This document summarizes Hadoop MapReduce, including its goals of distribution and reliability. It describes the roles of mappers, reducers, and other system components like the JobTracker and TaskTracker. Mappers process input splits in parallel and generate intermediate key-value pairs. Reducers sort and group the outputs by key before processing each unique key. The JobTracker coordinates jobs while the TaskTracker manages tasks on each node.
Map reduce definition
A Programming model and an associated implementation for processing and generating large data sets with a parallel*, distributed* algorithm on a cluster*.
A Parallel algorithm is an algorithm which can be executed a piece at a time on many different processing devices, and then combined together again at the end to get the correct result.
A distributed algorithm is an algorithm designed to run on computer hardware
constructed from interconnected processors.
A computer cluster consists of connected computers that work together so that, in many respects, they can be viewed as a single system. Computer clusters have each node set to perform the same task, controlled and scheduled by software.
Map reduce - division into two categories map and reduce
working of Jobtracker , TaskTracker ,Namenode , Datanode in mapreduce engine of hadoop
Fault tolerance in hadoop
Box class datatypes
Allowable file formats
wordcount job explained using animation in hadoop using mapreduce
fields where map reduce can be implimented
limitations of map reduce
Mapreduce examples starting from the basic WordCount to a more complex K-means algorithm. The code contained in these slides is available at https://github.com/andreaiacono/MapReduce
Map reduce definition
A Programming model and an associated implementation for processing and generating large data sets with a parallel*, distributed* algorithm on a cluster*.
A Parallel algorithm is an algorithm which can be executed a piece at a time on many different processing devices, and then combined together again at the end to get the correct result.
A distributed algorithm is an algorithm designed to run on computer hardware
constructed from interconnected processors.
A computer cluster consists of connected computers that work together so that, in many respects, they can be viewed as a single system. Computer clusters have each node set to perform the same task, controlled and scheduled by software.
Map reduce - division into two categories map and reduce
working of Jobtracker , TaskTracker ,Namenode , Datanode in mapreduce engine of hadoop
Fault tolerance in hadoop
Box class datatypes
Allowable file formats
wordcount job explained using animation in hadoop using mapreduce
fields where map reduce can be implimented
limitations of map reduce
Mapreduce examples starting from the basic WordCount to a more complex K-means algorithm. The code contained in these slides is available at https://github.com/andreaiacono/MapReduce
This slide deck is used as an introduction to Relational Algebra and its relation to the MapReduce programming model, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
Learning Objectives - In this module, you will understand Hadoop MapReduce framework and how MapReduce works on data stored in HDFS. Also, you will learn what are the different types of Input and Output formats in MapReduce framework and their usage.
In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.
This slide deck is used as an introduction to Relational Algebra and its relation to the MapReduce programming model, as part of the Distributed Systems and Cloud Computing course I hold at Eurecom.
Course website:
http://michiard.github.io/DISC-CLOUD-COURSE/
Sources available here:
https://github.com/michiard/DISC-CLOUD-COURSE
Learning Objectives - In this module, you will understand Hadoop MapReduce framework and how MapReduce works on data stored in HDFS. Also, you will learn what are the different types of Input and Output formats in MapReduce framework and their usage.
In this presentation , i provide in depth information about the how MapReduce works. It contains many details about the execution steps , Fault tolerance , master / worker responsibilities.
Pyshark is a wrapper around tshark comand line utility to capture a live Network packet or from a
capture file. Pyshark is useful in parsing capture data for analysis.
As MapReduce clusters have become popular these days, their scheduling is one of the important factor which is to be considered. In order to achieve good performance a MapReduce scheduler must avoid unnecessary data transmission. Hence different scheduling algorithms for MapReduce are necessary to provide good performance. This
slide provides an overview of many different scheduling algorithms for MapReduce.
A very categorized presentation about big data analytics Various topics like Introduction to Big Data,Hadoop,HDFS Map Reduce, Mahout,K-means Algorithm,H-Base are explained very clearly in simple language for everyone to understand easily.
Introduction to MapReduce | MapReduce Architecture | MapReduce FundamentalsSkillspeed
This Hadoop MapReduce tutorial will unravel MapReduce Programming, MapReduce Commands, MapReduce Fundamentals, Driver Class, Mapper Class, Reducer Class, Job Tracker & Task Tracker.
At the end, you'll have a strong knowledge regarding Hadoop MapReduce Basics.
PPT Agenda:
✓ Introduction to BIG Data & Hadoop
✓ What is MapReduce?
✓ MapReduce Data Flows
✓ MapReduce Programming
----------
What is MapReduce?
MapReduce is a programming framework for distributed processing of large data-sets via commodity computing clusters. It is based on the principal of parallel data processing, wherein data is broken into smaller blocks rather than processed as a single block. This ensures a faster, secure & scalable solution. Mapreduce commands are based in Java.
----------
What are MapReduce Components?
It has the following components:
1. Combiner: The combiner collates all the data from the sample set based on your desired filters. For example, you can collate data based on day, week, month and year. After this, the data is prepared and sent for parallel processing.
2. Job Tracker: This allocates the data across multiple servers.
3. Task Tracker: This executes the program across various servers.
4. Reducer: It will isolate the desired output from across the multiple servers.
----------
Applications of MapReduce
1. Data Mining
2. Document Indexing
3. Business Intelligence
4. Predictive Modelling
5. Hypothesis Testing
----------
Skillspeed is a live e-learning company focusing on high-technology courses. We provide live instructor led training in BIG Data & Hadoop featuring Realtime Projects, 24/7 Lifetime Support & 100% Placement Assistance.
Email: sales@skillspeed.com
Website: https://www.skillspeed.com
introduction to data processing using Hadoop and PigRicardo Varela
In this talk we make an introduction to data processing with big data and review the basic concepts in MapReduce programming with Hadoop. We also comment about the use of Pig to simplify the development of data processing applications
YDN Tuesdays are geek meetups organized the first Tuesday of each month by YDN in London
A tutorial presentation based on hadoop.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system.
This presentation explains the architecture of classic mapreduce or MapReduce 1 in Hadoop, Most of the sides are animated. So please download and read it.
Apache Flink Internals: Stream & Batch Processing in One System – Apache Flin...ucelebi
An in-depth look at Apache Flink’s Streaming Dataflow Engine. Flink executes data streaming programs directly as streams with low latency and flexible user-defined state and models batch programs as streaming programs on finite data streams.
The slides cover the general design of the runtime and show how the engine is able to support diverse features and workloads without compromising on performance or usability.
Flink Forward, Berlin
October 13, 2015
Map reduce - simplified data processing on large clustersCleverence Kombe
The paper introduces MapReduce, a programming model and an associated implementation for processing and generating large data sets. It exploits the inherent parallelism in the word load to split it into multiple independent subtasks that can be executed simultaneously.
The MapReduce consists of two phases: The first phase is mapping which reads data from distributed file system and performs filtering or transformation, and the second phase is reducing which aggregates the shuffled output from mapping phase. Programs are written in a functional style which automatically parallelized and executed on a large cluster of commodity machines. The run-time system (library code) handles the details about partitioning the input data, scheduling the program’s execution across a set of machines, take care of machine failures, and managing the inter-machine communication.
Unified stateful big data processing in Apache Beam (incubating)Aljoscha Krettek
Apache Beam lets you process unbounded, out-of-order, global-scale data with portable high-level pipelines, but not all use cases are pipelines of simple “map” and “combine” operations. Aljoscha Krettek introduces Beam’s new State API, which brings scalability and consistency to fine-grained stateful processing while interoperating with Beam’s other features such as consistent event-time windowing and windowed side inputs—all while remaining portable to any Beam runner, including Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Aljoscha covers the new state and timer features in Beam and shows how to use them to express common real-world use cases in a backend-agnostic manner.
Examples of new use cases unlocked by Beam’s new mutable state and timers include:
* Microservice-like streaming applications such as new user account verification and digital ordering
* Complex aggregations that cannot easily be expressed as an efficient associative combiner
* Output based on customized conditions, such as limiting to only “significant” changes in a learned model (resulting in potentially large cost savings in subsequent processing)
* Fine control over retrieval and storage of intermediate values during aggregation
* Reading from and writing to external systems with detailed management of the nature and size of requests
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Apache Beam's new State API brings scalability and consistency to fine-grained stateful processing while remaining portable to any Beam runner. Aljoscha Krettek introduces the new state and timer features in Beam and shows how to use them to express common real-world use cases in a backend-agnostic manner.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
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📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
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While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
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My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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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.
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2. Map/Reduce Goals
– Distribution
• The data is available where needed.
• Application does not care how many computers
are being used.
– Reliability
• Application does not care that computers or
networks may have temporary or permanent
failures.
2
3. Application Perspective
• Define Mapper and Reducer classes and a
“launching” program.
• Mapper
– Is given a stream of key1,value1 pairs
– Generates a stream of key2, value2 pairs
• Reducer
– Is given a key2 and a stream of value2’s
– Generates a stream of key3, value3 pairs
• Launching Program
– Creates a JobConf to define a job.
– Submits JobConf to JobTracker and waits for
completion. 3
5. Input & Output Formats
• The application also chooses input and output
formats, which define how the persistent data
is read and written. These are interfaces and
can be defined by the application.
• InputFormat
– Splits the input to determine the input to each map
task.
– Defines a RecordReader that reads key, value
pairs that are passed to the map task
• OutputFormat
– Given the key, value pairs and a filename, writes
the reduce task output to persistent store.
5
6. Output Ordering
• The application can control the sort order and
partitions of the output via
OutputKeyComparator and Partitioner.
• OutputKeyComparator
– Defines how to compare serialized keys.
– Defaults to WritableComparable, but should be
defined for any application defined key types.
• key1.compareTo(key2)
• Partitioner
– Given a map output key and the number of
reduces, chooses a reduce.
– Defaults to HashPartitioner
6
• key.hashCode % numReduces
7. Combiners
• Combiners are an optimization for jobs with
reducers that can merge multiple values into
a single value.
• Typically, the combiner is the same as the
reducer and runs on the map outputs before it
is transferred to the reducer’s machine.
• For example, WordCount’s mapper generates
(word, count) and the combiner and reducer
generate the sum for each word.
– Input: “hi Owen bye Owen”
– Map output: (“hi”, 1), (“Owen”, 1), (“bye”,1), (“Owen”,1)
– Combiner output: (“Owen”, 2), (“bye”, 1), (“hi”, 1) 7
8. Process Communication
• Use a custom RPC implementation
– Easy to change/extend
– Defined as Java interfaces
– Server objects implement the interface
– Client proxy objects automatically created
• All messages originate at the client
– Prevents cycles and therefore deadlocks
• Errors
– Include timeouts and communication problems.
– Are signaled to client via IOException.
– Are NEVER signaled to the server.
8
9. Map/Reduce Processes
• Launching Application
– User application code
– Submits a specific kind of Map/Reduce job
• JobTracker
– Handles all jobs
– Makes all scheduling decisions
• TaskTracker
– Manager for all tasks on a given node
• Task
– Runs an individual map or reduce fragment for a
given job
– Forks from the TaskTracker
9
11. Job Control Flow
• Application launcher creates and submits job.
• JobTracker initializes job, creates FileSplits,
and adds tasks to queue.
• TaskTrackers ask for a new map or reduce
task every 10 seconds or when the previous
task finishes.
• As tasks run, the TaskTracker reports status
to the JobTracker every 10 seconds.
• When job completes, the JobTracker tells the
TaskTrackers to delete temporary files.
• Application launcher notices job completion
and stops waiting. 11
12. Application Launcher
• Application code to create JobConf and set
the parameters.
– Mapper, Reducer classes
– InputFormat and OutputFormat classes
– Combiner class, if desired
• Writes JobConf and the application jar to DFS
and submits job to JobTracker.
• Can exit immediately or wait for the job to
complete or fail.
12
13. JobTracker
• Takes JobConf and creates an instance of
the InputFormat. Calls the getSplits method to
generate map inputs.
• Creates a JobInProgress object and a bunch
of TaskInProgress “TIP” and Task objects.
– JobInProgress is the status of the job.
– TaskInProgress is the status of a fragment of
work.
– Task is an attempt to do a TIP.
• As TaskTrackers request work, they are given
Tasks to execute. 13
14. TaskTracker
• All Tasks
– Create the TaskRunner
– Copy the job.jar and job.xml from DFS.
– Localize the JobConf for this Task.
– Call task.prepare() (details later)
– Launch the Task in a new JVM under
TaskTracker.Child.
– Catch output from Task and log it at the info level.
– Take Task status updates and send to JobTracker
every 10 seconds.
– If job is killed, kill the task.
– If task dies or completes, tell the JobTracker. 14
15. TaskTracker for Reduces
• For Reduces, the task.prepare() fetches all of
the relevant map outputs for this reduce.
• Files are fetched using http from the map’s
TaskTracker’s Jetty.
• Files are fetched in parallel threads, but only
1 to each host.
• When fetches fail, a backoff scheme is used
to keep from overloading TaskTrackers.
• Fetching accounts for the first 33% of the
reduce’s progress.
15
16. Map Tasks
• Use the InputFormat object to create a
RecordReader from the FileSplit.
• Loop through the keys and values in the
FileSplit and feed each to the mapper.
• For no combiner, a SequenceFile is written
for the keys to each reduce.
• With a combiner, the frameworks buffers
100,000 keys and values, sorts, combines,
and writes them to SequenceFile’s for each
reduce.
16
17. Reduce Tasks: Sort
• Sort
– 33% to 66% of reduce’s progress
– Base
• Read 100 (io.sort.mb) meg of keys and values into
memory.
• Sort the memory
• Write to disk
– Merge
• Read 10 (io.sort.factor) files and do a merge into 1 file.
• Repeat as many times as required (2 levels for 100 files,
3 levels for 1000 files, etc.)
17
18. Reduce Tasks: Reduce
• Reduce
– 66% to 100% of reduce’s progress
– Use a SequenceFile.Reader to read sorted input
and pass to reducer one key at a time along with
the associated values.
– Output keys and values are written to the
OutputFormat object, which usually writes a file to
DFS.
– The output from the reduce is NOT resorted, so it
is in the order and fragmentation of the map output
keys.
18