This document discusses using Python for Hadoop and data mining. It introduces Dumbo, which allows writing Hadoop programs in Python. K-means clustering in MapReduce is also covered. Dumbo provides a Pythonic API for MapReduce and allows extending Hadoop functionality. Examples demonstrate implementing K-means in Dumbo and optimizing it by computing partial centroids locally in mappers. The document also lists Python books and tools for data mining and scientific computing.
MapReduce is a programming model for processing large datasets in a distributed system. It involves a map step that performs filtering and sorting, and a reduce step that performs summary operations. Hadoop is an open-source framework that supports MapReduce. It orchestrates tasks across distributed servers, manages communications and fault tolerance. Main steps include mapping of input data, shuffling of data between nodes, and reducing of shuffled data.
This document provides performance optimization tips for Hadoop jobs, including recommendations around compression, speculative execution, number of maps/reducers, block size, sort size, JVM tuning, and more. It suggests how to configure properties like mapred.compress.map.output, mapred.map/reduce.tasks.speculative.execution, and dfs.block.size based on factors like cluster size, job characteristics, and data size. It also identifies antipatterns to avoid like processing thousands of small files or using many maps with very short runtimes.
MapReduce is a programming model for processing large datasets in parallel. It works by breaking the dataset into independent chunks which are processed by the map function, and then grouping the output of the maps into partitions to be processed by the reduce function. Hadoop uses MapReduce to provide fault tolerance by restarting failed tasks and monitoring the JobTracker and TaskTrackers. MapReduce programs can be written in languages other than Java using Hadoop Streaming.
This document provides a high-level overview of MapReduce and Hadoop. It begins with an introduction to MapReduce, describing it as a distributed computing framework that decomposes work into parallelized map and reduce tasks. Key concepts like mappers, reducers, and job tracking are defined. The structure of a MapReduce job is then outlined, showing how input is divided and processed by mappers, then shuffled and sorted before being combined by reducers. Example map and reduce functions for a word counting problem are presented to demonstrate how a full MapReduce job works.
Hadoop MapReduce is an open source framework for distributed processing of large datasets across clusters of computers. It allows parallel processing of large datasets by dividing the work across nodes. The framework handles scheduling, fault tolerance, and distribution of work. MapReduce consists of two main phases - the map phase where the data is processed key-value pairs and the reduce phase where the outputs of the map phase are aggregated together. It provides an easy programming model for developers to write distributed applications for large scale processing of structured and unstructured data.
Map Reduce is a parallel and distributed approach developed by Google for processing large data sets. It has two key components - the Map function which processes input data into key-value pairs, and the Reduce function which aggregates the intermediate output of the Map into a final result. Input data is split across multiple machines which apply the Map function in parallel, and the Reduce function is applied to aggregate the outputs.
The document provides an overview of MapReduce and how it addresses the problem of processing large datasets in a distributed computing environment. It explains how MapReduce inspired by functional programming works by splitting data, mapping functions to pieces in parallel, and then reducing the results. Examples are given of word count and sorting word counts to find the most frequent word. Finally, it discusses how Hadoop popularized MapReduce by providing an open-source implementation and ecosystem.
Large Scale Data Analysis with Map/Reduce, part IMarin Dimitrov
This document provides an overview of large scale data analysis using distributed computing frameworks like MapReduce. It describes MapReduce and related frameworks like Dryad, and open source MapReduce tools including Hadoop, Cloud MapReduce, Elastic MapReduce, and MR.Flow. Example MapReduce algorithms for tasks like graph analysis, text indexing and retrieval are also outlined. The document is the first part of a series on large scale data analysis using distributed frameworks.
Optimal Execution Of MapReduce Jobs In Cloud - Voices 2015Deanna Kosaraju
Optimal Execution Of MapReduce Jobs In Cloud
Anshul Aggarwal, Software Engineer, Cisco Systems
Session Length: 1 Hour
Tue March 10 21:30 PST
Wed March 11 0:30 EST
Wed March 11 4:30:00 UTC
Wed March 11 10:00 IST
Wed March 11 15:30 Sydney
Voices 2015 www.globaltechwomen.com
We use MapReduce programming paradigm because it lends itself well to most data-intensive analytics jobs run on cloud these days, given its ability to scale-out and leverage several machines to parallel process data. Research has demonstrates that existing approaches to provisioning other applications in the cloud are not immediately relevant to MapReduce -based applications. Provisioning a MapReduce job entails requesting optimum number of resource sets (RS) and configuring MapReduce parameters such that each resource set is maximally utilized.
Each application has a different bottleneck resource (CPU :Disk :Network), and different bottleneck resource utilization, and thus needs to pick a different combination of these parameters based on the job profile such that the bottleneck resource is maximally utilized.
The problem at hand is thus defining a resource provisioning framework for MapReduce jobs running in a cloud keeping in mind performance goals such as Optimal resource utilization with Minimum incurred cost, Lower execution time, Energy Awareness, Automatic handling of node failure and Highly scalable solution.
This document describes the MapReduce programming model for processing large datasets in a distributed manner. MapReduce allows users to write map and reduce functions that are automatically parallelized and run across large clusters. The input data is split and the map tasks run in parallel, producing intermediate key-value pairs. These are shuffled and input to the reduce tasks, which produce the final output. The system handles failures, scheduling and parallelization transparently, making it easy for programmers to write distributed applications.
MapReduce - Basics | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2skCodH
This CloudxLab Understanding MapReduce tutorial helps you to understand MapReduce in detail. Below are the topics covered in this tutorial:
1) Thinking in Map / Reduce
2) Understanding Unix Pipeline
3) Examples to understand MapReduce
4) Merging
5) Mappers & Reducers
6) Mapper Example
7) Input Split
8) mapper() & reducer() Code
9) Example - Count number of words in a file using MapReduce
10) Example - Compute Max Temperature using MapReduce
11) Hands-on - Count number of words in a file using MapReduce on CloudxLab
Hadoop - Introduction to map reduce programming - Reunião 12/04/2014soujavajug
Aaron Myers introduces MapReduce and Hadoop. MapReduce is a distributed programming paradigm that allows processing of large datasets across clusters. It works by splitting data, distributing it across nodes, processing it in parallel using map and reduce functions, and collecting the results. Hadoop is an open source software framework for distributed storage and processing of big data using MapReduce. It includes HDFS for storage and Hadoop MapReduce for distributed computing. Developers write MapReduce jobs in Java by implementing map and reduce functions.
This document provides an overview of MapReduce concepts including:
1. It describes the anatomy of MapReduce including the map and reduce phases, intermediate data, and final outputs.
2. It explains key MapReduce terminology like jobs, tasks, task attempts, and the roles of the master and slave nodes.
3. It discusses MapReduce data types, input formats, record readers, partitioning, sorting, and output formats.
This document discusses various concepts related to Hadoop MapReduce including combiners, speculative execution, custom counters, input formats, multiple inputs/outputs, distributed cache, and joins. It explains that a combiner acts as a mini-reducer between the map and reduce stages to reduce data shuffling. Speculative execution allows redundant tasks to improve performance. Custom counters can track specific metrics. Input formats handle input splitting and reading. Multiple inputs allow different mappers for different files. Distributed cache shares read-only files across nodes. Joins can correlate large datasets on a common key.
The document provides an introduction to MapReduce, including:
- MapReduce is a framework for executing parallel algorithms across large datasets using commodity computers. It is based on map and reduce functions.
- Mappers process input key-value pairs in parallel, and outputs are sorted and grouped by the reducers.
- Examples demonstrate how MapReduce can be used for tasks like building indexes, joins, and iterative algorithms.
This document describes how to set up a single-node Hadoop installation to perform MapReduce operations. It discusses supported platforms, required software including Java and SSH, and preparing the Hadoop cluster in either local, pseudo-distributed, or fully-distributed mode. The main components of the MapReduce execution pipeline are explained, including the driver, mapper, reducer, and input/output formats. Finally, a simple word count example MapReduce job is described to demonstrate how it works.
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.
Dache - a data aware cache system for big-data applications using the MapReduce framework.
Dache aim-extending the MapReduce framework and provisioning a cache layer for efficiently identifying and accessing cache items in a MapReduce job.
Pig is a data flow language that sits on top of Hadoop and allows users to quickly process large volumes of data across many servers simultaneously. It supports relational features like joins, groups, and aggregates, making it well-suited for extract, transform, load (ETL) tasks. Common ETL use cases for Pig include time-sensitive data loads from various sources into databases, and processing multiple data sources to gain insights into customer behavior. While Pig can handle ETL tasks, it is also capable of sampling large datasets for analysis and providing analytical insights beyond basic ETL functions.
This document provides a summary of the Unix and GNU/Linux command line. It begins with an overview of files and file systems in Unix, including that everything is treated as a file. It then discusses command line interpreters (shells), and commands for handling files and directories like ls, cd, cp, and rm. It also covers redirecting standard input/output, pipes, and controlling processes. The document is intended as training material and provides a detailed outline of its contents.
The document discusses the Linux file system at three levels: hardware space, kernel space, and user space. At the hardware level, it describes how data is organized on physical storage devices like hard disks using partitions, tracks, sectors, and block allocation. In kernel space, file system drivers decode the physical layout and interface with the virtual file system (VFS) to provide a unified view to user space. Common Linux file systems like ext2, ext3, and their data structures are also outlined.
This document provides an overview of Linux including:
- Different pronunciations of Linux and the origins of each pronunciation.
- A definition of Linux as a generic term for Unix-like operating systems with graphical user interfaces.
- Why Linux is significant as a powerful, free, and customizable operating system that runs on multiple hardware platforms.
- An introduction to key Linux concepts like multi-user systems, multiprocessing, multitasking and open source software.
- Examples of common Linux commands for file handling, text processing, and system administration.
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Setting up the Hadoop Cluster, Map-Reduce,PIG, HIVE, HBase, Zookeeper, SQOOP etc. will be covered in the course.
This document provides examples of web scraping using Python. It discusses fetching web pages using requests, parsing data using techniques like regular expressions and BeautifulSoup, and writing output to files like CSV and JSON. Specific examples demonstrated include scraping WTA tennis rankings, New York election board data, and engineering firm profiles. The document also covers related topics like handling authentication, exceptions, rate limiting and Unicode issues.
The document summarizes Jimmy Lin's MapReduce tutorial for WWW 2013. It discusses the MapReduce algorithm design and implementation. Specifically, it covers key aspects of MapReduce like local aggregation to reduce network traffic, sequencing computations by manipulating sort order, and using appropriate data structures to accumulate results incrementally. It also provides an example of building a term co-occurrence matrix to measure semantic distance between words.
Hybrid Map Task Scheduling for GPU-based Heterogeneous ClustersKoichi Shirahata
This document proposes a hybrid scheduling technique for MapReduce jobs on GPU-based heterogeneous clusters. It aims to accelerate MapReduce by efficiently scheduling Map tasks to both CPUs and GPUs to minimize total job execution time. The technique was implemented in Hadoop using its Pipes feature to invoke CUDA programs from Java. Evaluation on a K-means application showed the hybrid scheduling approach was 1.93 times faster than CPU-only execution at 64 nodes by better utilizing multiple GPUs.
This document discusses large scale computing with MapReduce. It provides background on the growth of digital data, noting that by 2020 there will be over 5,200 GB of data for every person on Earth. It introduces MapReduce as a programming model for processing large datasets in a distributed manner, describing the key aspects of Map and Reduce functions. Examples of MapReduce jobs are also provided, such as counting URL access frequencies and generating a reverse web link graph.
This document provides an overview and introduction to Spark, including:
- Spark is a general purpose computational framework that provides more flexibility than MapReduce while retaining properties like scalability and fault tolerance.
- Spark concepts include resilient distributed datasets (RDDs), transformations that create new RDDs lazily, and actions that run computations and return values to materialize RDDs.
- Spark can run on standalone clusters or as part of Cloudera's Enterprise Data Hub, and examples of its use include machine learning, streaming, and SQL queries.
1. Big Data - Introduction(what is bigdata).pdfAmanCSE050
Big Data Characteristics
Contents
Explosion in Quantity of Data
Importance of Big Data
Usage Example in Big Data
Challenges in Big Data
Hadoop Ecosystem
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It uses a simple programming model called MapReduce that automatically parallelizes and distributes work across nodes. Hadoop consists of Hadoop Distributed File System (HDFS) for storage and MapReduce execution engine for processing. HDFS stores data as blocks replicated across nodes for fault tolerance. MapReduce jobs are split into map and reduce tasks that process key-value pairs in parallel. Hadoop is well-suited for large-scale data analytics as it scales to petabytes of data and thousands of machines with commodity hardware.
In these slides we analyze why the aggregate data models change the way data is stored and manipulated. We introduce MapReduce and its open source implementation Hadoop. We consider how MapReduce jobs are written and executed by Hadoop.
Finally we introduce spark using a docker image and we show how to use anonymous function in spark.
The topics of the next slides will be
- Spark Shell (Scala, Python)
- Shark Shell
- Data Frames
- Spark Streaming
- Code Examples: Data Processing and Machine Learning
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.
MapReduce provides a programming model for processing large datasets in a distributed, parallel manner. It involves two main steps - the map step where the input data is converted into intermediate key-value pairs, and the reduce step where the intermediate outputs are aggregated based on keys to produce the final results. Hadoop is an open-source software framework that allows distributed processing of large datasets across clusters of computers using MapReduce.
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.
In this session you will learn:
Meet MapReduce
Word Count Algorithm – Traditional approach
Traditional approach on a Distributed System
Traditional approach – Drawbacks
MapReduce Approach
Input & Output Forms of a MR program
Map, Shuffle & Sort, Reduce Phase
WordCount Code walkthrough
Workflow & Transformation of Data
Input Split & HDFS Block
Relation between Split & Block
Data locality Optimization
Speculative Execution
MR Flow with Single Reduce Task
MR flow with multiple Reducers
Input Format & Hierarchy
Output Format & Hierarchy
To know more, click here: https://www.mindsmapped.com/courses/big-data-hadoop/big-data-and-hadoop-training-for-beginners/
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.
This document provides an overview of big data processing techniques including batch processing using MapReduce and Hive, iterative batch processing using Spark, stream processing using Apache Storm, and OLAP over big data using Dremel and Druid. It discusses techniques such as MapReduce, Hive, Spark RDDs, and Storm tuples for processing large datasets and compares small versus big data approaches. Example usages and technologies for different processing types are also outlined.
In this session you will learn:
1. Meet MapReduce
2. Word Count Algorithm – Traditional approach
3. Traditional approach on a Distributed System
4. Traditional approach – Drawbacks
5. MapReduce Approach
6. Input & Output Forms of a MR program
7. Map, Shuffle & Sort, Reduce Phase
8. WordCount Code walkthrough
9. Workflow & Transformation of Data
10. Input Split & HDFS Block
11. Relation between Split & Block
12. Data locality Optimization
13. Speculative Execution
14. MR Flow with Single Reduce Task
15. MR flow with multiple Reducers
16. Input Format & Hierarchy
17. Output Format & Hierarchy
Todd Lipcon gives a presentation introducing Apache Spark. He begins with an overview of Spark, explaining that it is a general purpose computational framework that improves on MapReduce by leveraging distributed memory for better performance and providing a more developer-friendly API. Lipcon then discusses Spark's Resilient Distributed Datasets (RDDs) and its expressive transformations and actions API. He provides examples of word count programs in Java and Scala. Lipcon also highlights Spark's integration with Hadoop, built-in machine learning library MLlib, and streaming capabilities through Spark Streaming.
A performance analysis of OpenStack Cloud vs Real System on Hadoop ClustersKumari Surabhi
It introduces the performance analysis of OpenStack Cloud with the commodity computers in the big data environments. It concludes that the data storage and analysis in hadoop cluster in cloud is more flexible and easily scalable than the real system cluster. It also concludes the cluster in commodities computers are faster than the cloud clusters.
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.
This document provides an introduction to MapReduce programming model. It describes how MapReduce inspired by Lisp functions works by dividing tasks into mapping and reducing parts that are distributed and processed in parallel. It then gives examples of using MapReduce for word counting and calculating total sales. It also provides details on MapReduce daemons in Hadoop and includes demo code for summing array elements in Java and doing word counting on a text file using the Hadoop framework in Python.
Docker in daeqaci provides the following benefits for testing:
1. Testing environments match production environments more closely by running tests inside Docker containers with the same base software environments.
2. Tests are isolated from each other and can be reproduced independently on different machines by defining the full testing environment through Docker Compose files.
3. Test initialization data is cached and reused through Docker images, speeding up test execution significantly compared to traditional testing.
This document discusses Docker and its use for the Douban App Engine (DAE). It covers:
- The history of adopting Docker for DAE applications from 2014 to 2016.
- How DAE uses Docker to build and deploy over 400 application images across different environments.
- Techniques used to optimize the Docker build process and reduce image sizes.
- Integrating Docker with the DAE monitoring, logging, and maintenance systems.
3. Hadoop in Python
• Jython: Happy
• Cython:
• Pydoop
• components(RecordReader , RecordWriter and Partitioner)
• Get configuration, set counters and report statuscpython use any module
Dumbo
• HDFS API
• Hadoopy: an other Cython
• Streaming:
• Dumbo
• Other small Map-Reduce wrapper
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5. Hadoop in Python Extention
Hadoop in Python
Integration with Pipes(C++) + Integration with libhdfs(C)
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6. Dumbo
• Dumbo is a project that allows you to easily write and
run Hadoop programs in Python. More generally, Dumbo can be
considered a convenient Python API for writing MapReduce
programs.
• Advantages:
• Easy: Dumbo strives to be as Pythonic as possible
• Efficient: Dumbo programs communicate with Hadoop in a very
effecient way by relying on typed bytes, a nifty serialisation
mechanism that was specifically added to Hadoop with Dumbo
in mind.
• Flexible: We can extend it
• Mature
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11. K-means in Map-Reduce
• Normal K-means:
• Inputs: a set of n d-dimensional points && a number of desired clusters k.
• Step 1: Random choice K points at the sample of n Points
• Step2 : Calculate every point to K initial centers. Choice closest
• Step3 : Using this assignation of points to cluster centers, each cluster center is
recalculated as the centroid of its member points.
• Step4: This process is then iterated until convergence is reached.
• Final: points are reassigned to centers, and centroids recalculated until the k
cluster centers shift by less than some delta value.
• k-means is a surprisingly parallelizable algorithm.
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12. K-means in Map-Reduce
• Key-points:
• we want to come up with a scheme where we can operate on
each point in the data set independently.
• a small amount of shared data (The cluster centers)
• when we partition points among MapReduce nodes, we
also distribute a copy of the cluster centers. This results
in a small amount of data duplication, but very minimal.
In this way each of the points can be operated on
independently.
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13. Hadoop Phase
• Map:
• In : points in the data set
• Output : (ClusterID, Point) pair for each point.
Where the ClusterID is the integer Id of the
cluster which is cloest to point.
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14. Hadoop Phase
• Reduce Phase:
• In : (ClusterID, Point)
• Operator:
• the outputs of the map phase are grouped by
ClusterID.
• for each ClusterID the centroid of the points
associated with that ClusterID is calculated.
• Output: (ClusterID, Centroid) pairs. Which represent the
newly calculated cluster centers.
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15. External Program
• Each iteration of the algorithm is structured as a single
MapReduce job.
• After each phase, our lib reads the output , determines
whether convergence has been reached by the calculating
by how much distance the clusters have moved. The runs
another Mapreduce job.
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20. Next
• Write n-times iteration wrapper
• Optimize K-means
• Result Visualization with Python
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21. Optimize
• If partial centroids for clusters are computed on the map
nodes are computed on the map nodes themselves.
(Mapper Local calculate!) and then a weighted average
of the centroids is taken later by the reducer. In
other words, the mapping was one to one, and so for
every point inputted , our mapper outputted a single
point which it was necessary to sort and transfer to a
reducer.
• We can use Combiner!
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22. Dumbo Usage
• Very easy
• You can write your own code for Dumbo
• Debug easy
• Command easy
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