[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-4351":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":30,"readmeContent":31,"aiSummary":32,"trendingCount":16,"starSnapshotCount":16,"syncStatus":19,"lastSyncTime":33,"discoverSource":34},4351,"hive","apache\u002Fhive","apache","Apache Hive","https:\u002F\u002Fhive.apache.org\u002F",null,"Java",5975,4791,304,61,0,8,15,2,41,"Apache License 2.0",false,"master",[7,25,26,27,5,28,29],"big-data","database","hadoop","java","sql","2026-06-12 02:01:02","\u003C!--\n{% comment %}\nLicensed to the Apache Software Foundation (ASF) under one or more\ncontributor license agreements.  See the NOTICE file distributed with\nthis work for additional information regarding copyright ownership.\nThe ASF licenses this file to you under the Apache License, Version 2.0\n(the \"License\"); you may not use this file except in compliance with\nthe License.  You may obtain a copy of the License at\n\nhttp:\u002F\u002Fwww.apache.org\u002Flicenses\u002FLICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n{% endcomment %}\n-->\nApache Hive (TM)\n================\n[![Master Build Status](https:\u002F\u002Ftravis-ci.org\u002Fapache\u002Fhive.svg?branch=master)](https:\u002F\u002Ftravis-ci.org\u002Fapache\u002Fhive\u002Fbranches)\n[![Maven Central](https:\u002F\u002Fmaven-badges.herokuapp.com\u002Fmaven-central\u002Forg.apache.hive\u002Fhive\u002Fbadge.svg)](http:\u002F\u002Fsearch.maven.org\u002F#search%7Cga%7C1%7Cg%3A%22org.apache.hive%22)\n\nThe Apache Hive (TM) data warehouse software facilitates reading,\nwriting, and managing large datasets residing in distributed storage\nusing SQL. Built on top of Apache Hadoop (TM), it provides:\n\n* Tools to enable easy access to data via SQL, thus enabling data\n  warehousing tasks such as extract\u002Ftransform\u002Fload (ETL), reporting,\n  and data analysis\n\n* A mechanism to impose structure on a variety of data formats\n\n* Access to files stored either directly in Apache HDFS (TM) or in other\n  data storage systems such as Apache HBase (TM)\n\n* Query execution using Apache Tez framework, designed for interactive query, \n  and has substantially reduced overheads versus MapReduce.\n\nHive provides standard SQL functionality, including many of the later\n2003 and 2011 features for analytics.  These include OLAP functions,\nsubqueries, common table expressions, and more.  Hive's SQL can also be\nextended with user code via user defined functions (UDFs), user defined\naggregates (UDAFs), and user defined table functions (UDTFs).\n\nHive is not designed for online transaction processing. It is best used\nfor traditional data warehousing tasks where the amount of data processed \nis large enough to require a distributed system. Hive is designed to maximize\nscalability (scale out with more machines added dynamically to the Hadoop\ncluster), performance, extensibility, fault-tolerance, and\nloose-coupling with its input formats.\n\n\nGeneral Info\n============\n\nFor the latest information about Hive, please visit out website at:\n\n  http:\u002F\u002Fhive.apache.org\u002F\n\n\nGetting Started\n===============\n\n- Installation Instructions and a quick tutorial:\n  https:\u002F\u002Fhive.apache.org\u002Fdevelopment\u002Fgettingstarted-latest\n  https:\u002F\u002Fhive.apache.org\u002Fdevelopment\u002Fquickstart\n\n- Instructions to build Hive from source:\n  https:\u002F\u002Fhive.apache.org\u002Fdevelopment\u002Fgettingstarted-latest\u002F#building-hive-from-source\n\n- A longer tutorial that covers more features of HiveQL:\n  https:\u002F\u002Fhive.apache.org\u002Fdocs\u002Flatest\u002Fuser\u002Ftutorial\n\n- The HiveQL Language Manual:\n  https:\u002F\u002Fhive.apache.org\u002Fdocs\u002Flatest\u002Flanguage\u002Flanguagemanual\n\n\nRequirements\n============\n\nJava\n------\n\n| Hive Version  | Java Version  |\n| ------------- |:-------------:|\n| Hive 4.0.1      | Java 8        |\n| Hive 4.1.x      | Java 17        |\n| Hive 4.2.x      | Java 21        |\n\n\nHadoop\n------\n\n- Hadoop 3.x\n\n\nUpgrading from older versions of Hive\n=====================================\n\n- Hive includes changes to the MetaStore schema. If\n  you are upgrading from an earlier version of Hive it is imperative\n  that you upgrade the MetaStore schema by running the appropriate\n  schema upgrade scripts located in the scripts\u002Fmetastore\u002Fupgrade\n  directory.\n\n- We have provided upgrade scripts for MySQL, PostgreSQL, Oracle,\n  Microsoft SQL Server, and Derby databases. If you are using a\n  different database for your MetaStore you will need to provide\n  your own upgrade script.\n\nUseful mailing lists\n====================\n\n1. user@hive.apache.org - To discuss and ask usage questions. Send an\n   empty email to user-subscribe@hive.apache.org in order to subscribe\n   to this mailing list.\n\n2. dev@hive.apache.org - For discussions about code, design and features.\n   Send an empty email to dev-subscribe@hive.apache.org in order to\n   subscribe to this mailing list.\n\n3. commits@hive.apache.org - In order to monitor commits to the source\n   repository. Send an empty email to commits-subscribe@hive.apache.org\n   in order to subscribe to this mailing list.\n","Apache Hive 是一个基于Hadoop的数据仓库工具，允许用户使用SQL查询大规模分布式存储的数据。其核心功能包括支持标准SQL查询、提供ETL（抽取、转换、加载）以及数据分析能力，并能够对多种数据格式施加结构。Hive利用Apache Tez执行框架进行查询处理，相比MapReduce具有更低的开销和更好的交互性能。此外，它还支持通过用户自定义函数(UDF)来扩展SQL功能。适用于需要处理大量数据的传统数据仓库场景，特别是在那些需要高度可扩展性和容错性的环境中表现优异。","2026-06-11 02:59:46","top_language"]