports a pluggable database backend for distributed metadata, although it currently only support MySQL Cluster as a backend. Hops opens up the potential for new directions for Hadoop when metadata is available for tinkering in a mature relational database.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
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.
2. Hadoop
Hadoop is an open-source software framework for distributed storage and
distributed processing of large structured, semi-structured, and unstructured data
sets across clusters of commodity servers
3. Google MR
Hadoop
becomes
Apaches top
level project
Yahoo!'s 4000 node cluster followed by Facebook's 2300 node cluster
are the largest clusters
FB
launche
s hive
Nutch:
Doug Cutting &
Mike Cafarella
NDFS &
MR to
N tch
Doug
Cutting
Joins
Yahoo!
Hadoop
Subproject
of Lucene
Spins out of
Nutch
Yahoo!
Fastest
sort of a
TB, 910
nodes, 3.5
mins
Google GFS
2002 2003 2004 2005 2006 2007 2008 2009
NY Times
converts 4
TB of Image
archives
over 100
EC2s
Cloudera
founded
Doug Cutting
joined
cloudera
Fastest sort of a
TB, 62 secs over
1460 nodes.
Petabyte Sort :
hrs: 16.25
Nodes :3658
5. Design of HDFS
Designed for
Very Large Files
Streaming Data Access
Commodity Hardware
Not meant for
Low Latency data access
Lots of Small Files
Multiple Writers, arbitrary file modifications
6. Hadoop Storage: HDFS Architecture
Datanodes, Block Replication, Namenode[FsImage, Edits log]
Block Replication/Data Replication determines how redundant data is stored in hdfs
Replication factor determins the number of copies
2 is store one copy on different rack
3 is store one copy on different rack and one on same rack
Datanodes store the actual data
stored as blocks
the size of blocks can be tuned
default is usually 64 or 128MB
smaller the block size(the more blocks) the more the namenode would have to manage
Namenode manages block locations
stores "metadata"
names nodes are a point of failure
RAM is important here
8. Hadoop 2.x Cluster Architecture
ResourceManager
Master that arbitrates all the available cluster resources
ApplicationMaster
Negotiates resources with the ResourceManager and for working with the
NodeManagers to start the containers.
Is the middleman between NM and RM
Allows for greater scalability
NodeManager
Takes instructions from the ResourceManager and manage resources
available on a single node.
9. Federation
allows for multiple namespaces
separation of namespace and storage
Namespace: manages directories, files and blocks. It supports file system
operations such as creation, modification, deletion and listing of files and
directories.
Block Storage: It supports block-related operations such as creation,
deletion, modification and getting location of the blocks. It also takes care
of replica placement and replication. stores the blocks and provides
read/write access to it.
improve scalability and isolation
without federation namespace does not scale as easily
10. HDFS Federation
Hadoop 1.0
Datanode 1
Namenode
Block Management
NS1
Datanode n
Hadoop 2.0
Block Pool
Datanode 1
NN 1
Pool1
NS1
NN 2
Pool2
NS2
NN n
Pooln
NS n
Datanode 2 Datanode n
Blockstorage
11. HDFS FED Example
Hadoop 2.0
Datanode 1
NN 1
NS1
/user/data/et
l
NN 2
NS2
/user/data/x
ml
NN n
NS n
/home/strea
ming/data/w
eather
Datanode 2 Datanode n
12. HA
Prior to Hadoop 2.0 –
One NameNode for metadata management
Single point of failure
HDFS High Availability –
Two NameNodes in the same cluster
Active NameNode: responsive for all client operations
Standby NameNode: maintain enough state to provide a fast failover
Shared storage
Active NN writes edit log
Standby NN reads edit log and applies to its own namespace
During failover, Standby NN reads all the edits and transitions to Active state
18. Coherency Model
First block is visible to read once more than a block’s worth of data is
written
The current block is the one that’s not always visible to reader
20. DataNode
HDFS
InputSplit
Memory
Buffer
p1 p2
p1 p2 p3
p3 p2
p1
p2
p3
Map 1
DataNode
p1
p1
Reduce
DataNode
p2
p2
Reduce
p1
p2
• Intermediate
map output
files.
• Sorted by key
• Part-m-00000
• Combine()
• Spills data to disk.
• Partitions data.
• Sorts by key
• Map takes <k,v>.
• Applies map() in <k,v>
• Writes the o/p to
mem
Merge.1file/partition
Sort/merge Reduce
Output
HDFS
Output
HDFS
DataNode n
p1 p4
p1 p2 p4
p4
p1
p2
p4
HDFS
InputSplit
Memory
Buffer
Map 1
100
MB
shuffleMap [o/p is sorted by key]
21. MR gotchas
Map takes input splits as key Value pairs
Output from mapper is always sorted but based on Key.
context.write(outKey, outValue);
then result will be sorted based on outKey
Default partition is hashing keys
Reducer reduces a set of intermediate values which share a key to a smaller set
of values.
reduce() function is called for each key
setNumOfReducetasks(0)