基于hadoop數(shù)據(jù)倉庫-展示頁
2025-05-14 00:42本頁面
【正文】 world” 0, “hello mapreduce” 0, “bye bye” “hello”, 1 “world”, 1 “bye”, 2 “hello”, 1 “mpareduce”, 1 “hello”, 2 “world”, 1 “mapreduce”, 1 “bye”, 2 files line offset, line content word, count word, count files 目錄 ? Hadoop簡介 –HDFS (Hadoop Distributed File System) –MapReduce ? Hive ? Hadoop的企業(yè)級應(yīng)用 What is HIVE ? 數(shù)據(jù)倉庫業(yè)務(wù)具有多樣性、多變性和邏輯復(fù)雜性，傳統(tǒng)的Parallel DBMSs只能使用 SQL語句，語言表達(dá)力不夠應(yīng)付現(xiàn)有的類似 google， facebook等的數(shù)據(jù)倉庫需求（若使用 UDF或 UDA自己定義 aggregate，則失去了其強(qiáng)大的優(yōu)化功能），而自己定制的 maper和 reducer的代碼較為低層比較繁瑣且重用性也不好，所以就有了 Hive，提供一個類 SQL的編程接口，簡單又不失靈活性，且基于mapreduce. What is HIVE ? （論文翻譯） hive是一個基于 hadoop的數(shù)據(jù)倉庫。 ? Hadoop在大量的公司中被使用和研究 Hadoop的體系架構(gòu) Hadoop由以下幾個部件組成 ： Hadoop Common: The mon utilities that support the other Hadoop subprojects. Avro: A data serialization system that provides dynamic integration with scripting languages. Chukwa: A data collection system for managing large distributed systems. HBase: A scalable, distributed database that supports structured data storage for large tables. HDFS: A distributed file system that provides high throughput access to application data. Hive: A data warehouse infrastructure that provides data summarization and ad hoc querying. MapReduce: A software framework for distributed processing of large data sets on pute clusters. Pig: A highlev
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