I recently researched the question "how many CPU consume the index during insert "
I did an empty table and load the data into it from pattern table size of 20MB.
Zero (base) run - loaded into a table without indexes and measured baseline statistics.
1nd run: created 10 indexes 1-column each and loaded data from the pattern table again.
And compared the CPU-statistics from each download.
2rd run: created 10 indexes 2-column each ...
3th run: created 10 indexes 3-column each
4th run: created 10 indexes 4-columns each
5th run: created 10 indexes 5-columns each
=============================================
grant dba to yu identified by yu;
conn yu/yu
drop table base;
drop table test;
create table BASE ... ;
drop table test;
purge recyclebin;
create table TEST as select * from BASE where 1=2;
conn yu/yu
insert into TEST select * from BASE;
commit;
set pages 100
col name for a50
select n.name, s.value from v$sesstat s, v$statname n where s.statistic# = n.statistic#
and s.sid = userenv('sid') and s.value>0
and n.name in ('CPU used by this session','db block changes','session logical reads','redo size')
order by n.name;
Run 1
create index I1 on test(1);
create index I2 on test(2);
create index I3 on test(3);
create index I4 on test(4);
create index I5 on test(5);
create index I6 on test(6);
create index I7 on test(7);
create index I8 on test(8);
create index I9 on test(9);
create index I10 on test(10);Run 2
create index I1 on test(1,2);
create index I2 on test(2,3);
create index I3 on test(3,4);
create index I4 on test(4,5);
create index I5 on test(5,6);
create index I6 on test(6,7);
create index I7 on test(7,8);
create index I8 on test(8,9);
create index I9 on test(9,10);
create index I10 on test(10,1);Run 3
create index I1 on test(1,2,3);
create index I2 on test(2,3,4);
create index I3 on test(3,4,5);
create index I4 on test(4,5,6);
create index I5 on test(5,6,7);
create index I6 on test(6,7,8);
create index I7 on test(7,8,9);
create index I8 on test(8,9,10);
create index I9 on test(9,10,1);
create index I10 on test(10,1,2);Run 4
create index I1 on test(1,2,3,4);
create index I2 on test(2,3,4,5);
create index I3 on test(3,4,5,6);
create index I4 on test(4,5,6,7);
create index I5 on test(5,6,7,8);
create index I6 on test(6,7,8,9);
create index I7 on test(7,8,9,10);
create index I8 on test(8,9,10,1);
create index I9 on test(9,10,1,2);
create index I10 on test(10,1,2,3);
Run 5
create index I1 on test(1,2,3,4,5);
create index I2 on test(2,3,4,5,6);
create index I3 on test(3,4,5,6,7);
create index I4 on test(4,5,6,7,8);
create index I5 on test(5,6,7,8,9);
create index I6 on test(6,7,8,9,10);
create index I7 on test(7,8,9,10,1);
create index I8 on test(8,9,10,1,2);
create index I9 on test(9,10,1,2,3);
create index I10 on test(10,1,2,3,4);
=============================================
The raw results in 1/100 second are in table:
![](data:image/png;base64,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)
You can see: baseline is 25 1/100 sec.
But with 10 indexes 1-column is 554 .
Let calculate one 1-col index CPU time: (554-25)/10 = 52.9 1/100 sec.
So, insert into one 1-col index takes 52.9/25 = 2.12 more CPU time as compared to insert into the table.
The normalized table is :
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqUAAABrCAIAAADBx2yIAAAW00lEQVR4nO2dXaLiIAyFXVcX1PW4j3l3JffNxTAPKiQhgVCrQpLvaW6lP+dQeoA6cvn3718KHPD39/frS/g4HjRmzIs1L1BJ+DBE2EWAhlwi753goRl40JgxL9a8QCXhwxBhF4Hm/SUIgiAIAotE3gdBEASBff5exHy+FzxMc3nQmDEv1rxAJeHDEGEXId7fe8RDM/CgMWNerHmBSsKHIcIuQuS9Rzw0Aw8aM+bFmheoJHwYIuwiRN57xEMz8KAxY16seYFKwochwi5C5L1HPDQDDxoz5sWaF6gkfBgi7CJE3k/Ibb9cLpf9dn7hJx6agVrjqIH363a5XLbr/eiVaU8xUKfmxZoXqCR8GCLsIkTen8qjji4XcC9oqo2Uad98Q4V5QK0/ds+Q42Q9eDsjkxzny+2agTT15yUzLfS0pi6fYpR38/7jYkFtvy33SN5/U+DbN/IH8/4LdzVo7W9W9e/z/sN2tZ+lo0TeTw66B+7X7VFVB/Jec5q37gOa949rru/h+3W7XLYNn4+XCY4zB0Aj0z15A7apf+IUx/L+82Jv++vSnud675k0mvefF0ja7kwP7u/e1SmlR8t+PAOWzvsv2HXbz9QXeT85TB8R3GT59qBjo7oMqulSfL91C8PPQQ7T7iaf95WA+3W7XPabakJh4ry/X7fLdr21h+XFrVdRrDL7uO07mxCtU+BToSPDMUHe8428/4LY6nD8x6eK/ZHA5rDvY7XZ4Os+wH0Fl37hg5Lv2qXL+5/aFXl/Hvy8V5XHZWSct5OqBX+i4fcOhuFcYRLe2/XO7J5Se3xP7u7Syai6FEjmxHn/oDMNj3pHxfra2GcRTmrnpV5VO+gUcOe3399/QWyRxF7oyWJ/I7AV9x+szQZf9uHZ42/d2b/xQcl37dLk/Y/tirw/FTj8ZnIdISctzftWB0LoHMBz1Pe3/P6exj0/08/IJMf5ffIfb+roKwnb9U42y8++dt4LtUP682DK5yt5f1hsEu+uT4j9skBmDPZhgUq+68NjpHtv3QY/8kHJ9+3inqS0yA/tirz/AK+nBVtt+Kbo5T3zzSGxMHuDsF88Eubz0evK/u2NZBoa31dNHe+qzXvywrCuHXqgcvJf5v2YWPIm/2NifyAw7wRGuN+pzQbf9AEMV8unk/ig5Ee3TWkbs9kVef8ZyiQNqjbhtmrlPdzE3Qc079l7kHwk5D0sRsf9Jdl5mZbz/pTxvfRAJV37/ZYONPWvi22+2P6A2N9MYMhTGB+tzQZf9AFO4olPgF/5oCRuG0Lk/Wnc86t58bXPow4f/0a3hFTp5Zj05ZF8h+QdtuuN2z3JeS/c3AkeW5BpOu/hvo03vppOP6idOzO5wk7hzCa2E/afEPtVgY/rbZ7mo7XZ4Ad3dfM0v/JByY9uG/lF16/tirw/EdQjLhWVN+fafRTY93JPoDJ1reMjioVhcThzQC6o+/6eaxbsHFU5ajUf8NV2zYC/mtu4NkVTT0Df85u7rDk9+bR2pFvmjbz/vFhm3NeYUzpJ7Fdrs3o9wfOx2pzHB3y05szVd31QErcNIfLeI/H7esYwL9a8QCXhwxBhFyHy3iMemoEHjRnzYs0LVBI+DBF2ESLvPeKhGXjQmDEv1rxAJeHDEGEXIfLeIx6agQeNGfNizQtUEj4MEXYRaN5fgiAIgiCwSOR9EARBENjn70XM53vBwzSXB40Z82LNC1QSPgwRdhHi/b1HPDQDDxoz5sWaF6gkfBgi7CJE3nvEQzPwoDFjXqx5gUrChyHCLkLkvUc8NAMPGjPmxZoXqCR8GCLsIkTee8RDM/CgMWNerHmBSsKHIcIugrG8Z3/n+ZSlXKSDKNYOOZ93T+qhGXjQmDEv1rxAJeHDEGEXYTDv4aoocOU1AF65BWbSF6LxxLwn1x95Py9srU+uEa6RIVVke41SuNeEYrsCpQLsoiMTClRy0IfyWEX39bo+KOnZhde9wSXqZekM2NUxhFm3nN5EkiGdvC8LpL3+LCun4sXchOV7Teb9khhoBi9u+2u1qpXyHiwqfL9uXKPgdMl7TSe2K1AocK8rMqU0oUAlx3y47XhNtokr+lz67eJ+3YQIue2Xbd83W3nfNwSBbpaeIe28l9IabQd5q8t7VIosDlz3V2BfB6goyxTuu5z31x0ej/QMSJzjXiRYpRgfhArgrpleY1dOXbLawvVF6HFKYNRXtHwzoDAxsYxGKeLan+FPphbbEkgK3Hah5NQClah9wAWRJxZ8UMLbJeX9Yzvdx5RdA+0oaQxp5r04OP9M3oP8vV93sJQ7TNZqueJn8HF5X8V83bngljSnmZq733hKo2zG14wuQSWnLtnZt74Y5EV1wSkZawYprZ33eACHkZv4QsO+lkBc4H7dLtuWO8Fgp6kFKtH7QIdp5Q8LPijh7eLHSK9egOm8790/8HONIa28J5P55Cw4e7Y6z6qS+LhC3jOvLnH+7jcS1f35fDafucn61nw+imB8PP7xPCgHlWQOy3cyGAeZC07JWDNIaeW8b3fapU8Xeq6NjErgPDaevpxYoJKx0RmYstu2zZIPSrp2gRukjPkXahejdA0Bca8z5J3xfd3nOm8+H6YpZr+RhNfmPRgCb9f7/brVOT2e9/U18za15LAHqbaUk1LBQh8m8n5OYMBx8I283mtasT2BuAAawSDt0wpUMuZD9Ym3vO/alUs9pqzhsNZk3ncNAcq1hjTzXhzgSx2BOnq7o2gmrcum5hfkD43v89B5Y+L+UN5LygfkyAfh35Vw4/v6g8j7CVE81Bhd7F5zih0OueoFpI28fyPsl3pxcxK6sM83CDdyeu1uwy6FIfAu0RrS+X4+fCH8+BvMvDMDf9JDEFIQ7A7evpevJZIBK/sF13zc/vt7cpD8NqjXkVDkPXfN5TRKOXVJbt/q/T3o2XHbI+9ng51Sqt/QEV38XmlGsV2B4qQaM7c/o0Alx3xABU0OWAU6dj0zJ29s3D8pJQt2qR4U+N0X3f3Q+L4cl3QZxLwvH74Q/x/F64C3Ig6cquxGei5VhwYdAZ1A+qb68zSNwfiFGY8L43v2mivnOnLqktIW3JOiR3eS97Qzu8aIsO6C0+9pcbqEvdKEYrsCJS1CG5pOoJJjPgATlurFvs2QXcxD21ze9x8UqTn+fy/vDdKbVzfJ8s1AwXoa4Xu3QdYQa16gkvBhiLCLcJIhHvO+OTthFpvNALOcxvLiZpwlxJoXqCR8GCLsIpxliL+8l/+XoW1MNgOCB40Z82LNC1QSPgwRdhF8571XPDQDDxoz5sWaF6gkfBgi7CLQvP8LgiAIgsA0Mb73wp+Dbq8HjRnzYs0LVBI+DBF2Ef5iPt8hHpqBB40Z82LNC1QSPgwRdhEi7z3ioRl40JgxL9a8QCXhwxBhFyHy3iMemoEHjRnzYs0LVBI+DBF2ESLvPeKhGXjQmDEv1rxAJeHDEGEXIfL+AI0FcTT8/jd+PDQDDxoz5sWaF6gkfBgi7CJE3h/gt3lfflKZWZWvbBbWGkgpmWsGbH3MrrGx2AJTBC2EUP96+IxiDwhkVvZ67jqjQCVtHyTJC1X0uXRvG8GZJR8COjp5wzkmuRh5f4A38/5N+EV8H+sobez6eNX12mgGKSW4GNJSTR0scSGsdtFZCnf2ZVIPCawPMe2CQFr6PtDS+22pij6Xnl28M4s+BDSI0nAB4lhZR5CspOc+75/rDV7BmBuuNwdMLqvw7TvKz86aTdV4nC5ST8cy0gWg4+1XtBLgbc/bmPVw6YzC+s2AsNp6uKgVsstf3PbOYp+owHRiDwlEYLXTCVTS9wHwkrxSRZ9Lz66GM+s9BAaQ+8ZjjkXev7IVZy1clLSKz2dIwz9gRwHXCxpm79USuqAQOkh9AVVhuDLys9ydz/vqhFaaQWaxpo4vl2vM9+v2nK0Bdydq2+iP2cQeFAhojEsWou8DAI9X16joc+nbJTuz3ENgBPHO6TlGJ0ki73FGk2jMeYuWzQX74OV0mcB/zr7Xb5rKjYr+li6AHvG1Sna5irxuNr+wtoXVxEUWa+rkcpmhLmyn9+vGVOq2bfPGwGGB8GMDd2zfh0IVZStU9Llo7JKcWe4hMIIy74Fj2SbcriLvmXwl5CCt854GPI7/6pDMCJ7uI1xAdbz9lt9F3PLxhPF9tQ6glWaQWaypD45j2NY+9bDvTYH1DrMJVKIf3ytnbBf1QcnQdEiM76sPmGKkLx153wrfwsj4nn1FB/ZpjefFCyCX8tijvOivJ+6lK07JTjPIrNbUu691kaBuXs4n9i2BdHCfJhSoRPv+npHMfrKqD0qGvu5APVvtITDA8ff3je/BRN4n8T/LwcCs39+jYTT9X3JwgoXmvfgCoHGf1y/72QtAeU96EVaaQWa5pg6aKf9PoIj5onLrtdwcHBfIzO7PKFBJ14eUBMm54NwVfS4qu/I23CqWewjoodI6Nt2vG/o+WozvM0xGkyn112fl+/mPGXQ8dman3snH1ZAe7cr9J2vumKhDwL1ocPX+nspcZg6Dzs2kxHTWuRuH7JNSmlPsAYGvIvVQZkaBSro+1JLXquhzadvFO7PsQ6APL01oR2SimbmD3Oe9S9ZvBn3W09ibv2ywhljzApWED0OEXYSTDIm894LNZoBZTmN59TPOEmLNC1QSPgwRdhHOMiTy3gsmmwHBg8aMebHmBSoJH4YIuwiR9x7x0Aw8aMyYF2teoJLwYYiwi0Dz/i8IgiAIAtPE+N4Lfw66vR40ZsyLNS9QSfgwRNhF+Iv5fId4aAYeNGbMizUvUEn4METYRYi894iHZuBBY8a8WPMClYQPQ4RdhMh7j3hoBh40ZsyLNS9QSfgwRNhFiLz3iIdm4EFjxrxY8wKVhA9DhF0EA3lf/bR968cIpAKNlW50Z1bRXwznO3hoBh40ZsyLNS9QSfgwRNhFiLyHB4m8XxK2HmbXyP96PAAu9E3Wil1CbFsgs+4zWfLRyooPx3wQan9hH5S81y7IZgt2DRjC/n4+2i3yHh4k8n45HmuN3FZbGgsskcIvEHPb0SpI5fZeRGxXYFV6vzX2mk+gksM+1LWf0sI+KDnWLsT1Yte3q2cI3IiXFWLDbNm8L6vV7XuV99e97tmA3ZgCJO9hrxtYTLpaIO8fO9DaoF0z+fKYTmt5uDcKgx34NZFKWaGXZ4LVlsJUrVoN70Y6kpld7NBC5lmQvNd0ApUc80Gu/VV9UHKwXZAGUljerp4h+FmQfbCW92CgXK9GL68tLxeoV5KF6y/Wkwe3fbve84d82lfl0dnBWcoCCMyCtkzhS3kq4OsE6yCjDsZD4W73qbFCBALw5fKDk1cl1p8uIFYjMAOX7Zb2mk2gkmM+JLn2F/VBycF2cb9ul23jZr1Xt6tvCOoQoJbEjFdXzXs0Ly7O53Nz9FIB8E886V6OXk/GPz973GnSVMJd2sRO7tOOTF0YTODcr1t9+bn0a36UmQRavRlULBCBEHK50vAEdWXFvVOaT6xS4OtD0GkV9ppNoJJjPpS/q9pf1AclB9sFnNMuz8SU1rdLYwicGt7YCQCwbcm8xyP3Tt43EhcUKIFJ93rtwfQe0LciuJmnZm8DD/8vgGbek/E9mtdAoFkCfIWrN4OKBSIQohjH4BRcbT5fP66Fn3ke31cTs3ztL+qDkoPtopr0Xv22yQzND9Vdxnrjknn//fH9c/6IH9+/3rHjM3V7G/mPahK/n/d0rqb3RUByMas3g4oFIhAx9lqOqFtBrPa9NX5C+X1/L0eWrQDrcKxdoK227Br6/gcb99iyNfO+inju/T18DZ+RCgiZW3cJ2Pf37Lx5Xb6V9/D6W3lfjfagLmaOAU4g2GkGlBUiEAGaJvtP2HTJlNwaYnsCU0q1MrnofAKVHPJBrv1lfVByrF2AFgHn9i3Ypbp/8rZXcuCZIvCwWDPvE5infg6vS0by32kHu3EF8FyANFFftoOhdp3UUnlxPr9Muu/7ri+ML4/dDpQY+hoLgL7JWKZPQ++NlJhvbJESS4ntCayeReJeaU6BSg75wNV+Skv7oORIu5CecybsahsChJN30FwzWjfvvdJ9Y6HBQjPosZ7G3nRdgzXEmheoJHwYIuwinGRI5P384ITnZ/H72GwGmOU0lpcv4ywh1rxAJeHDEGEX4SxDIu+XAE/qHurnmWwGBA8aM+bFmheoJHwYIuwiRN57xEMz8KAxY16seYFKwochwi4Czfu/IAiCIAhME+N7L/w56PZ60JgxL9a8QCXhwxBhF+Ev5vMd4qEZeNCYMS/WvEAl4cMQYRch8t4jHpqBB40Z82LNC1QSPgwRdhEi7z3ioRl40JgxL9a8QCXhwxBhFyHy3iMemoEHjRnzYs0LVBI+DBF2EWzk/dHfnUEc/bG6I2c/fC79wVs/yeChGXjQmDEv1rxAJeHDEGEXIfI+E3m/Kqzg2TUKv/hdgOtar/g74W2BzArOYFWqJQQqOVbR4BOvv5/ftYssYsrsZsOuzgOddUy4qWzk/Sl8NIO/eS5XeV8WQFoq78ESKdyqMSnddmZZeHGv+cR2BVal99tSApUcq+j8yb5vrvK+ZxfciBeNYR+m69slPt9wAeKYeFP9LO/BknC5omA3jVvsp14i6bmFxCe7PFB36TzNQapxCV2/nuttMV0tLu+fK91eq1V6W9fA2LTtO1qI12y3F7DEErGA7rLWaNur9S60PPzQut259hYSqORYRZcP6I29qg9KenZhO7JdhvP+AfN8e8I6Jt5Uv8p7sMrb/brnOsMRWRapRyU7+9ar2qNlZMtBuXupc5Cy+DwsXO0I17wvixtIu+DzC0v0FkP4A9br5W6CVynZaQaZxfIeXy7fmF81Xj5t7DWbWI3ATH5kLSRQybGKTvjJ7Sfv+3aheEM3DjMusmOX2IIkx9ibKv0279GFcKPr1wwfLsmsNA92RuvFwqyWlpMfOQiYO7lfNya8uePiCy+Pbj7vyVlrQ9gDMqvklu31BVlpBpml8x73wAGw59beazaxSoGvD0FDWkSgkmMVDcZnnvOetwtObm7sBADYZsUuZd4jx+hNlVKaYj4fDrkxcKwNh77VlpKN9N21EIntvBcPQsb31TibfS2PlfXynn2fIBoCr4vLe869ZKcZZJbOe7Yx4xTMtbzI8Fc/voefLSRQyaGKhrOxnvO+My1EBvvsRit2jY7vmafHgx9/Xw8PnVu1W38Jjc6zy0Pz+oMD4/uS5yi5mc+laQvV+B7nfW0If0BpfC+4Z6UZZBbL+8H3lK+/Fnq9rX1/jx/ZCwlUcqSir0xP/1VmVR+UDH3tg417bKgVu8be3yv7zd/L+/IOmkQW93UWUpLbV3qDLm3v5b1wENJXYnYkQ+vn9wPBKxV93suGMAeE5wXzOJxXKdlpBpnV8h42U/afsBmXOUp+rzSj2J7AlFI1+7qUQCXHKjrja3yvvG3yttcDD0/FAr+s2EVvg45N8k31q/E9GCnThCPzz3VJaQv+ohs9+lDeNw/CX2G9I4rfy+Wy7/tI3kunYw8INqNOBuuzmWaQ6tcey/Rp6H2SEm6nUBjurNNtaU6xPYHVw1ncK80pUMmxigY7e8r7nl3geUbet7IOrm8X/3yrRvVaS+L/3w/AzJq35pvmZf1m0Gc9jb35ywZriDUvUEn4METYRTjJkMj7Njjh+en2NbDZDDDLaSwvX8ZZQqx5gUrChyHCLsJZhkTed8GzK2uGfTLaDAgeNGbMizUvUEn4METYRYi894iHZuBBY8a8WPMClYQPQ4RdBJr3lyAIgiAILBJ5HwRBEAT2+XsR8/le8DDN5UFjxrxY8wKVhA9DhF0EaMh/6A1BrV5gIUEAAAAASUVORK5CYII=)
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)
I did an empty table and load the data into it from pattern table size of 20MB.
Zero (base) run - loaded into a table without indexes and measured baseline statistics.
1nd run: created 10 indexes 1-column each and loaded data from the pattern table again.
And compared the CPU-statistics from each download.
2rd run: created 10 indexes 2-column each ...
3th run: created 10 indexes 3-column each
4th run: created 10 indexes 4-columns each
5th run: created 10 indexes 5-columns each
=============================================
grant dba to yu identified by yu;
conn yu/yu
drop table base;
drop table test;
create table BASE ... ;
drop table test;
purge recyclebin;
create table TEST as select * from BASE where 1=2;
conn yu/yu
insert into TEST select * from BASE;
commit;
set pages 100
col name for a50
select n.name, s.value from v$sesstat s, v$statname n where s.statistic# = n.statistic#
and s.sid = userenv('sid') and s.value>0
and n.name in ('CPU used by this session','db block changes','session logical reads','redo size')
order by n.name;
Run 1
create index I1 on test(1);
create index I2 on test(2);
create index I3 on test(3);
create index I4 on test(4);
create index I5 on test(5);
create index I6 on test(6);
create index I7 on test(7);
create index I8 on test(8);
create index I9 on test(9);
create index I10 on test(10);Run 2
create index I1 on test(1,2);
create index I2 on test(2,3);
create index I3 on test(3,4);
create index I4 on test(4,5);
create index I5 on test(5,6);
create index I6 on test(6,7);
create index I7 on test(7,8);
create index I8 on test(8,9);
create index I9 on test(9,10);
create index I10 on test(10,1);Run 3
create index I1 on test(1,2,3);
create index I2 on test(2,3,4);
create index I3 on test(3,4,5);
create index I4 on test(4,5,6);
create index I5 on test(5,6,7);
create index I6 on test(6,7,8);
create index I7 on test(7,8,9);
create index I8 on test(8,9,10);
create index I9 on test(9,10,1);
create index I10 on test(10,1,2);Run 4
create index I1 on test(1,2,3,4);
create index I2 on test(2,3,4,5);
create index I3 on test(3,4,5,6);
create index I4 on test(4,5,6,7);
create index I5 on test(5,6,7,8);
create index I6 on test(6,7,8,9);
create index I7 on test(7,8,9,10);
create index I8 on test(8,9,10,1);
create index I9 on test(9,10,1,2);
create index I10 on test(10,1,2,3);
Run 5
create index I1 on test(1,2,3,4,5);
create index I2 on test(2,3,4,5,6);
create index I3 on test(3,4,5,6,7);
create index I4 on test(4,5,6,7,8);
create index I5 on test(5,6,7,8,9);
create index I6 on test(6,7,8,9,10);
create index I7 on test(7,8,9,10,1);
create index I8 on test(8,9,10,1,2);
create index I9 on test(9,10,1,2,3);
create index I10 on test(10,1,2,3,4);
=============================================
The raw results in 1/100 second are in table:
You can see: baseline is 25 1/100 sec.
But with 10 indexes 1-column is 554 .
Let calculate one 1-col index CPU time: (554-25)/10 = 52.9 1/100 sec.
So, insert into one 1-col index takes 52.9/25 = 2.12 more CPU time as compared to insert into the table.
The normalized table is :
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