Hadoop @ Java CCC 2008 Spring
Download as pptx, pdf0 likes1,356 views
- The document discusses using Lucene and Hadoop to build large-scale search engines. Lucene is introduced as a Java-based search engine library, while Hadoop is a framework for distributed storage and processing of large datasets. - Code examples are provided for creating a Lucene index from files and for performing searches on the indexed data. - The presentation agenda indicates it will cover Lucene, Hadoop, building search engines with Lucene+Hadoop, and questions. Reference materials on Lucene and Hadoop are also listed.
1 of 39
Downloaded 43 times
Ad
Recommended
6 12 march 17 county
6 12 march 17 countysnehalcnp
?
This document summarizes the events surrounding an encounter between police and alleged militants in Lucknow, India. It describes how news of the encounter spread quickly through the busy Machhli Bazaar market, causing people and shopkeepers to flee in panic. While such encounters have occurred before in Lucknow, the public spreading of information about an ongoing clash was new. Speculation arose about the number and affiliations of the militants. The encounter added to a sense of unease in the city following a recent jewelry store robbery.9 feb 15 feb 2015
9 feb 15 feb 2015snehalcnp
?
The Aam Aadmi Party (AAP) is predicted to win a majority in the upcoming Delhi state elections, according to several exit polls, marking a significant setback for the Bharatiya Janata Party (BJP). If these projections hold true, Arvind Kejriwal, founder of AAP and former Chief Minister, would resume leadership in a government free from coalition pressures. The anticipated victory could challenge the BJP's recent electoral dominance and affect Prime Minister Modi's political image.Presentation on google
Presentation on googletara
?
The presentation is about Google and how to use search functions effectively. It discusses what a search engine is and lists major search engines. It then focuses on Google, describing it as one of the top search engines. The presentation outlines various advanced search techniques that can be used on Google, such as searching within a file type, getting weather or time in other locations, tracking packages and stocks, and using mathematical or unit conversions. It emphasizes that Google search can be used to find many types of information beyond just web pages.Rainforest
Rainforestguest423910
?
The rainforest receives over 80 inches of rain annually and is home to millions of plant and animal species. It produces much of the world's oxygen and contains plants that can help develop new medicines. However, we are losing over 50,000 species each year due to deforestation, which is destroying an area the size of two football fields every second. Simple everyday actions like lowering your heat and reusing materials can help conserve this vital ecosystem.Optimization Techniques at the I/O Forwarding Layer
Optimization Techniques at the I/O Forwarding LayerKazuki Ohta
?
Kazuki Ohta presented on optimization techniques for the I/O forwarding layer on leadership-class computing systems. The talk discussed (1) the growing imbalance between high compute performance and lower storage throughput, (2) challenges of millions of concurrent I/O clients, and (3) two proposed optimizations - out-of-order I/O pipelining and an I/O request scheduler. Evaluations on a Linux cluster and Blue Gene/P supercomputer showed performance improvements of 29.5-42% over standard I/O software stacks. Future work includes event-driven forwarding, collaborative caching, and evaluation on additional leadership systems.Buzz words
Buzz wordscwensel
?
The document summarizes common patterns for processing large datasets using MapReduce. It describes how MapReduce works by applying map and reduce functions to key-value pairs in parallel. Common patterns discussed include filtering, parsing, counting, merging, binning, distributed tasks, grouping, finding unique values, secondary sorting, and joining datasets. Real-world applications are described as chaining many MapReduce jobs together to process large amounts of data.ブラウザでMap ReduceL龍のK双蛍柊I尖
ブラウザでMap ReduceL龍のK双蛍柊I尖Shinya Miyazaki
?
This document discusses MapReduce and its implementation using JavaScript and Ruby on Rails (JS + R). It provides an overview of MapReduce and how it works, describes a word count example to demonstrate MapReduce, and discusses implementing MapReduce across multiple servers using Ajax and Comet for real-time updates.Pairwise document similarity in large collections with map reduce
Pairwise document similarity in large collections with map reducenivedalk
?
The document describes a MapReduce framework for efficiently computing pairwise document similarity at scale. It discusses building an inverted index and calculating similarities between all document pairs in parallel. The proposed approach uses term filtering and compression techniques to reduce computation. Experimental results on a large news corpus demonstrate linear scalability. Some questions are raised about algorithm details and potential impact of aggressive term filtering.Pairwise document similarity in large collections with map reduce
Pairwise document similarity in large collections with map reducenivedalk
?
The document describes a MapReduce framework for computing pairwise document similarity. It discusses building an inverted index and calculating similarities between all document pairs in a distributed fashion. The algorithm uses term frequency information and document IDs to group related information together during the Map and Reduce phases. Experimental results on a large news article dataset show the approach can scale to billions of document pairs efficiently.Pairwise document similarity in large collections with map reduce
Pairwise document similarity in large collections with map reducenivedalk
?
The document describes a MapReduce framework for efficiently computing pairwise document similarity at scale. It discusses how an inverted index is constructed in the map phase and document similarity scores are calculated in the reduce phase. The algorithm uses techniques like document frequency cutting to reduce computation complexity from quadratic to linear while minimizing information loss. Evaluation on a large real-world dataset showed the approach can compute billions of similarity pairs efficiently in a distributed manner.EMC2, Владимир Суворов
EMC2, Владимир СуворовEYevseyeva
?
The document discusses Apache Hadoop and its MapReduce programming model, which allows for data-parallel processing across large clusters of commodity hardware, originally developed by Google. It covers the architecture, fault tolerance, and practical applications of MapReduce, including examples like word count and inverted index. Additionally, it introduces higher-level languages like Pig and Hive, which simplify development by capturing common job patterns.Mapreduce in Search
Mapreduce in SearchAmund Tveit
?
The document discusses how MapReduce can be used for various tasks related to search engines, including detecting duplicate web pages, processing document content, building inverted indexes, and analyzing search query logs. It provides examples of MapReduce jobs for normalizing document text, extracting entities, calculating ranking signals, and indexing individual words, phrases, stems and synonyms.Map Reduce In 5 Minutes
Map Reduce In 5 MinutesLinuxmalaysia Malaysia
?
MapReduce is a programming model and framework for processing large datasets in a distributed computing environment. It allows for massive datasets to be processed by splitting the work across many computers in a cluster. The MapReduce framework handles parallelization of computation, scheduling, and data distribution across nodes. It consists of two main phases - the map phase where data is processed on individual hosts, and the reduce phase where results are aggregated.Wprowadzenie do technologii Big Data / Intro to Big Data Ecosystem
Wprowadzenie do technologii Big Data / Intro to Big Data EcosystemSages
?
The document provides an introduction to big data technologies, covering key components such as MapReduce, Pig, and Hive, along with various data storage options like NoSQL databases. It discusses practical applications, including optimizing waste collection based on street data and fraud detection through transactional data analysis. Additionally, it illustrates the architecture and commands of Hadoop, emphasizing the importance of batch processing, scalability, and various data formats.lec8_ref.pdf
lec8_ref.pdfvishal choudhary
?
This document discusses several examples of using MapReduce to solve different problems. It begins with a word count example to illustrate the map and reduce functions. It then discusses counting words of different lengths, finding a word length histogram, and building an inverted index. Finally, it describes how to perform relational joins and find common friends using MapReduce. The examples show how complex tasks can be broken down and solved in parallel using the map and reduce model.Codepot - Pig i Hive: szybkie wprowadzenie / Pig and Hive crash course
Codepot - Pig i Hive: szybkie wprowadzenie / Pig and Hive crash courseSages
?
The document provides an introduction to data processing using Pig and Hive, focusing on MapReduce architecture and examples of data manipulation tasks. It includes details about using Hadoop ecosystem components, Pig Latin basics, and sample use cases involving data analytics projects like fraud detection and web crawling. The content elaborates on various programming constructs for data processing and optimization techniques.堰温糸看看沿秘壇とクラウド旋喘
堰温糸看看沿秘壇とクラウド旋喘Naoki Yanai
?
This document discusses Hadoop and related technologies. It introduces Hadoop, its components MapReduce and HDFS, and how they work together. It also briefly mentions related Apache projects like Mahout and how companies like Amazon, Yahoo and Facebook use Hadoop in their systems. Finally, it covers Amazon's Elastic MapReduce service, which allows running Hadoop jobs in the cloud.Geoff Rothman Presentation on Parallel Processing
Geoff Rothman Presentation on Parallel ProcessingGeoff Rothman
?
The document provides an overview of cloud computing and the Google MapReduce framework, discussing their characteristics, service models, and deployment options. It elaborates on the MapReduce programming model, its applications in data processing, and compares it with parallel database management systems (DBMS), concluding that while MapReduce is beneficial for specific tasks, DBMS should be used for others. Additionally, it addresses the ongoing debate between the two methodologies and outlines their complementary roles in data analysis.Intro to Map Reduce
Intro to Map ReduceDoron Vainrub
?
The document describes MapReduce, a programming model for processing large datasets in a distributed manner. It defines Map and Reduce functions. Map takes key-value pairs as input and outputs a list of intermediate key-value pairs. Reduce receives all intermediate values for a key and merges them. The document provides examples of word count and finding maximum temperature to illustrate how MapReduce works. It also includes pseudocode for the algorithms.07 2
07 2a_b_g
?
This document summarizes algorithms for large-scale data mining using MapReduce, including:
1) Information retrieval algorithms like distributed grep, calculating URL access frequency, and constructing the reverse web link graph.
2) Graph algorithms like PageRank, which is computed through an iterative process of message passing between nodes.
3) Clustering algorithms like canopy clustering, which uses two distance thresholds to create overlapping clusters in a single pass over the data.MapReduce with Hadoop
MapReduce with HadoopVitalie Scurtu
?
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers using MapReduce. It allows for the parallel processing of large datasets across clusters of computers using a simple programming model. MapReduce programs are divided into Map and Reduce phases, where the Map phase processes input key-value pairs to generate intermediate output and the Reduce phase merges all intermediate values associated with the same key. Experiments showed that Hadoop and MapReduce were able to process 1 million documents faster than a single computer by distributing the work across multiple machines in a cluster.Scoobi - Scala for Startups
Scoobi - Scala for Startupsbmlever
?
This document discusses Scoobi, a Scala library for developing MapReduce applications on Hadoop. Some key points:
1) Scoobi allows developers to write Hadoop MapReduce jobs using a functional programming style in Scala, inspired by Google's FlumeJava. It provides abstractions like DList and DObject to represent distributed datasets and computations.
2) Under the hood, Scoobi compiles Scala code into Java MapReduce jobs that run on Hadoop. It handles partitioning, parallelization, and distribution of data and computation across clusters.
3) Examples show how common operations like filtering, mapping, reducing can be expressed concisely using the Scoobi API, mirroring ScalaMastering Hadoop Map Reduce - Custom Types and Other Optimizations
Mastering Hadoop Map Reduce - Custom Types and Other Optimizationsscottcrespo
?
The document provides an overview of MapReduce and its optimization strategies, detailing its role in processing distributed data sets using Hadoop. It discusses various components like mappers, reducers, and custom data types, highlighting the importance of creating effective configurations and handling n-grams for tasks like speech recognition. Additionally, it emphasizes the performance benefits of implementing custom data types in MapReduce applications.MongoDB, Hadoop and humongous data - MongoSV 2012
MongoDB, Hadoop and humongous data - MongoSV 2012Steven Francia
?
The document discusses the concept of 'humongous data' and the exponential growth of data from 2000 to 2012, highlighting the advancements in data processing technologies such as MongoDB and Hadoop. It explains the applications of these technologies in handling large datasets, including methods like map-reduce and aggregation for processing data. The future of data is emphasized, with predictions of continued growth and the importance of tools like Hadoop and Storm in managing and analyzing big data.MongoDB Analytics
MongoDB Analyticsdatablend
?
This document discusses using MongoDB for chemical similarity analysis. It describes storing compound fingerprints in MongoDB collections and querying them to find similar compounds. The approach involves building a fingerprint index, mapping over compounds to calculate similarities, and reducing the results to find the closest matches. This allows evaluating chemical similarity for millions of compounds faster than pairwise comparisons.More Related Content
Similar to Hadoop @ Java CCC 2008 Spring (20)
Buzz words
Buzz wordscwensel
?
The document summarizes common patterns for processing large datasets using MapReduce. It describes how MapReduce works by applying map and reduce functions to key-value pairs in parallel. Common patterns discussed include filtering, parsing, counting, merging, binning, distributed tasks, grouping, finding unique values, secondary sorting, and joining datasets. Real-world applications are described as chaining many MapReduce jobs together to process large amounts of data.ブラウザでMap ReduceL龍のK双蛍柊I尖
ブラウザでMap ReduceL龍のK双蛍柊I尖Shinya Miyazaki
?
This document discusses MapReduce and its implementation using JavaScript and Ruby on Rails (JS + R). It provides an overview of MapReduce and how it works, describes a word count example to demonstrate MapReduce, and discusses implementing MapReduce across multiple servers using Ajax and Comet for real-time updates.Pairwise document similarity in large collections with map reduce
Pairwise document similarity in large collections with map reducenivedalk
?
The document describes a MapReduce framework for efficiently computing pairwise document similarity at scale. It discusses building an inverted index and calculating similarities between all document pairs in parallel. The proposed approach uses term filtering and compression techniques to reduce computation. Experimental results on a large news corpus demonstrate linear scalability. Some questions are raised about algorithm details and potential impact of aggressive term filtering.Pairwise document similarity in large collections with map reduce
Pairwise document similarity in large collections with map reducenivedalk
?
The document describes a MapReduce framework for computing pairwise document similarity. It discusses building an inverted index and calculating similarities between all document pairs in a distributed fashion. The algorithm uses term frequency information and document IDs to group related information together during the Map and Reduce phases. Experimental results on a large news article dataset show the approach can scale to billions of document pairs efficiently.Pairwise document similarity in large collections with map reduce
Pairwise document similarity in large collections with map reducenivedalk
?
The document describes a MapReduce framework for efficiently computing pairwise document similarity at scale. It discusses how an inverted index is constructed in the map phase and document similarity scores are calculated in the reduce phase. The algorithm uses techniques like document frequency cutting to reduce computation complexity from quadratic to linear while minimizing information loss. Evaluation on a large real-world dataset showed the approach can compute billions of similarity pairs efficiently in a distributed manner.EMC2, Владимир Суворов
EMC2, Владимир СуворовEYevseyeva
?
The document discusses Apache Hadoop and its MapReduce programming model, which allows for data-parallel processing across large clusters of commodity hardware, originally developed by Google. It covers the architecture, fault tolerance, and practical applications of MapReduce, including examples like word count and inverted index. Additionally, it introduces higher-level languages like Pig and Hive, which simplify development by capturing common job patterns.Mapreduce in Search
Mapreduce in SearchAmund Tveit
?
The document discusses how MapReduce can be used for various tasks related to search engines, including detecting duplicate web pages, processing document content, building inverted indexes, and analyzing search query logs. It provides examples of MapReduce jobs for normalizing document text, extracting entities, calculating ranking signals, and indexing individual words, phrases, stems and synonyms.Map Reduce In 5 Minutes
Map Reduce In 5 MinutesLinuxmalaysia Malaysia
?
MapReduce is a programming model and framework for processing large datasets in a distributed computing environment. It allows for massive datasets to be processed by splitting the work across many computers in a cluster. The MapReduce framework handles parallelization of computation, scheduling, and data distribution across nodes. It consists of two main phases - the map phase where data is processed on individual hosts, and the reduce phase where results are aggregated.Wprowadzenie do technologii Big Data / Intro to Big Data Ecosystem
Wprowadzenie do technologii Big Data / Intro to Big Data EcosystemSages
?
The document provides an introduction to big data technologies, covering key components such as MapReduce, Pig, and Hive, along with various data storage options like NoSQL databases. It discusses practical applications, including optimizing waste collection based on street data and fraud detection through transactional data analysis. Additionally, it illustrates the architecture and commands of Hadoop, emphasizing the importance of batch processing, scalability, and various data formats.lec8_ref.pdf
lec8_ref.pdfvishal choudhary
?
This document discusses several examples of using MapReduce to solve different problems. It begins with a word count example to illustrate the map and reduce functions. It then discusses counting words of different lengths, finding a word length histogram, and building an inverted index. Finally, it describes how to perform relational joins and find common friends using MapReduce. The examples show how complex tasks can be broken down and solved in parallel using the map and reduce model.Codepot - Pig i Hive: szybkie wprowadzenie / Pig and Hive crash course
Codepot - Pig i Hive: szybkie wprowadzenie / Pig and Hive crash courseSages
?
The document provides an introduction to data processing using Pig and Hive, focusing on MapReduce architecture and examples of data manipulation tasks. It includes details about using Hadoop ecosystem components, Pig Latin basics, and sample use cases involving data analytics projects like fraud detection and web crawling. The content elaborates on various programming constructs for data processing and optimization techniques.堰温糸看看沿秘壇とクラウド旋喘
堰温糸看看沿秘壇とクラウド旋喘Naoki Yanai
?
This document discusses Hadoop and related technologies. It introduces Hadoop, its components MapReduce and HDFS, and how they work together. It also briefly mentions related Apache projects like Mahout and how companies like Amazon, Yahoo and Facebook use Hadoop in their systems. Finally, it covers Amazon's Elastic MapReduce service, which allows running Hadoop jobs in the cloud.Geoff Rothman Presentation on Parallel Processing
Geoff Rothman Presentation on Parallel ProcessingGeoff Rothman
?
The document provides an overview of cloud computing and the Google MapReduce framework, discussing their characteristics, service models, and deployment options. It elaborates on the MapReduce programming model, its applications in data processing, and compares it with parallel database management systems (DBMS), concluding that while MapReduce is beneficial for specific tasks, DBMS should be used for others. Additionally, it addresses the ongoing debate between the two methodologies and outlines their complementary roles in data analysis.Intro to Map Reduce
Intro to Map ReduceDoron Vainrub
?
The document describes MapReduce, a programming model for processing large datasets in a distributed manner. It defines Map and Reduce functions. Map takes key-value pairs as input and outputs a list of intermediate key-value pairs. Reduce receives all intermediate values for a key and merges them. The document provides examples of word count and finding maximum temperature to illustrate how MapReduce works. It also includes pseudocode for the algorithms.07 2
07 2a_b_g
?
This document summarizes algorithms for large-scale data mining using MapReduce, including:
1) Information retrieval algorithms like distributed grep, calculating URL access frequency, and constructing the reverse web link graph.
2) Graph algorithms like PageRank, which is computed through an iterative process of message passing between nodes.
3) Clustering algorithms like canopy clustering, which uses two distance thresholds to create overlapping clusters in a single pass over the data.MapReduce with Hadoop
MapReduce with HadoopVitalie Scurtu
?
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers using MapReduce. It allows for the parallel processing of large datasets across clusters of computers using a simple programming model. MapReduce programs are divided into Map and Reduce phases, where the Map phase processes input key-value pairs to generate intermediate output and the Reduce phase merges all intermediate values associated with the same key. Experiments showed that Hadoop and MapReduce were able to process 1 million documents faster than a single computer by distributing the work across multiple machines in a cluster.Scoobi - Scala for Startups
Scoobi - Scala for Startupsbmlever
?
This document discusses Scoobi, a Scala library for developing MapReduce applications on Hadoop. Some key points:
1) Scoobi allows developers to write Hadoop MapReduce jobs using a functional programming style in Scala, inspired by Google's FlumeJava. It provides abstractions like DList and DObject to represent distributed datasets and computations.
2) Under the hood, Scoobi compiles Scala code into Java MapReduce jobs that run on Hadoop. It handles partitioning, parallelization, and distribution of data and computation across clusters.
3) Examples show how common operations like filtering, mapping, reducing can be expressed concisely using the Scoobi API, mirroring ScalaMastering Hadoop Map Reduce - Custom Types and Other Optimizations
Mastering Hadoop Map Reduce - Custom Types and Other Optimizationsscottcrespo
?
The document provides an overview of MapReduce and its optimization strategies, detailing its role in processing distributed data sets using Hadoop. It discusses various components like mappers, reducers, and custom data types, highlighting the importance of creating effective configurations and handling n-grams for tasks like speech recognition. Additionally, it emphasizes the performance benefits of implementing custom data types in MapReduce applications.MongoDB, Hadoop and humongous data - MongoSV 2012
MongoDB, Hadoop and humongous data - MongoSV 2012Steven Francia
?
The document discusses the concept of 'humongous data' and the exponential growth of data from 2000 to 2012, highlighting the advancements in data processing technologies such as MongoDB and Hadoop. It explains the applications of these technologies in handling large datasets, including methods like map-reduce and aggregation for processing data. The future of data is emphasized, with predictions of continued growth and the importance of tools like Hadoop and Storm in managing and analyzing big data.MongoDB Analytics
MongoDB Analyticsdatablend
?
This document discusses using MongoDB for chemical similarity analysis. It describes storing compound fingerprints in MongoDB collections and querying them to find similar compounds. The approach involves building a fingerprint index, mapping over compounds to calculate similarities, and reducing the results to find the closest matches. This allows evaluating chemical similarity for millions of compounds faster than pairwise comparisons.