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![54 | © 2019 Akamai | Confidential定番JSON加工ツール: jq(1)$ jq -r '[.data[] | select(.geo.country) | {"e": .geo.country,"v": .network.bw | tonumber}] | group_by(.e) | map({"i": "GB","e": .[0].e,"v": map(.v) | add})' < raw.json > status.json{"data": [{"type": "Raw",..."geo": {"country": "US",...},"network": {..."bw": "5000"},...}, {"type": "Raw",...左のDataStream Raw LogのJSON形式は上の7行ほどのjqコマンドでGIO.jsに渡すデータ構造に変換できる。つまり、ここもプログラミング不要に。この生成された結果をGIO.jsでロードする](/image.pl?url=https%3a%2f%2fimage.slidesharecdn.com%2f20190723-akamai-techconf-final-200831141114%2f75%2fDIY-Akamai-Globe-in-50-Minutes-54-2048.jpg&f=jpg&w=240)










Delivered in Akamai Japan Tech Conf 2019.Presented on how you can visualize a site traffic and build one's "Akamai Globe" which visualizes traffic happening worldwide across the globe. With appropriate selection of APIs and OSS stack, it is a doable DIY project in 50 minutes!





















































![54 | © 2019 Akamai | Confidential定番JSON加工ツール: jq(1)$ jq -r '[.data[] | select(.geo.country) | {"e": .geo.country,"v": .network.bw | tonumber}] | group_by(.e) | map({"i": "GB","e": .[0].e,"v": map(.v) | add})' < raw.json > status.json{"data": [{"type": "Raw",..."geo": {"country": "US",...},"network": {..."bw": "5000"},...}, {"type": "Raw",...左のDataStream Raw LogのJSON形式は上の7行ほどのjqコマンドでGIO.jsに渡すデータ構造に変換できる。つまり、ここもプログラミング不要に。この生成された結果をGIO.jsでロードする](/image.pl?url=https%3a%2f%2fimage.slidesharecdn.com%2f20190723-akamai-techconf-final-200831141114%2f75%2fDIY-Akamai-Globe-in-50-Minutes-54-2048.jpg&f=jpg&w=240)








