{"id":38290,"date":"2025-04-23T20:04:01","date_gmt":"2025-04-23T12:04:01","guid":{"rendered":"https:\/\/www.yunshan.net\/blog\/?p=38290"},"modified":"2025-04-23T20:04:01","modified_gmt":"2025-04-23T12:04:01","slug":"opentelemetry%e7%9a%84%e6%8c%87%e6%a0%87%e6%98%af%e5%a6%82%e4%bd%95%e8%ae%a1%e7%ae%97%e6%ac%a7%e5%87%a0%e9%87%8c%e5%be%97%e8%b7%9d%e7%a6%bb%e7%9a%84","status":"publish","type":"post","link":"https:\/\/www.yunshan.net\/blog\/archives\/38290.html","title":{"rendered":"OpenTelemetry\u7684\u6307\u6807\u662f\u5982\u4f55\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684"},"content":{"rendered":"

\n

\"OpenTelemetry\u7684\u6307\u6807\u662f\u5982\u4f55\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\"<\/p>\n<\/p>\n

\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\uff08Euclidean distance\uff09\u662f\u4e00\u79cd\u5728\u591a\u7ef4\u7a7a\u95f4\u4e2d\u8ba1\u7b97\u4e24\u70b9\u4e4b\u95f4\u76f4\u7ebf\u8ddd\u79bb\u7684\u65b9\u6cd5\uff0c\u4e5f\u79f0\u4e3aL2\u8303\u6570\u3002\u5b83\u662f\u6700\u5e38\u89c1\u7684\u8ddd\u79bb\u5ea6\u91cf\u4e4b\u4e00\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\u3002<\/p>\n

\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u8ba1\u7b97\u516c\u5f0f\u5982\u4e0b\uff1a<\/p>\n

$d(p,q)=sqrt{sum_{i=1}^{n}(p_i-q_i)^2}$<\/p>\n

\u5176\u4e2d\uff0c$p=(p_1,p_2,ldots,p_n)$\u548c$q=(q_1,q_2,ldots,q_n)$\u662fn\u7ef4\u7a7a\u95f4\u4e2d\u7684\u4e24\u4e2a\u70b9\uff0c$d(p,q)$\u8868\u793a\u5b83\u4eec\u4e4b\u95f4\u7684\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u3002<\/p>\n

\u4e8c\u3001OpenTelemetry\u4e2d\u7684\u6307\u6807\u548c\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb<\/h2>\n

OpenTelemetry\u662f\u4e00\u4e2a\u5f00\u653e\u7684\u9065\u6d4b\u6807\u51c6\uff0c\u7528\u4e8e\u6536\u96c6\u3001\u5904\u7406\u548c\u5bfc\u51fa\u5206\u5e03\u5f0f\u7cfb\u7edf\u7684\u76d1\u63a7\u6570\u636e\u3002\u867d\u7136OpenTelemetry\u672c\u8eab\u5e76\u6ca1\u6709\u76f4\u63a5\u63d0\u4f9b\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u529f\u80fd\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u7ed3\u5408\u4f7f\u7528\u5176\u6307\u6807\u548c\u9002\u5f53\u7684\u5de5\u5177\u6765\u5b9e\u73b0\u3002<\/p>\n

1. \u6570\u636e\u91c7\u96c6<\/h2>\n

\u9700\u8981\u4f7f\u7528OpenTelemetry\u91c7\u96c6\u76f8\u5173\u7684\u6307\u6807\u6570\u636e\u3002\u8fd9\u4e9b\u6307\u6807\u53ef\u4ee5\u662f\u4efb\u4f55\u4e0e\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u8ba1\u7b97\u76f8\u5173\u7684\u6570\u503c\uff0c\u4f8b\u5982\uff1a<\/p>\n

  • \u5411\u91cf\u7684\u5750\u6807\u503c\uff1a\u5982\u679c\u8981\u8ba1\u7b97\u5411\u91cf\u4e4b\u95f4\u7684\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\uff0c\u9700\u8981\u91c7\u96c6\u5411\u91cf\u7684\u5404\u4e2a\u5750\u6807\u503c\u4f5c\u4e3a\u6307\u6807\u3002<\/li>\n
  • \u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff1a\u5bf9\u4e8e\u6d89\u53ca\u65f6\u95f4\u5e8f\u5217\u7684\u5e94\u7528\uff0c\u53ef\u4ee5\u91c7\u96c6\u4e0d\u540c\u65f6\u95f4\u70b9\u4e0a\u7684\u6570\u503c\u4f5c\u4e3a\u6307\u6807\u3002<\/li>\n

    2. \u6570\u636e\u5904\u7406<\/h2>\n

    \u4e00\u65e6\u91c7\u96c6\u5230\u6307\u6807\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u7f16\u7a0b\u8bed\u8a00\u6216\u6570\u636e\u5904\u7406\u5de5\u5177\u6765\u8fdb\u884c\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u8ba1\u7b97\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Python\u548cNumPy\u5e93\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u793a\u4f8b\uff1a<\/p>\n

    python<\/p>\n

    import numpy as np<\/p>\n

    \u5b9a\u4e49\u4e24\u4e2a\u5411\u91cf<\/p>\n

    vector1 = np.array([1, 2, 3])<\/p>\n

    vector2 = np.array([4, 5, 6])<\/p>\n

    \u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb<\/p>\n

    distance = np.sqrt(((vector1<\/p>\n

  • vector2) 2).sum)<\/li>\n

    print(distance)<\/p>\n

    \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5b9a\u4e49\u4e86\u4e24\u4e2a\u5411\u91cf`vector1`\u548c`vector2`\uff0c\u7136\u540e\u4f7f\u7528NumPy\u7684\u6570\u7ec4\u64cd\u4f5c\u548c\u6570\u5b66\u51fd\u6570\u6765\u8ba1\u7b97\u5b83\u4eec\u4e4b\u95f4\u7684\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u3002<\/p>\n

    3. \u53ef\u89c6\u5316\u548c\u5206\u6790<\/h2>\n

    \u8ba1\u7b97\u51fa\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\uff08\u5982Matplotlib\u6216Grafana\uff09\u5c06\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\u5c55\u793a\u3002\u8fd9\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u548c\u8d8b\u52bf\u3002<\/p>\n

    \u8fd8\u53ef\u4ee5\u7ed3\u5408\u5176\u4ed6\u6570\u636e\u5206\u6790\u6280\u672f\uff0c\u5982\u805a\u7c7b\u5206\u6790\u6216\u5206\u7c7b\u7b97\u6cd5\uff0c\u8fdb\u4e00\u6b65\u6316\u6398\u6570\u636e\u4e2d\u7684\u4fe1\u606f\u3002\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u4f5c\u4e3a\u4e00\u79cd\u8ddd\u79bb\u5ea6\u91cf\uff0c\u53ef\u4ee5\u4f5c\u4e3a\u8fd9\u4e9b\u7b97\u6cd5\u7684\u8f93\u5165\uff0c\u5e2e\u52a9\u786e\u5b9a\u6570\u636e\u70b9\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u6216\u5dee\u5f02\u6027\u3002<\/p>\n

    \u867d\u7136OpenTelemetry\u672c\u8eab\u6ca1\u6709\u76f4\u63a5\u63d0\u4f9b\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u529f\u80fd\uff0c\u4f46\u901a\u8fc7\u5408\u7406\u5730\u91c7\u96c6\u3001\u5904\u7406\u548c\u5206\u6790\u6307\u6807\u6570\u636e\uff0c\u53ef\u4ee5\u5229\u7528\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u8fdb\u884c\u5404\u79cd\u76d1\u63a7\u548c\u5206\u6790\u4efb\u52a1\u3002\u8fd9\u79cd\u7075\u6d3b\u6027\u4f7f\u5f97OpenTelemetry\u6210\u4e3a\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u591a\u79cd\u5206\u5e03\u5f0f\u7cfb\u7edf\u7684\u76d1\u63a7\u548c\u7ba1\u7406\u573a\u666f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

    \u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\uff08Euclidean distance\uff09\u662f\u4e00\u79cd\u5728\u591a\u7ef4\u7a7a\u95f4\u4e2d\u8ba1\u7b97\u4e24\u70b9\u4e4b\u95f4\u76f4\u7ebf\u8ddd\u79bb\u7684\u65b9\u6cd5\uff0c\u4e5f\u79f0\u4e3aL2\u8303\u6570\u3002\u5b83\u662f\u6700\u5e38\u89c1\u7684\u8ddd\u79bb\u5ea6\u91cf\u4e4b\u4e00\uff0c\u5e7f\u6cdb\u5e94\u7528\u4e8e\u6570\u636e\u79d1\u5b66\u548c\u673a\u5668\u5b66\u4e60\u9886\u57df\u3002 \u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u8ba1\u7b97\u516c\u5f0f\u5982\u4e0b\uff1a $d(p,q)=sqrt{sum_{i=1}^{n}(p_i-q_i)^2}$ \u5176\u4e2d\uff0c$p=(p_1,p_2,ldots,p_n)$\u548c$q=(q_1,q_2,ldots,q_n)$\u662fn\u7ef4\u7a7a\u95f4\u4e2d\u7684\u4e24\u4e2a\u70b9\uff0c$d(p,q)$\u8868\u793a\u5b83\u4eec\u4e4b\u95f4\u7684\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u3002 \u4e8c\u3001OpenTelemetry\u4e2d\u7684\u6307\u6807\u548c\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb OpenTelemetry\u662f\u4e00\u4e2a\u5f00\u653e\u7684\u9065\u6d4b\u6807\u51c6\uff0c\u7528\u4e8e\u6536\u96c6\u3001\u5904\u7406\u548c\u5bfc\u51fa\u5206\u5e03\u5f0f\u7cfb\u7edf\u7684\u76d1\u63a7\u6570\u636e\u3002\u867d\u7136OpenTelemetry\u672c\u8eab\u5e76\u6ca1\u6709\u76f4\u63a5\u63d0\u4f9b\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u529f\u80fd\uff0c\u4f46\u53ef\u4ee5\u901a\u8fc7\u7ed3\u5408\u4f7f\u7528\u5176\u6307\u6807\u548c\u9002\u5f53\u7684\u5de5\u5177\u6765\u5b9e\u73b0\u3002 1. \u6570\u636e\u91c7\u96c6 \u9700\u8981\u4f7f\u7528OpenTelemetry\u91c7\u96c6\u76f8\u5173\u7684\u6307\u6807\u6570\u636e\u3002\u8fd9\u4e9b\u6307\u6807\u53ef\u4ee5\u662f\u4efb\u4f55\u4e0e\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u8ba1\u7b97\u76f8\u5173\u7684\u6570\u503c\uff0c\u4f8b\u5982\uff1a \u5411\u91cf\u7684\u5750\u6807\u503c\uff1a\u5982\u679c\u8981\u8ba1\u7b97\u5411\u91cf\u4e4b\u95f4\u7684\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\uff0c\u9700\u8981\u91c7\u96c6\u5411\u91cf\u7684\u5404\u4e2a\u5750\u6807\u503c\u4f5c\u4e3a\u6307\u6807\u3002 \u65f6\u95f4\u5e8f\u5217\u6570\u636e\uff1a\u5bf9\u4e8e\u6d89\u53ca\u65f6\u95f4\u5e8f\u5217\u7684\u5e94\u7528\uff0c\u53ef\u4ee5\u91c7\u96c6\u4e0d\u540c\u65f6\u95f4\u70b9\u4e0a\u7684\u6570\u503c\u4f5c\u4e3a\u6307\u6807\u3002 2. \u6570\u636e\u5904\u7406 \u4e00\u65e6\u91c7\u96c6\u5230\u6307\u6807\u6570\u636e\uff0c\u53ef\u4ee5\u4f7f\u7528\u7f16\u7a0b\u8bed\u8a00\u6216\u6570\u636e\u5904\u7406\u5de5\u5177\u6765\u8fdb\u884c\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u8ba1\u7b97\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u4f7f\u7528Python\u548cNumPy\u5e93\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u793a\u4f8b\uff1a python import numpy as np \u5b9a\u4e49\u4e24\u4e2a\u5411\u91cf vector1 = np.array([1, 2, 3]) vector2 = np.array([4, 5, 6]) \u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb distance = np.sqrt(((vector1 vector2) 2).sum) print(distance) \u5728\u8fd9\u4e2a\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9996\u5148\u5b9a\u4e49\u4e86\u4e24\u4e2a\u5411\u91cf`vector1`\u548c`vector2`\uff0c\u7136\u540e\u4f7f\u7528NumPy\u7684\u6570\u7ec4\u64cd\u4f5c\u548c\u6570\u5b66\u51fd\u6570\u6765\u8ba1\u7b97\u5b83\u4eec\u4e4b\u95f4\u7684\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u3002 3. \u53ef\u89c6\u5316\u548c\u5206\u6790 \u8ba1\u7b97\u51fa\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u540e\uff0c\u53ef\u4ee5\u4f7f\u7528\u6570\u636e\u53ef\u89c6\u5316\u5de5\u5177\uff08\u5982Matplotlib\u6216Grafana\uff09\u5c06\u7ed3\u679c\u8fdb\u884c\u53ef\u89c6\u5316\u5c55\u793a\u3002\u8fd9\u6709\u52a9\u4e8e\u66f4\u597d\u5730\u7406\u89e3\u6570\u636e\u4e4b\u95f4\u7684\u5173\u7cfb\u548c\u8d8b\u52bf\u3002 \u8fd8\u53ef\u4ee5\u7ed3\u5408\u5176\u4ed6\u6570\u636e\u5206\u6790\u6280\u672f\uff0c\u5982\u805a\u7c7b\u5206\u6790\u6216\u5206\u7c7b\u7b97\u6cd5\uff0c\u8fdb\u4e00\u6b65\u6316\u6398\u6570\u636e\u4e2d\u7684\u4fe1\u606f\u3002\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u4f5c\u4e3a\u4e00\u79cd\u8ddd\u79bb\u5ea6\u91cf\uff0c\u53ef\u4ee5\u4f5c\u4e3a\u8fd9\u4e9b\u7b97\u6cd5\u7684\u8f93\u5165\uff0c\u5e2e\u52a9\u786e\u5b9a\u6570\u636e\u70b9\u4e4b\u95f4\u7684\u76f8\u4f3c\u6027\u6216\u5dee\u5f02\u6027\u3002 \u867d\u7136OpenTelemetry\u672c\u8eab\u6ca1\u6709\u76f4\u63a5\u63d0\u4f9b\u8ba1\u7b97\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u7684\u529f\u80fd\uff0c\u4f46\u901a\u8fc7\u5408\u7406\u5730\u91c7\u96c6\u3001\u5904\u7406\u548c\u5206\u6790\u6307\u6807\u6570\u636e\uff0c\u53ef\u4ee5\u5229\u7528\u6b27\u51e0\u91cc\u5f97\u8ddd\u79bb\u8fdb\u884c\u5404\u79cd\u76d1\u63a7\u548c\u5206\u6790\u4efb\u52a1\u3002\u8fd9\u79cd\u7075\u6d3b\u6027\u4f7f\u5f97OpenTelemetry\u6210\u4e3a\u4e00\u4e2a\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u591a\u79cd\u5206\u5e03\u5f0f\u7cfb\u7edf\u7684\u76d1\u63a7\u548c\u7ba1\u7406\u573a\u666f\u3002<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-38290","post","type-post","status-publish","format-standard","hentry","category-discussion"],"_links":{"self":[{"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/posts\/38290","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/comments?post=38290"}],"version-history":[{"count":1,"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/posts\/38290\/revisions"}],"predecessor-version":[{"id":38291,"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/posts\/38290\/revisions\/38291"}],"wp:attachment":[{"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/media?parent=38290"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/categories?post=38290"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.yunshan.net\/blog\/wp-json\/wp\/v2\/tags?post=38290"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}