{"id":1822,"date":"2020-07-01T05:58:28","date_gmt":"2020-06-30T21:58:28","guid":{"rendered":"http:\/\/39.97.231.10\/?p=1822"},"modified":"2020-07-01T05:58:28","modified_gmt":"2020-06-30T21:58:28","slug":"hive%e5%86%85%e9%83%a8%e5%87%bd%e6%95%b0%e5%8f%8a%e6%9f%a5%e8%af%a2%e8%af%ad%e6%b3%95","status":"publish","type":"post","link":"http:\/\/www.weijing.co\/?p=1822","title":{"rendered":"Hive\u5185\u90e8\u51fd\u6570\u53ca\u67e5\u8be2\u8bed\u6cd5"},"content":{"rendered":"<div>\n<div>\n<div>\n<div>1.Hive\u5185\u7f6e\u51fd\u6570\uff1a<\/div>\n<\/div>\n<\/div>\n<\/div>\n<article>\u5728Hive\u4e2d \u7cfb\u7edf\u7ed9\u6211\u4eec\u5185\u7f6e\u4e86\u5f88\u591a\u51fd\u6570 \u5177\u4f53\u53c2\u8003<a href=\\\"https:\/\/cwiki.apache.org\/confluence\/display\/Hive\/LanguageManual+UDF#LanguageManualUDF-Built-inOperators\\\" target=\\\"_blank\\\" rel=\\\"nofollow noopener noreferrer\\\">\u5b98\u65b9\u5730\u5740<\/a><\/p>\n<ul>\n<li>\u770b\u4e0b\u5b98\u7f51\u7ed9\u6211\u4eec\u7684\u4ecb\u7ecd:<\/li>\n<\/ul>\n<div>\n<pre>SHOW FUNCTIONS; --\u67e5\u770b\u6240\u6709\u5185\u7f6e\u51fd\u6570\nDESCRIBE FUNCTION &lt;function_name&gt;; --\u67e5\u770b\u67d0\u4e2a\u51fd\u6570\u7684\u63cf\u8ff0\nDESCRIBE FUNCTION EXTENDED &lt;function_name&gt;; --\u67e5\u770b\u67d0\u4e2a\u51fd\u6570\u7684\u5177\u4f53\u4f7f\u7528\u65b9\u6cd5<\/pre>\n<\/div>\n<div>\n<pre>hive&gt; DESCRIBE FUNCTION case;\nOK\nCASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END - When a = b, returns c; when a = d, return e; else return f\nTime taken: 0.006 seconds, Fetched: 1 row(s)\nhive&gt; DESCRIBE FUNCTION EXTENDED case;\nOK\nCASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END - When a = b, returns c; when a = d, return e; else return f\nExample:\n SELECT\n CASE deptno\n   WHEN 1 THEN Engineering\n   WHEN 2 THEN Finance\n   ELSE admin\n END,\n CASE zone\n   WHEN 7 THEN Americas\n   ELSE Asia-Pac\n END\n FROM emp_details\nTime taken: 0.008 seconds, Fetched: 13 row(s)\n# DESCRIBE \u53ef\u7b80\u5199\u4e3adesc\nhive&gt; desc FUNCTION EXTENDED case;\nOK\nCASE a WHEN b THEN c [WHEN d THEN e]* [ELSE f] END - When a = b, returns c; when a = d, return e; else return f\nExample:\n SELECT\n CASE deptno\n   WHEN 1 THEN Engineering\n   WHEN 2 THEN Finance\n   ELSE admin\n END,\n CASE zone\n   WHEN 7 THEN Americas\n   ELSE Asia-Pac\n END\n FROM emp_details\nTime taken: 0.009 seconds, Fetched: 13 row(s)\n<\/pre>\n<\/div>\n<p>\u4e0b\u9762\u6211\u4eec\u4e86\u89e3\u4e0b\u5e38\u7528\u51fd\u6570\u7684\u4f7f\u7528\u65b9\u6cd5\uff1a<\/p>\n<div>\n<pre># \u4e3a\u4e86\u65b9\u4fbf\u6d4b\u8bd5 \u6211\u4eec\u521b\u5efa\u5e38\u7528\u7684dual\u8868\nhive&gt; create table dual(x string);\nOK\nTime taken: 0.11 seconds\nhive&gt; insert into table dual values(\\'\\');\nQuery ID = hadoop_20180702100505_f0566585-06b2-4c53-910a-b6a58791fc2d\nTotal jobs = 3\nLaunching Job 1 out of 3\n...\nOK\nTime taken: 29.535 seconds\nhive&gt; select * from dual;\nOK\nTime taken: 0.147 seconds, Fetched: 1 row(s)\n# \u6d4b\u8bd5\u5f53\u524d\u65f6\u95f4\nhive&gt; select current_date from dual;\nOK\n2018-07-02\nTime taken: 0.111 seconds, Fetched: 1 row(s)\n# \u6d4b\u8bd5\u5f53\u524d\u65f6\u95f4\u6233\nhive&gt; select current_timestamp from dual;\nOK\n2018-07-02 15:03:28.919\nTime taken: 0.117 seconds, Fetched: 1 row(s)\n# \u6d4b\u8bd5substr\u51fd\u6570 \u7528\u4e8e\u622a\u53d6\u5b57\u7b26\u4e32\nhive&gt; desc function extended substr;\nOK\nsubstr(str, pos[, len]) - returns the substring of str that starts at pos and is of length len orsubstr(bin, pos[, len]) - returns the slice of byte array that starts at pos and is of length len\nSynonyms: substring\npos is a 1-based index. If pos&lt;0 the starting position is determined by counting backwards from the end of str.\nExample:\n   &gt; SELECT substr(\\'Facebook\\', 5) FROM src LIMIT 1;\n  \\'book\\'\n  &gt; SELECT substr(\\'Facebook\\', -5) FROM src LIMIT 1;\n  \\'ebook\\'\n  &gt; SELECT substr(\\'Facebook\\', 5, 1) FROM src LIMIT 1;\n  \\'b\\'\nTime taken: 0.016 seconds, Fetched: 10 row(s)\nhive&gt; SELECT substr(\\'helloworld\\',-5) FROM dual;\nOK\nworld\nTime taken: 0.171 seconds, Fetched: 1 row(s)\nhive&gt; SELECT substr(\\'helloworld\\',5) FROM dual;\nOK\noworld\nTime taken: 0.12 seconds, Fetched: 1 row(s)\nhive&gt; SELECT substr(\\'helloworld\\',5,3) FROM dual;\nOK\nowo\nTime taken: 0.142 seconds, Fetched: 1 row(s)\n# \u6d4b\u8bd5\u51fd\u6570concat \u7528\u4e8e\u5c06\u5b57\u7b26\u8fde\u63a5\u8d77\u6765\nhive&gt; desc function extended concat_ws;\nOK\nconcat_ws(separator, [string | array(string)]+) - returns the concatenation of the strings separated by the separator.\nExample:\n  &gt; SELECT concat_ws(\\'.\\', \\'www\\', array(\\'facebook\\', \\'com\\')) FROM src LIMIT 1;\n  \\'www.facebook.com\\'\nTime taken: 0.019 seconds, Fetched: 4 row(s)\nhive&gt; select concat_ws(\\\".\\\",\\\"192\\\",\\\"168\\\",\\\"199\\\",\\\"151\\\") from dual;\nOK\n192.168.199.151\nTime taken: 0.152 seconds, Fetched: 1 row(s)\n# \u6d4b\u8bd5\u51fd\u6570split \u7528\u4e8e\u62c6\u5206\nhive&gt; desc function extended split;\nOK\nsplit(str, regex) - Splits str around occurances that match regex\nExample:\n  &gt; SELECT split(\\'oneAtwoBthreeC\\', \\'[ABC]\\') FROM src LIMIT 1;\n  [\\\"one\\\", \\\"two\\\", \\\"three\\\"]\nTime taken: 0.021 seconds, Fetched: 4 row(s)\nhive&gt; select split(\\\"192.168.199.151\\\",\\\"\\\\\\\\.\\\") from dual;\nOK\n[\\\"192\\\",\\\"168\\\",\\\"199\\\",\\\"151\\\"]\nTime taken: 0.169 seconds, Fetched: 1 row(s)\n<\/pre>\n<\/div>\n<h5>2.Hive\u67e5\u8be2\u8bed\u6cd5\uff1a<\/h5>\n<ul>\n<li>\u7b80\u5355select\u8bed\u6cd5\uff1a<\/li>\n<\/ul>\n<div>\n<pre># \u7b80\u5355select\u8bed\u6cd5\nhive&gt; select * from emp where deptno=10;\nOK\n7782    CLARK   MANAGER 7839    1981-6-9        2450.0  NULL    10\n7839    KING    PRESIDENT       NULL    1981-11-17      5000.0  NULL    10\n7934    MILLER  CLERK   7782    1982-1-23       1300.0  NULL    10\nTime taken: 0.899 seconds, Fetched: 3 row(s)\nhive&gt; select * from emp where empno &lt;= 7800;\nOK\n7369    SMITH   CLERK   7902    1980-12-17      800.0   NULL    20\n7499    ALLEN   SALESMAN        7698    1981-2-20       1600.0  300.0   30\n7521    WARD    SALESMAN        7698    1981-2-22       1250.0  500.0   30\n7566    JONES   MANAGER 7839    1981-4-2        2975.0  NULL    20\n7654    MARTIN  SALESMAN        7698    1981-9-28       1250.0  1400.0  30\n7698    BLAKE   MANAGER 7839    1981-5-1        2850.0  NULL    30\n7782    CLARK   MANAGER 7839    1981-6-9        2450.0  NULL    10\n7788    SCOTT   ANALYST 7566    1987-4-19       3000.0  NULL    20\nTime taken: 0.277 seconds, Fetched: 8 row(s)\nhive&gt; select * from emp where salary between 1000 and 1500;\nOK\n7521    WARD    SALESMAN        7698    1981-2-22       1250.0  500.0   30\n7654    MARTIN  SALESMAN        7698    1981-9-28       1250.0  1400.0  30\n7844    TURNER  SALESMAN        7698    1981-9-8        1500.0  0.0     30\n7876    ADAMS   CLERK   7788    1987-5-23       1100.0  NULL    20\n7934    MILLER  CLERK   7782    1982-1-23       1300.0  NULL    10\nTime taken: 0.187 seconds, Fetched: 5 row(s)\nhive&gt; select * from emp limit 5;\nOK\n7369    SMITH   CLERK   7902    1980-12-17      800.0   NULL    20\n7499    ALLEN   SALESMAN        7698    1981-2-20       1600.0  300.0   30\n7521    WARD    SALESMAN        7698    1981-2-22       1250.0  500.0   30\n7566    JONES   MANAGER 7839    1981-4-2        2975.0  NULL    20\n7654    MARTIN  SALESMAN        7698    1981-9-28       1250.0  1400.0  30\nTime taken: 0.154 seconds, Fetched: 5 row(s)\nhive&gt; select * from emp where empno in(7566,7499);\nOK\n7499    ALLEN   SALESMAN        7698    1981-2-20       1600.0  300.0   30\n7566    JONES   MANAGER 7839    1981-4-2        2975.0  NULL    20\nTime taken: 0.153 seconds, Fetched: 2 row(s)\nhive&gt; select * from emp where comm is not null;\nOK\n7499    ALLEN   SALESMAN        7698    1981-2-20       1600.0  300.0   30\n7521    WARD    SALESMAN        7698    1981-2-22       1250.0  500.0   30\n7654    MARTIN  SALESMAN        7698    1981-9-28       1250.0  1400.0  30\n7844    TURNER  SALESMAN        7698    1981-9-8        1500.0  0.0     30\nTime taken: 0.291 seconds, Fetched: 4 row(s)\n<\/pre>\n<\/div>\n<ul>\n<li>\u805a\u5408\u51fd\u6570\u53ca\u5206\u7ec4\u51fd\u6570\uff1a<\/li>\n<\/ul>\n<div>\n<pre># \u805a\u5408\u51fd\u6570\u53ca\u5206\u7ec4\u51fd\u6570\n#  max\/min\/count\/sum\/avg \u7279\u70b9\uff1a\u591a\u8fdb\u4e00\u51fa\uff0c\u8fdb\u6765\u5f88\u591a\u6761\u8bb0\u5f55\u51fa\u53bb\u53ea\u6709\u4e00\u6761\u8bb0\u5f55\n# \u67e5\u8be2\u90e8\u95e8\u7f16\u53f7\u4e3a10\u7684\u6709\u591a\u5c11\u6761\u8bb0\u5f55\nhive&gt; select count(1) from emp where deptno=10;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n3\nTime taken: 38.951 seconds, Fetched: 1 row(s)\n# \u6c42\u6700\u5927\u5de5\u8d44\uff0c\u6700\u5c0f\u5de5\u8d44\uff0c\u5e73\u5747\u5de5\u8d44\uff0c\u5de5\u8d44\u7684\u548c\nhive&gt; select max(salary),min(salary),avg(salary),sum(salary) from emp;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n5000.0  800.0   2073.214285714286       29025.0\nTime taken: 23.748 seconds, Fetched: 1 row(s)\n# \u5206\u7ec4\u51fd\u6570 group by\n# \u6c42\u90e8\u95e8\u7684\u5e73\u5747\u5de5\u8d44\n# \u6ce8\uff1aselect\u4e2d\u51fa\u73b0\u7684\u5b57\u6bb5\uff0c\u5982\u679c\u6ca1\u6709\u51fa\u73b0\u5728\u7ec4\u51fd\u6570\/\u805a\u5408\u51fd\u6570\u4e2d\uff0c\u5fc5\u987b\u51fa\u73b0\u5728group by\u91cc\u9762\uff0c\u5426\u5219\u5c31\u4f1a\u4ea7\u751f\u62a5\u9519\nhive&gt; select deptno,avg(salary) from emp group by deptno;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n10      2916.6666666666665\n20      2175.0\n30      1566.6666666666667\nTime taken: 36.502 seconds, Fetched: 3 row(s)\n# \u6c42\u6bcf\u4e2a\u90e8\u95e8(deptno)\u3001\u5de5\u4f5c\u5c97\u4f4d(job)\u7684\u6700\u9ad8\u5de5\u8d44(salary)\nhive&gt; select deptno,job,max(salary) from emp group by deptno,job;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n10      CLERK   1300.0\n10      MANAGER 2450.0\n10      PRESIDENT       5000.0\n20      ANALYST 3000.0\n20      CLERK   1100.0\n20      MANAGER 2975.0\n30      CLERK   950.0\n30      MANAGER 2850.0\n30      SALESMAN        1600.0\nTime taken: 36.096 seconds, Fetched: 9 row(s)\n# \u67e5\u8be2\u5e73\u5747\u5de5\u8d44\u5927\u4e8e2000\u7684\u90e8\u95e8\uff08\u4f7f\u7528having\u5b50\u53e5\u9650\u5b9a\u5206\u7ec4\u67e5\u8be2\uff09\nhive&gt; select deptno,avg(salary) from emp group by deptno having avg(salary) &gt;2000;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n10      2916.6666666666665\n20      2175.0\nTime taken: 24.71 seconds, Fetched: 2 row(s)\n# case when then end\uff08\u4e0d\u4f1a\u8dd1mr\uff09\nhive&gt; select ename, salary,\n    &gt; case\n    &gt; when salary &gt; 1 and salary &lt;= 1000 then \\'LOWER\\'\n    &gt; when salary &gt; 1000 and salary &lt;= 2000 then \\'MIDDLE\\'\n    &gt; when salary &gt; 2000 and salary &lt;= 4000 then \\'HIGH\\'\n    &gt; ELSE \\'HIGHEST\\'\n    &gt; end\n    &gt; from emp;\nOK\nSMITH   800.0   LOWER\nALLEN   1600.0  MIDDLE\nWARD    1250.0  MIDDLE\nJONES   2975.0  HIGH\nMARTIN  1250.0  MIDDLE\nBLAKE   2850.0  HIGH\nCLARK   2450.0  HIGH\nSCOTT   3000.0  HIGH\nKING    5000.0  HIGHEST\nTURNER  1500.0  MIDDLE\nADAMS   1100.0  MIDDLE\nJAMES   950.0   LOWER\nFORD    3000.0  HIGH\nMILLER  1300.0  MIDDLE\nTime taken: 0.096 seconds, Fetched: 14 row(s)\n<\/pre>\n<\/div>\n<ul>\n<li>\u591a\u8868join\u67e5\u8be2\uff1a<\/li>\n<\/ul>\n<div>\n<pre># \u521b\u5efa\u6d4b\u8bd5\u8868\nhive&gt; create table a(\n    &gt; id int, name string\n    &gt; ) row format delimited fields terminated by \\'\\\\t\\';\nOK\nTime taken: 0.311 seconds\nhive&gt; create table b(\n    &gt; id int, age int\n    &gt; ) row format delimited fields terminated by \\'\\\\t\\';\nOK\nTime taken: 0.142 seconds\n# insert\u6216load\u6570\u636e \u6700\u540e\u8868\u6570\u636e\u5982\u4e0b\nhive&gt; select * from a;\nOK\n1       zhangsan\n2       lisi\n3       wangwu\nhive&gt; select * from b;\nOK\n1       20\n2       30\n4       40\nTime taken: 0.2 seconds, Fetched: 3 row(s)\n# \u5185\u8fde\u63a5 inner join = join \u4ec5\u5217\u51fa\u88681\u548c\u88682\u7b26\u5408\u8fde\u63a5\u6761\u4ef6\u7684\u6570\u636e\nhive&gt; select a.id,a.name,b.age from a join b on a.id=b.id;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n1       zhangsan        20\n2       lisi    30\nTime taken: 24.415 seconds, Fetched: 2 row(s)\n# \u5de6\u5916\u8fde\u63a5\uff08left join\uff09 \u4ee5\u5de6\u8fb9\u7684\u4e3a\u57fa\u51c6\uff0c\u5de6\u8fb9\u7684\u6570\u636e\u5168\u90e8\u6570\u636e\u5168\u90e8\u51fa\u73b0\uff0c\u5982\u679c\u6ca1\u6709\u51fa\u73b0\u5c31\u8d4bnull\u503c\nhive&gt; select a.id,a.name,b.age from a left join b on a.id=b.id;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n1       zhangsan        20\n2       lisi    30\n3       wangwu  NULL\nTime taken: 26.218 seconds, Fetched: 3 row(s)\n# \u53f3\u5916\u8fde\u63a5\uff08right join\uff09 \u4ee5\u53f3\u8868\u4e3a\u57fa\u51c6\nhive&gt; select a.id,a.name,b.age from a right join b on a.id=b.id;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n1       zhangsan        20\n2       lisi    30\nNULL    NULL    40\nTime taken: 24.027 seconds, Fetched: 3 row(s)\n# \u5168\u8fde\u63a5\uff08full join\uff09\u76f8\u5f53\u4e8e\u88681\u548c\u88682\u7684\u6570\u636e\u90fd\u663e\u793a\uff0c\u5982\u679c\u6ca1\u6709\u5bf9\u5e94\u7684\u6570\u636e\uff0c\u5219\u663e\u793aNull.\nhive&gt; select a.id,a.name,b.age from a full join b on a.id=b.id;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n1       zhangsan        20\n2       lisi    30\n3       wangwu  NULL\nNULL    NULL    40\nTime taken: 32.94 seconds, Fetched: 4 row(s)\n# \u7b1b\u5361\u5c14\u79ef\uff08cross join\uff09 \u6ca1\u6709\u8fde\u63a5\u6761\u4ef6 \u4f1a\u9488\u5bf9\u88681\u548c\u88682\u7684\u6bcf\u6761\u6570\u636e\u505a\u8fde\u63a5\nhive&gt; select a.id,a.name,b.age from a cross join b;\nWarning: Map Join MAPJOIN[7][bigTable=a] in task \\'Stage-3:MAPRED\\' is a cross product\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\n1       zhangsan        20\n1       zhangsan        30\n1       zhangsan        40\n2       lisi    20\n2       lisi    30\n2       lisi    40\n3       wangwu  20\n3       wangwu  30\n3       wangwu  40\nTime taken: 29.825 seconds, Fetched: 9 row(s)\n<\/pre>\n<\/div>\n<h5>3.\u5229\u7528Hive sql\u5b9e\u73b0wordcount\uff1a<\/h5>\n<div>\n<pre># \u521b\u5efa\u8868 \u52a0\u8f7d\u6d4b\u8bd5\u6570\u636e\nhive&gt; create table hive_wc(sentence string);\nOK\nTime taken: 0.149 seconds\n[hadoop@hadoop000 ~]$ cat hive-wc.txt\nhello,world,welcome\nhello,welcome\nhive&gt; load data local inpath \\'\/home\/hadoop\/hive-wc.txt\\' into table hive_wc;\nLoading data to table default.hive_wc\nTable default.hive_wc stats: [numFiles=1, totalSize=34]\nOK\nTime taken: 0.729 seconds\nhive&gt; select * from hive_wc;\nOK\nhello,world,welcome\nhello,welcome\nTime taken: 0.13 seconds, Fetched: 2 row(s)\n# \u83b7\u53d6\u6bcf\u4e2a\u5355\u8bcd \u5229\u7528split\u5206\u5272\nhive&gt; select split(sentence,\\\",\\\") from hive_wc;\nOK\n[\\\"hello\\\",\\\"world\\\",\\\"welcome\\\"]\n[\\\"hello\\\",\\\"welcome\\\"]\nTime taken: 0.163 seconds, Fetched: 2 row(s)\n# explode\u628a\u6570\u7ec4\u8f6c\u6210\u591a\u884c \u7ed3\u5408split\u4f7f\u7528\u5982\u4e0b\nhive&gt; select explode(split(sentence,\\\",\\\")) from hive_wc;\nOK\nhello\nworld\nwelcome\nhello\nwelcome\nTime taken: 0.068 seconds, Fetched: 5 row(s)\n# \u505agroup by\u64cd\u4f5c \u4e00\u6761\u8bed\u53e5\u5373\u53ef\u5b9e\u73b0wordcount\u7edf\u8ba1\nhive&gt; select word, count(1) as c\n    &gt; from (select explode(split(sentence,\\\",\\\")) as word from hive_wc) t\n    &gt; group by word ;\nQuery ID = hadoop_20180703142525_af460dc7-287b-41b2-8af3-ba27cc0ea6ce\nTotal jobs = 1\n...\nOK\nhello   2\nwelcome 2\nworld   1\nTime taken: 34.168 seconds, Fetched: 3 row(s)<\/pre>\n<\/div>\n<\/article>\n","protected":false},"excerpt":{"rendered":"<p>1.Hive\u5185\u7f6e\u51fd\u6570\uff1a \u5728Hive\u4e2d \u7cfb\u7edf\u7ed9\u6211\u4eec\u5185\u7f6e\u4e86\u5f88\u591a\u51fd\u6570 \u5177\u4f53\u53c2\u8003\u5b98\u65b9\u5730\u5740 \u770b\u4e0b\u5b98\u7f51\u7ed9\u6211\u4eec\u7684\u4ecb\u7ecd: SH &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"http:\/\/www.weijing.co\/?p=1822\"> <span class=\"screen-reader-text\">Hive\u5185\u90e8\u51fd\u6570\u53ca\u67e5\u8be2\u8bed\u6cd5<\/span> \u67e5\u770b\u5168\u6587 &raquo;<\/a><\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"default","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":""},"categories":[137,118],"tags":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false,"mailpoet_newsletter_max":false,"woocommerce_thumbnail":false,"woocommerce_single":false,"woocommerce_gallery_thumbnail":false,"shop_catalog":false,"shop_single":false,"shop_thumbnail":false},"uagb_author_info":{"display_name":"weijing","author_link":"http:\/\/www.weijing.co\/?author=4"},"uagb_comment_info":0,"uagb_excerpt":"1.Hive\u5185\u7f6e\u51fd\u6570\uff1a \u5728Hive\u4e2d \u7cfb\u7edf\u7ed9\u6211\u4eec\u5185\u7f6e\u4e86\u5f88\u591a\u51fd\u6570 \u5177\u4f53\u53c2\u8003\u5b98\u65b9\u5730\u5740 \u770b\u4e0b\u5b98\u7f51\u7ed9\u6211\u4eec\u7684\u4ecb\u7ecd: SH&hellip;","_links":{"self":[{"href":"http:\/\/www.weijing.co\/index.php?rest_route=\/wp\/v2\/posts\/1822"}],"collection":[{"href":"http:\/\/www.weijing.co\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.weijing.co\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.weijing.co\/index.php?rest_route=\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"http:\/\/www.weijing.co\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1822"}],"version-history":[{"count":0,"href":"http:\/\/www.weijing.co\/index.php?rest_route=\/wp\/v2\/posts\/1822\/revisions"}],"wp:attachment":[{"href":"http:\/\/www.weijing.co\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1822"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.weijing.co\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1822"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.weijing.co\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1822"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}