Testing strings for multiple substrings

  • Hello all,
    I am stuck with how to approach this task.
    I have an error table that has error text held as a string.

    I have a lookup table with generic substrings in it and their corresponding categories.
    Example here:

    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

    What I want to do is test the error string for all of these substrings and then return the category if it finds a match.

    Does anyone have any experience doing this? I can't quite decide where to begin.

    Sorry to be so vague.

    Dave

  • One method is to use CHARINDEX. Performance on a large dataset will (probably) not be very good, however. Doing lots of pattern matching on a string in SQL Server is not it's forté. Have a look at the documentation and have a try; if you don't succeed post your attempt and we'll see where you went wrong.

    Thom~

    Excuse my typos and sometimes awful grammar. My fingers work faster than my brain does.
    Larnu.uk

  • Yes, or you can use LIKE.  I don't know whether it'll be more or less efficient than CHARINDEX, or exactly the same - you'll need to test.

    SELECT l.Category
    FROM ErrorTable e
    JOIN Lookup l ON e.ErrorText LIKE '%' + l.SearchString + '%'

    John

  • John Mitchell-245523 - Friday, October 5, 2018 6:33 AM

    Yes, or you can use LIKE.  I don't know whether it'll be more or less efficient than CHARINDEX, or exactly the same - you'll need to test.

    SELECT l.Category
    FROM ErrorTable e
    JOIN Lookup l ON e.ErrorText LIKE '%' + l.SearchString + '%'

    John

    Hi Thanks for the replies. I used Like and it worked just fine. I only had 25 substrings to test though so performance was never going to suffer in this instance.

    Regards

    Dave

  • Ok, Joe.  Let's say you took the time to setup FTS.  What would the query be to solve the OP's problem?

    --Jeff Moden


    RBAR is pronounced "ree-bar" and is a "Modenism" for Row-By-Agonizing-Row.
    First step towards the paradigm shift of writing Set Based code:
    ________Stop thinking about what you want to do to a ROW... think, instead, of what you want to do to a COLUMN.

    Change is inevitable... Change for the better is not.


    Helpful Links:
    How to post code problems
    How to Post Performance Problems
    Create a Tally Function (fnTally)

  • david_h_edmonds - Friday, October 5, 2018 5:53 AM

    Hello all,
    I am stuck with how to approach this task.
    I have an error table that has error text held as a string.

    I have a lookup table with generic substrings in it and their corresponding categories.
    Example here:

    data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAx8AAADgCAYAAABiiNv0AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAD8XSURBVHhe7d1Jiyvrn+D3n06e8699D9WbHqpBcRZ5st5A6BVIucmVwAuTDd2EbDBI2CQYLGxsy2DIjQQGI2GDEy8M4i4SDilxFz0tUj3cHqqqdXS7U4Kqcl+7urq63MO/+7a7XOeknyfiCcWgGDXl9P1c4h6FYnoiFPHMT2Tll7/85aMY707em08AAAAAsF/vzL8AAAAAcFAUPgAAAAAcRaTb1e/9s983nwAAAABgvyKFj99a/QvzCQAAAAD2i25XAAAAAI6CwgcAAACAo6DwAQAAAOAoKHwAAAAAOIrcAed/8c/8ivkk8vnzZ3Ecx8wBAIC36suXL/Lp0yczB+A1mP3G75hPh0PLBwAAAICjoPABAAAA4CgofAAAAAA4ihde+FjJoFaR1tTMAgAAAHi2dit8TFtSqVTW07MvBGSEdzWoSaXACUxbatvaQBV7AABAxGogtVA6WzS9LJoGA3j5ti986AimMRJn8iiPj3qaiPSecaZ8T+GtD9W2922pmnkAAKDoCj5rLM2ln84+yrI5lssB1XUAAjt2u7Ll1DIfpS7DSKbc6xIVtDS0JFKnEasdCSo8ptKq1GQw8FspzHap62t6G3+Z2jY1nksKrxdOqzMTGTXcfdTcHSSHw235WB/c7/aVcfxYa4s7UbsDAHhVVDroVvDdSztUO1dt38u9/0ViOp6WBgfL1mlnPB+Rmb5mbbuZvrciabvmbR+EBcC+bF/4qJ5L055Jx0rO7E9blnTOJkHtR38ujXXzq3rwL0Vu1stsGUVaIdR+OyITd/lQFRPU+pb6or80+4u2WowaPTk1NS0TR217HYmePKnhrUr73guDijXdfawjyo1wJEs/vo6M5yrY3rL1MYZpewIA4AVaPchcHLlITd7S0v30NDg3H5GavuqCQ8K2kQJINH0fXjgqMb8Nlq/uZDyzpXkeKkkB2IsdWj68CMPNbFu65iCUqV8NpDeypX8VxELVdlec2Vju3HWirSTV86bYs4UszbzmTEKZ/emtjFSk1l0XCqLbh2ta6joCmT+ECjK+jPBmiIQjRerx3cj4TD6aZdWPZylhAwDgBVsuVHY+S366H5GXj8hKX92CgyOTUEWfu63a4iGUAEfS9/qFWj6SW1P6WN2NZeZ0I604wFvwf//+H8jv/8Efyr/4f/6V/Mt//W/k3/zbfyf/7ud/L//vf/gj+f/++I/NWrvZsduVel71GAi3VkFirQp+Jt+fGuqxDriDy/xlVicn0lLsU1n3mNpBengPoPpRRY1BZDe9VVfg7OM68gUA4K0one5n5SOy0tfEgpAlp/ZMFqmlnbpc6dYYt/SxkrvxTJz0ZhwAO9i58OGrtm+kH3mwHdOcGZ5MC8G0JVbnLFiuSgK2t1G6rBqSLWyG9wDcmpkg8myMojUxAAC8CrGWgw3bpPtZ+Yis9NU6Tdj3Uhaz8LjPTW5rjO56ZVpOKHsAh/Hut3/7t8WfytA1GJGBWNNr6fgPtju+YiS9gs0K0+ucGhATqQX7m0qr5OtuM8Pr23eXKF37YvqwelN+Fy4AAF4e03LQiPYo2Eh7jcR0P5wG5+UjstJXs20jNIB8NejJyG5K5hAOfzvdKuNckF4DB/Lu13/918WfytAtB82xFTSHugO//LEPenzFMrpcT36BoT6UiaMecPN977SvChdZ6jL0+kmZffXk9Cb8Zq182eHVcY7uf9oRSy3b29st9HmK9/aO9VSy0AQAwEug32zlJdVBmmeNm3KjE9qcdH8zDS6Qj0hNX/W2E3HM27P0pMOxjLyRM0lVzptemwldroDDqTzqKgNj9hu/Yz4F/uKf+RXzSeTz58/iONnFBAR0jU88wtOv6u2dLkNv1AIA4OX58uWLfPr0ycwd18HSV/363oZ+CxY9FfA2fff9D3Ly7p2cnJyoyfwbmv/w/r1Zc3t7G/OBTcvFLDbAfCUPc/MRAABs5VDpqztwnS5XQKKK+XdXFD4OqD5cSn8ebha2ZNyk1QMAgF0cJn2dilf2oOgBHBKFj4Py/rZIMCAu/AcMAQDAdg6RvtZlqPbDSymBw8od87H8zb8pX79+lW/fvrlTrVYzSwAAAAC8Fj/+9HPqmI/3av79HsZ8MOAcAACU9pQDzgEcRtaA830VPuh2BQAAAOAoKHwAAAAA8MRfa6Xm9/WmK43CBwAAAICjoPABAAAA4CieReFD/1XSSmtq5l4Y/ddQawNZmVkAALA98gTA67aXwocbUVQqkhRXuMtKPojbbFPKaiC19R8mOvCxUq1kUAuFwUxPEd/6v19tEL0Kq0GtVAKQvX7y+caPmaTo/VA2vACA/SNPsA3yBEnHTEKe4OXbQ+FD/0VQWxzHltHt5o9cHz7K431byvzpn222KUzXSlhjaS6DP0y0bI7lssANfwh2fxn8kaSJI6NGsYdv32zbllnnWv2ah+VMguuupyJ/FOqg9wMAYI/IE+yCPAF5grdg98LH9FZGdlOurppij243blS3hLoueU6lVanJYKAedreU20q8saPbeKXj1lRv65WMK3ofkWcxXoJO3q97/MZI3ez3Er6/q+370A2fs69IDYkKx4P5fq1oWBLUh25kE33g8/aXvdwt+a+Xxa9bSLMrfXskjcxagrRjed9bnZmomNJdViqydK9pKGyx+ej9kHROOx4fALAf5AliioYlAXmCxHnyBMe3zzddaTsXPqa3I7Gb51KtnktT3ai93B94Jp2OyMQt5Q6lbr7NM2r05NTUTEwctY/r4MabtizpnE3WJedlfy6NpCa51YPMxZGL1IPqGzZhX+EH6lIFfl0z0ZVFZ+Qu8RUOS5r6hQrhSLwKo7zw5BxPPbCXnTNzrfUUjWCjLGnf9FVi0UuJjLLCUpX2vZ63dTWGu6xI7cVatS3q0NK51OH2r/FNclgTz2nH4wMA9oI8AXkC8gTIs1vhQ/3ovZEtzXP9o1blvGnLbHzn3eQZnEnxCMYXrpmoXzgi84f1w6TD0L8K9lhtd8WZjeUuHpDlQkVzGVZ3Mp45MhnG9qWipwe9L7O8u76J6zKchP7ie5mwFJEbniLH8yOtAtQD39WRuPvAx+SFpSDdhOzVTugpiDC9cHfksnYpHenLTWZEUeKcAADHQZ6APAF5AhSwU+FjdTeWmd0UN55RqudNsbd9qHaiHg4rfAM31K24hcSIyJJTeyaLpfqYF1G59hQWLS88rozjqYjjftmXuXm4izQ71ocT74GPr1soLPmi/TvDCY4Xac9mM3G6GX05tzgnAMDhkSdIQp4gC3mCt2mHwsdK7sbq1lM3peXf5FZH3YwzGR89plGl7/XN608JzYmR5ssE1qnY5mNgKYuZLaeW+pi4PK5gWNLo/rJqH24zcF54XDnH0w+m+91EzjpWgTdnmAe+cy135htXobDsQve9nUu/rwfYBbUfiUqfEwDgsMgTJCNPsB3yBK9ZpPChB5TEp1TTa+mom6wfekOEnnQfu2O8IWGtcL9SrS5XKnyjRnSQlR6s5JaWzb7CA6xWg547eM5rRf4oZyoaWB9rNZBaI1SHUSosCcz+7P6VV/rPDU+Z4+kaCfMxT/3KHWjW0YO1fHlh8flN3yVNWw0ZOV1pt71jZw9y8yWc05bHBwDsgDzBOg1fKxWWBOQJyBM8Q0llhbLT1i0felCZ6BsjfJMpXp+/Y/a/0wOLltIcW15Niz+lDOjSb7FY6oFMVrCuNW6a/oR6XxNxzNsR/GXL9Svd6jL0NvaWWwvpqvngXi8XFm3m78vdn/e6v2BgVF54co6nXyG4/t4bGBbqnplB7VcPNDNznrywqDXcJnav1iut6TPav9Osp8LZGDni9R01x04b5JZxTkWODwDYP/IEajl5AvIEr4YqIuhSgvqf+09ofh8qj7pqwvhbv/k75lPg1371V8wnkc+fP4vjhAZTAQCAN+nLly/y6dMnMwfgNfju+x/k5ORETe/k5N2JvNf/huY/fDgxa25vt7ddAQAAAEBBFD4AAAAAHAWFDwAAAABHkTvm4+E3/qZ8/fpVvn375k61Ws0sAQAAAPBa/PjTzwcf8xErfPyu+RT4tV/9hfnEgHMAAOBhwDnw+oQHnL9XhQ230BEpfLw3a26PblcAAAAAjoLCBwAAAICjoPABAAAA4CgofAAAAAA4imdf+Ji2KlJpTc0c3pRpSyq1gazMLMpayaBWER4fAADwXOyl8OEWECrHyeS4x9oxQ7oa1DYKNPvYbzIvA6ivT3iqDchSv1WHuteS7us824TFf96DqSXho/rL4/d4NHxJz0V0PwBeluPHDaSvwKFU1v8Lic9vaQ+Fj6ncjmxxHFtGt4fPOtSHj/J435aqmd+XQ+3X50zU/h+D6b59qCPhuTv0vVbGtmGx+8v1vbzsz6URyxzYti2zznVuYSK8n4kzksYxajAAHMxTxA2kr8DLsnvhY3orI7spV1dNsUe3sQhF10rUZLCaSmtdK6HnzeLc5ZvcmpNwJLQaSG29baj1JfF7r5bE6sxERg33e7+GZGO/GzUq4QjUW9aaFg93Mr292m7QCh0j6TstKzxp24Rlba9scR0jlyvSRWqbc1Aix1LbP5jvN+j9qOWp980+jh8+v+zt3Fq79bIgHGnfR+81/1qmnYuir+16mZkiF9/bR9J97Unfd/y+Twtzlmq7K46MJFL30OxK3y5XmKhfOCLzB3U2AF6Dp40bktKBbdKGtG0AbGvnwsf0diR281yq1XNpqgilt5FbmUnH6snp0q8JETUffnjzlmdRkYLVEVnXkExEBUBFI+r7S5EbUwuy7Nsycr+vSvvemxdn4i5LriHREZElnTNvHW8fmzU4o0YQ7omjzuN6myhJbadOYeIeZyj1xO+KhCdpP7687TOuY+L3RWyGZ9pKCMO6wKLCeBk+VlcWnZG7JJnaf959FTn+ttcgJ9yqwHLZOTPH0dO9uLdU2vcp0u8lFa7GXAXLW7a+d4fhXzj7vi58n5YM89rqQeZiy6ll5l2WtG/6Yo96hQow7u/fU7/32cdn0SIEYA+ePG7YTIfKpw1a0n4AbGu3wofKrPRGtjTPdZRQlfOmLbPxnZcpC3EmQSbGqwmZy0NopbzlqXSri1q7u84h1WXodiHx//VUz5tizxayNPO5VncynjkyCWXw8sKdVzMzaqTVquj9bEZmke8KhyclUszbPu06pl7fYqLn4N0r/atYGGZjudNhMGGMHGuS/df08++rEtcw7Vzzwu2K1eytpX2/KfVechPvM/non+fHs9I1gGXu0zJh9k2vOzKzm+JGA2HVtnR1YecyvcA661jmmbBk3FQFv0ihCsBLdqy44UnTVwCl7VT4WN2NIxGLl8kPZ8rSzGSRWRLIWx5in0qkUsWIdB+xVARovi9kuUhY35JTu0S4YqJ9UktGYruGp8j2Kdcx9futqMTGCicSDZXVNRLDWFbG9djlGmSFWyWi98u+zE3it+7ulPZ9WdWPqugRJIK6pfFgrQMlwhxkDCrSmPdlmVIorQ8nqqDWkcuUfa37dauCZpF+4ACet6eIG540fQVQ2g6Fj5XcjdUjqyIPy0Q0XiZ/JuPc0ke8GTYub3lIUovGtCVWuPuIylDZZlEh1mnC+ktZzEqEa592DU+R7dNahsq0GOVyQl16/MnUyieGsayM67HLNcgKt6Yz7e53EzlTCe+6K3Pa92W4LR9B4acxitbQ7V3BMIcHg2YPWPdasHTm4c58k6h+5fYD3+y2CeAleXFxw3NL74E3YPvCx/RaOurh9Pui+5Pudx6vpRg1goGrq0HPHaAebobNW56qfqGyheFIaSqthNeGuk2/5vNaVtcTM34lPCCuVLj2bdfw5G2fdh1Tr29VdO+f9dvNVgOpNdZtAclSxwQZbg1/aHmBfZa6b7a9BnnhjtC1ZeZjRNr3BehaOTOOw5tyavVKdslKt0OY40zmoaMHxKeqSrtL6wfwpjyHuGHX9BVAaVsXPtzuH043qP01vL6S0X7jzqQrC8uruXVbJGK1IXnL09Vl6I009lpeKj05vVHb1ofeq/nc7yrSO+2rMAW87mFei01y1xI9gHcijnlzkBuucTO1+biIaJ/Ust1wdg1P3vYp1zH1e7WFbjL392ctpJszPsMLw1Ka46BJ3p3WhcXYsfQ+c1qsyt03216DnHBH3kTlDVp0GybSvi9L38sShNmd1tcsKv++zrGvMG9Q11APMDVzqUwBsOyYEwAv1X7ihqdNXwGUVXnU1anG3/rN3zWfAr/2q78wn0Q+f/4sjpOXyQzz3iKx6D6mZGLylgNJ3s59o8cuxRNC/Xrc3umSd9kDeFJfvnyRT58+mTkAr8F33/8gJycn8v7knfvvyTvzr5n/8P69WXN7u73tCsBBLRez2ADzlTzMzUcAAIAXhsIH8IzVh0vpz8PdrrzXTtLqAQAAXqIDd7sCAACvEd2ugNcn6HYVdLUKd73aR7er3MLHw2/8Dfn69at8+/bNnWq1mlkCAAAA4LX48aefn77wQcsHAACIo+UDeH2O0fLBmA8AAAAAR0HhAwAAAMBRUPgAAAAAcBQUPgAAAABkqph/d/WMCx/6r1hXpDU1s2/KWz53AAC2RfoJPHc7FD68Bzz442d6asmxnvdpSx2vNlCh2KenPaftJIX5aSJe9zdRx64Nor/KalCTSokAZa+ffL7xYyYpes+UDS8AgPRzF6SfeEt2bvmw+0vRb+t9fFxK3x5J40g3XX2ojnnflkP8neenOqddBGFW08SRUaNYhLJvtm3LrHN98ATHmZhzNVORv/h9yHsGAED6uQvST7wVe+x2VZXzpi0yfwiVjOOl7HgtSN7ykGlLLa+JHx+4pfB1pObtpzWdSmu9r2Bdl7u9v8xMuZFi/Jz0/tV+B/6+THhXA6mF9hvstkC4UrfVMrbLUh+6EWg0Etvtt3BrM9bLMsLS7BZIcNKO5X1vdWaiYn93WakEwL2WobDF5qP3TNI57Xh8AIBB+ukj/QSiIoUPPZAkPhW3krvxTOzmuSkZ6xvRks7ZZF26Xvbn0og8KFnLQ3TBoTGX/vJesgroo0ZPTpfevibOTDrX/p5UJORu7x9HRYiOOu6wbpaniZ+TpvbbEZm4YR5KXe/7UuRmfQ62jHrRpsnMcFlqZ+tal4lIaNv07QqoX4gjI7l1N8m/1tNWwnK/iVVFQpedM3POesr6HSxp3/TFHvVSItissFSlfR/6fdSyIjUya9W2qENL51KHWx3nUl/bm+SwJp7TjscHABikn6SfpJ+vUVJZoey0c8vHrGOZkq96IGa2NM/Nzba6k/HMkUkog19td9UDPZcH/VDlLfc9qNJ3gYKH5kyCdeoXTlDjsnpQez2Tj2ZZ9eNZrIUmKvWcDGeiI01fXYahpsjqeVPs2UKWZl5LDdf0VkVvjnTXJxbdV+p2ZeX+FgPpjWzpX8WWz8Zytz6gHxEXoCKxro7s3UgspujvnkM3i3u/kZ6CRMALd0cua5fSkb7cZN40Jc4JAJCL9NNPh0g/8YqoEoMuNOzLHsd8qGlyJh3LNNMtFzLzVgmx5NSeyULHLHnLjVGnk7BeSdWPqugRPJzT25HI2cd1JBWXek4pIs2PVsnw2qfqrA+s0LVWkZ0VjpAaKmoxVGR4v+zL3ERYRZpS68OJF4nF1y34u+eJ9lmNJWa6yXw2E6eb0T91i3MCAGQj/dQT6SeQZY9jPhS3qdI8BNap2N63IUtZzGw51bFF3nJDPyTqHk+uBSjKbfkIIofGKFpzkCl8TkmmLbHCzY8qsJvnlSFWy7M3plboQp9moWutrsk6MvKnoObIjWzc7yZy1rFifWuTmEiscy135htXwd99e6aLXV8PGgxqdBKVPicAQGGknx7STyAiWvgId8jypzLcB9Y8BNVzacYGTq0GPRnZTXFbYfOWh1TbN9KXjljb3t26tsD0QfSmcEk/R/icCphel6i5Mf1Ke+taA/Xg7+P1wSvdVU2Fun/lnWfB3yIIRxZdy2I+5qlfuYPnOnoAmq/o775lE/m01ZCR05V22zt29sA9X8I5bdtEDwDwkH7GkH7iFUgqK5Sc9jjmQ03qgQ36WerBRxNxzFsP9GSNm7Jc98nMWx4WrLtV8159KBMJjuNOGZFU+jkl0Pt21ENq1u+d9lWEWFRdhm6zjn+8npzeZDR1ZoiE2RpLcxke7FXkt1hKcxzah578axR5U5g32K1Yw5Harx48Z+Y8+b+71+9XFTbVsrTfO9pn1aynwhm0apljpw3cyzinIscHAGwi/TQT6SfpJ1JVHnVTgPG3f+t3zafAX/jTvzCfRD5//iyOUzxqeC50n9L4A6pfG9c7XfI2BgAAtvDlyxf59OmTmQPwGnz3/Q9ycnIi79V0cvLO/az/ff/O+/fDh/dmze3td8zHM7VczGIDzFfyMDcfAQAAACTSvaX26U0UPurDpfTn4W5XloybtHoAAAAAx/QmCh9eP0l/sLk3UfAAAAAAjit3zMc/+Yd/Q75+/Srfvn1zp1qtZpYAAAAAeC1+/OnnjTEf7/W/exzz8SYGnAMAgP1iwDnw+iQNON934eONdLsCAAAA8NQofAAAAACIqZg3Xan/ux/2894rCh8AAAAAjiJW+NAlmvgEAAAAAEllhXLTM275WMmgVpHW1MxC4ZoAAN4i0r9NXBO8TDsUPrybPvjDfXpqybGegWlLHa82UKHYp6c9p5fO/U3UNasNor/KalCTSonYMW99/zgv4Tcqe+4A8DKRJ0AUeYJN5Ak8O7d82P2l+cN9S+nbI2kc6aLWh+qY9205xJ8KDM7pUSbO8c7pNbBtW2ad64M/+OHfaNmfS4MEAQCeHHkChJEnQJI9druqynnTFpk/hGoe4rUG8Zshb3nItKWW18QvQLsl3XUE4O2nNZ1Ka72vYF2Xu72/zEwFIpD6hVPinPQyfdxwOPTy8DaxcK0GUluvG24+1ftQ6w78cCdcm9g1cUvU633FjuMrfbwSv5HW7BZIcNL26X1vdWYio4a7LF5jkqTa7oojI7l1d7LdeaRfu6ztvGXJ99125wIArwN5Am+ZPm44HHp5eJtYuMgThPbpfU+e4PXZY+FjJXfjmdjNc1PzoC+0JZ2zSUppNG95iH6gGnPpL++lnVGtMWr05HTp107MpHPt70ndCO72/nFUhOio4w7rZnkaFcbeSOTs47o2ZdpKCHOkqVcd17qVC3e5qfmpWLLo+uuLdC799VW4LkVu1vuyZdSL7asjMnGXDyUS2vg1URHIZefMrKunpGtV/nj55xtnSfumL/aolxzRqS3Tf/eqtO+9cLm/j1p2n/WD+1YPMhdbTi0zX/Y8Mq5dkfNPvu+2PBcAeBXIE3jUcckTkCcgTxCxc+Fj1rFM6U79IDNbmufmYq7uZDxzZBJ6mL3S6Fwe9K+Ut9z3oErlBSIZzZkE60RqJ9wb8Uw+mmXVj2exmouo8DmNm8sgQlI3ZG9kS/8qFubZWO5CO3MmfqRgan7Ujebvwj32bCFLd64uw1AzcfW8KfZ6mSfYV0jqNfFL+mlKHq/g+W6otqWrH7h1hBpS9HcvYXrdkZndFP/W08qfR8K1K/x7p9x3APDGkCdISiPIE5AnQNgex3yoaXKmSvimiWm5UGXNOEtO7Zks9J2dt9wYqeLq5nolVT+qaCa4kae30ZqLuPU5TZyEvorqAbJ0JORPDXWLbi/StGcVO9fEa6Ie7vtlX+YNb19pzXnlj7fd+daHE/VAduQyHo6Cv3ueIDGoSGPel2VuX9+M88i8dvv9vQHgNSNPQJ4gCXkChO2x25VSv1ClVXPDWKeiyvcxS1nMTFNY3nLDmehmqnCz5BbcWo7ghmmMoqXsVPUrt4m0F7nx1LZ+xLqe8mtgEk1bYoWb9tSJbl6TTanXRD8w7r4mcqYexI0ullsdb9vzrcvQRNR35htXwd89TySBy41ktJzzSL12e/y9AeAtIU9QDnkCgzzBa7ffwsf0VpUAzQ1TPZemekjDg4xWg56M/KawvOUh1faN9KUj1saTU5AuWZs+dt6U0GyZqCrtbqimw4Q5GvHsj9tUaD7nyb4mutbAfMyQe7xdz9dE1B09wMpX9HffZzNlqfMIXbt9/d40uQJ4i8gT7IQ8gflCI0/wquxxzIeaGqNQXzc9uGYijhnVrydr3Aw1heUtDwvW3ertAPWhTCQ4jjtlDpAKcWtu/L5/OhxLaY5D51xmX3E6XPq1fWY/vdO+OlZRsWuiB5utw+QNiNqoyCl9vF3PV22vB5qZOU8Qbn9/8d/d63eqIlG1bKvfe0POeaReu13P/xDnAgDPF3mCEvuKI0/gTuQJnjH9B8r3oKJK/Y/ms/zt3/o/zafAX/jTH8wnkc+fP4vjFH8UngvdpzF+M+vX8vVOl7yBCACALXz58kU+ffpk5l4O8gRAuu++/0FOTk7kvZq8f9+5/56Yfz+8f2/W3N5+u109U8vFLDaYbCUPc/MRAAC8GeQJgKf1Jgof9eFS+vNwE6v3ujxqOAAAeFvIEwBP600UPrx+ev7AMm8ikgEA4C0iTwA8pdwxH//kH/51+fr1q3z79s2darWaWQIAAADgtfjxp58PPubjTQw4BwAA+/VSB5wDSNdsNs2nQOs//+/3Wvh4I92uAAAAAOQZj8fr6RAofAAAAAA4CgofAAAAAI6CwgcAAAAAlx734U+HwIBzAABQ2nMacK7/QnlDJvI4rJtvAGzj6H/hvJIw5VoNpLb+Qz1qqg1kZRZtbyWDWmifZmpNzeIj0hGaPnZtED2r1aAmlRIByl4/+Xzjx0zihq/ANS8bXgAASpm2ImnYUyY5RdPGYsiTJB0zCXmSV0j99ubD+v+7Trt1u9IRjTWW5jL4Qz3L5lguC9ygRdj9ZfBHgCaOjBrFbv59s21bZp1rOfRj4kyC66inIn/0qD5U6963hT+PBAB4MroisjEKpWMTkd6+Mv/lHSJtJE9CngT7sUPhYyotN6K5l/D9WG3fBzdorFUkKOSqbSs1GQz8WpJW/kNUH7oPe/SBi5fO4/vJXu6WvNfLVHjS4pBmV/r2SBqZpfS0Y3nfW52ZqJjKXVYqsnKvYShssXm3liEUrs1z2vH4AAAUYsupZT5KXYbrTKiXDkWSUF15ua4h18t1eqXzBuH0y12o5C3fFE8b/TAE6WM4P0CepDDyJNiD7QsfqweZiyMXqd0r1cN8KXJjSszLvi2jSC3ITDodkYm7fKiiqQLqF+qII7l172t9A1vSOZusS+XL/lwaoYd52kpY7kd26oG57JyZ4+spWoiKsqR90xd71EuJDLLCUpX2vXf+qqTmLitSe7BWbYs6tHQudbjVcS7VRevfJIc18Zx2PD4AAHmq59K0VbpuZWSaM+lte3JqelIsdbpnhTPnecuzZeYHXORJCiFPgj3YvvCxXKhHNUu41kPHS02xZwtZmnnNmRR8wJOs7mQ8c2QSGlxWbXdVRDCXB/0wqpu+N7KlfxVbPhvL3fph9SONAtQD13VU5OQ+cDF5YSlIN+EGNQRBhOWFuyOXtUvpSF9uMh/UEucEAMBeeJnKiU4nLb+W2ywqKNyTIikNzVueqlB+gDxJHHkSHEq08JE0KmQHkeY2q5NTWCkpsfBjyak9k8W6hONHgv7UUI+BoR7c+2Vf5ubhKtLsVx9OvAcuvm6hsOSL9q8MR4KqIKebd2czcboZfSm3OCcAAPbF7fOv0jCvZWLbVhBfXhpaJo3NyA/sA3mSTeRJXqekskLJafuWj0hzY4JpS6xwc5u6AW2zaGvTW3VE09XLOk3Y31IWs3CfU1XyXz84/hRqytQPhvvdRM46VoE3V5gHrnMtd+YbV6Gw7GIqrcZc+n09wC2nmbn0OQEAsF/V9o30S2Z2N+WloWXS2Jz8wDbIk5AneWN02WEfdhhwXpcrPY6jEa3Z0K0dSaXb6fWOLR+rgfsmDbt/5ZW+3f6l0QFXq0FPRnZTzvWDbJb3CpW0dY2A+ZinfuUO9OrowVK+vLD45g+bzaMFTFsNGTldabe9Y2cPMvMlnNOWxwcAIMtG2j+9ls46s1uVj2ciI7+20qTnceH8RFIamrc8Van8QEHkSciTYGs7FD7U/d2+N02rQTOiNW56/f/qQ5k46qY03/dO+6rMX85MlZL9/fqv9A0GJun+pRNxzNsS/GMv1+NM9PKlNMehfejJH9wVeR+5NzCr2N8mUvvVA73MnCcvLGoNd8xLRyy1LK3pMdq/0qynwtkY+X03zbHTBpllnFOR4wMAsA3d0hFJb3XN+DKo1Xe7CPlppLWQ7mQzR+BMurIw+Qm350Tsla15y9Pl5AcKIk+i1iNPgj2I/IXzv/OPNv/C+Z//U/yFcwAAELW/v3DuvZ1p0X1MyXDnLQewLxt/4fy9+ved99fN9V87//Bhz3/hHAAAAAAOhcIHAAAAgKOg8AEAAJ6QHqOQ1aUqbzmAg6rs701XWu6Yj3/8D/66fP36Vb59++ZOtVrNLAEAAADwWvz408+bYz5O3sn7d96/+xjzwYBzAABQ2v4GnAN4LhIHnO+58EG3KwAAAABHQeEDAAAAwFFQ+AAAAABwFBQ+AAAAACTa55uuNAofAAAAAI5i98LHaiC1SkUq/lQbyMos2tZqUJNKa2rmUhzguL5Cxy9p2iofRncbdW61QXSrsuHLW98/TjC1ZL9nvz+H+G0AALtayaDmpSGJUfS05aUvoXRwm3QxbNftkwXnEZ7KJDs7p1MHyt+QfuK52K3woSMTayzN5aPoN/bqadkcy2Uss7x3T3XcHdSHKpz3bama+aJs25ZZ5/rghQG7vwyuZX8ujWdcAAEAPE86zRr1NjPL09vRxqv6t00XfbtunyWcJj5OHBk1NisCD+IF5m+AsmKFD92rKz6lmUqroSKTyb20Q09+tX0v9+sv4jUI0QytWwpfL6vJYOWtb3Vmop509/vNh33X43rLWlO1n8ixg2Wbx9frqnUGpuZmvb/s8wtza2hCNQ6b524WxDW70rdH0sisrUgLR9r5ZKu2u6KiWrl1d7LduaefX9Z23rJyvw0A4Lk466r0YzaWu3D0vBpIb+TIxYWZN4qmi2nfR7fPSj8Mv/UlPBVpCagP3QJIpCIw1jrh7SYjnUpcP06FPS9/k7offWx9vmXST/+a6XUMfY3WLS3bpf947ZLKCuWm7Vs+Vg8yV1nUi7qZ36BvTks6Z5Og9B6uUVcP0GXnTCZm2eOjftiq0r7X69minj73+6BAYeQeVz87CceNNVuOGj05NTULE2cmnWsdqqzjq3U6YsI7FH34IsdJlHjuZtkGS9o3fbFHvWgkupZ1nQtczyTuNbbl1DLzZc894/wO89sAAJ6HulyoeHscKn2s7sYycy7ctCNVWrpRKr1MSz80nbGfS98sW6clw8xQBeoX0Uq5S5EbEya9L6+1Jy2dSls/Jjd/k7cfdb5WcP7Lvqj5HfICrj3mfQBj+8LHcqFuyQyrOxnPHJmEHmyvRn0uD+s71H+QS8g9rq5hsaV/FTturCYmXLNQv3BE5g+5D44z8R48V8HjpCtx7tW2dHUkepkUWRW5zuVMrzsys5tyHoqbyp97wvkd8LcBADwPOt4OWgmmopKUSLyfLi1dLJ5epqYfbsb+TD6aZdWPZzukLXUZhrp7Vc+bYs8WsjTzmwqun5e/KbCf8Pnvmhfw7Tfvg5ep4jVaeP/b2e4DztMkPkSWnNozWegnRWWo71WxfN7wmu3224VGl/7DTYINFXUdwpbH2eLc68OJerg7m/0+865zQbOOtT6Pxrwvy9x+tBnnnnl+x/ptAABPItxKML2VUawyK1FaurGvvEL1oyp6BBlxPQZFzj7mpHPpIl3BrE5COhxVdv005fdTLi9QDOk4drN94SPSBJnAOhXbfAwsZTELdefRkYrbbDeRM5X5LdL1Mve4LifUROtP2U2129nhOKXPvS5D0+f0znzjKnKdC4gMris0gC/n3FPP71i/DQDgadTlSncJUgm1zuTbzfNimfy0dGObvEKc2/IRZJobo2iPgVy6EKXSL7dL1LQlVrgrmCocbabDIUXXz8vflD2uq1xeoBjScexmh5YPE7k0ogO6dKncrZmonkvTjg6UXg16KTUguqbefPSlNocWO25v29oRX15z7L6Ok3TuaepX7uDzjh405it6nffZdanUuYfO71i/DQDgSbldgkYNN5PfLZ0rTUsXS6SXcbqXgBnv4E2hrkR5VgOpNVQhqn+VuI3bVdl8XstIpxLXd+Xkb2KS9hPeNj8vUBXd+0wXEl3mPDPtLe+Dt2ynblf6DQyq4B1pfrPGTblxIxo9wGkijnmzgr9s3Z0n8tYJb/CSXwnh9WPsiKWWJT1w+cddSnMcdCNypxKDofKO79nhOBnnnk0dUw8+N3OenOus1yh0PmXknHvq+R3rtwEAPCk3k6r+zRto7ktLN7ZOL2PqQ5lIkE66U0baE+6K7L/6dj1IW+/LGUnDLO+d9iX8EuGNdCpn/bDM/E2B/TiTrizMtm4rSU5ewO3S7ecfrIV0J2kh8+2ejgOVR10FYPydf/RPzafAn/9T780nkc+fP2+8pxsAALw9X758kU+fPpm55023HsQr5vSrenuny1fy5kTvzZeL7uN2hTPA+O77H+Tk5ETeq8n9973/+Z2cvDuRDx+CcsG2DjfgHAAA4BlYLmaxAeYreZibjwCOisIHAAB41erDpfTn4W5Xloybr6XVA3hZKHwAAIBXTo9VCL+d6bX9oVrv/OhyhZcgd8zHP/4Hf02+fv0q3759c6darWaWAAAAAHgtfvzp54OP+WDAOQAAKO0lDTgHUAwDzgEAAAC8GpHCRyVhAgAAAPDGqYJBvJywzUTLBwAAAICjoPABAAAA4CgofAAAAAA4it0LH6uB1NZ/tEdNtYGszKJtrQY1qbSmZu45WsmgFjpnMz1FkKct79i1QfSql72G2esnn2/8mEnc8BW4J57/bw4AyEWeYD2RJ9hEngDaboWPaUsq1liay+CP9iybY7kscAO+BnZ/GfzBookjo0axh2/fbNuWWedaDv2YOpPgd9ZTkT/QVB+qde/bwt+QBYBXjjxBkEaSJ0hEngDaDoWPqbQaI3Xz3Uv4fqu2780N6JWMIwVXHTGFSrxuyXZdaq7JYOVtY3Vmop7aWEk6XtJuhR4svUxvr8IUWR7eRi83q7uy9qf3o9YfqPBuLEtRH7qRTfSBzzqGlr188/qYBXHNrvTtkTQyawnSjuV9n3zNC3BruUJhi827tRyhcJX7zQEALwN5ggjyBInz5AmgRQsfSe/DSrN6kLk4crHtn/JXN+Rl50wm61KzjrCq0r5/lGXf1kXqUEla34yWdM687/S07M+lEXkwZ9KxbuXCXb70HryKJYuuv75I5zKI5KathP1FmgLV/jpiwjeUQqdZv1BXZCS3bqDyw5wZhsTr426WwJL2TV/sUS8lMsoKS9o1L6jaFnVoc23VcS7VRevfJIe11G8OAHgxyBNsIk9AnuA1SiorlJy2b/lYLtSjuCv/ocyxupPxzJHJMHjcq+2ueqjn8hB6sJyJHyFU5bzp3bj+JtWPZyruWMhSz6gbvjeypX8V299sLHeJ+9tCXpgLhaHg9dHUA991VOQYikzXCl6/PLoJ2aud0FMQYXrh7shl7VI60pebzIiixDkBAF4G8gTZyBOkIE/wFu0+4Hxb6sG4X/Zlbm7ezGa1xEjNklN7Jgs35tiGrhUJPzgN9QjsUaEwZ4ShzPUx6sOJ98DH193T9Yv27wxHwnUZ6ubl2UycbkZfzi3OCQDwBpAnUMgT4G3YvvARaU7ckr7x3Jt2ImcdK/3NENap2OZjYCmLmS2nlpktTZX61w+NP2U1YxYwvVVXxDQ7FwpzThiKXp8188B3ruXOfOM6yPUL031959Lv6wF24WbvBKXPCQDw7JEn2ESegDwBEu3Q8lGXq76tbqzooCc9eMgrvVZFt2qO/JhoNZBaI60eQZe4zUff/CFoKqyeS1P31wzdlatBT0Z2U863iRjM/nr7LGWb87P7V17pPy/MpcKQcH3S1K/cvq0dPVjLV/T6ha95CdNWQ0ZOV9pt79jZg9x8Ob85AOAFIU8QQZ6APAFS7dTtSr/FYqkHFllBM6E1bq7797lNfuZtBRVrIV1VAl/Tb7kw21Qq3sCndV/M86bYs45YapkfabXvQ/tSkz7OcuvXten9LaU5tkJhUFNkcFm+mSqlr7c1rxcMBkblhTknDBnXJ5varx5oZuY8+ddv85pvivbvNOupcDZGft9Rc+y0QW6lfnMAwEtCnoA8AXmC10yPFt+PyqNu1zP+7vyfmk+BP/cn35tPIp8/fxbHCUUWAADgTfry5Yt8+vTJzAF4Db77/gc5OTmR92py/30ffD45eSe/+BCUC7b1dAPOAQAAALwpFD4AAAAAHAWFDwAAAABHkTvm48e//9fk69ev8u3bN3eq1WpmCQAAAIDX4seffj74mA8GnAMAgNIYcA68PlkDzt+rwscHBpwDAAAAeCkofAAAAAA4CgofAAAAAI6CwgcAAHhRpq2KVFpTMwfgJXlBhY+VDGoVIa4BAOAtm8rtSMS5qJv5XT1d/uLohahpSyq1gTpj4OnsUPjwHtZKJTqVeYZWgxo1FwAAvAJuRjqSJ2ipYsIBTG9lZPflqj6VVuR44ck7thumA2e2yctkK/obcB3fjp1bPuz+UvTbet1p4sioUZHagDI1AABvTThPsOzPpXGAAsj0diR281yqUpehn/9Y9sVW//WXZv5xqJaK1Ifq831brYunwm+AuP12u6oP3QLIrHMdRDargdRCtRFeodZrNbE6M1GlFff7dYElcf2wcE1HTSLlnIxt3RJ14nbxFpysiDJrXR0utd9BK7Qs6Tut7H4AAHhZqu2uODKSWzcR89K91jQtDS+a9ukuV7Y0z4tlZTe6NeXmMRTdNSkStrQ02/s+MS8TVuSYrm3yDErW/iPL1L4fzPfKZpcv7zjJFcgZYXCPEbpesfn4cTbzY+nXMT3vhpds/2M+6hehyEY9SJciN+taEFtGPd30VpX2vTcvzsRddt/WEUna+oFRoyenpmZj2RfpWP4DkLGtehAuO2cyMcseH+/FPZwybVnSOfPC4G03l0ZK82D+ujPpdMQcx6t12fxOP2QJ+4lEJkn7AQDgBVk9yFxsObXMvBJOwyeOSuuug5SvUNrndrlqSsGyR4zKJ1jqAOvWmYlILI/hFjwac7VKkXxCWl4mLD9fE7V5DbLzHln7V/mNy/D5dmXRGblLtPqFo36Q2yDvsbqT8Sy5YJcZhmpbbnR+7FLP+8e8WV+/iMT8WMp1zMi74WU78IDzugxDTW3V86bYs4Uszfym/PWdSXDzebUqc3lw7/68bf0CUYi6sXsjW/pXQRTn7nM2lrt4zFBwXWeyGWFGvnMfbkcmw9h+1ufhSdoPAAAvxfS6I7NYQSGchruZ3/mDySh7stM+lbHt+V2utqALLiq17a5zsNF8gzwMpBYreJTKJyQqmw+K5xnyjp+xf5PfiJzvRF1zX6SyWK8+lpnT3czgF7gG3nxHLmuX0pG+3GzsJCwhP5aqzLp4KQ5c+ND3bKjJzFIRkfk+Tdn1dS3BwjzFqduqUvn9si/zhrcs2qQ4k45ltnGnhrrV05RZN8VykXBOlpzawXkAAPASzTrWOo1szPuyDGfud5VRM1+YfapS3GSjTlqeY7e0v3y+Ji77+Kn7T8xvhNXlSreUuLn7ldyNZxlvEMu7Bl7BZjZT++hm/OaZ+bGYMuviRYkVPioJU0mmZsG9f6ctscJNZu6AsAxl13eZJt28bfVN7C6byJmKHIPuh06oSc+f0pr2yqybwjpNOKelLFSEGm6aBgDgpYm8hGafBQ8ltWa+jIxWB2eiu/743YfCdkj7t8rXxGUcP2v/ifmNKLelRHe9Mq0k6W8vzrsGU2npVqO+fvFQbExKXGp+LEGZdXEkSWWFctN+Wz5WuslypCKfq6DJMMRtgjWf12JNrmFJ648awYCj1aCX2vcz8Vgu3cpgPlbPpWmPpFekNF1m3SxmP43I4Kv08wAAAHk18wWYbkZBOq4yzLExntX2jfSlI5afRhdN+zPyMmHpeZMUJfMekf1XP8pZ+HxNHi3Cz5PoFhPnIjHvViQM01ZDRrpg2L6SfiyPky6UH/OlXseEdfFi7Vz4CDexVqyxNJehAVf1oUwcdROa5b3TvnrwA17fRPWQ+81pOevrVo7+pCljy1vulvb9mpWsbaf+myP05A2a8oZc6EFOS2mOQ+egp1hk5Cmzbha9n4k45o0OerLGzf02TQMA8Jrk1swXUZeh27Thp+M9Ob2Jp71BGu1188lP+zfyMmG5+Zo8OcfP3H/sfK2FdDdaXqpy3vS+SS/Y5YRB5bEaI0e8saxq3Rt1jFEv+c1UqfmxhOuYsS5etsqjbjsz/u78J/Mp8Of+5In5JPL582dxnHKPDQAAeH2+fPkinz59MnOHpcc1WIuuPJL73D+dyW/oN2zxohuIfPf9D3JyciLv1eT++z70+eSdfPjwway5vYMPOAcAANjeHrpcIZX+o43q4lLwwNFQ+AAAAM+Y7vbzSJebg9B/tDGryxWwfxQ+AAAA3qS6DB8p2KEo/baq3UXGfPyQMOZj8ff/qnz9+lW+ffvmTrVazSwBAAAA8Fr8+NPPGWM+TuQXH96bNbeXW/j4sww4BwAAMccccA7gOLIHnO+n8EG3KwAAAABHQeEDAAAAwFFQ+AAAAABwFBQ+AAAAABwFhY/XQP910tpAVmYWAAAAeI52KHysZFCrSKUSnVpTs/iJrAY1qTxxIAqFYTWQWvjaPVHhYdoqf2x3GxXm2iC6Vdlrn7e+f5xgaskT316pnsN9BwBPJ8gTJEaFupJMx+Oh9Gab9Cds1+2T7Z632Tk9OFD+gHQKz0W08KH/dkh8ymH3l6Lf1utOE0dGjc1MKWJ0JGyNpbk0101Ny+ZYLp/gutWH6vj3bama+aJs25ZZ5/rghYHw/bXsz6XxjAsgAPDW6bRh1NvMLE9vRxuv6t82/fHtun2WJ8vbPKP8AZAoqaxQctpvt6v60H1Io5nSeC1COPOol9VksJpKa71cz5vFWqwGICi0623UugNTm6I+19RxrM5MVCzhfudFFEnH0GEIhyt2zNww63AkhdlbthmGMLVdQ0XCk3tph2LMavte7tdfZB1fiVwTdewH8/1azvYhbs1RqCbErRkJ7zsefF+zK317JI3MWpS0cBS5Tpuq7a6oJEBu3Z3Ef//ovjeP6Uk/v6ztvGXb/+YA8DacdVU8PRvLXTgaVGlWb+TIxYWZN4qmP2nfR7fPiqcNv/UlPGWmYUZS3iYxb5KRHqTmZcIK5A9S96OPrc+3TDrlXzO9jhHpxr1dOgvk2f+Yj/pFKIOo72NLOmeToASva68jTYgz6Vg9OTWl/GVf1Lx/I6sb/1LkZr1tvEZFbdsRmbjL1cN5762jnlx3/SAzr49xKxfueksv01yxZNENHfMy2G9+mPXzG4R54qj9X+sQV6WdGgZj9SBzdYUu6mZ+g36oE44ffugv1Umva2W6suiM3CW+IuFPpCK1y86ZuZ56ikaAUZa0b/pij3rRyH0t6zwKXKck7rWz5dQy85Hffyj6kmaee8b5HfQ3B4A3oy4XKn4ch0ofq7uxzJwLN45OlRY/l0qX0uJpTWfs5yrp9Jat4+xhZqgCkbxNWt4kLT3Iy8sYufmDAnmixPzULulUyXQWKOCwA85VpNEb2dK/Cp4kt/Y6VisSLuV7tdtzeXCX12UYalKtnjfFni1kaeY1Z+I9DHmC9apy3vQeQD/OqX48U8+X2e8WYa5fOCLzh2IP3nKhHuUMqzsZzxyZhCLEyDUxy7vriENdo0moKbtg+NMFBcdc1bZ0deQeKrit5Z3HFqbXHZnZTTkPxZmR37/QuSec36F/cwB4Q3T8GLQSTEVF3ZH4NV1a+lM8XUqNp92M/Zl8NMvcdH/rODw/bxJVcP28/EGB/aTnp7ZXPp0Fsh228OHSJfFw81xDRSN5ZrIwT1OkudVSmU/v6wPbJsx7khj5WHJqm2uSGzlpW4ZfFSbul32ZN7ztinQfqg8nKtLpbPZHzTuPgmYda30ejXlflrn9ezPOPfP8nvA3B4DXJNxKML2VUazSKFFa/LxFupSo+lEVPYKMuB6DImcfc9KTdGXzJvvKy5TfT7k0txjSS+xm/4UPHdGoaCdoNnRCzaX+FJTMk5muNdOWWOHmVhUB2d4KB7ZNmAuKdUvbYJ0mnONSFjNzTRKXx+0Qfh3Ru+tP5Exl/PO7w3otL7qW685848o7j4Iig/5yCx5azrmnnt8Bf3MAeFPqcqW7BKmETmfy7eZ5sUx+WvxcOl1K4LZ8BJnmxkjF+UW7XGnhvE3ZvEnR9fPyB1vlicqlucWQXmI3+y18rAZSa6iIpn/lNdFVz6Vpj6SXU1MxagSDwlaDXmotidvtxnzOtEt3mIJhzpUaBhMph85Z07UZbo2OOX54IHfkmri1N6HwmWu+tq/wu60U5mOe+pU7jqajB7P58s7Dt8+uS6XOPXR+B//NAeBtcbsEjRpuJj/oJlxUWvpTIl2K063xZryDNxXrsu2K521iEvMmGelBel4mJ38Qk7Sf3PxUJFxV0b3PdCHRFc9PJNlbHgNv2c6Fj3C3GP/1cMFAJj3IaSnNcWgdPcUGJjmTriwsb5lbqvdruOtDmTh6cLi3rHfaV+XtbF4fyI5Yav3tmmeLhTlLXhj0myuW7kCwYP/WuCk37nXTx5+IY95I4S8LuhvVZeht7C23FtKN1H7sEH79lov1Nt6AsmIVQ+qYevC5mfPknYdaY+ffKi7n3FPPb4drZuz/XADgBXMzqerfvIHmvrT4eet0KUbnJyRIj9wpI47PzNvk5E020oMSeZnM/EGB/aTmp5SkdMrtOu2n0zo/ER5Dmmj39BKoPOoqAOOHLz+ZT4E/+ydOzCeRz58/b7ynezfeG5H0W6e2ikwAAMCT+PLli3z69MnMPW+69SBeAaZf1ds7Xb6SNxSSn8J+fPf9D3JyciLv1eT++z70WU2/+MV7s+b2jjDgHAAA4OksF7PYAPOVPMzNRwBHReEDAAC8avXhUvrzcLcrS8bN19LqAbwsT1z40H0HaSIEAACH5OU3ggHnr+0PwpKfwsuRO+Zj8ff+qnz9+lW+ffvmTrVazSwBAAAA8Fr8+NPPBx/z8cQDzgEAwEv0kgacAyiGAecAAAAAXo3clo8ifven3zOfAAAv2fh/vTafAs2/fGU+AQBes6Q04D/5L3rH7XZVxE+/98/NJwDAS/Z/jP5HGY/HZk4VPJpN+Y+c/9LMAQBes6Q04FkWPv7ZP/9D8wkA8JL97//z/7CR8PzH/+l/ZeYAAK9ZUhrwLAsff/CH/8p8AgC8ZP/b//TfbSQ8f+k/+6/NHADgNUtKA55l4eNf/utfmk8AgJfsf+n/N+ZT4K90/lvzCQDwmiWlAc+y8PHLf/uz+QQAAADgNXl2hY+f//1/MJ8AAAAAvDbPqvDxR3/0x+YTAAAAgNfo2RQ+AAAAACAPf+EcAAAAwFFQ+AAAAABwFBQ+AAAAABwFhQ8AAAAARxEZcP73vvxf5hMAAAAA7JPI/w9r9kTlzuVJoQAAAABJRU5ErkJggg==

    What I want to do is test the error string for all of these substrings and then return the category if it finds a match.

    Does anyone have any experience doing this? I can't quite decide where to begin.

    Sorry to be so vague.

    Dave

    Have you some real samples of real error strings that we could take a look at?

    --Jeff Moden


    RBAR is pronounced "ree-bar" and is a "Modenism" for Row-By-Agonizing-Row.
    First step towards the paradigm shift of writing Set Based code:
    ________Stop thinking about what you want to do to a ROW... think, instead, of what you want to do to a COLUMN.

    Change is inevitable... Change for the better is not.


    Helpful Links:
    How to post code problems
    How to Post Performance Problems
    Create a Tally Function (fnTally)

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