SELECT @local_variable '=' Versus SELECT @local_variable '+='

  • Hello, I am trying to find out what is the purpose of using += when selecting a local variable to the value of an expression. So for my example, I am trying to generate and PRINT a series of TSQL statements for adding notifications to alerts in my SQL Server.

    When I query: SELECT name FROM msdb.dbo.sysalerts WHERE has_notification = 0;
     I get 3 alerts:
    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

    Then if use

    DECLARE @AlertText2 varchar(max) =''

    SELECT @AlertText2 += 'EXEC msdb.dbo.sp_add_notification

    @alert_name = ''' + name + ''',

    @notification_method = 1,

    @operator_name = ''DBA'';'

    + CHAR(10) + CHAR(13)

    FROM msdb.dbo.sysalerts

    WHERE has_notification = 0;

    PRINT @AlertText2

    ~

    I get the results as expected:
    data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAwMAAAERCAYAAAAno3uaAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsQAAA7EAZUrDhsAAE0DSURBVHhe7d1vjCPnfSf4H5k575vgDnc4A5t7cYf1SayavpkX7m7JgP0iL5wNYI+KdOYEdTIB0k0pkQ67AWxgluwhzvBgDC9a3e0B7Eu82NFCJluBtWn9aYnNlhQk6xdewAY8rm4d0EKrODKc3CLIHmDcApt1Xtg7qrrn9zxP/SOrimSx2c2e+n6AZ4ZVxafqeZ4qsp9/rCptfvM7HoHUvPnP9KtkP/jBD+T/f//3fy//j/pP/+nvaXX19/USsK27/4pe+EOUCQAAAMC8Kv385z9HY0D47s5rIxsDL710SM8/v6SXQqIM6atvvUX3nn9erwGGxgAAAADAfCvr/2FCPDpw+P+RDP/ubz4hXugNAAAAAAAXBBoDOTiOQx999BF5f23Tg5/9Z/ov7/9UbwEAAAAAuDhSpwn9n3v/j36V7DClJ/wv/uX/ol9dLONOE6Ilot/4u7+j//gf/yf6R//I0VuIfvSjf6B793JME/rpt+lzj3+FfvT8u+Td+4K/kr79ucfpK/Qt+uiHX6bH9NqLBtOEAAAAAOZb5sjA0tJSGLgWHAmxbToUwfMin5Zlyd8O8A+GOXxi4Sn6oz9apH/7k/9ML4lWEoef/exnOsYYPvtZ+uwHD0QTQPvpO/Taj/RrAAAAAIAZyWwMHIpKbRDkpPjIckIYOW/+Z/foi5/8JH1yINz8vt4+Ld7/F+/RBNXwif3t3/6t/MEwB84yNwDY8vIy/d4T/51sLHA5PPPM5gQNgmfomSuv0Tt+a+CjE9HqeJ4+qxcBAAAAAGYhtTHA/fz3nl8Kwh8tevJ/+54Kd2/8Inj9w//riqgEc6yX+J9sT3yDfqwr0zL8+Bv04d1TrsDPsFHwq1/9ik5OTmT4xS9+QL/+//57+i9lXYt/SeT/pRfky3/9r/9oogbBtS9dodd0a+C9tz+gZ760IF9LPJWoVKKSDJ+jb+vD/fTbn9PrSvQ5vTJp3Xsv+HFVeOE9uXpgvyJ87ttqdGKC4wEAAADAxTX2D4j/4R/+Qb9S/Hvt//KXv5SBfeITx/L/iXzqMbr8kwf013px3n3qU5+i3/zN3wwCTxkK8K1Fn79Hv/Vb/z39H//m38hV3CAYy+Oi8v/aO6Iy/h69/cEzdO1xvZ5/P7D6Gj3zkUeeJ8JHz9Brq1xpf4+2v3KF3pXrvkX0lW2xJmkd0Rfu6bh6/Qff4Pi836/QlXfD9WokYpLjAQAAAMBFltoY4B/H8l1zbNsOHrbF//uB8Q9q//Iv/5Leeqsnl3P52U/pwycq9E/k6+g0oi/SPd2p/rN7XwymFH2RVw72/MeWH9B3/vir9JOffJU+o9cNxZ+Sf1tROVVI/B/gUYEXXpANhqUlfiaBaByM+1OKx67RM3RCH733Nr10pRL+aFj+fuBH9JXHdU89/9j4Rzyl6HFa+OxL9EXupX/nGv3Qu0dfoKR1vI9ITz/H9/dL36KG/5tl30THAwAAAICLLHNkwDRNOReee8BZtEec76bzu7/7uHx95anfj91ZJxNX0nXFXIbPPKCb74oKtKi23/vjLtV+7E8fqlH3j7ky/336zlcv05/rKUX01e+INVkq9M//9Bv0BE9HkvudNP54/mf35/Qfyp9UC/5vJZ6/R3TvnmwsrP/Wa6ohMOp3FIHH6NozH9A3vvEBPf+lwWr286pHPgg/pC8/9hh9+Ydq+asnj4uK+wv0ntjH8Lr36IXHoz39/ghAlnGPBwAAAAAXWWpj4Je/NPWrYTwt6Dd+4+/k64/+638r/x9b7DcDP6ZvPPEKvcu185/9FXV/8hP66mf8RgL37nfpr372T6gi3vO73Kv/V/+U3v35Xfq82tOYpo0/jPMfNAT+Rv0nvcAjA8uyMbD5757RK8f32DUR50dXKNYW4BGDz75Eb8fm+YuKuPxfzef/wr2P6Fuf/YAevJewTk7tv0IVf6jhoxM1MiBHIr5C24M1+kmOh58NAAAAAFxoqY2Bjz++L+8Q5E8L6v/6r1Ov1wumBvHIwJ//+Uf0wcH3ZPjlL+O/KRjPp+iFm39Ar8jWAPsD1YMfhHfphU+J97yrlm8++IxoKNycsGd/2vgZREPgJVoOR0Xu3aOff+M9+sUvfkE3jLsTjApoj305YfoN98i/S/RFf9oO9/KL94j37vDUfTmd53F67Zkd+vIXEtY99gVqfOsDOb1Hxv+i/yNvsV/x5g+C/erpQ5Mc76I+AAEAAAAApMxpQvzsAH9aEFdsf/u3fzuYGsQV4KX/fYmuXFH32f/444/pV7+6qmNO4PNfpD945V36/qf+KdX8UQImfz8gKu7yf/X7gc/f5ZGED+mnosIa+9HxXz+gn+iXQ5LiT/GzAf4NxVtv/Qc6/JtDUSSHtET39BbhhWX65Fe/IBsDN1+9qVeOwA2ApAeLxdZ/ge7FpuzIleItP9TrPPqhXjlqnQz+fmXjQ69793mi4LcK4x8PAAAAAC6uzMZAFE8L8u8aFEwN+huiB666z35+ahrPu9/nHvw/J/pdf5oQ/37gLn3+Uy/Qn/JUfzl96DPUrf0pvfCpz9M//8aHcuqPfO/dD+kJvTdJ3qGIf5sgGhOJ8fX7cuC7BP3oR39CL730UhB8//YPv08vPX+Pjo5K9PzSvfF/R3Eu3qMX/NECOWLwAX1r6NfEAAAAAPAoK/385z/39OuY119/nRYWFuT8d54S5ItWcPl3BTydyPfyy/83/cVf/Eu9dLF8d+c1at78Z3ppMjs736M/+ZN/r5eG2XZk9KBAtu7+K3rhD39fLwEAAADAvMlsDPzZn/2ZnPrDzw8Y/J9FX/v29/f1q4tlmsYAJENjAAAAAGC+pTYGigaNgdOHxgAAAADAfBv7NwMAAAAAAPBoKW1+8zsYGdAwMnC6eGQAAAAAAOZXyeP7RIKsuKIxcLpQpgAAAADzDY0BAAAAAICCwm8GAAAAAAAKCo0BAAAAAICCQmMAAAAAAKCg0BgAAAAAACgoNAYAAAAAAAoKjQEAAAAAgIJCYwAAAAAAoKDQGAAAAAAAKCg0BgAAAAAACgqNAQAAAACAgkJjAAAAAACgoNAYAAAAAAAoKDQGAAAAAAAKCo0BAAAAAICCQmMAAAAAAKCg0BgAAAAAACgo2Rh4Y+9ALgAAAAAAQHFgZAAAAAAAoKDOrDFw2KxS89DTS0Sed0hNo0SlUpU6Xrj+vA2mM4vndaha7eil0xOWjQpG81Bvyd52Xg6bRuw8zqpcAAAAAOB0nUljgCuHd5zrtLVU0muIjtZvkNNyqW3pFXMgKZ1ZSqU1um3ujd14GNvOHVk2nqhcc+hvLekNQta2HE6j4r601Y+dx5mVCwAAAACcqrMZGdjZI7q+qhcUrkDur41X6Y7qVLlH3KBmp0mG7h2vdnSP9GG4LrpexQl70b1OVb3HaNKh7s2WEtIpK8vBPqui4hwfyVjcvE7OnR29NJ54OuP747SW6z3q1ctD27O2xfcpyic2ChPNg9jWFPkQZcNxzHKder263jYwCpG1T78MOSQ0JvKUCwAAAACcrTNpDBydEJlX9cKU1vY9shtE23WHWq7qIedGBVd4a8vhOte1ydwwZQV2tdsmy2oHveiltX2xD4sar27SkqjM+pLSebS+R6at92mb4g23aS0Sh+gqmXQSb1SMtECtyD731o/0epU/t22R1fZ7//eD46VtG857i5wb6zJNPK1o3dwI88Dbtntyf6WlLXJcVTZqf+FIQ+Y+uREhytbW22yxf9FGGZCnXAAAAADgLF3QHxAb1LC78Uo51+QbYUW9VFqizZZBzjEv6Yqp7unmXvFjsWZlUb410+KKSd1l1RNfXt6mvl4/leM9qp/mPkXe+9Sjelnvk3v7H3RpV7YxjskxWsHUJ57Csy8q6CNHZbL2eeyQ0QobUoPThAAAAADgYjiTxsDiAulK+QwlHMTv6eeGgajT0+6uQzXbJnPPpA1nITYqwJLSyb3nfe4N16Hl3IlN65GVbRreVxrZ4x7pVXfthmjaTEkk3Kg0gn2q0NcNANEQ6u8NpHlAX/Xgq+lEejpQ1j6vmtTfUKMEjEcKNoZGBiYrFwAAAAA4B6KS573+Zo//mxnXbXuW1dZLStsirklGguW1XVdvTSb3E4sjQmS/dqOSus1tWx5VGp6o3MrXVnv4WIPpdF3ba1Qi+xOh0rD1VsVuWF7Dzk73oKF0iuCnZ6hcdJpHbZP5i26LlKdocHiV2LbweCy63+j6zH3Gtony5H1E0pOnXAAAAADgbJX4H37o2NPXnxL1utnhW3burnTHvlPPeZkknbKXv0a0v7+m1wBDuQAAAABcDGfWGAAAAAAAgPlyQX9ADAAAAAAA00JjAAAAAACgoNAYAAAAAAAoKDQGAAAAAAAKCo0BAAAAAICCQmMAAAAAAKCgZtYY8DpVKpVKA6Eqn4Tbqaplo3ko3xtdzorH1FNy/fUGNTtNqur9zNph0wjSItNsNIOn8OY1i33Ok2j+BmVtOyvqekpOg+cdUtPwr7XwemVZ8WbhrI8HAAAAxTCzxkBpbZ/cdoMatisqMp4MbUttW9vn1xa1Nhfl8up1iyoNm/pbS5nxuHK2bm6Qqbe5bouc+jb11eaZW9rqB2lZ7drUMNTracxin/Mkmr9BWdtOi6xEVzt6aViptEb73j6ticr+kJ075LTC65CvT19mvBymSicAAABATmcyTYh7+6sdTzQCwsrM6m2TNso16hw2ydww6VXdMIgaine0S07t1eDpwKqCFK+kDQp6d6tiX2If3PPeHBiZUL2ufg+weJ94r98DGxupSKisHa+bwfZoz3GWvPv0RFkZfjwRuGzGkZY/fySiI3vo1fax8xBLS7zHOit/o/KeRMUxRLqT05lWLrzeLNep16snxlMjE7x+uMedy6Zc71GvXh56T1a8eFkb1Gyqa1huSymzadKZmvcRZQYAAADAZtwYcGh7uSwrVYNKS1vk2KJBsOxQy9mkJVFZCaXEO3YmHgUolZZo02mT1evLEQW71qWuaZPrtslwjuV7jtb3wtEGkSYyb8vGh6ykiYaK7apttrlBsST1t6nutOQ2Di1neWQFPe8+uZJZ47LS8VzXlvtpHmYfj6XlT45E0OR5UBaoFdnn3vqRXJuVv5F5T8GjRXaDqEcqnfLcdXfldKqscpHXmHivZbXlNg7RhiOPTPC6pNEJHr1yxQarrfbrRXrl0+IljlxtRzOYXGZ505mZ94wyAwAAAPDNuDFgyuk+XKkaFFRkuEFgrg9UUlLiXTUp9ywaq6VHFAyqrcRHIRZXTOqKxgf3npaXI9OOROPDaIUNlaFpLUaD7O6qXuDRDlH7OlEVvFR593l0QtRQlXgmGzktg3R7JlNq/liePLDjPapPWmaj8p7JENn303mVTP9CmKJcTt8xOYZ/nXFa1MjV/ppaTi2zvEbmPaXMAAAAALQzmSbEvZRcITpsVmWvZdiD2qU17hVtObRc29HvDg3GE7VaMrs3Yr3hPIVi2ukP3DPbF5U2v1e25dxR0zFE46O/ETZUuGd7Y6AX/04k2Tt3tokWhqc7xeTd5+ICDdZwuS5oXtULGVLzx3LkQTbkIj38rt0IG2lZ+RuV9zzGKZf+iTwmp7vKU3ci18/pEhXu/t7QVB6WWWa+SdM5xTUBAAAAIImKiff6mz3+71S5bYtrMgOh4jVs12tbetlqy/dGl7PiMddte1Z0m95HGte1vUYlfK/dqIjXltfW+6k02uF2HSoNW8cezIflWZzWSsNr834qYjkSNxqP+XHT1k+6T5X2cNtg3pOOF8u/Dv52uc3KzkOaobSIYLX1OUrJn6gIZ25LE8bh8zZ8/Ywql+D9IgRpHLyOZAivs2gcGfz0j4gnKvleZWC7f8ysMmN50pmW91FlBgAAAMBK/M8bewf09PWnRF0BikSO0NR2aaU7+JsNAAAAACiCM5kmBPNpp7ZM271tWi6XcacZAAAAgHkXzgWYLGRAY6DA+I45npyjHr97jZqzHt6uMhZm8FC0sz4eAAAAwIXEEznyhAyYJgQAAAAAUFAYGQAAAAAAKCg0BgAAAAAACgqNAQAAAACAgkJjAAAAAACgoNAYgELrVP07F43/ZGJ+6rWKU0182jBMjp9GbYgyrXbC8uTnYDQN//zoUO3orYN3oRLnr9OkavNwIN545zW2L32M8DzH05WH2n/y9TKYz2lu86uuZ5Xn2H7P8a5ck+Zv1PnD52+YPO+nfI79z6R/3jj4nwOvU42tj23L8fmb1jxdE7NKS9J3JMCp4bsJzeIJxADzTj6l139iLz/pd8Kn87Yt9XTf05Dn+I8S9aTl8MnK/NV0FiEq6RzYDStI06zwdRh9EvU0gieOR/JxmtdpHnnzJ+M1GiIkPxX9Uf385UmLepp89pPc84ieO3VtRZ9+Hn421Lb4+Rh1/mbhvK/1qEnTMuq8D35HApwmjAxAYe3sEbW7q7KXq1yuU0+vZ/FesfF7eOLx4r1iQa9tJ3wP9/JwHJOP36vreOP1DqveOYOq1bAHOxovKQ9Br121qnrCjSY19eiIHzcrD2miveixEOnJz3aVzIpBC4t68RyUSmt0nfaCc81lteuYtKLTlOfcsqyeQo5XrveoVy/H3hP2rqrjBMceq/fXooa5MdSDqNLh71+PhMj9jb4mssTLJd5zmZa/cfDn8/rmCpnO7tg93nnOEcfJ+vzFR6BEGYlyGicPWfHS0jkqLaMcr5tD8aa7lkKl0hJtvlqj7p0dvWaAYYpPcSjt/OUpT3nuRHo7ke+ZccslfjxRBk1xTD7vU5RL/PyNf03nvQaV8/+OhEcYtwgwMgBFpHpu2p5V4R61eK+Ma7e9tt/rZSf3bg32/Kiem3CdXJb7Dt9jNyrir8Zwj1Ge3kAm96fjDR4vLQ/RHiaOXxHr/eOrbdl5OAv81XQWYVCsJ3SozPKdW19aT2Faz/ngdTfOKEXYQxv21EaPG30d7U1W+Uu+JrIMl0u895il5S8L77fhl31K/LP6/MV6wFO+C5KkxRuVzqy0pFHlLq7pSLy2RYnXMht3xGuw7KNpU2UZfpYG35d2/vKU56j8+Yavifj1qMr+FMolx98HX55rEGDWMDIAYCzQUGfL8R7Vl1VvZnl5m/p6daajE/G+HtXLOh739Dzo0u6R3i4Z1LC7tCa2nw6xv9ur+vVVMg39kmXlwWrR1hKnwaCa3/XNxsrDsOlHBubE6m0y99Zlz+DRrkOmXzbncG5LS1t03bkjex25d/OOc12fs3Es0uarJu2tjzhxUWnXRBZRLuICDPIse49bBjnHcjG3o/UN2t5eVmXNIwt7Kb3RUTM6R4srJnUn/S4QUuPl/IyNZDTI7vrfBXwpN4hO1E6nu5YiZNp9XJYut6hJVLrJ3CgHo0JZ5y9veWblL90xOYZ/XavRv32R3v01vZy3XPL8fQic9t8AgOmhMQAFt0q3zQ31B7mnpojwH4Xahkm2q//Q2Q3x9T2GxQUyKuIPlo6nQn/8P7r9E1kJVcPa4dSBPM46D0tb/cj7I2F/Tb/jYuDK7IrpiIrZTrxiMO25zWn1tqrQH63vkRk0+sajKjo3aCNWU+mH9aedO7Q9WS1mmCiXwZo/tw/M6HyRCanpWbVYWdvmyeipGDP6/HE59oP9edTSlcdRUuONk8483wX9bYrO4Nm5s03ROSXTXEs+biDXEuKqRqAlkn0k0px9/vKW56j8JbtKZj+c+pdk0nLJ/d06jlP8GwAwEXExe5gmBEXEw9c8HSLJ4BA4Bx5a9oeZ49siw9Bin/Ftajg4Md7AkDAPe/vbBoe/k4THUscI4qcM43Ow2nq4Xb8vHLJW6ZPTQ1LycJbix59dSMLD/hWxbfAc5Dm3o66X6DmXIWFKlnzPmNMHwnMeTVt4/mJ5qFieJadeNEZeE1mGrrNIWsfJX1QwHSTy3nAfXG7Z5Xnan79YenQYVR5sVLxRn7GktGSR58A/nzpeUjonuZb8z4G/Pxl03OH0iyDP16jzN0V5Wsn5G/UZS8rHYJlOUi5s6JrX+8xKS95rcBzhZ3b8OABRJf7njb0Devr6U+L6AygO7sFaN5dp+4FaFl++wfAxwLzoVA06ue3MfBQCHn0X9VqS39W1XVrpbtLSDKbXXOTPmP93zGnh7xfkh2lCUFg8tL3VD4er8UUK80TdeaRE9d4D2k67gwvAGC76tbRTW6bt3jYtl8sT3V1plEfiM3a0S11qUM6ZXwASGgMAc0rNG9U/xB0ME94WEC5eea7thw1V/7cXZ52HWRzvUbiuL9p5mIdrKcuotFzthr8/6G8t6VjTm/dyGYf8DUZ/ayYjJlAcmCYEAAAAAFBQGBkAAAAAACgoNAYAAAAAAAoKjQEAAAAAgIJCYwAAAAAAoKDQGIBC828tVzqjpz2qO1VUE5+IyfeLbhrhnSuit9DLijcLZ32803TYNET5TZ/209pPlvCcX8yyBgCAiw+NASgsr1OlOrVFhcwj122Rcwr3mZaV6GpHLw0rldZo39untaTbwO3ckQ+O8W91F72FXma8HKZK55xIy8PSVp/all6YwmntJ8vR+g15zmd9HAAAgDRoDEBh7ewRtburslFQLtepp9fzMo8UVKvcM5zQS3/YJEOv51DtqB5dXm/yfnr1xHiqpzm5F5hHKMr1HvXq5aH3ZMVTPfjqWHJ0o1mNpSdMZxh3mnSm5n1EmSUJesWrIs0cx2hSU4/U+HHjxwtHb0blgZ2sm8E2P50sLQ9M5UNvy2gsRYXlNRDGiM8NDjzsDgAAzhMaA1BwO1TbMMl22+R3zpbW9slukGgctGQPvSu2Gd1d+bAZrnzXlh1quaoH33VtMkV8rqTyw18c3o+lRhsGe/e54sfrknqB+eE3rthgtf2RgbBXPi2eegz9Bpm2n5YWOdt+k4YtUMvfZpu0t34k1+ZNZ2beM8osDT8BetMR6ej1ZR7sWpe6pq3iOscJxxP5u7Eu9zkqD5ySbSdMC22oeFl5kA0MeS2obbYoW9E+G8kvr6GgH2IEAAAwz9AYADAWaFG/DBnUCJ7vfpVMQ788OiGxIaioywptyyBRdz0Hx+QYLdpa8tPCU3u8sKf5eI/qy2qkoby8TX21Nr+ReU8ps1EsPw8G1VYiZ0Icry8q9fWyzgOPBDzo0q5q04xgyVEfZczzd+yQ0doMnuTJlfxxpu9MMzIAAABw3tAYgIJbpdvmhqpo9vaGpsUMWVygwZo/1y/Nq3qB9U+CXujqTH+YLCq5/eQ0yx7wSC+3azdEVXvApOkcJ++nSRzPqDSCPKjQDxo/0mnm4apJfT2CwHikYAMjAwAA8KgTf7S819/s8X8AheK2La/SsPVSiNfzR4PI8tqu67Utfi2C1Zbb7UZFb4+v9wXvF8Fqu3Kd67Y9KxpHhorXsNX2aBwZKg1PVIJHxhOVfK8ysN0/5lA6I9tYnnSm5X1UmSVxXdtrVML3qX1zfJUGPjfhfv2g9u9LykOYxsnPX/x4lmdxXH0uZmHovA/kLyyT2RwfAACgxP+8sXdAT19/SvwtAigONed+mbYfqGVRmcSPOWFu+Ncn320I1yUAAMwKpglBYfF88a1+OK0DFS6YK0e71KUGBT/DAAAAmAE0BgBgZtRc/oQf13IwmsH8fBjGd0zq97eCHzQDAADMAhoDADAz/h2O/NGXWEBFFwAA4NyhMQAAAAAAUFBoDAAAAAAAFBQaAwAAAAAABYXGAAAAAABAQaExAIXWqfp3t5nlk4LhPPAThA1xbqud8Lz666J3NfK3e51qbH1sm3dITcNfn3ytJB0PAABg3qExAIXFlb86teWdbVy3Rc6dHb1lvsjbc1Y7egnGtrhABlXIvKqXBb5dp9O25APm1Hm3ydwwZeW+tLZPdsOihh3dVqOOeO0/k8LluI0aObtHeo8RCccDAACYd2gMQGHt7BG1u6uyUVAu16mn17PBHmS/t1eOJBhN6jSNYJvRPJTbWDxevAdZjUKIdZ3wPUHPcyxeVVZA/fUmp61X19uyjhfvlc46XhrVO26Ixsc4+VPpDHrNq1X1TAFRPk094uLHzSqXNIeRMo6FsRtGV8msGLSwqBcTcCV/89UaddMagoYp9hLia+b65gqZzm7CMxLSj+fnP1qWAAAA8wCNASi4HaptmGS7bbL0Gu6Jry071HKHe49XuzY1aJvqTktu49BylmUlezhei5wb60GlcW3fI7tBtF0P3xM+9XiBWn6PtG3S3rrqeZY92Zw2S41gcOhvLcltWelk2cdLpnrHSTSMVP5ccWyjG634DqdTVqgdkcZen0yxza51qWvaKq5znJDOeLmkWdrqy/cPhf01/Y5sqjd/n9ZEJTyT7NH39Wl7uSwbHeXyMjnXN4NnIXA+TszbYn8iv9cdGmw/ZB1PPkBMpN0/dwAAAPMCjQEAY4FinblHJ0QNrvSpSp2s7LYMEvVaxWiQ3V3VC0Srt0Xt+URU3kW8vqhG18t+ZbJOvQddis8oMahhd4crjMd7VPcrocvbYj9jGJVOKeV4mUSc237+rpIZ1pSz02m1aGuJj2NQbSVSomOVy7DpRwbGJNPn4/KKNq7KwWjK0foGbW8vyzSU6z3q7c3ntDIAAIBJoDEABbdKt80NVUHt7anpOYsLNFCjlvXuYC54fzvWK7xzZ5vk3BDuYa6IhoLuAVehryvI6WTPuRyd8HvcG6JKOqB/InvS+b1Vf5rNqHSesrHSmSRnuUw7MjCuo12HakHjJ6QaV5Yo+iNx3EPadWqxPNjmSTCdaxTVsBlvehQAAMCZEn/UvNff7PF/AIXiti2v0rD1UpzdqHCtLQxWW653XdtrWJZnVcJt0X3wPmPxyPLarivitT0rtl4EvU82dDwRrLart3pe20pen57O7OOlCdOv0h0cV8dNTqcoE788xPvUezi+SgOXT1q5nDXRgPEqsXSI4JfZUBpFqDQ8m8+5nz+5HCkXqngNOzsf8poR8dOuNQAAgPNU4n/e2Dugp68/Jf62ARQH9/aum8u0/UAt8x1mRs2pl3Fqu7TSDeeSA2SRPwK/QfSqg2sGAADmDxoDABPgO/TU9W2HKg0bPwgFAACAs8Pj0nlk9EXhNwMAE+A79Ij2swwXsSGgfnOQ8KNcDkZz5B1+AAAA4BxxpT5PyIDGAECBlEprtK8bM0Ohv4VpLAAAAAWDxgAAAAAAQEGhMQAAAAAAUFBoDAAAAAAAFBQaAwAAAAAABYXGAAAAAABAQaExAAAAAABQUGgMAAAAAAAUFBoDAAAAAAAFhcYAAAAAAEBBoTEAAAAAAFBQaAwAAAAAABQUGgMAAAAAAAWFxgAAAAAAQEGhMQAAAAAAUFBoDAAAAAAAFBQaAwAAAAAABYXGAAAAAABAQaExAAAAAABQUGgMAAAAAAAU1MwaA16nSqVSaSBUqeN51KmqZaN5KN8bXc6KxzyvQ9VgvUHNTpOqej+zdtg0grTINBtNOtTpymsW+5wn0fwNytp2VtT1lJwGzzukpuFfa+H1yrLizcJZHw8AAACKYWaNgdLaPrntBjVsV1RkPBnaltq2ts+vLWptLsrl1esWVRo29beWMuNx5Wzd3CBTb3PdFjn1beqrzTO3tNUP0rLatalhqNfTmMU+50k0f4Oytp0WWYmudvTSsFJpjfa9fVoTlf0hO3fIaYXXIV+fvsx4OUyVTgAAAICczmSaEPf2VzueaASElZnV2yZtlGvUOWySuWHSq7phEDUU72iXnNqrtLWk9qEqSPFK2qCgd7cq9iX2wT3vzYGRCdXr6vcAi/eJ9/o9sLGRioTK2vG6GWyP9hxnybtPT5SV4ccTgctmHGn580ciOrKHXm0fOw+xtMR7rLPyNyrvSVQcQ6Q7OZ1p5cLrzXKder16Yjw1MsHrh3vcuWzK9R716uWh92TFi5e1Qc2muobltpQymyadqXkfUWYAAAAAbMaNAYe2l8uyUjWotLRFji0aBMsOtZxNWhKVlVBKvGNn4lGAUmmJNp02Wb2+HFGwa13qmja5bpsM51i+52h9LxxtEGki87ZsfMhKmmio2K7aZpsbFEtSf5vqTktu49BylkdW0PPukyuZNS4rHc91bbmf5mH28Vha/uRIBE2eB2WBWpF97q0fybVZ+RuZ9xQ8WmQ3iHqk0inPXXdXTqfKKhd5jYn3WlZbbuMQbTjyyASvSxqd4NErV2yw2mq/XqRXPi1e4sjVdjSDyWWWN52Zec8oMwAAAADfjBsDppzuw5WqQUFFhhsE5vpAJSUl3lWTcs+isVp6RMGg2kp8FGJxxaSuaHxw72l5OTLtSDQ+jFbYUBma1mI0yO6u6gUe7RC1rxNVwUuVd59HJ0QNVYlnspHTMki3ZzKl5o/lyQM73qP6pGU2Ku+ZDJF9P51XyfQvhCnK5fQdk2P41xmnRY1c7a+p5dQyy2tk3lPKDAAAAEA7k2lC3EvJFaLDZlX2WoY9qF1a417RlkPLtR397tBgPFGrJbN7I9YbzlMopp3+wD2zfVFp83tlW84dNR1DND76G2FDhXu2NwZ68e9Ekr1zZ5toYXi6U0zefS4u0GANl+uC5lW9kCE1fyxHHmRDLtLD79qNsJGWlb9Rec9jnHLpn8hjcrqrPHUncv2cLlHh7u8NTeVhmWXmmzSdU1wTAAAAAJKomHivv9nj/06V27a4JjMQKl7Ddr22pZettnxvdDkrHnPdtmdFt+l9pHFd22tUwvfajYp4bXltvZ9Kox1u16HSsHXswXxYnsVprTS8Nu+nIpYjcaPxmB83bf2k+1RpD7cN5j3peLH86+Bvl9us7DykGUqLCFZbn6OU/ImKcOa2NGEcPm/D18+ocgneL0KQxsHrSIbwOovGkcFP/4h4opLvVQa2+8fMKjOWJ51peR9VZgAAAACsxP+8sXdAT19/StQVoEjkCE1tl1a6g7/ZAAAAAIAiOJNpQjCfdmrLtN3bpuVyGXeaAQAAACggNAYKjO+Y48k56vG716g56+HtKmNhBg9FO+vjAQAAAICCaUIAAAAAAAWFkQEAAAAAgIJCYwAAAAAAoKDQGAAAAAAAKCg0BgAAAAAACgqNASi0TtW/c9H4Tybmp16rONXEpw3D5Php1IYo02onLE9+DkbT8M+PDtWO3jp4Fypx/jpNqjYPB+KNd15j+9LHCM9zPF15qP0nXy+D+ZzmNr/qelZ5ju33HO/KlTd/XqcaxFEh+/Mm837B7z42izz4n61oWfrX83AZR7bl+BxNa56+W2eVlqTvOoBzx3cTmsUTiAHmnXxKr//EXn7S74RP521b6um+pyHP8R8l6knL6snK/LV0FmFQ0jmwG1bwtOdZ4esw+iTqaQRPHI/k4zSv0zymzR8/1dsa48no6onq2U8zP0t5PtOzykP0HKhrJPoU8/AaV9vi14uM2xjvHJyW875moyZNy6jzHv2uA5gXGBmAwtrZI2p3V2XvWLlcp55ez+K9aeP3DMXjxXvTgl7bTvge7h3iOCYfv1fX8cbrPVW9egZVq2EPdjReUh6C3r5qVfWEG01q6tERP25WHtJEe9FjIdKTn+0qmRWDFhb14jkoldboOu0F55rLatcxaUWnKc+5ZVk9jByvXO9Rr16OvSfslVXHCY49Vq+xRQ1zY6jnUaXD378eCZH7G31NZImXS7zHMy1/08g6HjteN4Ntsc9DbCRJ5FXkd5y0ZMVLuyZ4fZ7PtC8pD9NdE6FSaYk2X61R986OXjPAMMWnMcTfk9c3V8h0dmPHyVOe/shHJ/J9MW65xI8nyqApjsnfn1OUS/z8jX9tTvddfv7fdQBDuEWAkQEoItXj0/asCvfExXtzXLvttf3espSeycEeI9XjE66Ty3Lf4XvsRkX8tRnuacrTi8jk/nS8weOl5SHaM8XxK2K9f3y1LTsPs8ZfS2cRksR6UIfKLN+59aX1MKb1nA9ed+OMUoQ9u2EPb/S40dfRXmiVv+RrIstwucR7ndlpjgxkHU+9Fuc2kua2RcGxYz3gKZ/pJGnxhtMilqOfvzHKb9CoPOS5JtjgOYimTV234edi8H0NP78D+8hTnqPy5xv+bo1fV6rsT6FccnzP+9I+73nOO8B5w8gAgLFAQ500x3tUX1a9meXlberr1ZmOTsT7elQv63jcQ/SgS7tHertkUMPu0prYfjrE/m6v6tdXyTT0S5aVB6tFW0ucBoNqftc3GysPw6YfGZgTq7fJ3FuXPYpHuw6Zftmcw7ktLW3RdeeO7K3kXtE7znV9zsaxSJuvmrS3PuLERaVdE1lEuYgLMMiz7HVuGeQcy8VcZA9w2nUz6nhGg+yu/3ng09kgOlFlsLhiUnfSz7SQGi/nZ2WkjDxMd01EyLT7+Lp1uXVMotJN5kY5GG05Wt+g7e1llT8e4dkLRxPylmdW/tIdk2P41yef9zXaF+ndX9PLecslz/d84LS/ywHODxoDUHCrdNvcUH/Ie2qKCP8xqW2YZLv6D6TdEF/7Y1hcIKMi/tDpeCr0x/9j3T+RlVBZGYpMOcjjrPOwtNWPvD8S9tf0Oy4GrlyumI6o0O3EKxTTntucVm+rCv3R+h6ZQaNvPKqCdIM2YjWcfljv2rlD25PVfoaJchms+XN93YzOM8lDfxbY0W43rKSNOl5/m6KzX3bubJM/H4PLox+cO49auvI4Smq8ca6JPJ/pjDywaa4JHzd0awlxVePKEsk+EmnmaXK1WP5s8yQos7zlOSp/ya6S2Q+n8CWZtFxyf0eO4xS/ywHOhPgQYJoQFBIPe/N0iCSDQ+cceEjaH56Ob4sMX4t9xrepYeTEeANDyTxc7m8bHDZPEh5LHSOInzL8z8Fq62F6/b5wqFulT04PScnDWYkfe3YhDU8XqIjtg+cgz7kddb1Ez7kMCVOy5HvGnHYQnvNo2sLzF8tDxfIsOWWjMfKayDJ0nUXSOk7+ksTTmTQdK7JPfTy53s+T3uanPZieEok3Kl9sVLxRn5VJP9NZeYia5Jrwr2d/fzLouMPpF0GWdyTfuvzDvPD1O0V5WmnnKPuzkpSPwTKdpFzY0LWk95mVlqzPu2/S8+5T6YlfQwBnocT/vLF3QE9ff0pctwDFwT1f6+YybT9Qy+JLOxh2BpgXnapBJ7edmY9CwMVxUa8J+Z1b26WV7iYtzWB6zUX+rPh/j5wW/g7B2cM0ISgsHhLf6ofD3PgChnmi7lhSonrvAW2n3fkFCuWiXxM7tWXa7m3Tcrk80d2VRnkkPitHu9SlBuWc+QUwFTQGAOaUmm+qf4g7GCa8nSBcvPJc2w8bqv5vL846D7M43jydh4tWnvNwTWQZlZar3fD3B/2tJR1revNeLuOQv8Hob81kxARgFEwTAgAAAAAoKIwMAAAAAAAUFBoDAAAAAAAFhcYAAAAAAEBBoTEAAAAAAFBQaAxAofm3pCud0VMi1R0uqolP0uT7TDeN8I4X0VvvZcWbhbM+3mk6bBqi/KZP+2ntJ0t4zi9mWQMAwMWHxgAUltepUp3aokLmkeu2yDmF+1PLSnS1o5eGlUprtO/t01rS7eN27sgHzvi3yIveei8zXg5TpXNOpOVhaatPbUsvTOG09pPlaP2GPOezPg4AAEAaNAagsHb2iNrdVdkoKJfr1NPreZlHCqpV7hlO6KU/bJKh13OodlSPLq83eT+9emI81dOc3AvMIxTleo969fLQe7LiqR58dSw5utGsxtITpjOMO006U/M+osySBL3iVZFmjmM0qalHavy48eOFozej8sBO1s1gm59OlpYHpvKht2U0lqLC8hoIY8TnBgcedgcAAOcJjQEouB2qbZhku23yO2dLa/tkN0g0Dlqyh94V24zurnxIDVe+a8sOtVzVg++6NpkiPldS+aExDu/HUqMNg737XPHjdUm9wPzQHFdssNr+yEDYK58WTz2+foNM209Li5xtv0nDFqjlb7NN2ls/kmvzpjMz7xllloafAL3piHT0+jIPdq1LXdNWcZ3jhOOJ/N1Yl/sclQdOybYTpoU2VLysPMgGhrwW1DZblK1on43kl9dQ0A8/AgAAmGdoDAAYC7SoX4YMagTPhb9KpqFfHp2Q2BBU1GWFtmWQqLueg2NyjBZtLflp4ak9XtjTfLxH9WU10lBe3qa+WpvfyLynlNkolp8Hg2orkTMhjtcXlfp6WeeBRwIedGlXtWlGsOSojzLm+Tt2yGhtBk8A5Ur+ONN3phkZAAAAOG9oDEDBrdJtc0NVNHt7Q9Nihiwu0GDNn+uX5lW9wPonQS90daY/TBaV3H5ymmUPeKSX27Uboqo9YNJ0jpP30ySOZ1QaQR5U6AeNH+k083DVpL4eQWA8UrCBkQEAAHjUiT9a3utv9vg/gEJx25ZXadh6KcTr+aNBZHlt1/XaFr8WwWrL7XajorfH1/uC94tgtV25znXbnhWNI0PFa9hqezSODJWGJyrBI+OJSr5XGdjuH3MonZFtLE860/I+qsySuK7tNSrh+9S+Ob5KA5+bcL9+UPv3JeUhTOPk5y9+PMuzOK4+F7MwdN4H8heWyWyODwAAUOJ/3tg7oKevPyX+FgEUh5pzv0zbD9SyqEzix5wwN/zrk+82hOsSAABmBdOEoLB4vvhWP5zWgQoXzJWjXepSg4KfYQAAAMwAGgMAMDNqLn/Cj2s5GM1gfj4M4zsm9ftbwQ+aAQAAZgGNAQCYGf8OR/7oSyygogsAAHDu0BgAAAAAACgoNAYAAAAAAAoKjQEAAAAAgIJCYwAAAAAAoKDQGIBC61T9u9vM8knBcB74CcKGOLfVTnhe/XXRuxr5271ONbY+ts07pKbhr0++VpKOBwAAMO/QGIDC4spfndryzjau2yLnzo7eMl/k7TmrHb0EY1tcIIMqZF7VywLfrtNpW/IBc+q822RumLJyX1rbJ7thUcOObqtRR7z2n0nhctxGjZzdI73HiITjAQAAzDs0BqCwdvaI2t1V2Sgol+vU0+vZYA+y39srRxKMJnWaRrDNaB7KbSweL96DrEYhxLpO+J6g5zkWryoroP56k9PWq+ttWceL90pnHS+N6h03RONjnPypdAa95tWqeqaAKJ+mHnHx42aVS5rDSBnHwtgNo6tkVgxaWNSLCbiSv/lqjbppDUHDFHsJ8TVzfXOFTGc34RkJ6cfz8x8tSwAAgHmAxgAU3A7VNkyy3TZZeg33xNeWHWq5w73Hq12bGrRNdaclt3FoOcuykj0cr0XOjfWg0ri275HdINquh+8Jn3q8QC2/R9o2aW9d9TzLnmxOm6VGMDj0t5bktqx0suzjJVO94yQaRip/rji20Y1WfIfTKSvUjkhjr0+m2GbXutQ1bRXXOU5IZ7xc0ixt9eX7h8L+mn5HNtWbv09rohKeSfbo+/q0vVyWjY5yeZmc65vBsxA4HyfmbbE/kd/rDg22H7KOJx8gJtLunzsAAIB5gcYAgLFAsc7coxOiBlf6VKVOVnZbBol6rWI0yO6u6gWi1dui9nwiKu8iXl9Uo+tlvzJZp96DLsVnlBjUsLvDFcbjPar7ldDlbbGfMYxKp5RyvEwizm0/f1fJDGvK2em0WrS1xMcxqLYSKdGxymXY9CMDY5Lp83F5RRtX5WA05Wh9g7a3l2UayvUe9fbmc1oZAADAJNAYgIJbpdvmhqqg9vbU9JzFBRqoUct6dzAXvL8d6xXeubNNcm4I9zBXRENB94Cr0NcV5HSy51yOTvg97g1RJR3QP5E96fzeqj/NZlQ6T9lY6UySs1ymHRkY19GuQ7Wg8RNSjStLFP2ROO4h7Tq1WB5s8ySYzjWKatiMNz0KAADgTIk/at7rb/b4P4BCcduWV2nYeinOblS41hYGqy3Xu67tNSzLsyrhtug+eJ+xeGR5bdcV8dqeFVsvgt4nGzqeCFbb1Vs9r20lr09PZ/bx0oTpV+kOjqvjJqdTlIlfHuJ96j0cX6WByyetXM6aaMB4lVg6RPDLbCiNIlQans3n3M+fXI6UC1W8hp2dD3nNiPhp1xoAAMB5KvE/b+wd0NPXnxJ/2wCKg3t7181l2n6glvkOM6Pm1Ms4tV1a6YZzyQGyyB+B3yB61cE1AwAA8weNAYAJ8B166vq2Q5WGjR+EAgAAwIWG3wwATIDv0CPazzJcxIaA+s1Bwo9yORjNkXf4AQAAgEcLGgMABVIqrdG+bswMhf4WprEAAAAUDBoDAAAAAAAFhcYAAAAAAEBBoTEAAAAAAFBQaAwAAAAAABQUGgMAAAAAAAWFxgAAAAAAQEGhMQAAAAAAUFBoDAAAAAAAFBQaAwAAAAAABYXGAAAAAABAQaExAAAAAABQUGgMAAAAAAAUFBoDAAAAAAAXgZczZEBjAAAAAADgIijlDBnQGAAAAAAAKCg0BgAAAAAACgqNAQAAAACAgkJjAAAAAACgoNAYAAAAAAAoqJk1BrxOlUql0kCoUsfzqFNVy0bzUL43upwVj3leh6rBeoOanSZV9X5m7bBpBGmRaTaadKjTldcs9jlPovkblLXtrKjrKTkNnndITcO/1sLrlWXFm4WzPh4AAAAUw8waA6W1fXLbDWrYrqjIeDK0LbVtbZ9fW9TaXJTLq9ctqjRs6m8tZcbjytm6uUGm3ua6LXLq29RXm2duaasfpGW1a1PDUK+nMYt9zpNo/gZlbTstshJd7eilYaXSGu17+7QmKvtDdu6Q0wqvQ74+fZnxcpgqnQAAAAA5nck0Ie7tr3Y80QgIKzOrt03aKNeoc9gkc8OkV3XDIGoo3tEuObVXaWtJ7UNVkOKVtEFB725V7Evsg3vemwMjE6rX1e8BFu8T7/V7YGMjFQmVteN1M9ge7TnOknefnigrw48nApfNONLy549EdGQPvdo+dh5iaYn3WGflb1Tek6g4hkh3cjrTyoXXm+U69Xr1xHhqZILXD/e4c9mU6z3q1ctD78mKFy9rg5pNdQ3LbSllNk06U/M+oswAAAAA2IwbAw5tL5dlpWpQaWmLHFs0CJYdajmbtCQqK6GUeMfOxKMApdISbTptsnp9OaJg17rUNW1y3TYZzrF8z9H6XjjaINJE5m3Z+JCVNNFQsV21zTY3KJak/jbVnZbcxqHlLI+soOfdJ1cya1xWOp7r2nI/zcPs47G0/MmRCJo8D8oCtSL73Fs/kmuz8jcy7yl4tMhuEPVIpVOeu+6unE6VVS7yGhPvtay23MYh2nDkkQlelzQ6waNXrthgtdV+vUivfFq8xJGr7WgGk8ssbzoz855RZgAAAAC+GTcGTDndhytVg4KKDDcIzPWBSkpKvKsm5Z5FY7X0iIJBtZX4KMTiikld0fjg3tPycmTakWh8GK2woTI0rcVokN1d1Qs82iFqXyeqgpcq7z6PTogaqhLPZCOnZZBuz2RKzR/Lkwd2vEf1SctsVN4zGSL7fjqvkulfCFOUy+k7JsfwrzNOixq52l9Ty6llltfIvKeUGQAAAIB2JtOEuJeSK0SHzarstQx7ULu0xr2iLYeWazv63aHBeKJWS2b3Rqw3nKdQTDv9gXtm+6LS5vfKtpw7ajqGaHz0N8KGCvdsbwz04t+JJHvnzjbRwvB0p5i8+1xcoMEaLtcFzat6IUNq/liOPMiGXKSH37UbYSMtK3+j8p7HOOXSP5HH5HRXeepO5Po5XaLC3d8bmsrDMsvMN2k6p7gmAAAAACRRMfFef7PH/50qt21xTWYgVLyG7XptSy9bbfne6HJWPOa6bc+KbtP7SOO6tteohO+1GxXx2vLaej+VRjvcrkOlYevYg/mwPIvTWml4bd5PRSxH4kbjMT9u2vpJ96nSHm4bzHvS8WL518HfLrdZ2XlIM5QWEay2Pkcp+RMV4cxtacI4fN6Gr59R5RK8X4QgjYPXkQzhdRaNI4Of/hHxRCXfqwxs94+ZVWYsTzrT8j6qzAAAAABYif95Y++Anr7+lKgrQJHIEZraLq10B3+zAQAAAABFcCbThGA+7dSWabu3TcvlMu40AwAAAFBAaAwUGN8xx5Nz1ON3r1Fz1sPbVcbCDB6KdtbHAwAAAAAF04QAAAAAAAoKIwMAAAAAAAWFxgAAAAAAQEGhMQAAAAAAUFBoDAAAAAAAFBQaA1BoO7Vfo1/7NQ4mrR+Nd9eio3VTx6nRDu50dCq8o3UyRZnWdsLy9LwjWjf986ND5EnlnrdDtWCbOH8761RbPxqIN955je1LHyM8z/F05aH2n3y9DObTFHnIS13PKs+x/ZrrdHRO1+qk+Rt13rOM+mxmnYcseeOdF1UO46U37XN0WpLSUpTzcBrkZ/qUP7/+9230M+Z/x3k7tdj62LYc363Tmqe/t7NKS9LfvzPHdxOaxROIAead26l6Zf+JvW7bq1Y78vW42pZ6uu9pcN3OxMd/lKgnLYdPVvbJ9ZGnJtsNw6t2+EnQh17TMGJPJq+Wy54Rffp22/KsRsOrNg/1mmyDx2J2wxpK02nj67AaeRL1NNQTx8teOXItneZ1mkee/KWd93Gdd57nwThlMM7n6DSMez4e9e/BPPmTn2kr+yn9ecjvx+BJ9+IY4jpoHqrl6Pee2lb1OpHjT/rdehrm6TM9aVpGnXf5fZfw9+8sYWQACuuVPY9e7q7KnpBLl56jg0hLP95zMn4vQDxevOck6LXdCd/DPQEc5/KlZ+ng4Fkdb7zeYdWDY1Kt5vdWxOMl5SHo2anVVG+guU7renTEj5uVhzRhj8lAGLNHl+gqmRWDFhb1osDPmSiX69Tr1YPnTixv92l/rSzWL9FWv0/by+XgffuuS/3t5eC95XqPetvbtL+1FKwbDFGl0hpdp7eCc81l9VrfpBWdpjznlmX1JnG8S88e0MFzl2LvCXvg1HGCY4/VQ3iNbhobQ71M0R7aoDdY7m/0NZElXi7x3q20/E1qcaVK3sn78nXW8bJknYeh3vF1URZjnL+sayK+T7E/Ub7j5D2+zzDOyM97tEd33M/d+7vkVL9HW0vqs8Cfge7HH5OjnzuTVtbTpCWtPPlYWd+D45+HCdKZ9xxlxEu7Jkblb5QPbl0eijfd90SoVFqize9VqXvnFb1mgPhuvqJfslfeIrq+uUKGsxs7Tp7y9Ec+doLzO365pH1upymX+Pkb73pg0/19H/77d+a4RYCRASgi1boXLXaDe13iPZGu3fbafs+IndwDMtg7IFv/5WqwTvayGU3vMPIeu1HxypH3+JJ6pcch96fjyX1EjpeWB/m+suoN5B5X7gWU66od8f/oPJwV/nqadRjEPV5+D7R72PSquod0mnPrS+tNivbQRfE5syI9tOOMUqiRAT5O2JsXPW70dbTHMeuayDJcLvEeRpaWvyzy2NHPY6cq9zHO8VhaWbPhz21y7/hgmofjZV8Th81quM+Bc5kl67sn7fPO16ohv8dUPD6H5XJ6Gfi4XNNGAUaV9bRpSTpHg+c9yejzMF46Wd5zlBZv1DUxTv4GqfzwaF8Yr10th99TOb4n2ODnMpo2dc7EMXWIjuxxHpt+ftvV2IhdnvKU+ZOjmcn58036uc1dLjn+9vvS/gbkOe9nDSMDUHCi5V5ZoKEG+Qdv0XNPqt7MS0/epf44vQPvn1DfPaDnLul4PNrwUZd2VYemZtDN+2/T2kCvdH5if7dX9WvuXRD50UuZebh2S/cGVqgadH2L7WPlYVjYazcQxh4ZmBOrt8nYuyV7j97fdcjwy+Yczm1paYuu978ue6a4B+zr/etBD+5oi7T5PYP2bo04cVFp10QWUS7eza8FeZY9jLcq5BzLxem881xwHV3aMEhe5jM53jE5FT/vYe/4/tqIsh5xTXz6GYP2I5+/B2r1aJnfPSmf92OHKrc2aUmXy9KWQy9fky+zXTHE2U4xsqxPOS155U2nkPccpcYbcU3kVrlJ99/28yC+pr52Mxgpm+57IuL9DyP5r4jvsof0sfgcPHx4n4wXLwWjLe/f2qBv3n1S5e+5AzrYC0cTcl/zRnr+0mV/bnOXS56//YHT/RtwltAYgIJbpdvmi/JL+5131BQR/uL4kqh83H+ovwzv30z/gxn16ctU4S9tHU8FhzYXx/xiePChrISqoc9weDmPs87D4qYTeX8kdMMv+IuAKxIrhiP+eL9Cd5zfCf94THtuc1r9mqrQv39rj4yvTVaW/Mfwd5wb9GLsL/ID+tD/G/vK1+nu2H+tU4hyof4HekERdQoyonMK8rr2cljWzpaqXM7keFfIeBBODxvbiGuCy98J1n9Mt3TFJEvuz62o1D94UTViGU9NePEd+TLb4gqZ+78f+67hhr2cxpC3rPOmxTfp9+AU10Sec8RS4424JqQ83/MP7tLXIzN4Xvn6XaLLn9ZL031P+Ljzoxo0mkKqcXWN+qJyztNvdp1qLH/3jZOgzPKW56j8JRv9uZ20XHJ//sZxin/fZ4KHBzBNCIqIh0mNlB9ADQ6TyqHSjj9NIb6+XA6HpHnYNL5NTdVIjDcwBaNthdvGmVYhfwAt36+O0anG95ucBx5aDd+n3sPx254l1hlNOzUPZ42/nmYdkvDQsCHyPXgO8pzbUddLcM78EJlS4OMh8+gQepbDpn/O/bTxeQ2HtPmaD49leZYcnm+OuCaSPyO+oessktZx8jdIDf9H4gx8TtKOl1XWIz+3+pxHt0/3eR/Igwjj/ig3+XMr9jni8x5PS9Wz+PtkrPJW0yuCuJHzl1rWudOSXZ4s6Xtw1HnIk86852hUvFHfn5N+z8vPdEV8ViPHTErnJN8TciqX3lcQhsosEuS0LzvMt76uws83n4u85Wl7DSs5f3k/t1GTlAtL/fxlpCVx28D31qTn3ae+0+PX0CyU+J839g7o6etPib+NAMUhPma0fvlJuvuRWn7quw+puzrbnl4Y3+APfGdBfP3pV/OrUzXo5LaTb+gfAArhon5PyL/DtV1a6YZTy07TRf7+5JGYW5efoA9vPRw9bXFKaAwAAMwhvjvFswd64anvXrgpVwAwexf9eyKa/sf/xU/I2TydW+o8Ct+f8k5Ev+fR95zZNJSi8JsBgDml5hYO/CDXDxPeOg4uXnmudsO5t/4fsrPOwyyOV+Tr+lE4f4+6i3aO5uF7IsuotFx5O/z9wWk1BNi8l8s4Soub5PT176VmDCMDAAAAAAAFhZEBAAAAAICCQmMAAAAAAKCg0BgAAAAAACgoNAYAAAAAAAoKjQEoNL79mLqbwNk8EVDdzaCW+NREvqfwuhne3UA+BVTLijcLZ32808RPUP21U0j7ae0nS3jOL2ZZAwDAxYfGABSWt1OjZ72X5a3HHj68Rc6dyPPQc5KV6NqOXhpWKq1S9+MurSbdKuyVO+TcSr7NWma8HKZK55xIy8PipkMvX/PkI4ancVr7yfL+rRvynM/6OAAAAGnQGIDCemXPo5e7q7JRcOnSc3Sge2Z5mUcKajXuGU7opT9aJ1Ov51Db0fH4ASGXnqWDg2cT46me5uReYB6huPTsAR08d2noPVnxVA++OpYc3VivxdITpjOMO006U/M+osySBL3iNZFmjmOu07oeqfHjxo8Xjt6MygP78NblYJufTpaWB6byobdlNJaiwvIaCGPE5wZHd8ZPlgQAAMiCxgAUlqrnvkJf2jDo/sOX6ZpcS1Ra7dL9m55oHNzSowYvU6W7Kx9IwpXvLz3h0K2Hqgf/4cP7ZGxclpVUfkDIh7yfa2q0YbB3nyt+vC6pF5gfkPLw5Wt07WV/ZCDslU+Lpx5VvkHGfT8tt8j55oHOF7/hMt3yt903aO/W+3J13nRm5j2jzNKUSov04ociHQd9mYf71S51jfsqrnOccDyRvxu35D5H5YHoHfqmE6bF21DxsvIgGxjyWlDb7hsb9Nw7encZ/PIaChfwiZcAAFA8aAxAwYlKYGWBhp97aNDN235l7iqZFV0xfv+EvJtfozVdUS+VlmjzVoVE3fUcHJNTuUVbS35a1qgrKqH7fk/zB2/Rc0+qkYZLT96lfkbFfCwj855SZqNc8/NQoeqKPhOcVnG8vntAz13SeeDRm4+6tKvaNCNco5ffnvD8HTtUuRU+9n1pi6cJyZeZphkZAAAAOG9oDEDBrdJt80VZ0XznnbeGpsUM+fRlov4HekF5/0NRDb6iF9iDD4Ne6NpMf5h8hYwHyWmWPeCRXu6H92+KqvaASdM5Tt5PkzhepXIzyIMKDm0u6sYOO808XDHowYtqBIHxSMGLGBkAAIBHnSe8/maP/wMoFLdteUbzUC+F3E7VK5fLIlS9jut6nSq/FqHakdvthqG3++vbcr2vbYXbrLYr17lux6tG48hgeM1DtT04hh+Mpncojj0qnms3PGNge7Wjtg2lM7KN5UlnWt5HlVkS1z30mkb4PrVvjt/2LLHOaNriHPn79YPavy8pD4dNP42Tn7/48aqexfvX52IWhs77QP5UXuLrAAAATlOJ/3lj74Cevv6Ubh4AFIOoatH65Sfp7kdq+anvPqTuaqTXGeAcqd+EPEEf3noYTv0CAAA4ZZgmBIXF88W3nHBaBxoCMFfe36Wud5OCn2EAAADMABoDADAzai5/wo9rOZjrwfx8GMZ3THL6W8EPmgEAAGYBjQEAmBn18LLIj2qjwdmkRVR0AQAAzhUaAwAAAAAABYXGAAAAAABAQaExAAAAAABQUGgMAAAAAAAUFBoDUGg7Nf/uNrN8UjCcB36CsCnObW0nPK/+uuhdjfzt3k4ttj62zTuiddNfn3ytJB0PAABg3qExAIXFlb9nvZflnW0ePrxFzp1X9Jb5Im/PWdvRSzC2T1+mivsYGVf0ssC36/zw5Wt07eWH+rzfJ2Pjsqzcl1a7dP/mNbp5P7rtS7TjiW2lRdp0xDqOe7NKzu77eo8RCccDAACYd2gMQGG9sufRy91V2Si4dOk5Oojc836wB9nv7ZUjCeY67aybwTZz/UhuY/F48R5kNQoh1u2E7wl6nmPxarIC6q+/fOlZOjh4Vm/LOl68VzrreGlU77gpGh/j5E+lM+g1r9XUMwVE+azrERc/bla5pDmKlHEsjN0wukpmxaCFRb2YgB88t/m9KnXTGoIifrRu/8pbRNc3V8hwdhOekZB+PD//0bIEAACYB2gMQGGputwr9KUNg+4/fJmuybW8foe+9IRDtx4O9x7/wdv36ab3TXrWuaXulS/CLecJWckejneLnBu3gkrjavdjun/To28+G74neOqxd5lu+T3S9w3au6V6nmVPNqftmhrB4OBsqtpmVjpZ5vFSqN5xTzSMVP4eimNXupGKb0I6udf8xQ9FGg/6ZIht96td6hr3VVzneGS5pFncdOT7h4JowI1DPmG6v09ro55lsLhAlSAtD+juk5dko+PSpSepf30zeOgX5+PEuC32JxoQv+PQYPsh63jyAWIi7f65AwAAmBdoDEDBiUp8ZYFiVbT3T8i7+bWgUid7j29VSNRrFeMm3X87rJCufu0meSei8i7i9d0Deu6SX5l8jg4+6lJ8RolBN++/PVxh/OAtes6vhD55l/ojKsrSqHRKKcfLJOLc9vPHvd2ijPRSZjqv3aKtJT5OhaorukR5+1jlMmz6kYExvf+haAL4KvFpQi9eCkZT3r+1Qd+8+6RMw6XnDuhgbz6nlQEAAEwCjQEouFW6bb4oK6jvvPOWmp7z6ctE/Q/0dkXUF8O54A/u0tcj9cBXvn6X6PKn1Zzximgo6B5wFRzaXMyuiMueczk64fe43xRV0gEPPpQ96fzemj/NZlQ6T9lY6UySs1ymHRkY1/u7DlWDxk9INa6uUV809Hgq1K5TjeXhvnESTOcaRTVsxpseBQAAcKY84fU3e/wfQKG4bcszmod6Kc5uGF65XA5DtS3Xu67tNSzLs4xwm9Gw5TbmtqvxeOWq13FdEa/jVWPrRah2dKyE44lQ7bh6q+e1rXC91Q7Xp6cz+3hp3I6ffpXuTjUeNzmdh17TLw/xPvUejt/2LLHOaNqp5XLW3MOmZ8TSIYJfZkHeI8FoeLY450H+jKZ3GC2XsuE1D7Pz4bqqfCqR6wQAAGBelPifN/YO6OnrT+nmAUAxeN4hrV9+ku5+pJaf+u7DkXPqZZzaLq10w7nkAFnkj8B/z6PvObhmAABg/qAxADABvkPPswfq9eP/4if4QSgAAABcaPjNAMAE+A49/pzxi9gQUL85SPhRLgdzfeQdfgAAAODRgsYAQIGUSqvU1Y2ZoeBs0iKmsQAAABQKGgMAAAAAAAWFxgAAAAAAQEGhMQAAAAAAUFBoDAAAAAAAFBQaAwAAAAAABYXGAAAAAABAQaExAAAAAABQUGgMFBE/V+qiBwAAAAAYX1J9SgQ0BoqInyt10QMAAAAAjC+pPiUCGgMAAAAAAAVV8oQ39g7of/jHj+tVj77/7X/9H/WrYnr4X3+lX11cl/6bT+hXAAAAADBKWv0vaAz8Tu0LetWj7Qc//mnhGwOe6+pXF1epjEEtAAAAgHEl1/+I/n+hyg6mgBF2nAAAAABJRU5ErkJggg==

    but instead if I use just '=' instead of '+=' when selecting the local variable i.e.
    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

    I only get 1 value i.e. the last value that is returned.
    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

    My question is, how is it adding the += returning all the 3 values instead of the last. How is it looping through? I am trying to understand the concept here.

    Any help would be greatly appreciated.

    Regards

    Amir

  • SELECT @Var += SomeExpresion FROM SomeTable;
    is a shortcut to
    SELECT @Var = @Var +SomeExpresion FROM SomeTable;

    This is a common shortcut in many programming languages. I learned it when studying C++ in college.

    Luis C.
    General Disclaimer:
    Are you seriously taking the advice and code from someone from the internet without testing it? Do you at least understand it? Or can it easily kill your server?

    How to post data/code on a forum to get the best help: Option 1 / Option 2
  • amirmir - Wednesday, March 15, 2017 12:10 PM

    My question is, how is it adding the += returning all the 3 values instead of the last. How is it looping through? I am trying to understand the concept here.

    Any help would be greatly appreciated.

    Regards

    Amir

    I'm sorry, I might have missed an important part of the answer to your question. The query is returning the 3 values using something called "pseudo cursor" which is another way of saying that SQL Server internals will read row by row and "increment" the value. In the wrong option, it's simply assigning a value to the variable row by row and keeping only the last one.
    For more information about "pseudo cursors" you can read the following article: http://www.sqlservercentral.com/articles/T-SQL/74118/

    Luis C.
    General Disclaimer:
    Are you seriously taking the advice and code from someone from the internet without testing it? Do you at least understand it? Or can it easily kill your server?

    How to post data/code on a forum to get the best help: Option 1 / Option 2
  • Luis Cazares - Wednesday, March 15, 2017 12:17 PM

    SELECT @Var += SomeExpresion FROM SomeTable;
    is a shortcut to
    SELECT @Var = @Var +SomeExpresion FROM SomeTable;

    This is a common shortcut in many programming languages. I learned it when studying C++ in college.

    Hi Luis, this is exactly what I was looking for, perfectly explained. I do appreciate the link to the "pseudo cursor" and while that is more internal, my confusion was with this bit. God bless!!

  • amirmir - Wednesday, March 15, 2017 12:10 PM

    Hello, I am trying to find out what is the purpose of using += when selecting a local variable to the value of an expression. So for my example, I am trying to generate and PRINT a series of TSQL statements for adding notifications to alerts in my SQL Server.

    When I query: SELECT name FROM msdb.dbo.sysalerts WHERE has_notification = 0;
     I get 3 alerts:
    data:image/png;base64,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

    Then if use

    DECLARE @AlertText2 varchar(max) =''

    SELECT @AlertText2 += 'EXEC msdb.dbo.sp_add_notification

    @alert_name = ''' + name + ''',

    @notification_method = 1,

    @operator_name = ''DBA'';'

    + CHAR(10) + CHAR(13)

    FROM msdb.dbo.sysalerts

    WHERE has_notification = 0;

    PRINT @AlertText2

    ~

    I get the results as expected:
    data:image/png;base64,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

    but instead if I use just '=' instead of '+=' when selecting the local variable i.e.
    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

    I only get 1 value i.e. the last value that is returned.
    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

    My question is, how is it adding the += returning all the 3 values instead of the last. How is it looping through? I am trying to understand the concept here.

    Any help would be greatly appreciated.

    Regards

    Amir

    Please post DDL and follow ANSI/ISO standards when asking for help. 

  • jcelko212 32090 - Wednesday, March 22, 2017 11:04 AM

    amirmir - Wednesday, March 15, 2017 12:10 PM

    Hello, I am trying to find out what is the purpose of using += when selecting a local variable to the value of an expression. So for my example, I am trying to generate and PRINT a series of TSQL statements for adding notifications to alerts in my SQL Server.

    When I query: SELECT name FROM msdb.dbo.sysalerts WHERE has_notification = 0;
     I get 3 alerts:
    data:image/png;base64,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

    Then if use

    DECLARE @AlertText2 varchar(max) =''

    SELECT @AlertText2 += 'EXEC msdb.dbo.sp_add_notification

    @alert_name = ''' + name + ''',

    @notification_method = 1,

    @operator_name = ''DBA'';'

    + CHAR(10) + CHAR(13)

    FROM msdb.dbo.sysalerts

    WHERE has_notification = 0;

    PRINT @AlertText2

    ~

    I get the results as expected:
    data:image/png;base64,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

    but instead if I use just '=' instead of '+=' when selecting the local variable i.e.
    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

    I only get 1 value i.e. the last value that is returned.
    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

    My question is, how is it adding the += returning all the 3 values instead of the last. How is it looping through? I am trying to understand the concept here.

    Any help would be greatly appreciated.

    Regards

    Amir

    The + = notation is a left over from the early Sybase days.. It's how we first wrote (incorrectly, by the way) the left outer join outer join.. The table that is referenced on the left side (+) is the preserved table. Well, almost..

    A Bit of History

    Before the SQL-99 Standard, there was no Standard OUTER JOIN syntax, so you had to construct it by hand with a messy UNION in products like very early versions of DB2 from IBM like this:

    SELECT sup_id, sup_name, order_amt -- regular INNER JOIN
    FROM Suppliers, Orders
    WHERE Suppliers.sup_id = Orders.sup_id
    UNION ALL
    SELECT sup_id, sup_name, CAST(NULL AS INTEGER) -- preserved rows
    FROM Suppliers
    WHERE NOT EXISTS
      (SELECT *
       FROM Orders
       WHERE Suppliers.sup_id = Orders.sup_id);

    You have to use a NULL with the correct data type to make the UNION work, hence the CAST() functions. Some products are smart enough that just NULL by itself will be given the correct data type, but this is portable and safer.

    The other alternative is to insert a constant of some sort to give a more meaningful result. This is easy in the case of a CHARACTER column, where a message like '{{NONE}}' can be quickly understood. It is much harder in the case of a numeric column, where we could have a balance with a supplier that is positive, zero, or negative because of returns and credits. There really is a difference between a vendor that we did not use and a vendor whose returns and credits canceled out its orders.

    In the second edition of this book, I described the proprietary OUTER JOIN extensions in detail. Today, they are gone and replaced by the Standard syntax. The vendor extensions were all different in syntax or semantics or both. Since they are mercifully gone, I am not going to tell you about them in this edition.

    The name LEFT OUTER JOIN comes from the fact that the preserved table is on the left side of the operator. Likewise, a RIGHT OUTER JOIN would have the preserved table on the right-hand side and the FULL OUTER JOIN preserves both tables.

    Here is how OUTER JOINs work in Standard SQL. Assume you are given:

    Table1
    a b
    1 w
    2 x
    3 y
    4 z

    Table2
    a c
    1 r
    2 s
    3 t

    and the OUTER JOIN expression

    Table1
    LEFT OUTER JOIN
    Table2
    ON Table1.a = Table2.a  -- JOIN condition
     AND Table2.c = 't';  -- single table filter condition

    We call Table1 the "preserved table" and Table2 the "unpreserved table" in the query. What I am going to give you is a little different, but equivalent to the ANSI/ISO standards.

    1) We build the CROSS JOIN of the two tables. Scan each row in the result set.

    2) If all search conditions in the ON clause test TRUE for that row, then you keep it.

    3) If the predicate tests FALSE or UNKNOWN for that row, then keep the columns from the preserved table, convert all the columns from the unpreserved table to NULLs and remove the duplicates. So let us execute this by hand:

    Let â—„ = passed the join search condition
    Let â— = passed the filter search condition

    Step One and two:

    Table1 CROSS JOIN Table2
    a b a c Notes
    1 w 1 r â—„
    1 w 2 s
    1 w 3 t â—
    2 x 1 r
    2 x 2 s â—„
    2 x 3 t â—
    3 y 1 r
    3 y 2 s
    3 y 3 t â—„ â—
    4 z 1 r
    4 z 2 s
    4 z 3 t â—

    Step three:

    Table1 LEFT OUTER JOIN Table2

    a b a c Notes
    3 y 3 t â—„ row(s)from step two
    1 w NULL NULL Sets of duplicates
    1 w NULL NULL
    1 w NULL NULL
    2 x NULL NULL
    2 x NULL NULL
    2 x NULL NULL
    3 y NULL NULL â—„ removed in step two
    3 y NULL NULL â—„ removed in step two
    4 z NULL NULL
    4 z NULL NULL
    4 z NULL NULL

    the final results:
    Table1 LEFT OUTER JOIN Table2
    a b a c
    1 w NULL NULL
    2 x NULL NULL
    3 y 3 t
    4 z NULL NULL

    The basic rule is that every row in the preserved table is represented in the results in at least one result row.

    Consider the two famous Chris Date tables from his “Suppliers and Parts†database used in his textbooks.

    SupParts
    sup_id part_nbr part_qty
    S1 P1 100
    S1 P2 250
    S2 P1 100
    S2 P2 250

    Suppliers
    sup_id
    S1
    S2
    S3

    If you write the OUTER JOIN with only the join predicate in the ON clause, like this:

    SELECT Suppliers.sup_id, SupParts.part_nbr, SupParts.part_qty
    FROM Suppliers
       LEFT OUTER JOIN
       SupParts
       ON Supplier.sup_id = SupParts.sup_id
    WHERE part_qty < 200;

    You get:

    sup_id part_nbr part_qty
    S1 P1 100
    S2 P1 100

    but if we put the search predicate in the ON clause, we get this result.

    SELECT Suppliers.sup_id, SupParts.part_nbr, SupParts.part_qty
    FROM Suppliers
       LEFT OUTER JOIN
       SupParts
       ON Supplier.sup_id = SupParts.sup_id
        AND part_qty < 200;

    You get:

    sup_id part_nbr part_qty
    S1 P1 100
    S2 P1 100
    S3 NULL NULL

    Another problem was that you could not show the same table as preserved and unpreserved in the proprietary syntax options, but it is easy in Standard SQL. For example to find the students who have taken Math 101 and might have taken Math 102:

    SELECT C1.stud_nbr, C1.math_course, C2.math_course
    FROM (SELECT * FROM Courses WHERE math_course = 'Math 101') AS C1
       LEFT OUTER JOIN
       (SELECT * FROM Courses WHERE math_course = 'Math 102') AS C2
       ON C1.stud_nbr = C2.stud_nbr;

    A third problem is that the order of execution matters with a chain of OUTER JOINs. That is to say, ((T1 OUTER JOIN T2) OUTER JOIN T3) does not produce the same results as (T1 OUTER JOIN (T2 OUTER JOIN T3)).

    The

    Please post DDL and follow ANSI/ISO standards when asking for help. 

  • jcelko212 32090 - Wednesday, March 22, 2017 11:05 AM

    The + = notation is a left over from the early Sybase days.. It's how we first wrote (incorrectly, by the way) the left outer join outer join.. The table that is referenced on the left side (+) is the preserved table. Well, almost..

    Please read the questions before answering. You mistakenly explained a removed feature (which uses a different operator) instead of a feature introduced in 2008.
    FYI, SQL Server used *= and =* for OUTER JOINs, while the plus sign (+) was used in Oracle and is no longer recommended (link).

    Luis C.
    General Disclaimer:
    Are you seriously taking the advice and code from someone from the internet without testing it? Do you at least understand it? Or can it easily kill your server?

    How to post data/code on a forum to get the best help: Option 1 / Option 2
  • jcelko212 32090 - Wednesday, March 22, 2017 11:05 AM

    The + = notation is a left over from the early Sybase days.. It's how we first wrote (incorrectly, by the way) the left outer join outer join.. The table that is referenced on the left side (+) is the preserved table. Well, almost..

    I think you're thinking of *= and =* like in this article:
    http://sqlblog.com/blogs/aaron_bertrand/archive/2009/10/08/bad-habits-to-kick-using-old-style-joins.aspx

    the += is performing concatenation as previously explained

  • Correct that this has nothing to do with joins or anything that Joe was talking about. 

    The += syntax is an example of a compound assignment operator; These two statements do the exact same thing: 

    SET @variableA = @variableA + 1;
    SET @variableA += 1;

    These two statements do the exact same thing as well:
    SET @variableA = @variableA * 1;
    SET @variableA *= 1;
    ... and these:
    SET @variableA = @variableA - 1;
    SET @variableA -= 1;

    Here's the complete list with an explanation :

    +=+= (Add EQUALS) (Transact-SQL)Adds some amount to the original value and sets the original value to the result.
    -=-= (Subtract EQUALS) (Transact-SQL)Subtracts some amount from the original value and sets the original value to the result.
    *=*= (Multiply EQUALS) (Transact-SQL)Multiplies by an amount and sets the original value to the result.
    /=(Divide EQUALS) (Transact-SQL)Divides by an amount and sets the original value to the result.
    %=Modulo EQUALS (Transact-SQL)Divides by an amount and sets the original value to the modulo.
    &=&= (Bitwise AND EQUALS) (Transact-SQL)Performs a bitwise AND and sets the original value to the result.
    ^=^= (Bitwise Exclusive OR EQUALS) (Transact-SQL)Performs a bitwise exclusive OR and sets the original value to the result.
    |=|= (Bitwise OR EQUALS) (Transact-SQL)Performs a bitwise OR and sets the original value to the result.

    "I cant stress enough the importance of switching from a sequential files mindset to set-based thinking. After you make the switch, you can spend your time tuning and optimizing your queries instead of maintaining lengthy, poor-performing code."

    -- Itzik Ben-Gan 2001

  • Alan.B - Wednesday, March 22, 2017 6:58 PM

    Correct that this has nothing to do with joins or anything that Joe was talking about. 

    The += syntax is an example of a compound assignment operator; These two statements do the exact same thing: 

    SET @variableA = @variableA + 1;
    SET @variableA += 1;

    These two statements do the exact same thing as well:
    SET @variableA = @variableA * 1;
    SET @variableA *= 1;
    ... and these:
    SET @variableA = @variableA - 1;
    SET @variableA -= 1;

    Here's the complete list with an explanation :

    +=+= (Add EQUALS) (Transact-SQL)Adds some amount to the original value and sets the original value to the result.
    -=-= (Subtract EQUALS) (Transact-SQL)Subtracts some amount from the original value and sets the original value to the result.
    *=*= (Multiply EQUALS) (Transact-SQL)Multiplies by an amount and sets the original value to the result.
    /=(Divide EQUALS) (Transact-SQL)Divides by an amount and sets the original value to the result.
    %=Modulo EQUALS (Transact-SQL)Divides by an amount and sets the original value to the modulo.
    &=&= (Bitwise AND EQUALS) (Transact-SQL)Performs a bitwise AND and sets the original value to the result.
    ^=^= (Bitwise Exclusive OR EQUALS) (Transact-SQL)Performs a bitwise exclusive OR and sets the original value to the result.
    |=|= (Bitwise OR EQUALS) (Transact-SQL)Performs a bitwise OR and sets the original value to the result.

    Thank you Alan, I did take a look at this and it helps.

  • jcelko212 32090 - Wednesday, March 22, 2017 11:05 AM

    jcelko212 32090 - Wednesday, March 22, 2017 11:04 AM

    amirmir - Wednesday, March 15, 2017 12:10 PM

    Hello, I am trying to find out what is the purpose of using += when selecting a local variable to the value of an expression. So for my example, I am trying to generate and PRINT a series of TSQL statements for adding notifications to alerts in my SQL Server.

    When I query: SELECT name FROM msdb.dbo.sysalerts WHERE has_notification = 0;
     I get 3 alerts:
    data:image/png;base64,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

    Then if use

    DECLARE @AlertText2 varchar(max) =''

    SELECT @AlertText2 += 'EXEC msdb.dbo.sp_add_notification

    @alert_name = ''' + name + ''',

    @notification_method = 1,

    @operator_name = ''DBA'';'

    + CHAR(10) + CHAR(13)

    FROM msdb.dbo.sysalerts

    WHERE has_notification = 0;

    PRINT @AlertText2

    ~

    I get the results as expected:
    data:image/png;base64,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

    but instead if I use just '=' instead of '+=' when selecting the local variable i.e.
    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

    I only get 1 value i.e. the last value that is returned.
    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

    My question is, how is it adding the += returning all the 3 values instead of the last. How is it looping through? I am trying to understand the concept here.

    Any help would be greatly appreciated.

    Regards

    Amir

    The + = notation is a left over from the early Sybase days.. It's how we first wrote (incorrectly, by the way) the left outer join outer join.. The table that is referenced on the left side (+) is the preserved table. Well, almost..

    A Bit of History

    Before the SQL-99 Standard, there was no Standard OUTER JOIN syntax, so you had to construct it by hand with a messy UNION in products like very early versions of DB2 from IBM like this:

    SELECT sup_id, sup_name, order_amt -- regular INNER JOIN
    FROM Suppliers, Orders
    WHERE Suppliers.sup_id = Orders.sup_id
    UNION ALL
    SELECT sup_id, sup_name, CAST(NULL AS INTEGER) -- preserved rows
    FROM Suppliers
    WHERE NOT EXISTS
      (SELECT *
       FROM Orders
       WHERE Suppliers.sup_id = Orders.sup_id);

    You have to use a NULL with the correct data type to make the UNION work, hence the CAST() functions. Some products are smart enough that just NULL by itself will be given the correct data type, but this is portable and safer.

    The other alternative is to insert a constant of some sort to give a more meaningful result. This is easy in the case of a CHARACTER column, where a message like '{{NONE}}' can be quickly understood. It is much harder in the case of a numeric column, where we could have a balance with a supplier that is positive, zero, or negative because of returns and credits. There really is a difference between a vendor that we did not use and a vendor whose returns and credits canceled out its orders.

    In the second edition of this book, I described the proprietary OUTER JOIN extensions in detail. Today, they are gone and replaced by the Standard syntax. The vendor extensions were all different in syntax or semantics or both. Since they are mercifully gone, I am not going to tell you about them in this edition.

    The name LEFT OUTER JOIN comes from the fact that the preserved table is on the left side of the operator. Likewise, a RIGHT OUTER JOIN would have the preserved table on the right-hand side and the FULL OUTER JOIN preserves both tables.

    Here is how OUTER JOINs work in Standard SQL. Assume you are given:

    Table1
    a b
    1 w
    2 x
    3 y
    4 z

    Table2
    a c
    1 r
    2 s
    3 t

    and the OUTER JOIN expression

    Table1
    LEFT OUTER JOIN
    Table2
    ON Table1.a = Table2.a  -- JOIN condition
     AND Table2.c = 't';  -- single table filter condition

    We call Table1 the "preserved table" and Table2 the "unpreserved table" in the query. What I am going to give you is a little different, but equivalent to the ANSI/ISO standards.

    1) We build the CROSS JOIN of the two tables. Scan each row in the result set.

    2) If all search conditions in the ON clause test TRUE for that row, then you keep it.

    3) If the predicate tests FALSE or UNKNOWN for that row, then keep the columns from the preserved table, convert all the columns from the unpreserved table to NULLs and remove the duplicates. So let us execute this by hand:

    Let â—„ = passed the join search condition
    Let â— = passed the filter search condition

    Step One and two:

    Table1 CROSS JOIN Table2
    a b a c Notes
    1 w 1 r â—„
    1 w 2 s
    1 w 3 t â—
    2 x 1 r
    2 x 2 s â—„
    2 x 3 t â—
    3 y 1 r
    3 y 2 s
    3 y 3 t â—„ â—
    4 z 1 r
    4 z 2 s
    4 z 3 t â—

    Step three:

    Table1 LEFT OUTER JOIN Table2

    a b a c Notes
    3 y 3 t â—„ row(s)from step two
    1 w NULL NULL Sets of duplicates
    1 w NULL NULL
    1 w NULL NULL
    2 x NULL NULL
    2 x NULL NULL
    2 x NULL NULL
    3 y NULL NULL â—„ removed in step two
    3 y NULL NULL â—„ removed in step two
    4 z NULL NULL
    4 z NULL NULL
    4 z NULL NULL

    the final results:
    Table1 LEFT OUTER JOIN Table2
    a b a c
    1 w NULL NULL
    2 x NULL NULL
    3 y 3 t
    4 z NULL NULL

    The basic rule is that every row in the preserved table is represented in the results in at least one result row.

    Consider the two famous Chris Date tables from his “Suppliers and Parts†database used in his textbooks.

    SupParts
    sup_id part_nbr part_qty
    S1 P1 100
    S1 P2 250
    S2 P1 100
    S2 P2 250

    Suppliers
    sup_id
    S1
    S2
    S3

    If you write the OUTER JOIN with only the join predicate in the ON clause, like this:

    SELECT Suppliers.sup_id, SupParts.part_nbr, SupParts.part_qty
    FROM Suppliers
       LEFT OUTER JOIN
       SupParts
       ON Supplier.sup_id = SupParts.sup_id
    WHERE part_qty < 200;

    You get:

    sup_id part_nbr part_qty
    S1 P1 100
    S2 P1 100

    but if we put the search predicate in the ON clause, we get this result.

    SELECT Suppliers.sup_id, SupParts.part_nbr, SupParts.part_qty
    FROM Suppliers
       LEFT OUTER JOIN
       SupParts
       ON Supplier.sup_id = SupParts.sup_id
        AND part_qty < 200;

    You get:

    sup_id part_nbr part_qty
    S1 P1 100
    S2 P1 100
    S3 NULL NULL

    Another problem was that you could not show the same table as preserved and unpreserved in the proprietary syntax options, but it is easy in Standard SQL. For example to find the students who have taken Math 101 and might have taken Math 102:

    SELECT C1.stud_nbr, C1.math_course, C2.math_course
    FROM (SELECT * FROM Courses WHERE math_course = 'Math 101') AS C1
       LEFT OUTER JOIN
       (SELECT * FROM Courses WHERE math_course = 'Math 102') AS C2
       ON C1.stud_nbr = C2.stud_nbr;

    A third problem is that the order of execution matters with a chain of OUTER JOINs. That is to say, ((T1 OUTER JOIN T2) OUTER JOIN T3) does not produce the same results as (T1 OUTER JOIN (T2 OUTER JOIN T3)).

    The

    Hello Jcelko, thank you taking the time and explaining in such great detail, though your post is not the exact answer to my question, I still appreciate your time and effort to contribute. Your time is very much appreciated!

    Regards
    Amir

  • No problem. Glad to help.

    "I cant stress enough the importance of switching from a sequential files mindset to set-based thinking. After you make the switch, you can spend your time tuning and optimizing your queries instead of maintaining lengthy, poor-performing code."

    -- Itzik Ben-Gan 2001

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