{"id":476736,"date":"2023-08-09T07:35:16","date_gmt":"2023-08-09T07:35:16","guid":{"rendered":""},"modified":"2023-09-05T11:13:20","modified_gmt":"2023-09-05T11:13:20","slug":"database-partitioning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/database-partitioning\/","title":{"rendered":"\u6570\u636e\u5e93\u5206\u533a"},"content":{"rendered":"

\u6570\u636e\u5e93\u5206\u533a\u662f\u4e00\u79cd\u5c06\u5927\u578b\u6570\u636e\u5e93\u5206\u89e3\u4e3a\u66f4\u5c0f\u3001\u66f4\u6613\u4e8e\u7ba1\u7406\u7684\u90e8\u5206\u6216\u5206\u533a\u7684\u65b9\u6cd5\uff0c\u8fd9\u4e9b\u90e8\u5206\u6216\u5206\u533a\u53ef\u4ee5\u5355\u72ec\u5b58\u50a8\u3001\u5904\u7406\u548c\u8bbf\u95ee\u3002\u8be5\u65b9\u6cd5\u65e8\u5728\u4f18\u5316\u6570\u636e\u5e93\u6027\u80fd\u3001\u7b80\u5316\u7ba1\u7406\u3001\u63d0\u9ad8\u53ef\u6269\u5c55\u6027\u3002<\/p>\n

\u6570\u636e\u5e93\u5206\u533a\u7684\u5386\u53f2<\/h2>\n

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IBM \u7684 System R \u662f\u5bf9\u6570\u636e\u5e93\u5206\u533a\u7684\u6700\u65e9\u5f15\u7528\u4e4b\u4e00\uff0c\u8be5\u7cfb\u7edf\u5bf9 SQL \u8bed\u8a00\u548c\u73b0\u4ee3\u6570\u636e\u5e93\u7cfb\u7edf\u7684\u5f00\u53d1\u4ea7\u751f\u4e86\u91cd\u5927\u5f71\u54cd\u3002\u8be5\u7cfb\u7edf\u5305\u542b\u4e86\u5206\u533a\u7684\u6700\u521d\u6982\u5ff5\uff0c\u540e\u6765\u5f97\u5230\u589e\u5f3a\u5e76\u6f14\u53d8\u6210\u6211\u4eec\u4eca\u5929\u6240\u77e5\u7684\u6570\u636e\u5e93\u5206\u533a\u3002<\/p>\n

\u6269\u5c55\u4e3b\u9898\uff1a\u6570\u636e\u5e93\u5206\u533a<\/h2>\n

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\u6570\u636e\u5e93\u5206\u533a\u7684\u5185\u90e8\u7ed3\u6784\u548c\u529f\u80fd<\/h2>\n

\u5728\u5206\u533a\u6570\u636e\u5e93\u4e2d\uff0c\u6570\u636e\u6839\u636e\u7279\u5b9a\u6807\u51c6\u5212\u5206\u4e3a\u903b\u8f91\u5b50\u96c6\uff0c\u4f8b\u5982\u503c\u7684\u8303\u56f4\uff08\u8303\u56f4\u5206\u533a\uff09\u3001\u503c\u5217\u8868\uff08\u5217\u8868\u5206\u533a\uff09\u3001\u54c8\u5e0c\u51fd\u6570\uff08\u54c8\u5e0c\u5206\u533a\uff09\u6216\u8fd9\u4e9b\u7684\u7ec4\u5408\uff08\u590d\u5408\u5206\u533a\uff09\u3002\u5206\u533a\uff09\u3002<\/p>\n

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\u6570\u636e\u5e93\u5206\u533a\u7684\u5173\u952e\u7279\u6027\u5206\u6790<\/h2>\n

\u6570\u636e\u5e93\u5206\u533a\u7684\u4e3b\u8981\u529f\u80fd\u5305\u62ec\uff1a<\/p>\n

    \n
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  3. \u6613\u4e8e\u7ba1\u7406<\/strong>\uff1a\u6bcf\u4e2a\u5206\u533a\u90fd\u53ef\u4ee5\u72ec\u7acb\u7ba1\u7406\u548c\u7ef4\u62a4\uff0c\u4f7f\u6570\u636e\u7ba1\u7406\u4efb\u52a1\u66f4\u6613\u4e8e\u7ba1\u7406\u3002<\/li>\n
  4. \u66f4\u597d\u7684\u53ef\u7528\u6027\u548c\u6062\u590d<\/strong>\uff1a\u5982\u679c\u67d0\u4e2a\u5206\u533a\u53d1\u751f\u6545\u969c\uff0c\u5219\u4ec5\u8be5\u5206\u533a\u4e2d\u7684\u6570\u636e\u53d7\u5230\u5f71\u54cd\u3002\u5176\u4ed6\u5206\u533a\u4ecd\u7136\u53ef\u7528\uff0c\u6062\u590d\u5de5\u4f5c\u53ef\u4ee5\u4ec5\u96c6\u4e2d\u4e8e\u6545\u969c\u5206\u533a\u3002<\/li>\n<\/ol>\n

    \u6570\u636e\u5e93\u5206\u533a\u7684\u7c7b\u578b<\/h2>\n

    \u4e0b\u8868\u663e\u793a\u4e86\u6570\u636e\u5e93\u5206\u533a\u7684\u4e3b\u8981\u7c7b\u578b\uff1a<\/p>\n\n\n\n\n\n\n\n\n
    \u5206\u533a\u7c7b\u578b<\/th>\n\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n
    \u8303\u56f4\u5206\u533a<\/td>\n\u6570\u636e\u6839\u636e\u4e00\u7cfb\u5217\u503c\u8fdb\u884c\u5206\u533a\u3002\u4f8b\u5982\uff0c\u5b58\u50a8\u5ba2\u6237\u6570\u636e\u7684\u8868\u53ef\u80fd\u6309\u5e74\u9f84\u8303\u56f4\uff080-18\u300119-30\u300131-50 \u7b49\uff09\u8fdb\u884c\u5206\u533a<\/td>\n<\/tr>\n
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    \u590d\u5408\u5206\u533a<\/td>\n\u8fd9\u6d89\u53ca\u5230\u4f7f\u7528\u591a\u79cd\u5206\u533a\u65b9\u6cd5\u3002\u4f8b\u5982\uff0c\u8303\u56f4\u5217\u8868\u5206\u533a\u53ef\u4ee5\u9996\u5148\u6309\u5e74\u9f84\u8303\u56f4\u5206\u533a\u6570\u636e\uff0c\u7136\u540e\u6309\u56fd\u5bb6\/\u5730\u533a\u5206\u533a\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u4f7f\u7528\u6570\u636e\u5e93\u5206\u533a\uff1a\u95ee\u9898\u548c\u89e3\u51b3\u65b9\u6848<\/h2>\n

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    \u4e0e\u7c7b\u4f3c\u6982\u5ff5\u7684\u6bd4\u8f83<\/h2>\n\n\n\n\n\n\n\n
    \u5b66\u671f<\/th>\n\u63cf\u8ff0<\/th>\n\u5b83\u4e0e\u5206\u533a\u6709\u4f55\u5173\u7cfb<\/th>\n<\/tr>\n<\/thead>\n
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    \u4ee3\u7406\u670d\u52a1\u5668\u548c\u6570\u636e\u5e93\u5206\u533a<\/h2>\n

    \u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u4e0e\u5206\u533a\u6570\u636e\u5e93\u534f\u540c\u5de5\u4f5c\uff0c\u4ee5\u589e\u5f3a\u6027\u80fd\u548c\u5b89\u5168\u6027\u3002\u4f8b\u5982\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u7528\u4e8e\u5c06\u8bf7\u6c42\u5206\u53d1\u5230\u4e0d\u540c\u7684\u6570\u636e\u5e93\u5206\u533a\uff0c\u5e73\u8861\u8d1f\u8f7d\u5e76\u63d0\u9ad8\u6574\u4f53\u7cfb\u7edf\u6027\u80fd\u3002<\/p>\n

    \u6b64\u5916\uff0c\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u4e3a\u5206\u533a\u6570\u636e\u5e93\u63d0\u4f9b\u989d\u5916\u7684\u5b89\u5168\u5c42\u3002\u5b83\u4eec\u53ef\u4ee5\u63a7\u5236\u5bf9\u4e0d\u540c\u5206\u533a\u7684\u8bbf\u95ee\uff0c\u4ece\u800c\u4fdd\u62a4\u654f\u611f\u6570\u636e\u3002<\/p>\n

    \u76f8\u5173\u94fe\u63a5<\/h2>\n

    \u6709\u5173\u6570\u636e\u5e93\u5206\u533a\u7684\u66f4\u591a\u4fe1\u606f\uff0c\u60a8\u53ef\u4ee5\u8bbf\u95ee\u4ee5\u4e0b\u8d44\u6e90\uff1a<\/p>\n

      \n
    1. \u6570\u636e\u5e93\u5206\u533a\uff1aOracle<\/a><\/li>\n
    2. SQL Server \u4e2d\u7684\u6570\u636e\u5e93\u5206\u533a<\/a><\/li>\n
    3. PostgreSQL \u5206\u533a\u7b80\u4ecb<\/a><\/li>\n
    4. \u6570\u636e\u5e93\u5206\u533a\uff1aMySQL<\/a><\/li>\n
    5. CJ Date \u7684\u6570\u636e\u5e93\u7cfb\u7edf\u7b80\u4ecb<\/a><\/li>\n
    6. \u6570\u636e\u5e93\u7cfb\u7edf\u6982\u5ff5\uff0c\u4f5c\u8005\uff1aAbraham Silberschatz\u3001Henry F. Korth \u548c S. Sudarshan<\/a><\/li>\n<\/ol>\n

      \u672c\u6587\u5168\u9762\u4ecb\u7ecd\u4e86\u6570\u636e\u5e93\u5206\u533a\u3001\u5176\u8d77\u6e90\u3001\u7ed3\u6784\u3001\u4e3b\u8981\u529f\u80fd\u3001\u7c7b\u578b\u4ee5\u53ca\u5982\u4f55\u4e0e\u4ee3\u7406\u670d\u52a1\u5668\u7ed3\u5408\u4f7f\u7528\u3002\u968f\u7740\u6570\u636e\u5e93\u7684\u89c4\u6a21\u548c\u590d\u6742\u6027\u4e0d\u65ad\u589e\u957f\uff0c\u638c\u63e1\u6570\u636e\u5e93\u5206\u533a\u6280\u672f\u4ecd\u7136\u662f\u6570\u636e\u5e93\u7ba1\u7406\u7684\u4e00\u9879\u57fa\u672c\u6280\u80fd\u3002<\/p>","protected":false},"featured_media":0,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476736","wiki","type-wiki","status-publish","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Database Partitioning: A Comprehensive Overview<\/mark>","faq_items":[{"question":"What is Database Partitioning?","answer":"

      Database partitioning is a technique of dividing a large database into smaller, more manageable parts, or partitions, that can be stored, processed, and accessed individually. This is done to enhance database performance, simplify management, and improve scalability.<\/p>"},{"question":"When was Database Partitioning introduced?","answer":"

      The concept of database partitioning came into being in the late 20th century as a response to the exponential growth of data. One of its early implementations is found in IBM\u2019s System R, a system that significantly influenced the development of the SQL language and modern database systems.<\/p>"},{"question":"How does Database Partitioning work?","answer":"

      Database partitioning divides data into logical subsets based on specific criteria like range of values, list of values, or a hash function. Each partition functions independently, allowing for parallel execution of queries and operations. Moreover, each partition can be located on a different physical location, further improving access speed and performance.<\/p>"},{"question":"What are the key features of Database Partitioning?","answer":"

      The key features of database partitioning include improved performance, enhanced scalability, ease of management, and better availability and recovery. By splitting data into smaller subsets, database queries can run more efficiently. Also, partitioning allows for horizontal scalability and independent management of each partition.<\/p>"},{"question":"What are the types of Database Partitioning?","answer":"

      There are four major types of database partitioning: range partitioning (based on a range of values), list partitioning (based on a predefined list of values), hash partitioning (using a hash function), and composite partitioning (a combination of these methods).<\/p>"},{"question":"What are some potential problems with Database Partitioning and how can they be solved?","answer":"

      Potential problems with database partitioning include the risk of over-partitioning or under-partitioning and increased complexity of transactions involving multiple partitions. These issues can be mitigated through careful planning and design, understanding the data and usage patterns thoroughly, and choosing the appropriate partitioning strategy.<\/p>"},{"question":"How is Database Partitioning related to Proxy Servers?","answer":"

      A proxy server can work together with a partitioned database to enhance performance and security. For instance, a proxy server can distribute requests to different database partitions, balancing the load and increasing the overall system performance. Furthermore, proxy servers can control access to different partitions, thereby enhancing the security of sensitive data.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476736","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476736\/revisions"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=476736"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}