How to build_a_search_engine
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This document discusses how to build a small distributed search engine using open source software. It describes the main subsystems of a search engine, including a page database, crawler, parser, indexer and link graph database. It then introduces Apache Hadoop and Apache Lucene as open source tools that can be used to build each subsystem in a distributed manner. Hadoop provides HDFS for distributed storage and MapReduce for distributed processing, while Lucene handles full-text indexing and search. The document outlines how Lucene indexes and searches document contents, and how its components can be integrated with HDFS to build a distributed search index and query system.
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hadoop distributed file systems complete information
The document provides an overview of the Hadoop Distributed File System (HDFS). It discusses that HDFS is the storage unit of Hadoop and relies on distributed file system principles. It has a master-slave architecture with the NameNode as the master and DataNodes as slaves. HDFS allows files to be broken into blocks which are replicated across DataNodes for fault tolerance. The document outlines the key components of HDFS and how read and write operations work in HDFS.
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This is a presentation on apache hadoop technology. This presentation may be helpful for the beginners to know about the terminologies of hadoop. This presentation contains some pictures which describes about the working function of this technology. I hope it will be helpful for the beginners.
Thank you.
Apache hadoop technology : Beginners
This presentation is about apache hadoop technology. It may be helpful for the beginners to know some terminologies of hadoop.
Apache hadoop technology : Beginners
This presentation is about apache hadoop technology. This may be helpful for the beginners. The beginners will know about some terminologies of hadoop technology. There is also some diagrams which will show the working of this technology.
Thank you.
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BIGDATA ppts
Business intelligence analyzes data to provide actionable information for decision making. Big data is a $50 billion market by 2017, referring to technologies that capture, store, manage and analyze large variable data collections. Hadoop is an open source framework for distributed storage and processing of large data sets on commodity hardware, enabling businesses to gain insight from massive amounts of structured and unstructured data. It involves components like HDFS for data storage, MapReduce for processing, and others for accessing, storing, integrating, and managing data.
Hadoop
Hadoop is an open-source software framework for distributed storage and processing of large datasets across clusters of computers. It has four main modules - Hadoop Common, HDFS, YARN and MapReduce. HDFS provides a distributed file system that stores data reliably across commodity hardware. MapReduce is a programming model used to process large amounts of data in parallel. Hadoop architecture uses a master-slave model, with a NameNode master and DataNode slaves. It provides fault tolerance, high throughput access to application data and scales to thousands of machines.
Big data analytics with hadoop volume 2
This document discusses big data analytics using Hadoop. It provides an overview of loading clickstream data from websites into Hadoop using Flume and refining the data with MapReduce. It also describes how Hive and HCatalog can be used to query and manage the data, presenting it in a SQL-like interface. Key components and processes discussed include loading data into a sandbox, Flume's architecture and data flow, using MapReduce for parallel processing, how HCatalog exposes Hive metadata, and how Hive allows querying data using SQL queries.
Basic System Design Geliyoo Search Engine
This document provides an overview of the basic system design for the Geliyoo search engine. It describes the key components including Apache Nutch for crawling, Elasticsearch for indexing, and Apache Cassandra for data storage. It outlines the basic workflow including crawling websites to extract links and content, indexing the content using Elasticsearch, and allowing searching of indexed data. The system is designed to distribute tasks across Hadoop, Elasticsearch, and Cassandra clusters for scalability.
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Indexing with solr search server and hadoop framework
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How to build_a_search_engine
- 1. How to build a small distributed search engine using open source software
- 2. Building a distributed search engine Search engine subsytems: ● Page database ● List of the pages to retrieve ● Pages retrieval and save ● Page content parsing ● Full-text indexing of the contents ● Graph database of the links for ranking
- 3. Building a distributed search engine Open Source Software • Apache Hadoop • • • • MapReduce HDFS HBase Apache Lucene
- 4. Building a distributed search engine HDFS Hadoop Distributed File System
- 5. Building a distributed search engine HDFS – Assumptions and goals ● Hardware failure ● Big data ● Write once / read many ● Moving computation, not data
- 6. Building a distributed search engine
- 7. Building a distributed search engine
- 8. Building a distributed search engine Lucene
- 9. Building a distributed search engine Lucene - Inverse Indexing Term Doc Id Weight JUG 301 198 120 0.97 0.65 0.43 301 278 451 103 763 0.94 0.15 0.87 0.45 0.77 Lugano
- 10. Building a distributed search engine Lucene - Indexing main classes IndexWriter Directory Analyzer Document Field
- 11. Building a distributed search engine Lucene - Searching main classes IndexSearcher Collector Query TopDocs ScoreDoc
- 12. Building a distributed search engine Lucene - Analyzers StopWords ”the book is on the table” → [book, table] Stemming [paint, paints, painted, …] → paint Synonims [cat, feline] → cat
- 13. Building a distributed search engine Lucene - Search options Fields Wildcards Title: JUG body: ”JUG Lugano” J?G → [JUG, JAG, ...] J*G →[JUG, JEEG, JUNG, …] Fuzzy (basata su vocabolario) JUG~[n] → [MUG, JAG, …]
- 14. Building a distributed search engine Lucene - Search options Range Boost JUG^5 Lugano ”JUG Lugano”^5 Proximity Year: [2002 TO 2012] Name: {Alberto TO Andrea} ”JUG Lugano”~5 Boolean and existance AND, OR, NOT, (), +, -
- 15. Building a distributed search engine HDFS - Lucene Integration File copy from/to HDFS Patch IndexWriter/Director IndexWriter/Directory Rewrite of IndexWriter on RAM Lucene 4
- 16. Building a distributed search engine And now... Hands on!