This document provides an overview of the MapReduce paradigm and Hadoop framework. It describes how MapReduce uses a map and reduce phase to process large amounts of distributed data in parallel. Hadoop is an open-source implementation of MapReduce that stores data in HDFS. It allows applications to work with thousands of computers and petabytes of data. Key advantages of MapReduce include fault tolerance, scalability, and flexibility. While it is well-suited for batch processing, it may not replace traditional databases for data warehousing. Overall efficiency remains an area for improvement.
The document summarizes the history and evolution of non-relational databases, known as NoSQL databases. It discusses early database systems like MUMPS and IMS, the development of the relational model in the 1970s, and more recent NoSQL databases developed by companies like Google, Amazon, Facebook to handle large, dynamic datasets across many servers. Pioneering systems like Google's Bigtable and Amazon's Dynamo used techniques like distributed indexing, versioning, and eventual consistency that influenced many open-source NoSQL databases today.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
This document discusses virtualization, containers, and hyperconvergence. It provides an overview of virtualization and its benefits including hardware abstraction and multi-tenancy. However, virtualization also has challenges like significant overhead and repetitive configuration tasks. Containers provide similar benefits with less overhead by abstracting at the operating system level. The document then discusses how hyperconvergence combines compute, storage, and networking to simplify deployment and operations. It notes that many hyperconverged solutions still face virtualization challenges. The presentation argues that combining containers and hyperconvergence can provide both the benefits of containers' efficiency and hyperconvergence's scale. Stratoscale is presented as a solution that provides containers as a service with multi-tenancy, SLA-driven performance
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Here is how you can solve this problem using MapReduce and Unix commands:
Map step:
grep -o 'Blue\|Green' input.txt | wc -l > output
This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches).
Reduce step:
cat output
This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green.
So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Slides from AIS and Microsoft's half-day session on the recently-announced Windows Azure Infrastructure as a Service (IaaS) offering. After a brief overview of the Azure Platform as a Service (PaaS) model, we will focus on key IaaS concepts. Additionally, we will walk you through a number of scenarios enabled by Azure IaaS and several demonstrations.
Agenda:
Overview of Windows Azure Platform
Azure IaaS
Why IaaS?
IaaS Core Concepts
Supported Applications
Azure Virtual Machines
Disk Mobility
VM export / Import
Availability
Azure Virtual Network
Virtualization allows multiple operating systems and applications to run on the same physical server at the same time. This increases hardware utilization and flexibility while reducing IT costs. VMware virtualization solutions can reduce energy costs by 80% through server consolidation and powering down unused servers without affecting applications or users. Virtualization makes hardware resources independent of operating systems and applications, treating them as single unified units that can be more easily deployed, maintained, and supported.
The document discusses several security challenges related to cloud computing. It covers topics like data breaches, misconfiguration issues, lack of cloud security strategy, insufficient identity and access management, account hijacking, insider threats, and insecure application programming interfaces. The document emphasizes that securing customer data and applications is critical for cloud service providers to maintain trust and meet compliance requirements.
The document summarizes two papers about MapReduce frameworks for cloud computing. The first paper describes Hadoop, which uses MapReduce and HDFS to process large amounts of distributed data across clusters. HDFS stores data across cluster nodes in a fault-tolerant manner, while MapReduce splits jobs into parallel map and reduce tasks. The second paper discusses P2P-MapReduce, which allows for a dynamic cloud environment where nodes can join and leave. It uses a peer-to-peer model where nodes can be masters or slaves, and maintains backup masters to prevent job loss if the primary master fails.
The document discusses Hadoop, an open-source software framework that allows distributed processing of large datasets across clusters of computers. It describes Hadoop as having two main components - the Hadoop Distributed File System (HDFS) which stores data across infrastructure, and MapReduce which processes the data in a parallel, distributed manner. HDFS provides redundancy, scalability, and fault tolerance. Together these components provide a solution for businesses to efficiently analyze the large, unstructured "Big Data" they collect.
Introduction to Hadoop and Hadoop component rebeccatho
This document provides an introduction to Apache Hadoop, which is an open-source software framework for distributed storage and processing of large datasets. It discusses Hadoop's main components of MapReduce and HDFS. MapReduce is a programming model for processing large datasets in a distributed manner, while HDFS provides distributed, fault-tolerant storage. Hadoop runs on commodity computer clusters and can scale to thousands of nodes.
The document summarizes the history and evolution of non-relational databases, known as NoSQL databases. It discusses early database systems like MUMPS and IMS, the development of the relational model in the 1970s, and more recent NoSQL databases developed by companies like Google, Amazon, Facebook to handle large, dynamic datasets across many servers. Pioneering systems like Google's Bigtable and Amazon's Dynamo used techniques like distributed indexing, versioning, and eventual consistency that influenced many open-source NoSQL databases today.
This document discusses different architectures for big data systems, including traditional, streaming, lambda, kappa, and unified architectures. The traditional architecture focuses on batch processing stored data using Hadoop. Streaming architectures enable low-latency analysis of real-time data streams. Lambda architecture combines batch and streaming for flexibility. Kappa architecture avoids duplicating processing logic. Finally, a unified architecture trains models on batch data and applies them to real-time streams. Choosing the right architecture depends on use cases and available components.
The document provides an introduction to NoSQL and HBase. It discusses what NoSQL is, the different types of NoSQL databases, and compares NoSQL to SQL databases. It then focuses on HBase, describing its architecture and components like HMaster, regionservers, Zookeeper. It explains how HBase stores and retrieves data, the write process involving memstores and compaction. It also covers HBase shell commands for creating, inserting, querying and deleting data.
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
This document discusses virtualization, containers, and hyperconvergence. It provides an overview of virtualization and its benefits including hardware abstraction and multi-tenancy. However, virtualization also has challenges like significant overhead and repetitive configuration tasks. Containers provide similar benefits with less overhead by abstracting at the operating system level. The document then discusses how hyperconvergence combines compute, storage, and networking to simplify deployment and operations. It notes that many hyperconverged solutions still face virtualization challenges. The presentation argues that combining containers and hyperconvergence can provide both the benefits of containers' efficiency and hyperconvergence's scale. Stratoscale is presented as a solution that provides containers as a service with multi-tenancy, SLA-driven performance
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
Here is how you can solve this problem using MapReduce and Unix commands:
Map step:
grep -o 'Blue\|Green' input.txt | wc -l > output
This uses grep to search the input file for the strings "Blue" or "Green" and print only the matches. The matches are piped to wc which counts the lines (matches).
Reduce step:
cat output
This isn't really needed as there is only one mapper. Cat prints the contents of the output file which has the count of Blue and Green.
So MapReduce has been simulated using grep for the map and cat for the reduce functionality. The key aspects are - grep extracts the relevant data (map
The document provides an overview of Hadoop and its ecosystem. It discusses the history and architecture of Hadoop, describing how it uses distributed storage and processing to handle large datasets across clusters of commodity hardware. The key components of Hadoop include HDFS for storage, MapReduce for processing, and an ecosystem of related projects like Hive, HBase, Pig and Zookeeper that provide additional functions. Advantages are its ability to handle unlimited data storage and high speed processing, while disadvantages include lower speeds for small datasets and limitations on data storage size.
Slides from AIS and Microsoft's half-day session on the recently-announced Windows Azure Infrastructure as a Service (IaaS) offering. After a brief overview of the Azure Platform as a Service (PaaS) model, we will focus on key IaaS concepts. Additionally, we will walk you through a number of scenarios enabled by Azure IaaS and several demonstrations.
Agenda:
Overview of Windows Azure Platform
Azure IaaS
Why IaaS?
IaaS Core Concepts
Supported Applications
Azure Virtual Machines
Disk Mobility
VM export / Import
Availability
Azure Virtual Network
Virtualization allows multiple operating systems and applications to run on the same physical server at the same time. This increases hardware utilization and flexibility while reducing IT costs. VMware virtualization solutions can reduce energy costs by 80% through server consolidation and powering down unused servers without affecting applications or users. Virtualization makes hardware resources independent of operating systems and applications, treating them as single unified units that can be more easily deployed, maintained, and supported.
The document discusses several security challenges related to cloud computing. It covers topics like data breaches, misconfiguration issues, lack of cloud security strategy, insufficient identity and access management, account hijacking, insider threats, and insecure application programming interfaces. The document emphasizes that securing customer data and applications is critical for cloud service providers to maintain trust and meet compliance requirements.
The document summarizes two papers about MapReduce frameworks for cloud computing. The first paper describes Hadoop, which uses MapReduce and HDFS to process large amounts of distributed data across clusters. HDFS stores data across cluster nodes in a fault-tolerant manner, while MapReduce splits jobs into parallel map and reduce tasks. The second paper discusses P2P-MapReduce, which allows for a dynamic cloud environment where nodes can join and leave. It uses a peer-to-peer model where nodes can be masters or slaves, and maintains backup masters to prevent job loss if the primary master fails.
This document discusses efficient analysis of big data using the MapReduce framework. It introduces the challenges of analyzing large and complex datasets, and describes how MapReduce addresses these challenges through its map and reduce functions. MapReduce allows distributed processing of big data across clusters of computers using a simple programming model.
High Performance & High Throughput Computing - EUDAT Summer School (Giuseppe ...EUDAT
Giuseppe will present the differences between high-performance and high-throughput applications. High-throughput computing (HTC) refers to computations where individual tasks do not need to interact while running. It differs from High-performance (HPC) where frequent and rapid exchanges of intermediate results is required to perform the computations. HPC codes are based on tightly coupled MPI, OpenMP, GPGPU, and hybrid programs and require low latency interconnected nodes. HTC makes use of unreliable components distributing the work out to every node and collecting results at the end of all parallel tasks.
Visit: https://www.eudat.eu/eudat-summer-school
Hadoop/MapReduce is an open source software framework for distributed storage and processing of large datasets across clusters of computers. It uses MapReduce, a programming model where input data is processed by "map" functions in parallel, and results are combined by "reduce" functions, to process and generate outputs from large amounts of data and nodes. The core components are the Hadoop Distributed File System for data storage, and the MapReduce programming model and framework. MapReduce jobs involve mapping data to intermediate key-value pairs, shuffling and sorting the data, and reducing to output results.
This document discusses a research project on scheduling schemes for Hadoop clusters. It begins with an introduction to Hadoop and its two main components, MapReduce and HDFS. It then reviews existing scheduling systems like FIFO, Facebook's Fair Scheduler, and Yahoo's Capacity Scheduler. The proposed system aims to address issues like CPU and I/O underutilization in the existing systems by using a predictive scheduler and prefetching mechanism. This predictive scheduler would assign tasks to appropriate task trackers and allow prefetching of data blocks. The prefetching module would help avoid I/O stalls and maximize CPU utilization. In comparison to existing systems, the proposed system is expected to provide higher I/O performance,
Hadoop is an open-source framework that uses clusters of commodity hardware to store and process big data using the MapReduce programming model. It consists of four main components: MapReduce for distributed processing, HDFS for storage, YARN for resource management and scheduling, and common utilities. HDFS stores large files as blocks across nodes for fault tolerance. MapReduce jobs are split into map and reduce phases to process data in parallel. YARN schedules resources and manages job execution. The common utilities provide libraries and scripts used by all Hadoop components. Major companies use Hadoop to analyze large amounts of data.
Apache hadoop, hdfs and map reduce OverviewNisanth Simon
This document provides an overview of Apache Hadoop, HDFS, and MapReduce. It describes how Hadoop uses a distributed file system (HDFS) to store large amounts of data across commodity hardware. It also explains how MapReduce allows distributed processing of that data by allocating map and reduce tasks across nodes. Key components discussed include the HDFS architecture with NameNodes and DataNodes, data replication for fault tolerance, and how the MapReduce engine works with a JobTracker and TaskTrackers to parallelize jobs.
Hadoop is an open source framework for running large-scale data processing jobs across clusters of computers. It has two main components: HDFS for reliable storage and Hadoop MapReduce for distributed processing. HDFS stores large files across nodes through replication and uses a master-slave architecture. MapReduce allows users to write map and reduce functions to process large datasets in parallel and generate results. Hadoop has seen widespread adoption for processing massive datasets due to its scalability, reliability and ease of use.
This document summarizes a proposal to improve fault tolerance in Hadoop clusters. It proposes adding a "Backup" state to store intermediate MapReduce data, so reducers can continue working even if mappers fail. It also proposes a "supernode" protocol where neighboring slave nodes communicate task information. If one node fails, a neighbor can take over its tasks without involving the JobTracker. This would improve fault tolerance by allowing computation to continue locally between nodes after failures.
The document discusses cloud computing systems and MapReduce. It provides background on MapReduce, describing how it works and how it was inspired by functional programming concepts like map and reduce. It also discusses some limitations of MapReduce, noting that it is not designed for general-purpose parallel processing and can be inefficient for certain types of workloads. Alternative approaches like MRlite and DCell are proposed to provide more flexible and efficient distributed processing frameworks.
Hadoop and Pig are tools for analyzing large datasets. Hadoop uses MapReduce and HDFS for distributed processing and storage. Pig provides a high-level language for expressing data analysis jobs that are compiled into MapReduce programs. Common tasks like joins, filters, and grouping are built into Pig for easier programming compared to lower-level MapReduce.
Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of commodity hardware. It implements Google's MapReduce programming model and the Hadoop Distributed File System (HDFS) for reliable data storage. Key components include a JobTracker that coordinates jobs, TaskTrackers that run tasks on worker nodes, and a NameNode that manages the HDFS namespace and DataNodes that store application data. The framework provides fault tolerance, parallelization, and scalability.
Hadoop Summit 2010 Tuning Hadoop To Deliver Performance To Your ApplicationYahoo Developer Network
This document provides guidelines for tuning Hadoop for performance. It discusses key factors that influence Hadoop performance like hardware configuration, application logic, and system bottlenecks. It also outlines various configuration parameters that can be tuned at the cluster and job level to optimize CPU, memory, disk throughput, and task granularity. Sample tuning gains are shown for a webmap application where tuning multiple parameters improved job execution time by up to 22%.
This document discusses the growth of data and challenges in storing and analyzing large datasets. It introduces Hadoop as a solution for processing large datasets in parallel across commodity servers. Key aspects of Hadoop covered include its core components HDFS for storage and MapReduce for distributed processing. Example uses by large companies like Amazon and Facebook are listed. The document contrasts Hadoop with RDBMS and explains when Hadoop is preferable to use.
Hadoop is a software framework for distributed processing of large datasets across large clusters of computers
Large datasets Terabytes or petabytes of data
Large clusters hundreds or thousands of nodes
This document provides an introduction to Apache Spark, including its architecture and programming model. Spark is a cluster computing framework that provides fast, in-memory processing of large datasets across multiple cores and nodes. It improves upon Hadoop MapReduce by allowing iterative algorithms and interactive querying of datasets through its use of resilient distributed datasets (RDDs) that can be cached in memory. RDDs act as immutable distributed collections that can be manipulated using transformations and actions to implement parallel operations.
Hadoop eco system with mapreduce hive and pigKhanKhaja1
This document provides an overview of MapReduce architecture and components. It discusses how MapReduce processes data using map and reduce tasks on key-value pairs. The JobTracker manages jobs by scheduling tasks on TaskTrackers. Data is partitioned and sorted during the shuffle and sort phase before being processed by reducers. Components like Hive, Pig, partitions, combiners, and HBase are described in the context of how they integrate with and optimize MapReduce processing.
MapReduce provides an easy way to process large datasets in a distributed manner. It uses mappers to process input data and generate intermediate key-value pairs, and reducers to combine those intermediate pairs into the final output. Key aspects include job tracking, splitting data into tasks, and storing intermediate output locally rather than on HDFS for efficiency, since it is discarded after reducing.
Spark improves on Hadoop MapReduce by keeping data in-memory between jobs. It reads data into resilient distributed datasets (RDDs) that can be transformed and cached in memory across nodes for faster iterative jobs. RDDs are immutable, partitioned collections distributed across a Spark cluster. Transformations define operations on RDDs, while actions trigger computation by passing data to the driver program.
we are interested in performing Big Data analytics, we need to
learn Hadoop to perform operations with Hadoop MapReduce. In this Presentation, we
will discuss what MapReduce is, why it is necessary, how MapReduce programs can
be developed through Apache Hadoop, and more.
Hadoop is a framework for distributed storage and processing of large datasets across clusters of computers. It addresses problems like hardware failure and combining data after analysis. The core components are HDFS for distributed storage and MapReduce for distributed processing. HDFS stores data as blocks across nodes and handles replication for reliability. The Namenode manages the file system namespace and metadata, while Datanodes store and retrieve blocks. Hadoop supports reliable analysis of large datasets in a distributed manner through its scalable architecture.
The document provides an overview of developing a big data strategy. It discusses defining a big data strategy by identifying opportunities and economic value of data, defining a big data architecture, selecting technologies, understanding data science, developing analytics, and institutionalizing big data. A good strategy explores these subject domains and aligns them to organizational objectives to accomplish a data-driven vision and direct the organization.
Views in Odoo - Advanced Views - Pivot View in Odoo 17Celine George
In Odoo, the pivot view is a graphical representation of data that allows users to analyze and summarize large datasets quickly. It's a powerful tool for generating insights from your business data.
The pivot view in Odoo is a valuable tool for analyzing and summarizing large datasets, helping you gain insights into your business operations.
Delegation Inheritance in Odoo 17 and Its Use CasesCeline George
There are 3 types of inheritance in odoo Classical, Extension, and Delegation. Delegation inheritance is used to sink other models to our custom model. And there is no change in the views. This slide will discuss delegation inheritance and its use cases in odoo 17.
Integrated Marketing Communications (IMC)- Concept, Features, Elements, Role of advertising in IMC
Advertising: Concept, Features, Evolution of Advertising, Active Participants, Benefits of advertising to Business firms and consumers.
Classification of advertising: Geographic, Media, Target audience and Functions.
Is Email Marketing Really Effective In 2024?Rakesh Jalan
Slide 1
Is Email Marketing Really Effective in 2024?
Yes, Email Marketing is still a great method for direct marketing.
Slide 2
In this article we will cover:
- What is Email Marketing?
- Pros and cons of Email Marketing.
- Tools available for Email Marketing.
- Ways to make Email Marketing effective.
Slide 3
What Is Email Marketing?
Using email to contact customers is called Email Marketing. It's a quiet and effective communication method. Mastering it can significantly boost business. In digital marketing, two long-term assets are your website and your email list. Social media apps may change, but your website and email list remain constant.
Slide 4
Types of Email Marketing:
1. Welcome Emails
2. Information Emails
3. Transactional Emails
4. Newsletter Emails
5. Lead Nurturing Emails
6. Sponsorship Emails
7. Sales Letter Emails
8. Re-Engagement Emails
9. Brand Story Emails
10. Review Request Emails
Slide 5
Advantages Of Email Marketing
1. Cost-Effective: Cheaper than other methods.
2. Easy: Simple to learn and use.
3. Targeted Audience: Reach your exact audience.
4. Detailed Messages: Convey clear, detailed messages.
5. Non-Disturbing: Less intrusive than social media.
6. Non-Irritating: Customers are less likely to get annoyed.
7. Long Format: Use detailed text, photos, and videos.
8. Easy to Unsubscribe: Customers can easily opt out.
9. Easy Tracking: Track delivery, open rates, and clicks.
10. Professional: Seen as more professional; customers read carefully.
Slide 6
Disadvantages Of Email Marketing:
1. Irrelevant Emails: Costs can rise with irrelevant emails.
2. Poor Content: Boring emails can lead to disengagement.
3. Easy Unsubscribe: Customers can easily leave your list.
Slide 7
Email Marketing Tools
Choosing a good tool involves considering:
1. Deliverability: Email delivery rate.
2. Inbox Placement: Reaching inbox, not spam or promotions.
3. Ease of Use: Simplicity of use.
4. Cost: Affordability.
5. List Maintenance: Keeping the list clean.
6. Features: Regular features like Broadcast and Sequence.
7. Automation: Better with automation.
Slide 8
Top 5 Email Marketing Tools:
1. ConvertKit
2. Get Response
3. Mailchimp
4. Active Campaign
5. Aweber
Slide 9
Email Marketing Strategy
To get good results, consider:
1. Build your own list.
2. Never buy leads.
3. Respect your customers.
4. Always provide value.
5. Don’t email just to sell.
6. Write heartfelt emails.
7. Stick to a schedule.
8. Use photos and videos.
9. Segment your list.
10. Personalize emails.
11. Ensure mobile-friendliness.
12. Optimize timing.
13. Keep designs clean.
14. Remove cold leads.
Slide 10
Uses of Email Marketing:
1. Affiliate Marketing
2. Blogging
3. Customer Relationship Management (CRM)
4. Newsletter Circulation
5. Transaction Notifications
6. Information Dissemination
7. Gathering Feedback
8. Selling Courses
9. Selling Products/Services
Read Full Article:
https://digitalsamaaj.com/is-email-marketing-effective-in-2024/
Principles of Roods Approach!!!!!!!.pptxibtesaam huma
Principles of Rood’s Approach
Treatment technique used in physiotherapy for neurological patients which aids them to recover and improve quality of life
Facilitatory techniques
Inhibitory techniques
Credit limit improvement system in odoo 17Celine George
In Odoo 17, confirmed and uninvoiced sales orders are now factored into a partner's total receivables. As a result, the credit limit warning system now considers this updated calculation, leading to more accurate and effective credit management.
How to Handle the Separate Discount Account on Invoice in Odoo 17Celine George
In Odoo, separate discount account can be set up to accurately track and manage discounts applied on various transaction and ensure precise financial reporting and analysis
4. MapReduce Paradigm
• Splits input files into blocks (typically of 64MB
each)
• Operates on key/value pairs
• Mappers filter & transform input data
• Reducers aggregate mappers output
• Efficient way to process the cluster:
– Move code to data
– Run code on all machines
5. • Map
Hash Function
(K1,v1) List(k2,v2)
• Reduce
Aggregate Function List(k3,v3)
(k2,list(v2))
6. Advanced MapReduce
• Hadoop Streaming
– Lets you stream Mapper and reducer written in
other languages such as python, ruby, etc.,
• Chaining MapReduce jobs
• Joining data
• Bloom filters
7. Hadoop
• Open Source Implementation of MapReduce by
Apache Software Foundation.
• Created by Doug Cutting.
• Derived from Google's MapReduce and Google
File System (GFS) papers.
• Apache Hadoop is a software framework that
supports data-intensive distributed applications
under a free license
• It enables applications to work with thousands of
computational independent computers and
petabytes of data.
8. Hadoop Architecture
• Hadoop MapReduce
– Single master node, many worker nodes
– Client submits a job to master node
– Master splits each job into tasks (MapReduce),
and assigns tasks to worker nodes
• Hadoop Distributed File System (HDFS)
– Single name node, many data nodes
– Files stored as large, fixed-size (e.g. 64MB) blocks
– HDFS typically holds map input and reduce output
10. Job Scheduling in Hadoop
• One map task for each block of the input file
– Applies user-defined map function to each record in
the block
– Record = <key, value>
• User-defined number of reduce tasks
– Each reduce task is assigned a set of record groups
– For each group, apply user-defined reduce function to
the record values in that group
• Reduce tasks read from every map task
– Each read returns the record groups for that reduce
task
11. Dataflow in Hadoop
• Map tasks write their output to local disk
– Output available after map task has completed
• Reduce tasks write their output to HDFS
– Once job is finished, next job’s map tasks can be
scheduled, and will read input from HDFS
• Therefore, fault tolerance is simple: simply re-
run tasks on failure
– No consumers see partial operator output
16. HDFS
• Data is distributed and replicated over
multiple machines.
• Files are not stored in contiguously on servers
broken up into blocks.
• Designed for large files (large means GB or TB)
• Block Oriented
• Linux Style commands (eg. ls, cp, mkdir, mv)
18. Hadoop Applicability by Workflow[MTAGS11]
Score Meaning:
• Score Zero implies Easily adaptable to the workflow
• Score 0.5 implies Moderately adaptable to the
workflow
• Score 1 indicates one of the potential workflow areas
where Hadoop needs improvement
19. Relative Merits and Demerits of
Hadoop Over DBMS
Pros Cons
• Fault tolerance • No high level language like
• Self Healing rebalances files SQL in DBMS
across cluster • No schema and no index
• Highly Scalable • Low efficiency
• Highly Flexible as it does not • Very young (since 2004)
have any dependency on compared to over 40years
data model and schema of DBMS
Hadoop Relational
Scale out (add more Scaling is difficult
machines)
Key/Value pairs Tables
Say how to process the data Say what you want (SQL)
Offline/ batch Online/ realtime
20. Conclusions and Future Work
• MapReduce is easy to program
• Hadoop=HDFS+MapReduce
• Distributed, Parallel processing
• Designed for fault tolerance and high scalability
• MapReduce is unlikely to substitute DBMS in
data warehousing instead we expect them to
complement each other and help in data analysis
of scientific data patterns
• Finally, Efficiency and especially I/O costs needs
to be addressed for successful implications
21. References
[LLCCM12] Kyong-Ha Lee, Yoon-Joon Lee, Hyunsik Choi, Yon Dohn
Chung, and Bongki Moon, “Parallel data processing with MapReduce:
a survey,” SIGMOD, January 2012, pp. 11-20.
[MTAGS11] Elif Dede, Madhusudhan Govindaraju, Daniel Gunter, and
Lavanya Ramakrishnan, “ Riding the Elephant: Managing Ensembles
with Hadoop,” Proceedings of the 2011 ACM international workshop
on Many task computing on grids and supercomputers, ACM, New
York, NY, USA, pp. 49-58.
[DG08]Jeffrey Dean and Sanjay Ghemawat, “MapReduce: simplified
data processing on large clusters,” January 2008, pp. 107-113. ACM.
[CAHER10]Tyson Condie, Neil Conway, Peter Alvaro, Joseph M.
Hellerstein, Khaled Elmeleegy, and Russell Sears, “MapReduce online,”
Proceedings of the 7th USENIX conference on Networked systems
design and implementation (NSDI'10), USENIX Association, Berkeley,
CA, USA, 2010, pp. 21-37.
If Distributed Computing is so hard, Do we need it?
Run code on machines unlike conventional systems where we move data to code, do processing and then store them back.
- Out of the scope of papers
The master (Job-Tracker) is ress. Each worker runs a Task- Tracker process that manages the execution of the tasks currently assigned to that node. Each TaskTracker has a fixed number of slots for executing tasks. Each map task is assigned a portion of the input file called a split. By default, a split contains a single HDFS block, so the total number of file blocks determines the number of map tasks.
Reducers begin processing data as soon as it is produced by mappers, they can generate and refine an approximation of their final answer during the course of executionMapReduce jobs can run continuously, accepting new data as it arrives and analyzing it immediately. This allows MapReduce to be used for applications such as event monitoring and stream processing.Data Node: Store actual file blocks on disk. Does not store entire files!Report block info to Namenode.Receive instructions from namenode.Secondary Namenode: Snapshot of namenode.Not a flipover server of namenode.Help minimize downtime/data loss ifNameNode failsJobTracker: Partition tasks across the cluster. Track MapReduce tasks. Re start failed tasks on different nodes.TaskTracker does the task processing and logs each and every event.
The input to a job is an input specification that is in key-value pairs. Each job consists of two stages: first, a user defin map function is applied to each input record to produce a list of intermediate key-value pairs. Second, a user-defined reduce function is called once for each distinct key in the map output and passed the list of intermediate values associated with that key. Reduce - The shuffle phase (Each reduce task is assigned a partition of the keyrange produced by the map step, so the reduce task must fetch the content of this partition from every map task’s output). The sort phase groups records with the same key. Apply the user-defined reduce function
The buffer content is written to the local file system as an index file and a data file . Index file for indexing and The data file contains only the records, which are sorted by the key within each partition segment. A reduce task fetches data from each map task by issuing HTTP requests to a configurablenumber of TaskTrackers at once (5 by default). The Job- Tracker relays the location of every TaskTracker that hosts map output to every TaskTracker that is executing a reduce task. a reduce task cannot fetch the output of a map task until the map has finished executing and committed its final output to disk.
The map phase reads the task’s split/HDFS blocks from HDFS, parses it into records (key/value pairs), and applies the map function to each record.After the map function has been applied to each input record, the commit phase registers the final output with the TaskTracker, which then informs theJobTracker that the task has finished executing.
a reduce task fetches data from ach map task by issuing HTTP requests to a configurable number of TaskTrackers at once (5 by default). The Job-Tracker relays the location of every TaskTracker that hosts map output to every TaskTracker that is executing a reduce task. Note that a reduce task cannot fetch the output of a map task until the map has finished executing and committed its final output to disk
In this design, the output of both map and reduce tasks is written to disk before it can be consumed. This is particularly expensive for reduce tasks, because their output is written to HDFS. Output materialization simplifies fault tolerance, because it reduces the amount of state that must be restored to consistency after a node failure. If any task (either map or reduce) fails, the JobTracker simply schedules a new task to perform the same work as the failed task.
While it was possible to implement all patterns in the framework but the level of difficulty varied.This evaluation helps in identifying if an applications workflow will be suitable to run in MapReduce Framework or not.
Fault tolerant when node fails due to high data replication. Scalable just by adding nodes we can process as much data as we want.Low efficiency:- with fault tolerance and scalability as its primary goals, MapReduce operations are not always optimized for I/O efficiency. Also Map and Reduce are blocking operations
-Easy since it hides implementation details of parallelization, fault tolerance, local optimization and load balanace. Horizontal scale out helps in processing as much as data we want by simply adding as many nodes as you want.