Hadoop installation and Running KMeans Clustering with MapReduce Program on H...Titus Damaiyanti
1. The document discusses installing Hadoop in single node cluster mode on Ubuntu, including installing Java, configuring SSH, extracting and configuring Hadoop files. Key configuration files like core-site.xml and hdfs-site.xml are edited.
2. Formatting the HDFS namenode clears all data. Hadoop is started using start-all.sh and the jps command checks if daemons are running.
3. The document then moves to discussing running a KMeans clustering MapReduce program on the installed Hadoop framework.
The document discusses parallel k-means clustering algorithms implemented using MapReduce and Spark. It first describes the standard k-means algorithm, which assigns data points to clusters based on distance to centroids. It then presents a MapReduce-based parallel k-means approach where the distance calculations between data points and centroids are distributed across nodes. The map tasks calculate distances and assign points to clusters, combine tasks aggregate results, and reduce tasks calculate new centroids. Experimental results show sub-linear speedup and good scaling to larger datasets. Finally, it briefly mentions k-means implementations on Spark.
This document discusses Hadoop design and k-means clustering. It outlines Hadoop's fault tolerance through task tracking and task replication. It describes Hadoop's data flow including input splitting, mapping and reducing. It also discusses optimizations like combiners. Finally it explains the k-means clustering algorithm and different approaches to implementing it in Hadoop including iterative MapReduce and partitioning large numbers of clusters.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
The document discusses clustering and k-means clustering algorithms. It provides examples of scenarios where clustering can be used, such as placing cell phone towers or opening new offices. It then defines clustering as organizing data into groups where objects within each group are similar to each other and dissimilar to objects in other groups. The document proceeds to explain k-means clustering, including the process of initializing cluster centers, assigning data points to the closest center, recomputing the centers, and iterating until centers converge. It provides a use case of using k-means to determine locations for new schools.
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.
Hadoop installation and Running KMeans Clustering with MapReduce Program on H...Titus Damaiyanti
1. The document discusses installing Hadoop in single node cluster mode on Ubuntu, including installing Java, configuring SSH, extracting and configuring Hadoop files. Key configuration files like core-site.xml and hdfs-site.xml are edited.
2. Formatting the HDFS namenode clears all data. Hadoop is started using start-all.sh and the jps command checks if daemons are running.
3. The document then moves to discussing running a KMeans clustering MapReduce program on the installed Hadoop framework.
The document discusses parallel k-means clustering algorithms implemented using MapReduce and Spark. It first describes the standard k-means algorithm, which assigns data points to clusters based on distance to centroids. It then presents a MapReduce-based parallel k-means approach where the distance calculations between data points and centroids are distributed across nodes. The map tasks calculate distances and assign points to clusters, combine tasks aggregate results, and reduce tasks calculate new centroids. Experimental results show sub-linear speedup and good scaling to larger datasets. Finally, it briefly mentions k-means implementations on Spark.
This document discusses Hadoop design and k-means clustering. It outlines Hadoop's fault tolerance through task tracking and task replication. It describes Hadoop's data flow including input splitting, mapping and reducing. It also discusses optimizations like combiners. Finally it explains the k-means clustering algorithm and different approaches to implementing it in Hadoop including iterative MapReduce and partitioning large numbers of clusters.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
This document provides an overview of the Hadoop MapReduce Fundamentals course. It discusses what Hadoop is, why it is used, common business problems it can address, and companies that use Hadoop. It also outlines the core parts of Hadoop distributions and the Hadoop ecosystem. Additionally, it covers common MapReduce concepts like HDFS, the MapReduce programming model, and Hadoop distributions. The document includes several code examples and screenshots related to Hadoop and MapReduce.
The document discusses clustering and k-means clustering algorithms. It provides examples of scenarios where clustering can be used, such as placing cell phone towers or opening new offices. It then defines clustering as organizing data into groups where objects within each group are similar to each other and dissimilar to objects in other groups. The document proceeds to explain k-means clustering, including the process of initializing cluster centers, assigning data points to the closest center, recomputing the centers, and iterating until centers converge. It provides a use case of using k-means to determine locations for new schools.
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.
This document provides an introduction to MapReduce and Hadoop, including an overview of computing PageRank using MapReduce. It discusses how MapReduce addresses challenges of parallel programming by hiding details of distributed systems. It also demonstrates computing PageRank on Hadoop through parallel matrix multiplication and implementing custom file formats.
Machine learning is used widely on the web today. Apache Mahout provides scalable machine learning libraries for common tasks like recommendation, clustering, classification and pattern mining. It implements many algorithms like k-means clustering in a MapReduce framework allowing them to scale to large datasets. Mahout functionality includes collaborative filtering, document clustering, categorization and frequent pattern mining.
Big data Clustering Algorithms And StrategiesFarzad Nozarian
The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.
This talk was prepared for the November 2013 DataPhilly Meetup: Data in Practice ( http://www.meetup.com/DataPhilly/events/149515412/ )
Map Reduce: Beyond Word Count by Jeff Patti
Have you ever wondered what map reduce can be used for beyond the word count example you see in all the introductory articles about map reduce? Using Python and mrjob, this talk will cover a few simple map reduce algorithms that in part power Monetate's information pipeline
Bio: Jeff Patti is a backend engineer at Monetate with a passion for algorithms, big data, and long walks on the beach. Prior to working at Monetate he performed software R&D for Lockheed Martin, where he worked on projects ranging from social network analysis to robotics.
This document discusses information retrieval techniques. It begins by defining information retrieval as selecting the most relevant documents from a large collection based on a query. It then discusses some key aspects of information retrieval including document representation, indexing, query representation, and ranking models. The document also covers specific techniques used in information retrieval systems like parsing documents, tokenization, removing stop words, normalization, stemming, and lemmatization.
This document provides a summary of MapReduce algorithms. It begins with background on the author's experience blogging about MapReduce algorithms in academic papers. It then provides an overview of MapReduce concepts including the mapper and reducer functions. Several examples of recently published MapReduce algorithms are described for tasks like machine learning, finance, and software engineering. One algorithm is examined in depth for building a low-latency key-value store. Finally, recommendations are provided for designing MapReduce algorithms including patterns, performance, and cost/maintainability considerations. An appendix lists additional MapReduce algorithms from academic papers in areas such as AI, biology, machine learning, and mathematics.
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
How do we manage more than one thousand of Pegasus clusters - backend partacelyc1112009
A presentation in Apache Pegasus meetup in 2021 from Wang Dan.
Know more about Pegasus https://pegasus.apache.org, https://github.com/apache/incubator-pegasus
my experience in spark tunning. All tests are made in production environment(600+ node hadoop cluster). The tunning result is useful for Spark SQL use case.
This document provides an introduction to MapReduce and Hadoop, including an overview of computing PageRank using MapReduce. It discusses how MapReduce addresses challenges of parallel programming by hiding details of distributed systems. It also demonstrates computing PageRank on Hadoop through parallel matrix multiplication and implementing custom file formats.
Machine learning is used widely on the web today. Apache Mahout provides scalable machine learning libraries for common tasks like recommendation, clustering, classification and pattern mining. It implements many algorithms like k-means clustering in a MapReduce framework allowing them to scale to large datasets. Mahout functionality includes collaborative filtering, document clustering, categorization and frequent pattern mining.
Big data Clustering Algorithms And StrategiesFarzad Nozarian
The document discusses various algorithms for big data clustering. It begins by covering preprocessing techniques such as data reduction. It then covers hierarchical, prototype-based, density-based, grid-based, and scalability clustering algorithms. Specific algorithms discussed include K-means, K-medoids, PAM, CLARA/CLARANS, DBSCAN, OPTICS, MR-DBSCAN, DBCURE, and hierarchical algorithms like PINK and l-SL. The document emphasizes techniques for scaling these algorithms to large datasets, including partitioning, sampling, approximation strategies, and MapReduce implementations.
This talk was prepared for the November 2013 DataPhilly Meetup: Data in Practice ( http://www.meetup.com/DataPhilly/events/149515412/ )
Map Reduce: Beyond Word Count by Jeff Patti
Have you ever wondered what map reduce can be used for beyond the word count example you see in all the introductory articles about map reduce? Using Python and mrjob, this talk will cover a few simple map reduce algorithms that in part power Monetate's information pipeline
Bio: Jeff Patti is a backend engineer at Monetate with a passion for algorithms, big data, and long walks on the beach. Prior to working at Monetate he performed software R&D for Lockheed Martin, where he worked on projects ranging from social network analysis to robotics.
This document discusses information retrieval techniques. It begins by defining information retrieval as selecting the most relevant documents from a large collection based on a query. It then discusses some key aspects of information retrieval including document representation, indexing, query representation, and ranking models. The document also covers specific techniques used in information retrieval systems like parsing documents, tokenization, removing stop words, normalization, stemming, and lemmatization.
This document provides a summary of MapReduce algorithms. It begins with background on the author's experience blogging about MapReduce algorithms in academic papers. It then provides an overview of MapReduce concepts including the mapper and reducer functions. Several examples of recently published MapReduce algorithms are described for tasks like machine learning, finance, and software engineering. One algorithm is examined in depth for building a low-latency key-value store. Finally, recommendations are provided for designing MapReduce algorithms including patterns, performance, and cost/maintainability considerations. An appendix lists additional MapReduce algorithms from academic papers in areas such as AI, biology, machine learning, and mathematics.
K-means clustering is an algorithm that groups data points into k number of clusters based on their similarity. It works by randomly selecting k data points as initial cluster centroids and then assigning each remaining point to the closest centroid. It then recalculates the centroids and reassigns points in an iterative process until centroids stabilize. While efficient, k-means clustering has weaknesses in that it requires specifying k, can get stuck in local optima, and is not suitable for non-convex shaped clusters or noisy data.
This document provides an overview of big data and Hadoop. It discusses why Hadoop is useful for extremely large datasets that are difficult to manage in relational databases. It then summarizes what Hadoop is, including its core components like HDFS, MapReduce, HBase, Pig, Hive, Chukwa, and ZooKeeper. The document also outlines Hadoop's design principles and provides examples of how some of its components like MapReduce and Hive work.
How do we manage more than one thousand of Pegasus clusters - backend partacelyc1112009
A presentation in Apache Pegasus meetup in 2021 from Wang Dan.
Know more about Pegasus https://pegasus.apache.org, https://github.com/apache/incubator-pegasus
my experience in spark tunning. All tests are made in production environment(600+ node hadoop cluster). The tunning result is useful for Spark SQL use case.
MySQL 5.6 GA版本已经发布了,其中包含了大量的新特性,了解这些新特性,不仅对数据库内核研发有帮助,对于更好的使用MySQL数据库也有着极大的意义。本分享将深入剖析MySQL 5.6新特性的实现细节,一共分为两期:分别是InnoDB引擎以及MySQL Server。本次为第一期,分享 MySQL 5.6 InnoDB引擎中的性能优化与功能增强。
In the last year, we've gone from millions of pieces of data to billions of pieces of data. I will speak on a solution for scaling up and about the challenges presented. Also covered will be the future of data at Qihoo 360 with MongoDB.
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. K-means in Hadoop
• Programs:
• Kmeans.py: k-means core algorithm
• Wrapper.py: local control iterations of k-means
• Generator.py: generate data in random of
range
• Graph.py: draw data
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5. Kmeans.py
• use “in-mapper combining” technology, for
implementing combiner functionality within every
map task. Notice, not combiner phase.
• It makes a discrete Combine step between Map and Reduce
unnecessary. Typically, it is not guaranteed that a combiner
function will be called on every mapper or that ,if called , it
will only be called once.
• In-mapper combiner design patten, we will guarantee that
combiner-like key aggregation occurs in every mapper,
instead of optionally in some mappers.
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6. Kmeans.py
• The aggregation is done entirely in the memory, without
touching disk and it happens before any emission code has
been called
• But it can not assure “Memory Leak” issue. We
should use python to control this condition.
• Results (3.6G Test Dataset)
• Old: 30+ min
• Current: 9+ min, in reduce phase we only use
1~2 second. Saving significant time.
2012-12-20 6
13. Plan
• 27 PCs run properly in Hadoop
• Remote management : write some shell scripts,
power saving, task submit from everyone etc.
• Build Mesos, spark, ZooKeeper, Hbase in our
platform.
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