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8.Spark集群测试
2017-10-10 12:13:36 】 浏览:4409
Tags:8.Spark 集群 测试

Spark集群测试

 

把Spark安装包下的”README.txt”上传到hdfs

wps2EF.tmp

通过hdfs的web控制台可以发现成功上传了文件:

wps2F0.tmp

启动Spark shell:

接下来通过以下命令读取刚刚上传到HDFS上的“README.md”文件 :

val file = sc.textFile("hdfs://192.168.0.49:9000/dmy/README.md")

wps301.tmp

对读取的文件进行以下操作:

val count = file.flatMap(line => line.split(" ")).map(word => (word,1)).reduceByKey(_+_)


14EJWw9D9wd2DVh/1/1f21Cs4/kx32e7WM7ydB4Jl2zON9z2aw3X/bGver8EPhiBGvKHxsONVB/JuVyFhEuoVYauVSdmW5LVP4O90DAACs4osmSsN+3TUk+881nI0MsLISmZM0v5A5IimTCgBAZgkQJ4/jvePliV6/pG7Jzl0fKUAQsYDDMH8nU7gUXuzOctD+J6nlk5uzqAwAwnpfvhw58a4PGN/4yXJNEBWAyGBLrJDo2jlFSYJBRSKYcwR881/wGL9S5jwJknjy8Z12T4MfzcyoXB6XSvp9bkkgsvhSJLn8K1WLm8HPRBKDa8mh6L4geN5J3aW71p1aPHArOxwhAACQ3SKOFmVknOhWtiSZvg9Xknmm9OixCkV3OlNzY3PPvLs3Tm9bO4QSdqSlIivot7W3bOfJy73Hfu01ZejkfGcEk/b2I99N9jtyZuSlItqve6l24fLJnkQxZ+NfrHdXPHr62bP7yvQgbR0icZ0L6ur2uuvxTrQIOnN/qdD6V5UAAACy51YGcyXvcWJSy0udBjKYJg4IO/GyNwUAANzPzN58sJlvbVDgsaFPenbS336gaRFoon2lDVis62/1uWGEHzJLgCAIgljxWXQzAAAzKoMjtkIQBOHQAQCoLB6CIIiVhMukmWnOEcnK+vr1+KGYcyRWCIJYCTjmBAAySyDhbv0NQuVJRRaap6GxEb45gUzlixEEQRAxR++DrLyZLdtcIksgQRBEItC2CySGpRRBEAmPAgBmJCabw6CQNOXnMmlmZAuxmMMTIggitWSQAMxoHGFQXvvH3QcQxErIZb6X71N4zpG1M5N10YEa/GjAiVhp8QMAYM5Paxo2wA/BnCOwQhArqdiCbqJHRKKYC71Sr3y+oMEPlbDeFGYEIleEIAiCSLhkACBReSJpfMujK5VRCCLh0CiQPvoP9Wdn9/uYJ9U+fPipsjqOzGCJpQiCICJLJgEA9KaQChgkXRmokcOfKQzbVNdTE3r8mBFJHIEUQRAtfog0Sz6Hw0e0pjMDACBTLUSau7Df7qhmRBKHa0mjvK1P6Julutmqxw/Zgo8giESoxQ+RxhGI+CIJgiASlvaeJLbICkEQqciSSjbbeD0ijSmSWCF62xqYgk4kaA8Kzn95v1mPHzJbZOhmZJbAa3fBYnchgiBige5RzbGRKxVOujN80LIaDDs382w03fRj8RGsCS8727Y8k6f/Arc+c+fxMfc1N7T22t9Yd9yOveEpzMgWHL7YysrKSiASIVI+i0gw8AoxhwwABCrHksPhWyFWUksLuvYSBElaRUNRkDMAkCgMDptJ1P6BaM6x5DBIZlSOBEEQKV9nCgKDzeVw+QiCIFKRBZUMZLYEQSRhGXOX3gk/VI6Ix6K+5SCC5c6CutPx3mQAIonO4Vjo3huBwbLkmlOAoimVUOerZnQLDocrQBAEsRKz6BQgsyQIIgncMzJjGj8aJeOLeVF6LpiH51a3HAo0+LvD+ZWHh1w2awY2mALIlhIEQRAJzwIAwIzE5AqtEISPza0M5kqINL6AQwYAIoVjydWUnEy1EFkhCILoWkdtC2Yl4lPJZpA++mymAAEACOz79kmRu+7xAYBAZllamifVrc5lk6lO+c3zyqYknSkspVYIYmXFZhABqFyJUFsRqEyJkEMGALJb+VR3NmsL+pjRmGwOfYv2j+yRcLypZA/3bW/yHWWAHwt7bOzSPI7jeCNekopQiFQLcdbpzo7uNhzHT2W4MwHsIzNxHG/tbO+uPGqrsZwxfj7yTSg7hzc2Nfa1n/OSsiWRuX09LRiWk+BrSyWYLIB9zYMvzgYBAFhny182+TKFtgdLcBzHFZemMr21wx5b40filiK/MITjeGNlbtA2rhnw99TOTynwiYWpsiAhMG3zzuMZ3rYAEFxxoTsviu5S8OTbxc5GvGfm8pUzcUAky5KO987d+25lGsfPH00J5r2PJZEDNQM3nz65OdhbW33cmwccWURJhfz+d+q1QSpj/HBtgs619zU2Nba1DtSeSBBu+MqxtPLHGic+U7+4hNefOrLLhrneFBRzTkohjuP40OyF0gh74NllFpQP3/3+s2sDOF6aJEOI6aM/PB6rP15cI2/LKZ1TVsdJ3KOKK3Ecx/t78SAbLuhMoZgaq93jo7mvRcypr5aqDL/aDPFDtuAn5OKXH349l62xftSg8qK8CW/t7b/YW7FdwiCQaLvz23r6O3F8YGakyttw6MqUyBb8fYXVZ8+cwrAMd8vNjzPGjyjkYHPr+I0r8nA7AAAx2nLj7nhrAz508+FIhj0AWHlnjswM43hzf09P/m4ZaZ37kVnBRyrHu3pxvAGvLI7dLiYZmOIc6qI9zBg/0pjjXX0L1y/imqEwcXRe59ilz1cu4zhenBursZiw6Oat9lTdyyQisqi4oKDYgyVpXqYfy5wTtDMhaseuIwcD1xrxuM4vpop89f+1dPDv7e/xs98w5MeWlfRMTfdN3P9WNT2+eGe23p3H0HvF8PxEQbAtCIPaLk4NtDe1d7b3151AWGQAIEvdavv6k90RAEDcd9bXFko1JaZswzqHz0f7RVYsLkzgY7PjOlPwUmoUl+bGh3AcrzoeaucYUDS7dBFvlE98php8F/w47Cpta67DsOwoh02pQOJvK+0ezgqyNwMQ2EXUNle4acZ5yNLM2j48OSKobOnKND40PqLzVXZCSc/cwvQwjuM1p6JdZH7Y2PV5vKFn9NMVxTvjBxwTi270FRoesG/wm/48zw3ngaEpBrSmEDpHV41OD+B454XhNlQGLCS0smektxNvmnu8MpgrkUTNLOKeAID44Ap5qoNYU0G65B04jlfkeFustWBN8q7e/N0yUqYeP1Znbn9dGUbfgTUpTscBAMPas6qjCT3RvTpXlHGqren0Pr4FGQCYQtmJ2p7WtuamxuGm0ylStu2Z6487d/IAQLKr8s5gugiA7rl7fqrZxXwLdDCj8s7jyQ6bHwBWgSnTfTVS7m/T3zXAj03K0re3j1kDAFliY80lEqxjCgdaS5yZJACQ2trqb0iUbK/u69/v8RHAevxoRWHsaxgvittOAhBuj0DRlBPnWscmJjvyAmGDpEVLi2W+ADaFE2NlBgPc7OPLV/AUTZO3FX4sth2TDzaEOwAAk8vlMZlge2Tu/nAUB3jJdT+N7jGJn8evl5J5QAs58snVelvt09fcwuM2M1NIftekXkNdyUE2Jg5iy0oUg6dcDXvtLo2PNsEPhYOWNHRkx1IJwEJ8a9qaQhxMdRMlUTPPJgy/m02awuKg4uu5Y5qq6lm1MlOgaz3TRz+71lxbOXFl7Ag7e05ZvfaAceenK/f6UnSm4EQdv9+XBzpTPOhIMby+IX4AAECY2ba8hp+VxTJkzSsY7ocU400BXAoAc9/gczxl/cCs9e6KSQPVZ/lSuNKA8FgUPdwyOjnZcdYZMTVgbowfAKAxI3omdfhJbF0YK3NlaE3BYzuVXhir95ECEGx3HFXUZ5qv+/xh2uZ1T7bv9wAAJlskFXIJBqZ4NpitPcwYPwAgdkiVK3D9TIwwKH0GzzAqJv5ivGCzlutdxcqdNXxZRMn26pbmYJlo/XFsWUlna6aTc+n8lyczIxoGm+MN2lxW7uynihyeMKht+VKlizlZYHeqsSPAiQcATFvvhpZGXzs2ALDtfGvrz9kJgkuG68K8PGs6GqMiCpXfLSXzgIuW6UzBS6mZvdESqZ10FKPz393KFgHTO+WSsn8jfrhuMXXdirV3XJUCBOI27ygUTStvH5hU9OfFO298arrYobK5M2o7DwDMpU7lDU0hDgGlk23RHk7FTS27Ek5c/+l+mgBYwVk6X2UnlCiWO5O03UZB1Njz5aMSYLjGji0MvTt+qL7p14dKDQ/46MSVydLITc7WmkL7hSEOG3txM9+WCUDxPTaoOmu/fW9NX00WQgWk7L5J/GgqiAebDABW9vb0tRbMDAnKUNRnmh8a//G7Ty5NTk5ODpzdFW3LBFZwlnKmxh7A3u9QfUUWsqt69Q8vvv7Ti6b0IHMAIJB9D5T3nj4gohJIdKuzHb1x2y0kR2eejx0UgCizsbPcVwgAwqD0qbZCJm2LYYa344fstGO4uxr5jUbeDfBjRmYEnH32Rq1WfzVeEkclgEMy3noq0fAd2u6rU6vV6tdv/vKHG4e9bQHW40cac/z+8+/Vr1799OenBqNMvJSa2df/b1U3XrnugQ4q7uHbt++5MnCKDGBu7dF0+XO1Wq3++e/6dnYr/PC8auQNqKGreVZdvVhoDQCRpatPa+1N4eehqtoBjFq0rfFDpjNZelkwqWRTXblfhB+6OB2/+uef3rxSq1+9fv2XZ5fifUx1E7bED5ktKhl/plar1T/97dulUhvt0xvh59lMWRbWM14ZCtlzyuo4UVj20uPv1Gr1j+qXeHqQkSmua9tZm5SabyaOGZbiLfiZbfAHrScc9rZlRrR+/fe/vFGr1Wr1jz/+OFESvu6ZCFQGy0DmawP9ngNfra6+XDTRzsLb8BOHy8/v5QNASu+3S6WIMKht+emPr16p1erXP/789zvNLMa6YUACxTZ+8NZTtfpPH8/W+fEBDEzx8l6j1oLvhZ+rVTEmyv9L5FZ+zxA/5jZenX1y0/hpwZMdxNiFl/m7vGp68TgxZ80rfv77s5kCRBjUNtoQwaQB1xrrGDjkZQMATFvvrj55kEwIAGCxDTtfn7Szdv7pg8GaQ834GccdZVpTBOfrTMFLqZE3oLovCfvMlU8at8Omcz9mJIo502LtHTP0X9xIwcyz1Z//KDe1cIUudsB7h7UVgSHOOIPnHDo/9/mLiZrUmvrz3vHFLz5vlAGAx16dr7ITSlobUhy159ukLN1p8ICt5n40WocfUcSR+2PlhgeE4g+3xI+xKd78+JNarVarX//8t3/MHPDOxGuP7rAA3dzPBvwww/DR9gP8tet51cw+fPP6lVqtfvXmp7/faWblTr2YP+XCYrFY8cNffjdyxAkIXrXKKzWekN106cx+L7O46tXLmEtEyuXpAReEA0Ra+ImpH37+6ZVarX716i+fX08L2wbk1ItfX8nZiU4NN23jMwFAGJS+NFKzoSJo5Vm1ola/+vHnv/71p9fqP/1htm4f39RhXM/Ei8ONvwN+yGyp0BwAiI6RAxNd/ubUbTvLh5qP2VEJAMDm8cjCoLbl0WwAknR7jUKhxY/A+ZS8/4CTBGCt/TWjMPa3zJQnerEEtkd6bqlUN1oPh1lsPgtwqOdud3dN59EAAPA+3N5XtlcEwC28rm9nLbLGlV3Zpifx2M4lI0OVXtYAQGUwzKlUcDqxuNIbSgfzhKq/TaQCw/pwdVNusD0AxONzGvw8WKmyh/X4Wdl8Bi+2cl6l142FQ9GOJg56K34YvP34UNUuHyoAUHm7T1QX7/IjmQEQyRZcLp0MAEDnItusBWvT6RvwY2gKcWLzpDzfDoCdPWaIn/liXSudPvpspkBrt+w5ZVt+Xnd/RbBMYwoNfh6/mk8wB3po3spAvvYsj0OfqNp9DW7qcmJUUZZkMDBmjJ8Z3BfW8GPuc2Kgs8COQQIAMy4i2OCoDlndKgONlsa4JuRdWLyvUl1M3bb+4DV5Zty902nYfV6Hn96aFB7o8GPpfq6vJdXOEgCAaiHg0s3MAAgUS+k2WxEDAIBAYXM4VAoBKIzk+gs1+/2pBqZ4MXJYew+/I88edhiaYiN+FpoPGZVz5/DNUex9JpzXRMme+r4zba2HLXaN6Olp9kB0/WOyhbWDM8IxjR+9V3Dy5rX4WZ45Zc0g8myL6tsCnQUAAJaOJzt783w05hYkn2oYurhQ472769JQW0UO4pl9+4f5BHNg7CjWmYKXUtOLx+naXCR56Y/KNBZQPXbNXundiB++X8rwwu21d9x7mGXtfnbkjkqlrIzdfFKfheQ0yk9GuhIBANixR84PX710zmVnz6XeltqTjt5pK3+7nswEml+GzlfZCSUta1/rkviLLy9ns4HivGN4tl+LHxJDYutobWn0vWiMH4J36tnZesNKRi1Y+GNdsu4dmxEthDa2Uo5uQnCdKdBLd/sieVQAAHOBNRc80hv6qjKkJOAWXl8ZzJXwwy4sdYTTgGwb0D4xlOogNvc7OTFR40InAgBHICCznUtaGw+6SQEAKOYCLt0sQz/4Bpy8+bv1sQBgk9b5YqB88OrsTkeAuOrVuWwA0Z6KqcW2aA6BEphZfi43jkUyAzMzcy5PM08UVn791uULQ9V7LTXTPbY7hma7YrcaKX1r78fMMfLAaGuxiPXbLD4wwA/X/eiZYgzDSuobx85hDDKRzkWOVfWcqyjGMKwwK4TDtDk+cEGBYYVnam/cmdfih2qZVNakqCjPPRRtRROm1nYpKsrzC05MLN8sT/SyicwtTXF/ayEck+XPn19M97EFAEc0XzEycBrDauZe6vFDd96juDJXje33tTSx5Ng2OGd0uAnDsJLCjBB7oRlIs9qvyBuwtrGplgQEgBmcfX6y9zyGYcPKe5vhh+uec+uGAsNyd0e4vedaSSP8MGVBiTlYzfJ3P082Y/siHGlkACI9KPusoqP1BLbTFkAoi27saCs4no8VnMjN2olwAAC8D4/dmSlb+8zdgB+9KTyZNEufdMXsZCWGVY0/0ONHGtNz91ovhmWE2AgJ6/DTkJFU1qSordGYQoOfJ9/fbj6DNcgH27P0nLQ9c/1alffaFBgSlqOYUJzFUrZTSBaWvvux0uEbLx4rsOy0JHv39fghkukZpX0NtaUYhh2tqNGsO99CZAt+QkqqDNn0W1Urjqz68uIQlrc31psLwuC9mQVFbbcf3mmtxHZ6MtfjB8A+8vj0hUYMw7CSspydLmSiZsDtlrIuGACAahmcfOh40fH8gsKewZ5EmRAMTNGmn6jhujffutGv9QpJ9KHc01WDdz5ews9iidspAMC0iZ68qMAwLC3JX/e16NsyPxwmfu/lRQDskCHVxXiD5a3OOwrrSveL1roQCTPPfhhNN40fvVecm36ixc/c5RG8sLjsrLzp7DZLzVWYO7Bz7dlRAABA9dx/7qsng/50i+orT3pyoixAlNx6e6wNax4a05nCuM0FXsz563O9WEm9/Jlq5F3mfuwSizLf6gpAD8443Z6fRCOaAZBdEkpUn0z6ApTOf6YoSrQEQULLylQXVtfVq/NVY/wAN/LswpV+7GRNxycr41r8WEf037rXHqGbjLPyPpSb17f0bLy9LDlQAgBAFuwpri2Jd1krhWXsBdVElN79KZzE8oXr8jTdF/86U4BXWs+FwRoMw7Az1Vm+YLkt5nxvb+VprGbu5cpgrgQk2SOLl+uxosqme/emUx3EmgpSe64Ew7BTeZFcgxYMO1mas9OFfGD0h0dTZzEMw7Ch6fmDISIAAEH8pVerKyN5EtDjBziyneMP7/RGO3IQz7MNnaWnCrH8Y0dy99nxAAD4vqceff9pRZK3bhRKeLBjqGOH7eb2fxt+CIzQjDMVWRHmv2jvyOYyWvlmYe+PoiiKRrpJeWYAAASuVBYei6Io6iICAODZe6MoGh0dHR7k/pFuWT1LYh8Zg8bGePIAOIgsMgaNj48PDvB0EG1cqWNadAuH8HAfLpMKAFQm1yccRVHUx8cnwNVKYzgzIsk1GEXRUAcLk7Wabu8fhaJohJ+MTQMAMJe670BRNMpbAxIq3zo4Mg5F0SB/r+3WfALTNiTYkQEAHCQk0FW33ojrFhGNorGBbjbvuWODxLTz9LDTlpCGyPxiUK1C3KSatVIMnjQwEkVRfxEAAMXKNSAuHkVjIvWNL/sjzyAf+7XFclS+T7iX1OAmelPY0ShECs0tBEVR1N/fP8rfXmcaaQCKomiEq5BtJvUM97HVXgzx8XcSsCT2YdGoxhQyKZfgUvDJg/OJwSi6w19qMCcpSeq6s1SqH54n0y08Q1EUDbYhEekWDqG654oO85cI+R4+MjYAUJn27lqvYPK2BcegKIrGeBqW/ddK5BKCovEh3vZM4LiEROpKgfrb0agCmZuThAwAErcof3saAADDMTgWRVE0wk+ocQsiw3p7oL8zV2dajVdoLghgYAqhwUYWiVuEziu4njGx+pv62Wi6qEwn/zAURcP8nfQL1+JPK86lv/c6aXZYwcCVOm+D0+2L+6ZyIvhrVZ4m9Anf4SkFIDHtXGRic6qNV4SNiO3kJhNQyXqv8PPzC/expQmD2hT4vrAkNG6Hp51QfxHW9j0XFtsidaYI97IDAnGbR4BMyiUAEHkuUSiK7vDVmYIscXKTCQxmrVmOkSgaERGxI2Q7j2h6WdF7iOkY1zfbgTJpAEDhSgM8nQHgI/eg7dZ8IgCwZVEoikYH6nyVJLRzkYkNTGVhH4GikZGRkcFumpJbhWfNjp13puteKM8+JjZe8waDnLkAIHRMauysCFyr9Dy0+tJcueNaI2BGFjn4BuiqsAlTAMtd44zBrgBaN4tDUR8fn2APayoA09YHRdEdO3ZEBLlpSs7kbQuNQVEUddN+G2pbMDQyUMimgdQTNfAz3V2c8BerHWkCAACBE6rtgNNsfcJjPKUAJKGd5444FI2PddPtSuOIE4cWxmJd1x6FF1qsetRg2Js3FpFv7WRkT2OZWwZW9XQmebxrw/5WfYh68N8tfUfwg/5TTSEMalPgYb/d1pwP+tfIKffF6tyms9OmpB+G/T+rD/j575ZNcnfnwd+yh/Lvq/9UU3Bc80/nutN/XRyBD/qXyi48bWDacDHK2xV2YmRhamTXdpN/ZPonnZ5QGsn44iQn//wR4wPwvb99fJEP+PmgD/qgD/qgf4E+4Oe/Q75ZysHcdw4bJkptGNcE6BGFZY/PXlFenjy9P+CfGGTnP0Qe2LBSqVRONURabbGfluQalVt2yP/3ih70HvpP7Qh+0P8xGeHHrXRpdXX1zePZRC8eAIgdUpf+uKrRD89mEvStF9NWv3738Ng3q6urXw2kav6CJJQ8++Fv//jrm7ZM41F0m5Slb6dSADRrdv/6j9XXX6myNFNgnlVfra6urq5+M3YYTMmlYGZ1dfWnF8u6ME0Ur8Tyx29WV1dXTe4j0m4f0epFrhMASHIHV1ZXV//0VLl3q3h7KZdff1uTYqOxxReaC9xvNbVSXnvB1dXV1T/fOuxtC9lzf1BNojIAAFbu1GTpnoQSxQ9/0x7y/UK5mEPhI8UPVldXV1eVxb95/Mi3Ka569QXuZOIPLq2f6221ulJler/khn0/htpgiveSfWbbA1wfks4Jf7FWqhf45nOlv0RMW+/z+HlvW1NBtDbInI/IH6yVYfXN4/JEr43rfTbu+1kv3W6P9yz0uykOv6X12/eVS8HM2tt/r2kwSVTeW0zx76nE7i/0VfhfXZb/NK3v/VA44kK8V4efZLylRLah1UHcE7vazrkItZOZkqi8RTzZ8IAw/IExfhhoWc/k0SAAondK0VD5AQaZIHbM7JCXe4i8CuvSEQAA7u6m5eXjpps4Io2Ziys0+GEJfZsH5eHbLAACG+9dO+SxcS2cJ75oAEugBmdVKE4lA4Ctd25n+wmEZWqhDtemfGCkfVhTjZ3P3X1UEwgAFmjVxcWizWiR3a+s1uIse+6vf3rSdSyOQ17Dz8Qp17VjLXYMfXIv0xkAnEuX7pwLM24EKVxXz4BwFI2ODgoPR6M8PwKDRYCxmnV6NKGPv8zFD0Vjw2VSriYoH1u0TeYVEBMTi8bH+DhJSLrlXjHBnjwmCQDMpe7RKBp0vHsz/DQ+UhtusCOzRT6hMQZrcgDW42dtPaSHtb5HtGYKoqUsIMQ7OAJF0UhHlnZ1lqtfjKaEZACwsA/UrNPj2ceEyphkuo1n6OHm+ZfTpSga4ydDqOCEv1g1GowmMuw8g+P0pgAAYMrCwnx9QzTrg+IjInagkSEhkbGxqL+9BQDw7L1j41HNci8SAEg9Uw4VTkxPFB5KQVGfdx7GDu59eb9Uv++U5RiJomhM8DYeEwAsHALSSxrvTjfpV74JnPxRFEXjIjXrxwDejh/h9ggURYNdhUQzAAJVai/zCAiPi0cjgz34VABz6/BwmWcQikYHfsRmABB41jJ7KZcAQLL2CXC1onCQkMiYyumnE3WHUDTEbmOsaArX1VPm4htnuPLNOQhFURSN8tQuWfQJx+R3P7tQgKI7PG2EZP0iQHTHdiEVDLwiJspfaGoRA9c56MCZLkNTgLl1OKr1VRP1jcx2cjP2CgCquVjmFxQevgNF0XAfWxqF6xoQjqKoZv0YAACwPaNQFEUDHDQbMLTLveLjYl2tGLC2djfG10FENgMzMlXmvwNFURSNeLevDtPieSX24oUf8POba2v87B29cXOgDcdxfKdBQxpzpPNUuo9+Nd9b8cOwCjrfWhksYAKRtedkbcEONxIAib/tRG1zjMfaDpe0zms3i0xHNjDEj9D3bFfTAU3b6FE635rtAQAgiSwqK/DVhuP37Hz42UwPjuN4li8AFTN1QBAAACAASURBVMmqrD/iZw8AZKlbRX2tzMoCzEh2oQewtCCuFl4095zSnr2Buq/I7bUr98+4AwAjqmxi6dRmi0eM8PPw1tRE//kwxMI0fqIanq/UaiYCeTnj1yqNg08Lg9rmZjsw/NrTez2DM6q5CgSAwZXEp2EYhjVemD4eaQ9IwszKtb5z2NnzdT0VuRw6EYDilVg6d+tq28lCLC8n3t/eVbvZpbC2obM0w5NMlGa1X+mqw8oHr78jfmgC2/j9pXMvXhv2LA3xY7AbDO/pPO2u21WkNwUzvOmuaqqqBKuZurV8wgvYshL59FhryYnikr7OOm8rBriV3tLsUoosVT9qdKFabI/eXzl845ulVgzLSQySMTbgx3JbTJO8o6wYG1Fez9JuSHJpevbDXE99CYZhtXNfvFSO9IzfujJeMrLyuH8fB8DKOzY3Dys8VdrZcNYT4YBjdFFF/eLSYn1FEYah2q2PgoDimqott6MY4oer2exS3drRVbibRjKz9N1V2jT0aGlYv+/H2j8Jw7CisrM9VccQjW+9DT+24VlNQ0sLDSiDDEAXp1ePzinqjudjvZMz2cEScMp98WqlrRyr6+rtyIsBIi08Fy9LD6Lr418IZXszc7qWXi72V2DYPs+NK9xM7fsJTMYwDDsnH1Fk+QKD6xOfVjP16OvL9RiWFb3dhqrbAlXRP3PzXBwQ6d4Zp/qaGk5g2PH84tQo2UYASQKTy9rHDUzBCjjaf1Oh9VXxxj3nPK+asTkjr9A0OPfvD5w7nY9hafE+biG5fQN1GIYNKvqjHVgA4JulGJGfwzCsoni3FEC/2eV4QWNH43E7R/ezC/P9GIZhheUnM11YdI5XWs+g/CyGYdjp4uwQ0zvLqJaByVguaruFE3zAz++krfBDorBtnV1dXV1dXU9P3lboCLSzZ3kk3nZtoPpt+KH7J1f1VO7h0syAwTvSIi84eGrs5p2i3dZYWx8W6kgAp8p5lUp1uTY5znqTNCqG+LHKG53qrKxsGm/LFCEFk1OVSWwAcCt9pB/NA5qVk6bYOXNfqY7udj7V3ZuTUjKjUh3dIyvr6d1lLwQiLfzE9E0Fprkj09a/sf6kE5euww9ZYOt05oJKpbo9ULrPQbDZtgYj/Cx3lxRUtFXG2+nx8/LLpyqVSqVaOBvnTU/p1e8MhZReVfNOoysJg9rGm0OAflZxLTU5YXTWaG2+qODS1ab9lkjCzLNLeRwg2wY0t5634jE045CTcszaQrNP3PncxbEzDjwAMyQ8R1GfZe5RvLjS60cDZnLTZvhpefE/373Q7k2P1/5oU7r07Wb4MYiFQc9tnz0cxl9nCmZE45XZKg8qUFM63tw6jbBlJYoLZ7cLCBaCnPM9+0Ns1uMHAEwNvv3hc02p+ncCeKQ3dBXt4hHB+uTiWJFmy6FL4+PXp+PYRABwK719syIxMqu3NksQWbGyVKrvrppZ2hR2DmGhjgSTg29OuU9/Vm8aXQXACD+SeP1W/6nuUgs6GTYZfCOIXZtHenfZCwHeZfCNEpSOj57X4QcfatkfQDGDqHOTbYfDGE65L1a1W/2V8iIT+AGArQffTEY9AAAA6s761UfnNKetG3zTBIAghxd+cbvegcJJLFcMn0LZZCASzblcC5P1wWjwjR/UeXO6kKv1VZePNoxq8LxqZieNvAJA7JA6NN0VbkcFAKAKUqq6G/YGAEBsxeipZFewSb75+vp+SyoAsHg8GoESmFvXfXIfn2RGogkKWkfR3YeXvr13wgEAiGyepTmRKE5smxsocmQCAE0g5pneqW+4GXkTfcDP76St8GMo18JhPMsXgEDLW7hcHmX4p63xY8YUZVa17Q+xBQAgs9NO1x+JkBEBgGudX9NsEOhse9nCo88bTad5MsSPOLquRxPdC8Dm2HgPZiKM6Zqy55St2JGa+kxthDqXM/X1rtbrPoMofkkVtTmohMXa03q3IdOFSgnv+/zb9iQA4O5rXHzaErXhutqrG+GnLVPodeCeoliQZ6r3k9z9zTVtEhfCvr6VxgSjKwmD2hS4L0Bxv3Ivqo0jwLYP6L729Su1+tWPPy63ZQqRhBllgw8AID74cG+8mA1A8UosxAsTtfWC59W29OT1q1dqjZbqLIJrr14slMJWcz/rej8au26BH4fU/m//8pP2Hp/fK9Iu7TTATxiu0DSLO5vfrFTZs2UlrQ2omAPm/LTG4TOJnuR3w49h78c7Ez+bEcQAYOXNLLWk8wEAXPAHK9om16301lJpdHhae0UaP6j01lIpYkZ0OdT+w/d/Vr96/fMP906Ey4i/cO5HJwP82KSsBTqb7QhlUmEdfsxI7vlDP6vV6tdv/vrdVS1yfil+yvDccBlRF5+Q6ZSrD3SmXGxzez/8rIv5RqRhFz5/o1arf/qr3itMz/3oQ95x3c+M3v5Brf76pXLXNtNrJIzwo48EiPjgw717HTZEq9OHatR5BWHdaD9dnH4Gz4uQAUBw+VjnkQhzvSk0ItLCT0z+4ecfta747FpKsC24FD5Uq9Wvv5uvSuZRzYDMTqm6+I1arf75ebEX5b03S37Az++krfBjbu1mpx3olaa0XSzfYw+80OkX13cbr4nZGj887yOd7cVO2lg51PCsMw3ZsVQCcD4Kq22rC/BzD3bWLAqiWh/q+Wl0j8lSGuKHK01u7a/yYJMBJEdGbxRHCQAALOxDI0O0oQOknj7aJobn03p/ojAyvqAG3xcMAALn+Mams7Y8CgCBt8070NOGTgYgUr2z6zSReW+/+POzaz0p+07feDCwwwIAgJdc93r6yCbW24AfsC+YUc5N3zSBH+uDi89nkyV0AG5S1/XeVOP5JFP40YS/4wK4VN7R4ufZcpEUaPbBbW3nrXj09fix2Fba1YF5iwEAmEJ7hE20OzJ3fziYBYK0jjX8mFuHo6guZeE74cfl+LDibLLmk8Q6Mr+1OteaSQIAJuKkSxO5JX4U0+f9hUSWEMP79gV/BNbHlu43etCBl1z3+nGTHj8vOvVIXo+f7Xtruk+lCMggO3tzpCBAU6it8CMMalsezWMxyAK7syPTevzowzyDzhRhsTH2WyXYMMCPIEof5nm6+5QFnQQAwqD0K+0695BEDa5cyAagIu49M6Na5Ig9axR92U5brCN7G35WtWGelb2FQKCGZNbXHN3BIui8AgAA4hvuNKSbiCENoOn9GEe89sWvKqs9wMgrXApmnjaGak9Zhx8zMk9szWOTAcD19KW+gh0mw4JIovLWTMH1bVqeK0O0vuqCsNYfzfOqmZ018op1+KHwdp3pbMkMBoBdNZPFu5xBGj3/h/uHrM0BQGRryyKQffeV1BUkW1IJYEZkI/Y8vsTNwRI0UZnHO0NFLIqlAyKkgT74t0n7GMfCMKkP+PmdZIwfnwP9w6P3Hj+/enGkIIImDkppHxiTy+Vy+XV5dZwdD5wPdT9o8jJ0Jfvk0tHLd759vNTd0RrvDGAbfLa+beHxn5/cmCg/4M+kQmLbyvl9+plDYEt9z7YPyfv6BuRTpQdc2B+5Hq3ukMvlcvno7ctDG3tdAAAe+/oGBm8//vz6vKI4UUqisvYcax0cG5HLl4fqo7ST34aDb7bBLUNTcrlcLr80J09zlwDXOuS8XCGXy4fkisK9DnQirBt800v7FWluHVU//fG0XC6XX1JOnYt13VAmdlj2Gbn85ovvHs60nAq15uvwA2znE4/+/rMJ/AAnMrN/bnlcLp+fkac7rxuNMIUf2/CMCwvLw3J578KKFj+3p3ra5WPjIx1F+y0ohPX4ARC5pgzMT8vlcnl3c3q0I5HATay8ODcj75y6sYYfp9wXq/rGfR1+iE7+B+rl4x9/9/PzZXn9KW1iXbHPbsWVm2PysggahWIuTC/uGFAMy+Xy6pJMa+56U5jAT9fwUH//4FD/SEuJsyUVwLpw4dHyuLxzYvlvT7X4sdh28ObTa3J5W2FKMGsDfpjSwPMDYyPD8uXrEwnbNN8yW+KHJjjYNn6tf6BnQHHj5kUNfkhM2+N1Fy5MKeTy/O06U/yCwTcwdz86cUMpn7o4hh+MohDNAIAmCO5fuCSXy88V7ULo4rz+hZtyec/QhYf3LmjxQ2Ynnm67orjQ0XrYFB98iuT989cfv/xkcaBfvi/IFH6+X+jplM/MXajZFwhA+Mg3bWju0uiAzisAAEAW06G8Oy+Xl8dyN/TthEFt0xf6uvuGRwYHzudLmSTgefXeeDgjN/IKtvOJlZfX5fLGnAg3xjr8ECh2ofvPd/TK5fLpa1Mpdqabcbp4x5opgO6wp2HlptZXeRuzKfK8aoYVxl6xbq0TUeCxv2d8RC6XT480+InpAGAXXq6Yn5TL5R2lO7gADJ5Dfk1v/9CAvLenvGCvUGy9t6xRLpePXBwfKc7gUknmtqFlrb1yufziw+Wz3pssIHrb4Bs3tmjk4tXnj++NDvcf8NnsqA96Hxnjh8FFdOIzzQhEsqVI+18OHYDnXTfQtMN49weVLdQeYSVlUQEo5poMkgiCiLgMgixv8WqNzCi/kS7Fp0SkCcuqySCJIAgi3GRlL52jL5WARQYACp1lnCsQgMwS6S4IANqsrfqMnEBkWooQBJGKBbpF1GZUC57QkrkuWhWNI2QziEamkPCoZht77doLIgiCSIXmFBKY8zUJXgHofMSKx6LTLSx5xjOuBKKF5hyBxYbaSKByLDkUAAtLAYNGs+RzKAD6xK98Pl/AMScgCTOLeAgfQazEHF0EewrdgmNBN3gIbYpPRCLQ5A/VJH4VCoVr2WNJTDGCsLXTx2SOSGqYhZLK4Ep01tbnEtV5gpBpZmaYA1fApm00hRmVY6kxBZ1jJWKT2LKSFny3qwuCWPHZDE1R6VwJoiuVzkZUntRKlyeXxBHrS6h5XSQLS5EVgliJ2LqhHzJHJNK8KyCzhAIWjcrgshgEKksoYBEBqCyeFYJYWVmJhJYW2sBlBAaLL0UQBOHT9Kawkm6ZhNMgaySAJsWnLsel7ieBWJ/tlMYWIAgilUolQq4+e6wmBbCVlGfqPgydqyIIgnAYRAaLw6SSQZ+Z1yn3xQvcT6hzMwAzMtVSJF3zCs2DaV1LZLEx/JowqE2Bx9nYGWY71dRaI68AuiZ/pyWTRiBbiDR21iXKJFDofE3qWAl38y6AkSk0bqbxVROjXjyvmt71XmGc4RSAQOUIxJrMvLoKQ+FJ1tqBtRSfiJUmlaouX7OEq7kpgajJzIsg/E1DwJqRmFyhgL2pExAt1l7RW1I3f9Av1Idtp/8+QhK0iUP+vcSWlbTgcZsnX/mgreSU+8L0pN076/9mzDeeNj73v7ocH/Sv1Af8/PuI646dSPnoX12KXyy6JG5/ynb2h+/G95IorKYmxVT2vXeWhX1qTooj1fSyr3+ZzK13ZX7wiv92fcDPB33QB33QB/0L9AE/AARq8MGqo3Hu/9TvQ5owpajqYLDj+2RKkXpW9bTGigymypJanlws/M06Rv6HH3z27befPTisXwZPE6ZUtZbHeP4KE7mcXzK44L9C1h4H2tuKnNb2vWwvu3jvT3/4um6f7W9zA1FY58gmYYuMFdv0UJPSnk0GAOD7pcx+/I1arToRbld18YVarVarat8a88Yie2SzjMhvl8CpaOS+Wq0eTTf9d7pFzpJa/Wy8RMT+p3ebQgu/uFX9q8Yb/8tEoJofHX6qVqvVjzrc3n74/yH99vgx2kDwbyEiLTwXL0z0+qcuqzRYYvuLhfjgCrlRKnvDPa2GSh/96+tvHqlUKtWTHxbLrVw8yyZVXz37VKVSzchPelhaEHc2/8/Pf/hUpVLdnTwe66O9on63h04824D2vq5AWyYAkNmyIy0zH3+iUqlU062YM5sGAL74i8Hcf2aLIUptnHrw8Os//umbTxdbQ/kmcuACgGfVykyBQRZLBpJdWX84yKhh3xAg6hdIa4qHOlPIErSJX7cWLX36Ze/aQisz5s7CurOoz1ozH1mqftT4Vmtq9/34Zqk+++KpSqVSffLF9YEQVxFy8srr77/4RKVS3exA7cR0GTp579mLhw9UKzd6Kg7YaRat6FLTGl5Q6lqx/HBRF1kR2LLwX7Ta+DeL+WawG+yXKTj/5bdfP1WpVKpPlfKjLqIN+5Nk6GR3GYtBJtMcjtZN3FUU/AalBQCAZHyxJOGfHshxnQxyN7+fdp+/9Oizr5/L9wMAiFwKOmY1G7+fvPj6h5tdUu6vSOC7idbhhxeegWEYti/CkUQAIDEcg5IOYxiWEWIJAHRxaHSQEAAYXJ/ISBcBC4RByftiktMwDMv04wMwhUGJGfroHRGO0nf6cOL7ZWIYdijZRaBZ0U1GtgcERSZhGIYdTg9yFJMs3TMwLCsrK2NPhAPHxGCxxC0iOxfDjmRFe9lQAIDvl7I/Kmk/hh2M09Qkvr1PWjaGYVhKlKtmKRyRbBV3EMMwbJevpQY/dRUleblYzsEkewsjUxyM8yATTazc4bhGYRiGYbnJ4dtZZABh0K69cSkZGJYZpfkgFcqCMnMw7OiReG3yTaAyrIN2puzffwDDsLQ9cXla/PCj0nUlXy8ysj0gKHznelMUVc7MyxPFHCBQbX0TcjBsZ9X4ZvjRJdsWJCheTjVm6xsd18yzispcSVr7G80S2498O5aWG/z4ACbwY59Y3121X6T9d9Nwy6GPaAAAVm7RgTvCk9Ky8aU/3r9wDsPSAiVcADOu1DUobndWVg529EhSqJPQxutgTl5udoY2jhzPe19qeMI+DMMOhliRAQDo4tCk9KMYtmtnbLSXzTt+bzNYue26bFoM66CdKQn7D2AYluHNA+DaxO7Nqr/89aOpGm3ecQAAalBW5UCxUWKTX4MfjSk0ObCt3KID0QODM3gqimFYblKQI4MEZGlQdKgTHQBsgw+n+LGBYu8fn1Mz99Wf7jRjOQm+tlQCLzyreGjpztVu/DiW4a7ZArceP8KYAxiGYQdiXAAAKFyv6JQjGLa3Xoefm61uAAAM95y2J31pSOHUrCbvuFfa3PLMbnT/5HxnBJMGdG7i2bZ+TYLtjfixDR6eHevqUbwjfhyjD2EYhuWkeEi5ZiaC7myUgVfk5cT729PAwjvpIIZhOfvjbQU0AOA4RGVjWMoZ+U+fNrqQGI4+Qa5SLgBIfGPCtyNkA1Ps8tVYimLjFZ11BDt65HCoE9vAb3n7ahU9aVIa0z0q2kuzVs/SKzY689h8dxmLIU0uUPThB6w0ARQELsmHMAzD0oOEAExZUHSIAwcAgCkMijbR4LCljrsPYBiGpcW7M2Ete/ds91kMS5HBVmLaeKXFegGYcaUe+3MwDMMiNK5nYZ90UGsWoxZMV0HMke0pB3IxDDuQEoVomjBh0EEMw7KT1kq4Hj/aFiwrwZdBJTiE7A6SmQ5pZqiNmziBQPE/UIUfS/itEmwbygg/gXmK2a5jKIrmHd0ro5I5yI7avt7jWWjj5U+qUUS/N2XNd33xjz9dOJWNYvLlj6tCgcaW+UXoYxe6SXlEACCZ+6SUVeWFbrK8Vbyv58GFavR4detgcRIAADCj8tru3Jk6oY1X6Liz+tLlRjQVK7t1b87k7nGujVt0LJqcntXWUhdgzwPPqoefL589jJ7pmVJk2gOAPsN0TfdgepANAMSXLfbWHkZR9MTJVFsiLfz48JWxir270PLxOzUpzgamSK3tHS2L38A8rtvpidFWFEXR3UePZofYCsEXv6eaOnEQrb6w3LPPFnR5xxN3JTe1texwFQEAR5xw4eHD7pLcJBQNDw06XIbnhkel1c2Pt+Row2KuFzMqr23l9liBKVMkijnmloFV/SPFh9GTisdvw480c/4P4/WZ+kaHbeerGGhzOT74Rhfb2Lf0yje9OwFM4Aetv9p2JERTQqfs4XsX6zytdDZhSfzDok9Mf7fccQRFw525TACiLPz45ZXlmqxUND4uwNWKLbSLisGmP32Zr/nmdyt9/M3dc3no4ebJxw0oAMUp+sRAV8nuJLRt8YmiJOEdwxIY4kec0H7n9kh+Bnps6O5SaSAwhd4hOwoufHZXjmnzjgMAgE3c8TtDpwwv8mvwozGFoz4GpyRq8Oad0TI05dCxyZ5zjmLz9VuggCRx8onBhl58v3hKGxbTYnvMvsaZq5NVJ5L0YTGN8RNZujzceARFDzQOTxSGEqw809p76w7uR88tfGWMH4vAY/JPe/eu4Qcgu+d+Te0ZLX4ArMIOah9/PX64aEtXQ6CzfnM3vA0/Us8YFEXTC8tHa/IYVKKJkKPrZegVMT5OEvf4kxf7ylA0CStpqivcQSXaFgwt40Uo1rrwl6ctLrrhAdBvgVozBXriZKotAM8+oK6lLSs9eVdySUd3petevd+KMhvHuvZKWEK/7qHOAFsGAKR13CgpOTTb3ZTbsXih4YA1X9seZZ2bqCk5gKJHhq8v795OizreNlcRBwA8tx2NTac9ReubLQZPGhiJoiha2XvhSKS9Jnt37fTDwXNZKBq8WQ+ISo+rm5y8dKHrYJwHkcrIqxktwVJRFC04tlsAFoE57bPtGXtSM3p62j2tGPoWTFtBKJyks42dx4+gKJqWlrM3yo0G0kzFi54zaBHeO1SwQ3sPY/zoW7Cq9qGzuwW2ISn44MXJyd5sV4M1kPaZDS0VQQYNx0b8kBnu5YrxAwHc3yrBtqEM8PNR0qUvL6ZTCQBAJJOJAA57m0Yac6zNgHxwYqUxwSR+lMu1XgQg7Gp9c6/cFgA2Dr5ROInlC9flaXyT6WJsU++8mk8mAcFrv7LvJAAAMKPyuidrIrWslcZNPJk6SAYza7+msf6tgpdwrfM7BwvCnAmeVbcetAUQgRhd9o+ZA4aHhFaOdx6JoDkdWfnjRAyZAAAkCoVApIWf6G49EMEEsDkxc/FcMnfNFGZOBztUG7ekSaIGV27VegIAkMhkEoEAvvjCdJkrAUjJbX8a2b92JJmR0TRyJtGTDMARJ/bODEQ5EAE0g2+9Vx6+fNSGMiibTQAxo/K6p8u9CaZMkSjmiOPxib4CBwKQUwc2w8///P0vr9VqtfopHsaif7R9rdERuTQ217gWDuvxQ9jVqp0K2ICfZPzqqURtlE4CWbKnUPH5m1dqtXqp86CmS2Q8+EaUhZ8Y6SnYZmnosW7N99fws/LFQBgRCFGn1Q9xJ5oota63bqcfBcDzzPR74iexebq/wIEAhH19314t0cQYWD/4BmDmuW+pv8Twl83wQ2aLSsafqfV6fCkl2HbdMRpTfPlaZwpJ1ODKZC4ZQOTSONK710G0AT/ax79vOPhG4aTj8vObDb7ZH3z098uJFAKAmcvRkXtVfmHH2zuPJVqaAe/YvBY/X/zxjVqtVn89U7FHyiIZ4se9SIE31ejxY+aSoH18Y/yIwg4tNO6jGcQWgXcbfDNzSZia69TEH3rb4JuxV3DdK6eGS+xFAMD33tWLFzD9Kq59rK22rx41msDPmimARKEQCNTQ462dx3ZamgGRxj7aqEgsqHz5w6s3arVa/aT7QAyPZAZM/rHOweNhTgRIGbjfFrAdnbz/9d/+5/97eadOu+/Ys+ry1TpvkhkAIbD585Z0W2JAzsfXmtyA7B1/qiY/0XLzRtf99ORwcbwGtFsMvonCsu8///67L4fjWSyWOZ1gZmYeUn1x8owD0QwAyFSqGdejanrk5DY+MPmZVR3xPlaga8G0FYQuzuu+KD/kBQAEAolMJoF99qf/uBxNBGJAzsdDuiFEQ/wYtGB2+2pv1kWDGYHGsGCx3KtufqP+6lFVmhcAgC/+0DgHzUb8MENrZyfLXN9njvrtMsCPTcrSSovhyLXDnob2siRLAEgffXIhT2QKPzODuRIwql2/bO7HPnPlSasnAHjsVS5oNsAzo/LO4xne2gOQhJlrLYGweewsIt3rMP7grkr1yeOvX948ES4jelYp54u3AUBk6eqjczYEinNy0dKNByqV6vkXT7vzouguBQ8f1RkFj9LN/Wirrk3K0hefa8KFqlQqVdteEyW3SZ5RqVSPHlzqLtrOo4AvPtaVrTXFrdMIgeqWXn77lkr14MGXL++XJ3pRADjiOLy3RhvYgS5Or5u7dfv6F3++dlhs6mNRb4pkB5Om0OCnvy5VCFvN/Wh7P0FHv3p9fb+Lp77RYW3zUQy0uxQM6V+cZ/H89wN7AEzgZ1fjcvPhwA0e6IJfupkf8xGYwE8uXpYuNhorNsKPUcw3uji9vDE/0pWoCy73PviJw3s1bW5K77e6kKMb8fNRTP7dkdOGv2yGHzMiSWijDbjr6urq6rSNY75FQ+yCX7qZn31IO/cjkml89TfAj1Pui7/98ETnidfqk8PzGiszQ8wN5340vR/Eu35uusbL2hA/Bzrv1OIVevxIQjK1j2+IH5I0o6Yb3x3j7u13bmChNM2HTSHB1vihcBI777xUqVRPPn/12bj24m/Hj4FX8Lxqpm4/e/SJ9sFGz5oHnddXW7VJ/Djlvviqdc0tibTwgrHnXz5/oLnCoiKuqEHnt8i++rmn8mQAekQW3lOSGN/++Ma5MP3cT373g5rcECoJwBd//uevH+nMW5/lC+B65uq19nikVD6bs8N2/UOYET8KT59QPnioUj398qU+EOIW+OH7pUxdvXv3pjzN1dXVHqGRicwwfKwre20gjOdVM9CcJOGAOT8NH8RCHQmeVYszBQgYTIPx/aqGF1Qq1Y2loWRnJsEp98VXrTIA8M1SLTVoXcUQP+tasK5MMxJFbOvs6hpTffG26sZCwc6NkVwANuKHyjtz9Ztz6O81rWWAH7as7trtuiAhACAymZREsg4rHuwqcWbB9sr7yxUhYO5Vc2HggATMbbw7Zme3wM/n7TFrdyDSHEL2ZaCuJnK2AQA3fPSLT447gigyT9lzDADW44fl03zzRuV2YDmHD12a1OKHLg7fe3h/AAtAEzxquBDh00QOdZOXNPi5/3QalYBNRuuf+1HD5HhZfbe786LofL++x5U+jgAAB5BJREFUBx8fdeMCgKOfn2gjftiyupn5k5rPQJugXR5kAABzJCY9b58PFQCAaRseYA8AFCv3+v7+Xe5W4ItfV7YG8sHx+PTn7THAlpUoBk+5WhOozLKxq6bxU4bnhss8do1cu9yoS1sAYs9dB1OCdT1FY/wYmyJRzOF7HRseqfbkguyUcg0/orDD+Ye0F1wbfAOP+ufKiXMdukZHtr9EcQ6zSu/QvjiJe+Pla21hIgAT+HHY3dhTuVdTYSwdwgK1V5fk982fSLAHAF/8xXSxflnbL8QPiYMWtXfmhVMJkNJ9X48fY1OY0LvgZ7kixOAMSuDByuFSo2Cvm+GHSGOG7Cso0+tYpuuGeOzrTZF3dB1+zB1PKy6ccjYHx+PTvwA/3tjnX/drrcnz6rtz65DGaezCd7mRfVKreyvTRDQIbnpsPPgGyJ7qT5cqYvX42b5rUjm/d1eaFj80dvyppqEjsQDG+DHnB6fklZWVna2omr31aLLliKslE7bGj33mypuZnQD8wDTl9WE9fpa0VdikjL2CLj5Y3VaX7AYAwLX28bAmS/eP39VW2zefNruQ+MlFzeWpPqCrtkyeV9/z5ye9eKCptmYk98SjzWXZIiYJCGSJR5D1zjW/RfZUfyrPAABzZ1Q+cvH2D0+OO64tPeBv261YHNwv44MkqkvRrR309NirGYbiJzQ/uzM7O9MauHEKi8JJLO9pSgs3B62vavCzt2m5cu9W8z5kqk9WWRne1FR5KIrukNB3oTdATAeA7UFBbPq2/N6JpmgxmS0+2Toc7S6EdfghMWVubnwOBQB21c/WHwyk8GJm//w8wwYkaMnd3hztPWR7l1X92pbXsAVDfFAPhseeIrypqazseIzhLLMkKudImkxk+IMRfvgJzV/er/xVeQy3lNHcD99+/9Cte0qlcrw+VUAhUDjOWOvFG7eUTx5cQKUMAEZAYe9XK8qFxeUHd8Y2ww9Dmnv7+ydK5URRlDsN3jb4BjTrfX0fP1LevzNbhmrIbYwfoErRugdPlHOL1z69N6LFD8+rZvbxZU3OUBIz8Wzfx0s3Ll1dvntzQoOfj59MjE4oH9yfzrBlApEWmlt9ffmuUqm8pLzUnRdFB7D2zh+7eUepVI6UhZhvxI+BKZRjDV7WFABN6MYXCxgLAIDCCT2KK5XKa7eV49VHbBhk8MVvqaYnZpSPPp7YZ20OREb0iZY7y7evKpduXJ/ZAj9EECR03VruytUsCPY+PHZnpkznEMb4MTZFophDYlgfrJ+5dUc5NH9rDT+eVWvh7wzwA6I9k8+//fzZ82crt5VK5WhvaYCES9rZ/D9vvrijVCqVo8XJIdo1yRvww98W0t7b7m9tDgB0a/9j7RNKpVKpXFloP6Jpnpi2FTe//ESpHMxxtd6AH0Fsbt2C8t7Xb/7yQqXsLopi+6+LeE1gOe3suriwtKycWVzU4yewYmah7oCV6XUIguSqgWvXn33z3Ys7E+cDLJkm8WNuXXLr60dK5Ui+vxMJAOhWB8rrj4Q4GF7o18z9rDeFS/w6/JDo28rnPvn4pnJk/tYvwA9NWLL4xSfK6aq0cC6ASHZYcfOuUqlUKmqdBMCwCqoanLt2QzmycGMdfoDl3Xrv2a2bj/705eMbSqXyaudej21MGTp55/GDa8tK5ULnuRwXFgPA9Mo34rsPvtGEJYtfPFIqZy5f/1LVp8EPhRPacfm6UqkcwLNMJcha/1HCkoQ2zC4rlUrlxaEDsS4EM/Pwoom7K8qxhZt/e9rkAiS7mKPz127rqy3TwBQjZSHmAFTmR1kV/ZeWrymvXm4sSmJH57/8+rN7SqVSqVRdksd4SQEAyNZH+27/7W65Jazhh0Dh7K8YWektAAD/pPKp67eUSqWyvxjRwIQRPPLVX+ZrkkwsczIjuSUdvXTtwTWl1lc1Z7jtaL7xdEWp7IjbeIqBqJZWXs5WZgRyeGrNxWs3lEolnolQgcQPwK7cXby2vHihAbNiENfjh0Bxjs8dmrmiVCoX51oj+eYAdNuMsfsPlCt3LxaFO+uvnjt064nySkvRLolhCzbeEuxkIdjmZivcMKxgPPjmmtN668Fz9VePryxcTPUEAKfihWX5zndOzfjL9e+z7wfxwZtqNn6BmpD+5f0z5asbZvkPUHD+yz/+/PMfdZ0V0OShMW4ifx/p8noAAFRcWMmLkmx9/C+Srffh/r4yg289j2rlUxOJ4f8bJJKVzTxfXV2dyzb9dwYLu/8fkmFamNm2PJO32fi2SZkMA/9Bv73+DfBjk1xc8/+3d/+qCQMBAMYPIRQ0lFJEEUQK0sXqIkUQFQ4yWkoLpoMtFA3iYhXvDUTBuXR2cu2qrxeXTKIhgiZc/H6vcHDfwf2z669fw9/ZuHDv9zykh/zo5q7UcLrvrUZ9sfrv1i642sJVSRg3j9L5W89fzADzhhBCCDPzULWXm63S6uqirjTIjxBpy1Hqpy+f8oc/utqTbXbenk9a7ZxBTrat8vF9CvhLla2PyVT1wh84xJeRvG1/q0+raAQ+uJWryMFIeX8o4sK0yA8AIG7IDwAgAuQHABAB8gMAiAD5AQBEwHVd4QIAELoddC4XY7t/GVwAAAAASUVORK5CYII=" alt="" />

接下来使用collect命令提交并执行Job:

count.collect

wps313.tmp

wps323.tmp

从控制台可以看到我们的程序成功在集群上运行.

使用Spark交互模式:

1. 运行./spark-shell.sh

2. scala> val data = Array(1, 2, 3, 4, 5) //产生data

data: Array[Int] = Array(1, 2, 3, 4, 5)

3. scala> val distData = sc.parallelize(data) //将data处理成RDD

distData: spark.RDD[Int] = spark.ParallelCollection@7a0ec850 (显示出的类型为RDD)

4. scala> distData.reduce(_+_) //在RDD上进行运算,对data里面元素进行加和

12/05/10 09:36:20 INFO spark.SparkContext: Starting job...

5. 最后运行得到

12/05/10 09:36:20 INFO spark.SparkContext: Job finished in 0.076729174 s

res2: Int = 15

wps324.tmp


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array(4) { ["type"]=> int(8) ["message"]=> string(24) "Undefined variable: jobs" ["file"]=> string(32) "/mnt/wp/cppentry/do/bencandy.php" ["line"]=> int(217) }