Post Syndicated from Curious Droid original https://www.youtube.com/watch?v=bjM3jOYQYz4
How we think about browsers
Post Syndicated from Keith Cirkel original https://github.blog/2022-06-10-how-we-think-about-browsers/
At GitHub, we believe it’s not fully shipped until it’s fast. JavaScript makes a big impact on how pages perform. One way we work to improve JavaScript performance is to make changes to the native syntax and polyfills we ship. For example, in January of this year, we updated our compiler to output native ES2019 code, shipping native syntax for optional catch binding.
JavaScript doesn’t get executed on very old browsers when native syntax for new language features is encountered. However, thanks to GitHub being built following the principle of progressive enhancement, users of older browsers still get to interact with basic features of GitHub, while users with more capable browsers get a faster experience.
GitHub will soon be serving JavaScript using syntax features found in the ECMAScript 2020 standard, which includes the optional chaining and nullish coalescing operators. This change will lead to a 10kb reduction in JavaScript across the site.
We want to take this opportunity to go into detail about how we think about browser support. We will share data about our customers’ browser usage patterns and introduce you to some of the tools we use to make sure our customers are getting the best experience, including our recently open-sourced browser support library.
What browsers do our customers use?
To monitor the performance of pages, we collect some usage information. We parse User-Agent headers as part of our analytics, which lets us make informed decisions based on the browsers our users are running. Some of these decisions include, what browsers we execute automated tests on, the configuration of our static analysis tools, and even what features we ship. Around 95% of requests to our web services come from browsers with an identifying user agent string. Another 1% of requests have no User-Agent header, and the remaining 4% make up scripts, like Python (2%) or programs like cURL (0.5%).
We encourage users to use the latest versions of Chrome, Edge, Firefox, or Safari, and our data shows that a majority of users do so. Here’s what the browser market share looked like for visits to github.com between May 9-13, 2022:
| Beta | Latest | -1 | -2 | -3 | -4 | -5 | -6 | -7 | -8 | Total | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Chrome | 0.2950% | 53.0551% | 12.7103% | 1.8120% | 0.8076% | 0.4737% | 0.5504% | 0.1728% | 0.1677% | 0.1029% | 70.1478% |
| Edge | 0.0000% | 6.3404% | 0.5328% | 0.0978% | 0.0432% | 0.0202% | 0.0143% | 0.0063% | 0.0058% | 0.0046% | 7.0657% |
| Firefox | 0.6525% | 7.7374% | 2.9717% | 0.2243% | 0.1041% | 0.1018% | 0.0541% | 0.0396% | 0.0219% | 0.0172% | 11.9249% |
| Safari | 0.0000% | 2.8802% | 0.7049% | 0.2110% | 0.0000% | 0.3288% | 0.0000% | 0.0696% | 0.0000% | 0.0094% | 4.2038% |
| Opera | 0.0030% | 0.2650% | 1.1173% | 0.0112% | 0.0044% | 0.0043% | 0.0016% | 0.0017% | 0.0015% | 0.0011% | 1.4112% |
| Internet Explorer | 0.0000% | 0.0658% | 0.0001% | 0.0001% | 0.0000% | 0.0000% | 0.0001% | 0.0000% | 0.0000% | 0.0000% | 0.0662% |
| Samsung Internet | 0.0000% | 0.0276% | 0.0007% | 0.0012% | 0.0008% | 0.0000% | 0.0000% | 0.0000% | 0.0000% | 0.0000% | 0.0302% |
| Total | 0.9507% | 70.3716% | 18.0379% | 2.3576% | 0.9602% | 0.9289% | 0.6207% | 0.2901% | 0.1968% | 0.1352% | 94.8498% |
The above graph shows two dimensions: market share across browser vendors, and market share across versions. Looking at traffic with a branded user-agent string shows that roughly 95% of requests are coming from one of seven browsers. It also shows us that—perhaps unsurprisingly—the majority of requests come from Google Chrome (more than 70%), 12% from Firefox, 7% from Edge, 4.2% from Safari, and 1.4% from Opera (all other browser vendors represent significantly less than 1% of traffic).
The fall-off for outdated versions of browsers is very steep. While over 70% of requests come from the latest release of a browser, 18% come from the previous release. Requests coming from three versions behind the latest fall to less than 1%. These numbers tell us that we can make the most impact by concentrating on Chrome, Firefox, Edge, and Safari, in that order. That’s not the whole story, though. Another vector to look at is over time:
| 15.4 | 15.3 | 15.2 | 15.1 | 15.0 | 14.1 | 14.0 | 13.1 | 13.0 | 12.1 | 12.0 | <12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-01-01 | 0.2130% | 1.9933% | 31.9792% | 25.3472% | 6.1477% | 11.5005% | 3.9045% | 2.3446% | 0.5210% | 0.9379% | 0.0336% | 15.0773% |
| 2022-01-02 | 0.2548% | 2.0334% | 32.8731% | 25.9360% | 6.4715% | 12.0837% | 3.7401% | 2.4652% | 0.5292% | 0.8598% | 0.0297% | 12.7235% |
| 2022-01-03 | 0.3078% | 1.6285% | 34.9256% | 27.0666% | 7.3738% | 13.7125% | 3.7582% | 2.1204% | 0.4217% | 0.6152% | 0.0238% | 8.0459% |
| 2022-01-04 | 0.3637% | 1.4860% | 35.6528% | 26.9236% | 7.6980% | 14.2046% | 3.6510% | 2.0938% | 0.3804% | 0.5613% | 0.0305% | 6.9543% |
| 2022-01-05 | 0.3519% | 1.4723% | 35.8533% | 26.3673% | 7.6227% | 14.5006% | 3.7131% | 2.1403% | 0.3793% | 0.5682% | 0.0254% | 7.0055% |
| 2022-01-06 | 0.3575% | 1.5431% | 36.6670% | 25.8058% | 7.5075% | 14.2149% | 3.7570% | 2.1580% | 0.3888% | 0.6040% | 0.0242% | 6.9722% |
| 2022-01-07 | 0.4123% | 1.6277% | 37.4426% | 25.2663% | 7.3924% | 13.8618% | 3.6753% | 2.0874% | 0.4024% | 0.5904% | 0.0270% | 7.2144% |
| 2022-01-08 | 0.3237% | 1.9625% | 35.9640% | 24.3500% | 6.2977% | 12.0691% | 3.7139% | 2.3841% | 0.5170% | 0.8028% | 0.0266% | 11.5885% |
| 2022-01-09 | 0.2964% | 1.9599% | 36.0700% | 24.2496% | 6.3270% | 12.0979% | 3.7857% | 2.3146% | 0.4816% | 0.8567% | 0.0242% | 11.5363% |
| 2022-01-10 | 0.3488% | 1.5101% | 39.0599% | 24.5018% | 7.2861% | 14.0757% | 3.6064% | 2.0192% | 0.3818% | 0.5383% | 0.0285% | 6.6433% |
| 2022-01-11 | 0.4108% | 1.5541% | 39.4265% | 24.3465% | 7.3778% | 14.1840% | 3.5905% | 1.9870% | 0.3366% | 0.5555% | 0.0253% | 6.2052% |
| 2022-01-12 | 0.3743% | 1.5182% | 40.0054% | 23.9508% | 7.3054% | 14.1456% | 3.5695% | 2.0163% | 0.3643% | 0.5105% | 0.0308% | 6.2090% |
| 2022-01-13 | 0.3380% | 1.5659% | 40.3951% | 23.5803% | 7.2104% | 14.1495% | 3.6099% | 1.9705% | 0.3716% | 0.5117% | 0.0229% | 6.2743% |
| 2022-01-14 | 0.3709% | 1.6172% | 40.8321% | 23.4113% | 6.9690% | 13.5323% | 3.5354% | 1.9806% | 0.3559% | 0.5424% | 0.0251% | 6.8279% |
| 2022-01-15 | 0.2870% | 2.0547% | 39.7351% | 22.0067% | 5.9847% | 11.7234% | 3.6011% | 2.2909% | 0.4668% | 0.7720% | 0.0287% | 11.0489% |
| 2022-01-16 | 0.2964% | 2.0923% | 40.8441% | 20.6853% | 5.9118% | 11.8049% | 3.6625% | 2.3851% | 0.4599% | 0.8312% | 0.0294% | 10.9970% |
| 2022-01-17 | 0.3043% | 1.6554% | 43.8724% | 20.6116% | 6.6334% | 13.7081% | 3.5519% | 2.0195% | 0.3721% | 0.5356% | 0.0287% | 6.7071% |
| 2022-01-18 | 0.3448% | 1.5978% | 45.3308% | 19.6763% | 6.7137% | 13.6977% | 3.5498% | 1.9990% | 0.3478% | 0.5166% | 0.0289% | 6.1968% |
| 2022-01-19 | 0.3490% | 1.6179% | 46.3810% | 19.0037% | 6.5909% | 13.7031% | 3.4676% | 1.9497% | 0.3358% | 0.4847% | 0.0264% | 6.0901% |
| 2022-01-20 | 0.3410% | 1.6362% | 47.2639% | 18.3797% | 6.4656% | 13.3978% | 3.3907% | 1.9803% | 0.3393% | 0.5244% | 0.0245% | 6.2566% |
| 2022-01-21 | 0.3553% | 1.7170% | 48.0184% | 17.4454% | 6.3012% | 13.1411% | 3.3914% | 2.0109% | 0.3696% | 0.4934% | 0.0230% | 6.7332% |
| 2022-01-22 | 0.2929% | 2.3538% | 46.1479% | 16.4726% | 5.4806% | 11.2732% | 3.4515% | 2.1378% | 0.4547% | 0.7435% | 0.0291% | 11.1624% |
| 2022-01-23 | 0.2595% | 2.3385% | 47.0822% | 15.5800% | 5.4940% | 11.0466% | 3.5465% | 2.2365% | 0.4565% | 0.7749% | 0.0233% | 11.1614% |
| 2022-01-24 | 0.3607% | 1.7504% | 50.7307% | 15.3784% | 6.1093% | 12.9047% | 3.4166% | 2.0184% | 0.3442% | 0.4872% | 0.0225% | 6.4769% |
| 2022-01-25 | 0.3654% | 1.7706% | 51.9246% | 14.6195% | 6.0739% | 12.9834% | 3.3441% | 1.9226% | 0.3283% | 0.4742% | 0.0197% | 6.1736% |
| 2022-01-26 | 0.3465% | 2.1688% | 52.4595% | 14.0287% | 5.9250% | 12.4463% | 3.3065% | 1.9205% | 0.3530% | 0.5013% | 0.0244% | 6.5195% |
| 2022-01-27 | 0.3628% | 7.7522% | 47.3489% | 13.5902% | 5.8790% | 12.5425% | 3.2687% | 1.9584% | 0.3513% | 0.4820% | 0.0285% | 6.4356% |
| 2022-01-28 | 0.8512% | 12.2593% | 43.2173% | 12.7719% | 5.7684% | 12.2779% | 3.2807% | 1.8948% | 0.3661% | 0.4896% | 0.0249% | 6.7981% |
| 2022-01-29 | 1.5324% | 15.5746% | 37.9759% | 11.4900% | 5.0904% | 10.8157% | 3.2414% | 2.2146% | 0.4751% | 0.7246% | 0.0226% | 10.8425% |
| 2022-01-30 | 1.8095% | 17.1024% | 36.5444% | 11.5112% | 5.0038% | 10.5058% | 3.3404% | 2.2842% | 0.4604% | 0.7569% | 0.0187% | 10.6623% |
| 2022-01-31 | 1.5814% | 17.6461% | 38.7703% | 12.5933% | 5.6880% | 12.0274% | 3.1897% | 1.8416% | 0.3408% | 0.4923% | 0.0240% | 5.8050% |
| 2022-02-01 | 1.7441% | 19.2814% | 37.2947% | 12.3450% | 5.5508% | 11.9390% | 3.1856% | 1.8109% | 0.3369% | 0.4689% | 0.0228% | 6.0199% |
| 2022-02-02 | 1.8425% | 20.6234% | 36.1439% | 12.2229% | 5.5517% | 11.8100% | 3.0868% | 1.7966% | 0.3369% | 0.4872% | 0.0285% | 6.0697% |
| 2022-02-03 | 1.8914% | 21.5787% | 34.9534% | 12.0932% | 5.4199% | 11.7927% | 3.1686% | 1.8609% | 0.3504% | 0.4656% | 0.0240% | 6.4013% |
| 2022-02-04 | 1.9648% | 22.7768% | 34.0393% | 11.7468% | 5.2886% | 11.4763% | 3.0458% | 1.8618% | 0.3508% | 0.5207% | 0.0221% | 6.9061% |
| 2022-02-05 | 2.3963% | 23.4144% | 30.8252% | 10.7756% | 4.6826% | 10.0675% | 3.2277% | 2.1561% | 0.4480% | 0.7145% | 0.0214% | 11.2706% |
| 2022-02-06 | 2.3912% | 24.0953% | 30.5678% | 10.4257% | 4.7046% | 10.0236% | 3.3234% | 2.1215% | 0.4327% | 0.7056% | 0.0193% | 11.1893% |
| 2022-02-07 | 2.0336% | 24.6938% | 32.2185% | 11.5380% | 5.1985% | 11.8112% | 3.1986% | 1.9324% | 0.3535% | 0.4776% | 0.0249% | 6.5194% |
| 2022-02-08 | 2.0578% | 25.5825% | 31.5513% | 11.4319% | 5.1997% | 11.8368% | 3.1809% | 1.8839% | 0.3255% | 0.4600% | 0.0220% | 6.4678% |
| 2022-02-09 | 2.1357% | 26.4126% | 31.2722% | 11.3999% | 5.2737% | 11.9741% | 3.1823% | 1.9032% | 0.2298% | 0.2883% | 0.0204% | 5.9077% |
| 2022-02-10 | 2.1586% | 27.2403% | 30.8552% | 11.3862% | 5.2045% | 11.8815% | 3.1880% | 1.5341% | 0.2342% | 0.2931% | 0.0234% | 6.0009% |
| 2022-02-11 | 2.3263% | 28.7838% | 30.1344% | 11.3683% | 5.1761% | 11.6652% | 3.1655% | 0.8880% | 0.1781% | 0.2133% | 0.0214% | 6.0796% |
| 2022-02-12 | 2.7622% | 28.4764% | 26.9469% | 9.7973% | 4.4372% | 9.9020% | 3.1473% | 2.1256% | 0.4193% | 0.7154% | 0.0241% | 11.2464% |
| 2022-02-13 | 2.6300% | 28.9074% | 26.5648% | 9.9005% | 4.4070% | 9.9237% | 3.1472% | 2.2069% | 0.4375% | 0.7176% | 0.0234% | 11.1339% |
| 2022-02-14 | 2.2108% | 30.1253% | 28.0680% | 10.8367% | 5.0225% | 11.6307% | 3.1060% | 1.7727% | 0.3190% | 0.4699% | 0.0230% | 6.4155% |
| 2022-02-15 | 2.2626% | 31.0756% | 27.6637% | 10.8023% | 4.9224% | 11.5579% | 3.0697% | 1.7311% | 0.3060% | 0.4568% | 0.0263% | 6.1257% |
| 2022-02-16 | 2.3030% | 31.5893% | 27.2155% | 10.7267% | 4.8788% | 11.4932% | 2.9605% | 1.7814% | 0.3026% | 0.4668% | 0.0264% | 6.2558% |
| 2022-02-17 | 2.3139% | 32.1564% | 26.7888% | 10.6523% | 4.7749% | 11.4135% | 3.0497% | 1.8037% | 0.3309% | 0.4543% | 0.0254% | 6.2362% |
| 2022-02-18 | 2.3419% | 33.8505% | 25.1471% | 10.3232% | 4.6872% | 11.2973% | 2.9507% | 1.8080% | 0.3524% | 0.4658% | 0.0234% | 6.7524% |
| 2022-02-19 | 2.8255% | 35.4968% | 21.2425% | 9.0705% | 4.3585% | 9.6129% | 3.0839% | 2.1391% | 0.4321% | 0.7214% | 0.0240% | 10.9927% |
| 2022-02-20 | 2.7597% | 37.2786% | 19.6970% | 9.0995% | 4.3411% | 9.6230% | 3.0634% | 2.1577% | 0.4466% | 0.6930% | 0.0223% | 10.8180% |
| 2022-02-21 | 2.2972% | 39.5270% | 20.2617% | 9.8308% | 4.5785% | 11.0922% | 3.0427% | 1.8432% | 0.3359% | 0.4971% | 0.0203% | 6.6735% |
| 2022-02-22 | 2.3285% | 41.6072% | 18.8362% | 9.5382% | 4.5626% | 11.1426% | 3.0037% | 1.8037% | 0.3116% | 0.4570% | 0.0206% | 6.3882% |
| 2022-02-23 | 2.3564% | 42.9442% | 17.7843% | 9.3653% | 4.5609% | 11.0347% | 2.8992% | 1.7738% | 0.3189% | 0.4421% | 0.0198% | 6.5005% |
| 2022-02-24 | 2.3331% | 43.8819% | 16.9144% | 9.1178% | 4.5580% | 11.1665% | 2.9641% | 1.7680% | 0.3063% | 0.4369% | 0.0224% | 6.5306% |
| 2022-02-25 | 2.3644% | 45.0140% | 15.8175% | 8.8273% | 4.4317% | 10.9711% | 2.9355% | 1.7624% | 0.3324% | 0.4556% | 0.0238% | 7.0641% |
| 2022-02-26 | 2.9596% | 44.4252% | 13.1945% | 7.6195% | 3.9776% | 9.4234% | 3.1355% | 2.0300% | 0.5206% | 0.7039% | 0.0223% | 11.9878% |
| 2022-02-27 | 2.8501% | 45.0292% | 12.5025% | 7.7242% | 4.0507% | 9.4992% | 3.0954% | 2.1008% | 0.4980% | 0.6989% | 0.0251% | 11.9258% |
| 2022-02-28 | 2.3807% | 47.0753% | 14.2634% | 8.4807% | 4.2220% | 10.9576% | 3.0524% | 1.8148% | 0.3434% | 0.4632% | 0.0242% | 6.9222% |
| 2022-03-01 | 2.3748% | 48.2034% | 13.7801% | 8.3157% | 4.2605% | 10.7307% | 2.9418% | 1.8095% | 0.3259% | 0.4437% | 0.0224% | 6.7913% |
| 2022-03-02 | 2.3629% | 48.7995% | 13.5339% | 8.1894% | 4.2284% | 10.7893% | 2.9092% | 1.7475% | 0.3293% | 0.4560% | 0.0220% | 6.6327% |
| 2022-03-03 | 2.4486% | 49.7154% | 12.6928% | 8.1700% | 4.1801% | 10.6387% | 2.8873% | 1.7340% | 0.3109% | 0.4497% | 0.0246% | 6.7479% |
| 2022-03-04 | 2.5373% | 50.2026% | 12.4981% | 7.7686% | 4.1271% | 10.5053% | 2.8372% | 1.7236% | 0.3080% | 0.4493% | 0.0255% | 7.0174% |
| 2022-03-05 | 3.0231% | 49.3317% | 10.5426% | 6.5147% | 3.7707% | 9.0290% | 3.0784% | 2.0464% | 0.4182% | 0.6288% | 0.0208% | 11.5956% |
| 2022-03-06 | 3.0723% | 49.6284% | 10.0200% | 6.7391% | 3.7704% | 9.0282% | 3.0924% | 2.1063% | 0.4288% | 0.6881% | 0.0213% | 11.4046% |
| 2022-03-07 | 2.4667% | 51.7157% | 11.7176% | 7.6974% | 4.1000% | 10.2989% | 2.8448% | 1.7253% | 0.3185% | 0.4412% | 0.0236% | 6.6503% |
| 2022-03-08 | 2.4190% | 52.4292% | 11.3917% | 7.3946% | 4.1027% | 10.3559% | 2.8335% | 1.7378% | 0.3259% | 0.4500% | 0.0230% | 6.5368% |
| 2022-03-09 | 2.4744% | 52.9758% | 11.0708% | 7.4840% | 4.0474% | 10.3307% | 2.8409% | 1.6682% | 0.3015% | 0.4183% | 0.0210% | 6.3667% |
| 2022-03-10 | 2.5404% | 53.2418% | 10.8388% | 7.3800% | 3.9569% | 10.2019% | 2.8462% | 1.7425% | 0.3027% | 0.4212% | 0.0254% | 6.5022% |
| 2022-03-11 | 2.6346% | 53.8851% | 10.3123% | 7.1008% | 3.8969% | 9.8746% | 2.7686% | 1.6714% | 0.2988% | 0.4224% | 0.0252% | 7.1092% |
| 2022-03-12 | 3.2418% | 52.4146% | 8.4171% | 6.0948% | 3.5575% | 8.6494% | 3.0048% | 2.0180% | 0.3927% | 0.7005% | 0.0251% | 11.4838% |
| 2022-03-13 | 3.2069% | 52.3926% | 8.5002% | 6.1721% | 3.5239% | 8.4507% | 2.9757% | 2.1148% | 0.3755% | 0.7167% | 0.0254% | 11.5456% |
| 2022-03-14 | 3.2185% | 54.3662% | 9.8584% | 7.0464% | 3.8492% | 9.8257% | 2.7413% | 1.6965% | 0.2680% | 0.4273% | 0.0270% | 6.6756% |
| 2022-03-15 | 11.1192% | 47.4059% | 9.5438% | 6.8719% | 3.7611% | 9.7005% | 2.7719% | 1.6308% | 0.2637% | 0.4211% | 0.0250% | 6.4849% |
| 2022-03-16 | 17.9069% | 41.3967% | 9.1987% | 6.6590% | 3.7184% | 9.5772% | 2.6822% | 1.6273% | 0.2877% | 0.4349% | 0.0197% | 6.4914% |
| 2022-03-17 | 21.7348% | 38.1323% | 8.8607% | 6.5819% | 3.6503% | 9.4056% | 2.6279% | 1.6295% | 0.2814% | 0.4449% | 0.0250% | 6.6258% |
| 2022-03-18 | 24.4165% | 35.7041% | 8.5482% | 6.3399% | 3.5433% | 9.0851% | 2.5852% | 1.6362% | 0.2685% | 0.4610% | 0.0258% | 7.3863% |
| 2022-03-19 | 26.2368% | 31.9489% | 6.8779% | 5.5836% | 3.3195% | 7.9382% | 2.7793% | 1.9717% | 0.3594% | 0.6485% | 0.0219% | 12.3144% |
| 2022-03-20 | 27.3687% | 30.7753% | 7.0252% | 5.5489% | 3.3195% | 7.9491% | 2.8717% | 1.9881% | 0.3587% | 0.6629% | 0.0231% | 12.1088% |
| 2022-03-21 | 28.2620% | 32.3271% | 8.3673% | 6.3448% | 3.4783% | 9.2887% | 2.6006% | 1.6620% | 0.2865% | 0.4242% | 0.0238% | 6.9347% |
| 2022-03-22 | 29.5670% | 31.4166% | 8.2768% | 6.3061% | 3.4969% | 9.2905% | 2.5700% | 1.5872% | 0.2591% | 0.4296% | 0.0227% | 6.7774% |
| 2022-03-23 | 30.6539% | 30.6544% | 8.0608% | 6.2326% | 3.4798% | 9.1248% | 2.5844% | 1.6372% | 0.2534% | 0.4191% | 0.0235% | 6.8761% |
| 2022-03-24 | 32.0481% | 29.9540% | 8.0759% | 6.0714% | 3.4595% | 9.0259% | 2.5544% | 1.5634% | 0.2705% | 0.4064% | 0.0225% | 6.5478% |
| 2022-03-25 | 32.7566% | 28.9962% | 7.7413% | 5.9692% | 3.3709% | 8.7142% | 2.5446% | 1.5842% | 0.2667% | 0.4180% | 0.0235% | 7.6147% |
| 2022-03-26 | 32.5970% | 26.7095% | 6.2462% | 5.0950% | 3.2226% | 7.6540% | 2.7770% | 1.8228% | 0.3847% | 0.6553% | 0.0210% | 12.8149% |
| 2022-03-27 | 33.0257% | 26.3003% | 6.3665% | 5.3195% | 3.2245% | 7.8100% | 2.8208% | 1.8248% | 0.3827% | 0.6404% | 0.0235% | 12.2614% |
| 2022-03-28 | 34.6919% | 27.5399% | 7.6902% | 5.9742% | 3.3578% | 8.8958% | 2.5193% | 1.6230% | 0.2658% | 0.3997% | 0.0232% | 7.0192% |
| 2022-03-29 | 35.6580% | 27.0895% | 7.6128% | 5.9695% | 3.3326% | 8.7237% | 2.4484% | 1.5631% | 0.2741% | 0.3802% | 0.0223% | 6.9258% |
| 2022-03-30 | 36.0933% | 26.8011% | 7.4668% | 5.9183% | 3.3682% | 8.6179% | 2.4670% | 1.5698% | 0.2556% | 0.4017% | 0.0195% | 7.0208% |
| 2022-03-31 | 36.7627% | 26.3586% | 7.4152% | 5.8338% | 3.2992% | 8.6070% | 2.5025% | 1.5928% | 0.2742% | 0.4120% | 0.0184% | 6.9235% |
| 2022-04-01 | 37.7935% | 25.6391% | 7.0944% | 5.6951% | 3.3207% | 8.4395% | 2.3936% | 1.5895% | 0.2895% | 0.4032% | 0.0177% | 7.3242% |
| 2022-04-02 | 36.7583% | 23.8424% | 6.0495% | 4.9378% | 3.1161% | 7.6109% | 2.7326% | 1.8943% | 0.3616% | 0.6358% | 0.0209% | 12.0398% |
| 2022-04-03 | 38.0555% | 23.4329% | 5.9867% | 5.0717% | 3.1091% | 7.4917% | 2.6295% | 1.8512% | 0.3456% | 0.6151% | 0.0185% | 11.3926% |
| 2022-04-04 | 39.5734% | 24.4753% | 7.2343% | 5.6909% | 3.1580% | 8.4157% | 2.4270% | 1.5334% | 0.2433% | 0.4101% | 0.0237% | 6.8149% |
| 2022-04-05 | 40.1999% | 24.3237% | 7.1533% | 5.5920% | 3.2204% | 8.3465% | 2.3998% | 1.4640% | 0.2627% | 0.3814% | 0.0251% | 6.6309% |
| 2022-04-06 | 40.3972% | 23.9005% | 7.0750% | 5.5978% | 3.4175% | 8.4546% | 2.3345% | 1.5084% | 0.2411% | 0.4040% | 0.0226% | 6.6467% |
| 2022-04-07 | 40.6483% | 23.5724% | 6.9376% | 5.5906% | 3.5983% | 8.4082% | 2.3696% | 1.5432% | 0.2714% | 0.3932% | 0.0221% | 6.6452% |
| 2022-04-08 | 41.0291% | 23.0447% | 6.7669% | 5.3345% | 3.7167% | 8.1355% | 2.3707% | 1.5204% | 0.2979% | 0.4360% | 0.0227% | 7.3250% |
| 2022-04-09 | 39.0096% | 21.5707% | 5.5139% | 4.6910% | 3.8459% | 7.1101% | 2.6170% | 1.8408% | 0.3681% | 0.6555% | 0.0163% | 12.7611% |
| 2022-04-10 | 38.9654% | 21.5612% | 5.7157% | 4.7531% | 3.9948% | 7.0060% | 2.6146% | 1.9593% | 0.3643% | 0.6320% | 0.0189% | 12.4146% |
| 2022-04-11 | 41.7134% | 22.3259% | 6.8796% | 5.4925% | 3.8380% | 8.2313% | 2.3455% | 1.5951% | 0.2604% | 0.3967% | 0.0202% | 6.9013% |
| 2022-04-12 | 42.9776% | 21.4756% | 6.6304% | 5.3460% | 3.9136% | 8.1430% | 2.3224% | 1.4970% | 0.2704% | 0.4073% | 0.0232% | 6.9935% |
| 2022-04-13 | 44.8529% | 19.5508% | 6.5201% | 5.2781% | 3.9081% | 8.1321% | 2.3117% | 1.4651% | 0.2597% | 0.3798% | 0.0203% | 7.3213% |
| 2022-04-14 | 47.2604% | 18.5562% | 6.3329% | 5.0969% | 3.9568% | 7.8447% | 2.2326% | 1.4409% | 0.2660% | 0.3694% | 0.0195% | 6.6238% |
| 2022-04-15 | 46.1738% | 17.2112% | 5.7005% | 4.8802% | 4.1301% | 7.6028% | 2.4450% | 1.5947% | 0.3140% | 0.4888% | 0.0221% | 9.4369% |
| 2022-04-16 | 45.0816% | 15.9900% | 4.9427% | 4.2670% | 4.3097% | 6.9089% | 2.5115% | 1.7646% | 0.3776% | 0.6567% | 0.0196% | 13.1700% |
| 2022-04-17 | 45.9303% | 15.1178% | 4.8665% | 4.3033% | 4.3275% | 6.9231% | 2.6406% | 1.7807% | 0.3541% | 0.6456% | 0.0216% | 13.0890% |
| 2022-04-18 | 48.5945% | 15.2044% | 5.8556% | 4.8611% | 4.2658% | 7.9850% | 2.4404% | 1.6419% | 0.2887% | 0.4655% | 0.0209% | 8.3763% |
| 2022-04-19 | 50.8857% | 14.9250% | 5.8652% | 5.0294% | 3.9808% | 7.8429% | 2.2999% | 1.4581% | 0.2670% | 0.3945% | 0.0178% | 7.0337% |
| 2022-04-20 | 51.9700% | 14.2590% | 5.8156% | 4.8890% | 4.0109% | 7.6977% | 2.2176% | 1.4981% | 0.2546% | 0.4005% | 0.0209% | 6.9662% |
| 2022-04-21 | 52.5838% | 13.5549% | 5.7156% | 4.8685% | 3.9548% | 7.6767% | 2.3164% | 1.4839% | 0.2606% | 0.4122% | 0.0211% | 7.1515% |
| 2022-04-22 | 53.0145% | 12.8874% | 5.4692% | 4.7749% | 4.0399% | 7.5126% | 2.2910% | 1.4816% | 0.2738% | 0.3989% | 0.0203% | 7.8361% |
| 2022-04-23 | 51.2057% | 11.0804% | 4.4180% | 4.1472% | 4.4620% | 6.6022% | 2.4416% | 1.8031% | 0.3768% | 0.5984% | 0.0245% | 12.8400% |
| 2022-04-24 | 51.2867% | 10.9186% | 4.6488% | 4.1452% | 4.2797% | 6.9482% | 2.6263% | 1.8961% | 0.3964% | 0.6026% | 0.0243% | 12.2271% |
| 2022-04-25 | 54.1650% | 12.3169% | 5.5367% | 4.7520% | 4.0855% | 7.5898% | 2.2639% | 1.4719% | 0.2677% | 0.4090% | 0.0216% | 7.1201% |
| 2022-04-26 | 55.1848% | 11.9726% | 5.3847% | 4.6786% | 4.0406% | 7.4071% | 2.2462% | 1.4609% | 0.2643% | 0.3727% | 0.0237% | 6.9636% |
| 2022-04-27 | 55.9856% | 11.5124% | 5.3171% | 4.5683% | 3.9992% | 7.3195% | 2.2536% | 1.4565% | 0.2617% | 0.3671% | 0.0254% | 6.9336% |
| 2022-04-28 | 56.1709% | 11.2112% | 5.3278% | 4.5597% | 4.0453% | 7.2469% | 2.1800% | 1.4478% | 0.2588% | 0.3927% | 0.0223% | 7.1366% |
| 2022-04-29 | 56.4630% | 10.8456% | 5.0356% | 4.3545% | 4.1464% | 7.2195% | 2.1173% | 1.4117% | 0.2479% | 0.4089% | 0.0203% | 7.7295% |
| 2022-04-30 | 53.9976% | 8.9824% | 4.1147% | 3.7867% | 4.3880% | 6.3945% | 2.5134% | 1.7417% | 0.3505% | 0.6254% | 0.0265% | 13.0786% |
Safari releases a major version each year alongside macOS and iOS. The above shows the release cadence from January-April for Safari traffic on GitHub.com. While we see older versions used quite heavily, we also see regular upgrade cadence from Safari users, especially 15.x releases, with peak-to-peak usage approximately every eight weeks.
| 101 | 100 | 99 | 98 | 97 | 96 | <90 | 95 | 94 | 93 | 92 | 91 | 90 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022-01-01 | 0.0000% | 0.3491% | 0.1468% | 0.2108% | 0.3386% | 86.6783% | 5.1358% | 2.2244% | 1.7838% | 0.5934% | 1.5257% | 0.5474% | 0.4658% |
| 2022-01-02 | 0.0000% | 0.3455% | 0.1363% | 0.1842% | 0.3181% | 88.7497% | 3.2558% | 2.2186% | 1.8004% | 0.5739% | 1.4336% | 0.5302% | 0.4538% |
| 2022-01-03 | 0.0000% | 0.2731% | 0.0937% | 0.1450% | 0.2434% | 90.4934% | 2.1269% | 2.5113% | 1.5836% | 0.5793% | 1.0352% | 0.5289% | 0.3861% |
| 2022-01-04 | 0.0000% | 0.2561% | 0.0908% | 0.1459% | 0.2757% | 90.3926% | 2.2204% | 2.5350% | 1.5445% | 0.5799% | 1.0151% | 0.5280% | 0.4160% |
| 2022-01-05 | 0.0000% | 0.2556% | 0.0899% | 0.1520% | 4.1497% | 86.6672% | 2.2260% | 2.4636% | 1.5101% | 0.5757% | 0.9840% | 0.5220% | 0.4042% |
| 2022-01-06 | 0.0000% | 0.2508% | 0.0815% | 0.1412% | 10.7723% | 80.2589% | 2.1902% | 2.3842% | 1.4689% | 0.5581% | 0.9913% | 0.5089% | 0.3937% |
| 2022-01-07 | 0.0000% | 0.1932% | 0.0962% | 0.1629% | 21.5610% | 69.5394% | 2.2636% | 2.2909% | 1.4494% | 0.5384% | 1.0015% | 0.5018% | 0.4017% |
| 2022-01-08 | 0.0000% | 0.1744% | 0.1480% | 0.2586% | 33.5510% | 56.3914% | 2.9587% | 2.0026% | 1.7096% | 0.5449% | 1.3334% | 0.5045% | 0.4230% |
| 2022-01-09 | 0.0000% | 0.1518% | 0.1521% | 0.2564% | 37.8032% | 52.5723% | 2.8295% | 1.8732% | 1.6458% | 0.5064% | 1.3001% | 0.4884% | 0.4208% |
| 2022-01-10 | 0.0000% | 0.1330% | 0.1059% | 0.1782% | 36.7369% | 54.4084% | 2.4167% | 2.1394% | 1.4727% | 0.5493% | 0.9584% | 0.5085% | 0.3927% |
| 2022-01-11 | 0.0000% | 0.1219% | 0.1051% | 0.2332% | 45.4664% | 46.0626% | 2.2293% | 2.0457% | 1.4169% | 0.5245% | 0.9212% | 0.4878% | 0.3853% |
| 2022-01-12 | 0.0000% | 0.1134% | 0.1468% | 0.2459% | 57.1392% | 34.5425% | 2.1934% | 1.9506% | 1.3772% | 0.5162% | 0.9154% | 0.4807% | 0.3786% |
| 2022-01-13 | 0.0000% | 0.1046% | 0.1624% | 0.2693% | 65.4838% | 26.4436% | 2.0266% | 1.8866% | 1.3646% | 0.4987% | 0.9004% | 0.4861% | 0.3733% |
| 2022-01-14 | 0.0000% | 0.1031% | 0.1829% | 0.3063% | 70.0467% | 21.6414% | 2.2699% | 1.8110% | 1.3665% | 0.4882% | 0.9326% | 0.4758% | 0.3755% |
| 2022-01-15 | 0.0000% | 0.1537% | 0.2627% | 0.4051% | 74.7938% | 15.8317% | 2.8385% | 1.6009% | 1.5320% | 0.4885% | 1.2465% | 0.4664% | 0.3802% |
| 2022-01-16 | 0.0000% | 0.1602% | 0.2558% | 0.3909% | 76.5597% | 14.3898% | 2.7115% | 1.5114% | 1.5108% | 0.4653% | 1.2131% | 0.4474% | 0.3840% |
| 2022-01-17 | 0.0000% | 0.1317% | 0.1893% | 0.2867% | 76.2164% | 15.8049% | 2.0853% | 1.6876% | 1.3236% | 0.4815% | 0.9184% | 0.4969% | 0.3777% |
| 2022-01-18 | 0.0000% | 0.1289% | 0.1898% | 0.2709% | 77.6409% | 14.7878% | 1.9411% | 1.6397% | 1.2295% | 0.4692% | 0.8660% | 0.4755% | 0.3606% |
| 2022-01-19 | 0.0000% | 0.1319% | 0.1909% | 0.2707% | 79.0723% | 13.6180% | 1.9163% | 1.5781% | 1.0862% | 0.4649% | 0.8503% | 0.4634% | 0.3568% |
| 2022-01-20 | 0.0000% | 0.1221% | 0.1687% | 0.2831% | 80.3988% | 12.5592% | 1.8927% | 1.4921% | 1.0036% | 0.4499% | 0.8351% | 0.4446% | 0.3500% |
| 2022-01-21 | 0.0000% | 0.1234% | 0.1599% | 0.2858% | 81.4219% | 11.6477% | 1.9316% | 1.3819% | 0.9494% | 0.4453% | 0.8615% | 0.4436% | 0.3480% |
| 2022-01-22 | 0.0000% | 0.1797% | 0.2379% | 0.3808% | 82.0847% | 9.9598% | 2.6312% | 1.2211% | 0.9332% | 0.4429% | 1.1404% | 0.4392% | 0.3493% |
| 2022-01-23 | 0.0000% | 0.1942% | 0.2317% | 0.3695% | 82.6698% | 9.5777% | 2.5169% | 1.1724% | 0.9056% | 0.4432% | 1.1244% | 0.4327% | 0.3617% |
| 2022-01-24 | 0.0000% | 0.1529% | 0.1677% | 0.2628% | 83.1674% | 10.2728% | 1.8032% | 1.2686% | 0.8822% | 0.4320% | 0.8178% | 0.4349% | 0.3377% |
| 2022-01-25 | 0.0000% | 0.1588% | 0.1748% | 0.2607% | 83.9039% | 9.7041% | 1.7497% | 1.2257% | 0.8594% | 0.4230% | 0.7947% | 0.4221% | 0.3230% |
| 2022-01-26 | 0.0000% | 0.1649% | 0.1919% | 0.2684% | 84.5595% | 9.1948% | 1.7411% | 1.1326% | 0.8188% | 0.4088% | 0.7871% | 0.4108% | 0.3213% |
| 2022-01-27 | 0.0000% | 0.1706% | 0.1723% | 0.2653% | 85.0102% | 8.7534% | 1.7195% | 1.1492% | 0.8218% | 0.4177% | 0.7826% | 0.4201% | 0.3175% |
| 2022-01-28 | 0.0000% | 0.1863% | 0.1721% | 0.2998% | 85.4656% | 8.3345% | 1.7354% | 1.1011% | 0.7879% | 0.4023% | 0.7755% | 0.4212% | 0.3184% |
| 2022-01-29 | 0.0000% | 0.2432% | 0.2524% | 0.4486% | 85.0456% | 7.6439% | 2.4780% | 0.9756% | 0.7196% | 0.4008% | 1.0169% | 0.4348% | 0.3406% |
| 2022-01-30 | 0.0000% | 0.2498% | 0.2378% | 0.4736% | 85.6022% | 7.4084% | 2.3117% | 0.9470% | 0.6814% | 0.3705% | 0.9848% | 0.4109% | 0.3219% |
| 2022-01-31 | 0.0000% | 0.1921% | 0.1645% | 0.3336% | 86.8529% | 7.5929% | 1.4485% | 0.9938% | 0.7046% | 0.3647% | 0.6753% | 0.4006% | 0.2765% |
| 2022-02-01 | 0.0000% | 0.1961% | 0.1595% | 0.4035% | 87.0774% | 7.3390% | 1.4790% | 0.9652% | 0.6843% | 0.3597% | 0.6694% | 0.3948% | 0.2720% |
| 2022-02-02 | 0.0000% | 0.1956% | 0.1629% | 4.0875% | 83.8040% | 7.0391% | 1.4431% | 0.9144% | 0.6703% | 0.3552% | 0.6702% | 0.3885% | 0.2692% |
| 2022-02-03 | 0.0000% | 0.1961% | 0.1534% | 10.7877% | 77.4577% | 6.7343% | 1.4876% | 0.8727% | 0.6411% | 0.3440% | 0.6779% | 0.3804% | 0.2672% |
| 2022-02-04 | 0.0000% | 0.2091% | 0.2088% | 19.5037% | 68.9628% | 6.4236% | 1.5359% | 0.8393% | 0.6403% | 0.3368% | 0.6862% | 0.3818% | 0.2718% |
| 2022-02-05 | 0.0000% | 0.3378% | 0.2795% | 27.7997% | 59.7330% | 6.1880% | 2.2839% | 0.7829% | 0.5944% | 0.3307% | 0.9933% | 0.3800% | 0.2968% |
| 2022-02-06 | 0.0000% | 0.3490% | 0.2758% | 30.7760% | 56.9533% | 6.0346% | 2.2841% | 0.7652% | 0.5892% | 0.3248% | 0.9688% | 0.3856% | 0.2937% |
| 2022-02-07 | 0.0000% | 0.2698% | 0.2073% | 31.2145% | 57.4092% | 6.0665% | 1.6698% | 0.8326% | 0.6459% | 0.3355% | 0.6748% | 0.3836% | 0.2905% |
| 2022-02-08 | 0.0000% | 0.2835% | 0.2034% | 39.6256% | 49.2262% | 5.8524% | 1.7011% | 0.8005% | 0.6267% | 0.3329% | 0.6700% | 0.3867% | 0.2913% |
| 2022-02-09 | 0.0000% | 0.2854% | 0.2040% | 51.7434% | 37.4438% | 5.5967% | 1.6149% | 0.7902% | 0.6179% | 0.3346% | 0.6811% | 0.3886% | 0.2994% |
| 2022-02-10 | 0.0000% | 0.3035% | 0.2056% | 64.1751% | 25.4399% | 5.1914% | 1.6026% | 0.7773% | 0.6148% | 0.3323% | 0.6678% | 0.3856% | 0.3042% |
| 2022-02-11 | 0.0000% | 0.3200% | 0.2123% | 70.9156% | 18.9909% | 4.9838% | 1.4181% | 0.7774% | 0.6214% | 0.3416% | 0.7113% | 0.3908% | 0.3168% |
| 2022-02-12 | 0.0001% | 0.4216% | 0.2739% | 75.8758% | 12.7559% | 4.8394% | 2.5110% | 0.7159% | 0.6017% | 0.3329% | 0.9718% | 0.3929% | 0.3072% |
| 2022-02-13 | 0.0000% | 0.4237% | 0.2763% | 77.6752% | 11.3726% | 4.6607% | 2.3649% | 0.6861% | 0.5655% | 0.3140% | 0.9684% | 0.3837% | 0.3091% |
| 2022-02-14 | 0.0000% | 0.3152% | 0.2083% | 76.9611% | 13.1654% | 4.6464% | 1.7167% | 0.7342% | 0.5899% | 0.3201% | 0.6560% | 0.3933% | 0.2934% |
| 2022-02-15 | 0.0000% | 0.3124% | 0.2021% | 79.1282% | 11.3529% | 4.4636% | 1.6466% | 0.7071% | 0.5653% | 0.3201% | 0.6274% | 0.3832% | 0.2912% |
| 2022-02-16 | 0.0000% | 0.3243% | 0.2038% | 80.6639% | 9.9946% | 4.2701% | 1.6595% | 0.7158% | 0.5658% | 0.3122% | 0.6302% | 0.3760% | 0.2838% |
| 2022-02-17 | 0.0000% | 0.3285% | 0.2009% | 81.8338% | 8.9708% | 4.1527% | 1.6762% | 0.6951% | 0.5514% | 0.3076% | 0.6269% | 0.3747% | 0.2814% |
| 2022-02-18 | 0.0008% | 0.3497% | 0.2078% | 82.6826% | 8.0582% | 4.0522% | 1.7689% | 0.6878% | 0.5474% | 0.3209% | 0.6655% | 0.3684% | 0.2897% |
| 2022-02-19 | 0.0334% | 0.3962% | 0.3184% | 83.4303% | 6.2674% | 4.0039% | 2.4328% | 0.6835% | 0.5080% | 0.3128% | 0.9336% | 0.3811% | 0.2987% |
| 2022-02-20 | 0.0797% | 0.3311% | 0.3194% | 84.0234% | 5.9904% | 3.8715% | 2.3334% | 0.6594% | 0.5073% | 0.3056% | 0.9356% | 0.3620% | 0.2810% |
| 2022-02-21 | 0.0587% | 0.2598% | 0.2468% | 84.1764% | 6.8024% | 3.8159% | 1.8127% | 0.6649% | 0.5265% | 0.3067% | 0.6597% | 0.3832% | 0.2862% |
| 2022-02-22 | 0.0632% | 0.2399% | 0.2450% | 84.9382% | 6.4595% | 3.6142% | 1.7137% | 0.6378% | 0.5159% | 0.3049% | 0.6299% | 0.3638% | 0.2741% |
| 2022-02-23 | 0.0752% | 0.2359% | 0.2761% | 85.3787% | 6.0287% | 3.5009% | 1.7839% | 0.6317% | 0.5087% | 0.3049% | 0.6374% | 0.3672% | 0.2707% |
| 2022-02-24 | 0.0705% | 0.2362% | 0.2634% | 85.9954% | 5.6871% | 3.4246% | 1.7009% | 0.5988% | 0.4996% | 0.2919% | 0.6033% | 0.3600% | 0.2683% |
| 2022-02-25 | 0.0768% | 0.2443% | 0.2955% | 86.3641% | 5.2829% | 3.3680% | 1.7568% | 0.5657% | 0.4905% | 0.2913% | 0.6322% | 0.3625% | 0.2695% |
| 2022-02-26 | 0.0986% | 0.3235% | 0.4231% | 85.9440% | 4.4333% | 3.3909% | 2.5171% | 0.5412% | 0.4798% | 0.2909% | 0.9168% | 0.3703% | 0.2705% |
| 2022-02-27 | 0.1076% | 0.2852% | 0.4442% | 86.3178% | 4.2554% | 3.3355% | 2.4455% | 0.5217% | 0.4657% | 0.2732% | 0.8975% | 0.3688% | 0.2820% |
| 2022-02-28 | 0.0805% | 0.2264% | 0.3290% | 87.0944% | 4.7665% | 3.2002% | 1.7667% | 0.5552% | 0.4824% | 0.2741% | 0.6065% | 0.3504% | 0.2676% |
| 2022-03-01 | 0.0823% | 0.2243% | 0.4175% | 87.4211% | 4.5314% | 3.0594% | 1.7720% | 0.5578% | 0.4607% | 0.2697% | 0.5930% | 0.3512% | 0.2596% |
| 2022-03-02 | 0.0813% | 0.2251% | 3.9680% | 84.2254% | 4.3094% | 3.0111% | 1.6974% | 0.5485% | 0.4639% | 0.2678% | 0.6025% | 0.3420% | 0.2575% |
| 2022-03-03 | 0.0860% | 0.2245% | 11.9552% | 76.5380% | 4.1113% | 2.9298% | 1.6869% | 0.5471% | 0.4651% | 0.2670% | 0.5852% | 0.3437% | 0.2603% |
| 2022-03-04 | 0.1481% | 0.2362% | 22.5161% | 66.1485% | 3.8667% | 2.8832% | 1.7433% | 0.5229% | 0.4527% | 0.2609% | 0.6226% | 0.3430% | 0.2558% |
| 2022-03-05 | 0.2483% | 0.3164% | 33.6288% | 54.2056% | 3.4509% | 3.0747% | 2.3631% | 0.4963% | 0.4430% | 0.2635% | 0.8942% | 0.3452% | 0.2699% |
| 2022-03-06 | 0.2651% | 0.3227% | 37.2905% | 50.9078% | 3.3400% | 2.9203% | 2.2912% | 0.4958% | 0.4426% | 0.2506% | 0.8726% | 0.3363% | 0.2644% |
| 2022-03-07 | 0.1837% | 0.2660% | 36.9753% | 52.3521% | 3.5951% | 2.6073% | 1.6510% | 0.4961% | 0.4436% | 0.2591% | 0.5845% | 0.3299% | 0.2562% |
| 2022-03-08 | 0.1921% | 0.2728% | 46.1578% | 43.5044% | 3.4375% | 2.5065% | 1.5931% | 0.4913% | 0.4273% | 0.2568% | 0.5841% | 0.3277% | 0.2486% |
| 2022-03-09 | 0.1910% | 0.2750% | 58.6384% | 31.1182% | 3.3291% | 2.5030% | 1.5923% | 0.4941% | 0.4443% | 0.2631% | 0.5759% | 0.3217% | 0.2541% |
| 2022-03-10 | 0.1983% | 0.2759% | 66.6023% | 23.3853% | 3.1888% | 2.4334% | 1.6060% | 0.4924% | 0.4216% | 0.2532% | 0.5752% | 0.3215% | 0.2462% |
| 2022-03-11 | 0.2169% | 0.2953% | 71.2820% | 18.7575% | 3.0712% | 2.4137% | 1.6315% | 0.4822% | 0.4248% | 0.2533% | 0.6013% | 0.3233% | 0.2471% |
| 2022-03-12 | 0.3178% | 0.3579% | 75.0959% | 13.7713% | 2.8331% | 2.6181% | 2.3502% | 0.5067% | 0.4230% | 0.2547% | 0.8853% | 0.3329% | 0.2531% |
| 2022-03-13 | 0.2925% | 0.3544% | 76.5015% | 12.4441% | 2.7972% | 2.6467% | 2.3365% | 0.5097% | 0.4227% | 0.2400% | 0.8676% | 0.3224% | 0.2645% |
| 2022-03-14 | 0.2183% | 0.2980% | 76.5209% | 13.9213% | 2.8796% | 2.3086% | 1.5790% | 0.4721% | 0.4184% | 0.2446% | 0.5742% | 0.3196% | 0.2454% |
| 2022-03-15 | 0.2178% | 0.2821% | 78.3268% | 12.3836% | 2.7799% | 2.2183% | 1.5574% | 0.4742% | 0.4004% | 0.2423% | 0.5740% | 0.3035% | 0.2396% |
| 2022-03-16 | 0.2279% | 0.2746% | 79.9200% | 11.0179% | 2.6655% | 2.1855% | 1.5131% | 0.4623% | 0.3887% | 0.2386% | 0.5568% | 0.3099% | 0.2394% |
| 2022-03-17 | 0.2330% | 0.2813% | 81.1027% | 9.9678% | 2.5655% | 2.1601% | 1.5065% | 0.4504% | 0.3991% | 0.2343% | 0.5593% | 0.3033% | 0.2366% |
| 2022-03-18 | 0.2561% | 0.2912% | 82.1716% | 9.0172% | 2.4236% | 2.0762% | 1.5907% | 0.4408% | 0.3820% | 0.2322% | 0.5850% | 0.3003% | 0.2331% |
| 2022-03-19 | 0.3151% | 0.3508% | 82.1984% | 7.4544% | 2.3516% | 2.4225% | 2.3421% | 0.4546% | 0.4032% | 0.2363% | 0.8967% | 0.3170% | 0.2574% |
| 2022-03-20 | 0.2713% | 0.3462% | 82.7299% | 7.2246% | 2.2897% | 2.3687% | 2.3127% | 0.4359% | 0.4056% | 0.2296% | 0.8447% | 0.2944% | 0.2466% |
| 2022-03-21 | 0.1971% | 0.2725% | 83.3152% | 8.0553% | 2.3554% | 2.0378% | 1.6154% | 0.4333% | 0.3793% | 0.2315% | 0.5662% | 0.3034% | 0.2376% |
| 2022-03-22 | 0.1822% | 0.2902% | 84.1726% | 7.4951% | 2.2640% | 1.9560% | 1.5519% | 0.4160% | 0.3707% | 0.2215% | 0.5526% | 0.3038% | 0.2235% |
| 2022-03-23 | 0.1787% | 0.2905% | 84.8110% | 7.0357% | 2.1387% | 1.9110% | 1.5677% | 0.3992% | 0.3727% | 0.2213% | 0.5465% | 0.2992% | 0.2279% |
| 2022-03-24 | 0.1816% | 0.2881% | 85.3078% | 6.6819% | 2.0346% | 1.8871% | 1.5665% | 0.4017% | 0.3653% | 0.2162% | 0.5431% | 0.2961% | 0.2300% |
| 2022-03-25 | 0.1850% | 0.3259% | 85.5512% | 6.3257% | 1.9862% | 1.8161% | 1.6912% | 0.3979% | 0.3712% | 0.2314% | 0.5782% | 0.3023% | 0.2376% |
| 2022-03-26 | 0.2513% | 0.4347% | 84.9980% | 5.7174% | 1.8887% | 1.8396% | 2.4636% | 0.3976% | 0.3775% | 0.2233% | 0.8562% | 0.3074% | 0.2447% |
| 2022-03-27 | 0.2380% | 0.4364% | 85.5432% | 5.6370% | 1.7971% | 1.7317% | 2.3124% | 0.3832% | 0.3692% | 0.2104% | 0.8099% | 0.2877% | 0.2441% |
| 2022-03-28 | 0.1666% | 0.3208% | 86.8461% | 5.7222% | 1.8063% | 1.5579% | 1.5932% | 0.3879% | 0.3505% | 0.2161% | 0.5225% | 0.2842% | 0.2257% |
| 2022-03-29 | 0.1513% | 0.5652% | 87.1876% | 5.3979% | 1.7323% | 1.4451% | 1.5669% | 0.3681% | 0.3506% | 0.2009% | 0.5336% | 0.2811% | 0.2194% |
| 2022-03-30 | 0.1576% | 5.5490% | 82.6303% | 5.1355% | 1.6597% | 1.3612% | 1.5718% | 0.3627% | 0.3415% | 0.2026% | 0.5277% | 0.2815% | 0.2187% |
| 2022-03-31 | 0.1559% | 14.2487% | 74.3161% | 4.9095% | 1.5865% | 1.3213% | 1.5609% | 0.3582% | 0.3308% | 0.1997% | 0.5151% | 0.2800% | 0.2173% |
| 2022-04-01 | 0.1813% | 25.5917% | 63.0197% | 4.6924% | 1.5348% | 1.3091% | 1.7270% | 0.3518% | 0.3366% | 0.2027% | 0.5498% | 0.2824% | 0.2208% |
| 2022-04-02 | 0.2617% | 36.0589% | 50.8073% | 4.7315% | 1.5903% | 1.4032% | 2.7742% | 0.3796% | 0.3699% | 0.2150% | 0.8255% | 0.3100% | 0.2731% |
| 2022-04-03 | 0.2696% | 40.1393% | 47.6733% | 4.5903% | 1.4216% | 1.2821% | 2.3981% | 0.3682% | 0.3410% | 0.1911% | 0.8276% | 0.2667% | 0.2313% |
| 2022-04-04 | 0.2050% | 40.4830% | 49.2044% | 4.2831% | 1.3593% | 1.1840% | 1.5060% | 0.3320% | 0.3082% | 0.1899% | 0.5062% | 0.2527% | 0.1862% |
| 2022-04-05 | 0.2067% | 43.9099% | 46.1357% | 4.0916% | 1.3287% | 1.1513% | 1.4396% | 0.3215% | 0.3041% | 0.1849% | 0.4934% | 0.2481% | 0.1847% |
| 2022-04-06 | 0.2187% | 45.9304% | 43.8938% | 3.9022% | 1.3749% | 1.1920% | 1.6659% | 0.3339% | 0.3118% | 0.1924% | 0.5080% | 0.2672% | 0.2089% |
| 2022-04-07 | 0.2248% | 55.3760% | 34.7892% | 3.6035% | 1.3432% | 1.1667% | 1.6971% | 0.3292% | 0.3116% | 0.1860% | 0.5093% | 0.2567% | 0.2067% |
| 2022-04-08 | 0.2437% | 65.3241% | 24.9055% | 3.4441% | 1.3174% | 1.1591% | 1.7565% | 0.3383% | 0.3080% | 0.1865% | 0.5392% | 0.2649% | 0.2129% |
| 2022-04-09 | 0.3552% | 72.6629% | 16.1023% | 3.4380% | 1.3360% | 1.2065% | 2.6180% | 0.3881% | 0.3343% | 0.2036% | 0.8520% | 0.2712% | 0.2320% |
| 2022-04-10 | 0.3588% | 75.0152% | 14.1712% | 3.3374% | 1.2959% | 1.1573% | 2.4802% | 0.3769% | 0.3273% | 0.1896% | 0.8174% | 0.2547% | 0.2182% |
| 2022-04-11 | 0.2514% | 75.1833% | 15.7071% | 3.0419% | 1.2542% | 1.1076% | 1.6913% | 0.3187% | 0.3001% | 0.1846% | 0.5052% | 0.2561% | 0.1983% |
| 2022-04-12 | 0.2525% | 77.6105% | 13.5018% | 2.8775% | 1.2313% | 1.0921% | 1.6838% | 0.3182% | 0.2940% | 0.1846% | 0.4997% | 0.2535% | 0.2004% |
| 2022-04-13 | 0.2515% | 79.3135% | 11.8381% | 2.7657% | 1.1999% | 1.1073% | 1.7596% | 0.3188% | 0.2942% | 0.1894% | 0.4959% | 0.2597% | 0.2064% |
| 2022-04-14 | 0.2638% | 81.6428% | 10.2230% | 2.6689% | 1.0457% | 1.0041% | 1.5028% | 0.2964% | 0.2720% | 0.1751% | 0.4884% | 0.2337% | 0.1832% |
| 2022-04-15 | 0.3107% | 82.1871% | 8.7132% | 2.6720% | 1.0914% | 1.0746% | 2.0693% | 0.3225% | 0.2960% | 0.1897% | 0.6075% | 0.2495% | 0.2165% |
| 2022-04-16 | 0.4049% | 82.9440% | 6.8725% | 2.7691% | 1.0718% | 1.0948% | 2.6616% | 0.3438% | 0.3227% | 0.1963% | 0.8405% | 0.2559% | 0.2221% |
| 2022-04-17 | 0.4047% | 83.4272% | 6.5785% | 2.7740% | 1.0408% | 1.0796% | 2.5529% | 0.3526% | 0.3187% | 0.1759% | 0.8376% | 0.2427% | 0.2149% |
| 2022-04-18 | 0.2857% | 83.8299% | 7.5944% | 2.5342% | 1.0325% | 1.0743% | 1.8479% | 0.3261% | 0.2894% | 0.1820% | 0.5436% | 0.2572% | 0.2028% |
| 2022-04-19 | 0.2766% | 84.8091% | 7.2881% | 2.3503% | 0.9702% | 0.9922% | 1.6394% | 0.3148% | 0.2756% | 0.1719% | 0.4804% | 0.2454% | 0.1860% |
| 2022-04-20 | 0.2799% | 85.4701% | 6.7614% | 2.2479% | 0.9362% | 0.9852% | 1.6643% | 0.3012% | 0.2773% | 0.1684% | 0.4834% | 0.2422% | 0.1825% |
| 2022-04-21 | 0.2973% | 86.0018% | 6.2328% | 2.2261% | 0.9132% | 0.9700% | 1.7047% | 0.2971% | 0.2696% | 0.1666% | 0.4986% | 0.2399% | 0.1823% |
| 2022-04-22 | 0.3213% | 86.3868% | 5.7889% | 2.1593% | 0.9105% | 0.9627% | 1.7757% | 0.2977% | 0.2819% | 0.1722% | 0.5237% | 0.2371% | 0.1822% |
| 2022-04-23 | 0.4968% | 85.8966% | 4.7296% | 2.4019% | 0.8974% | 0.9904% | 2.5055% | 0.3403% | 0.3033% | 0.1721% | 0.8126% | 0.2451% | 0.2084% |
| 2022-04-24 | 0.4777% | 85.4802% | 4.7939% | 2.3608% | 0.9737% | 1.0529% | 2.7088% | 0.3398% | 0.3156% | 0.1795% | 0.7897% | 0.2788% | 0.2486% |
| 2022-04-25 | 0.3257% | 87.3284% | 5.1208% | 2.0780% | 0.8546% | 0.9471% | 1.6892% | 0.2974% | 0.2703% | 0.1654% | 0.5006% | 0.2404% | 0.1822% |
| 2022-04-26 | 0.4103% | 87.7223% | 4.8262% | 2.0045% | 0.8395% | 0.9266% | 1.6524% | 0.2915% | 0.2682% | 0.1640% | 0.4741% | 0.2361% | 0.1842% |
| 2022-04-27 | 4.8221% | 83.6717% | 4.5406% | 1.9638% | 0.8197% | 0.9201% | 1.6594% | 0.2805% | 0.2673% | 0.1625% | 0.4782% | 0.2347% | 0.1792% |
| 2022-04-28 | 10.2662% | 78.5895% | 4.2556% | 1.9180% | 0.8044% | 0.9025% | 1.6505% | 0.2883% | 0.2675% | 0.1639% | 0.4822% | 0.2347% | 0.1769% |
| 2022-04-29 | 14.2843% | 74.6277% | 3.9706% | 1.9208% | 0.8075% | 0.9010% | 1.8151% | 0.2902% | 0.2755% | 0.1664% | 0.5181% | 0.2346% | 0.1882% |
| 2022-04-30 | 18.8788% | 69.1757% | 3.2994% | 2.1357% | 0.8096% | 0.9441% | 2.6950% | 0.3253% | 0.2932% | 0.1724% | 0.8318% | 0.2364% | 0.2026% |
Chrome, Edge, and Firefox all have similar release cycles with releases every four weeks. Graphing Chrome traffic by version from January through April shows us how quickly older versions of these evergreen browsers fall off. We see peak-to-peak traffic around every four weeks, with a two week period where a single version represents more than 80% of all traffic for that browser.
This shows us that the promise of evergreen browsers is here today. The days of targeting one specific version of one browser are long gone. In fact, trying to do so today would be untenable. The Web Systems Team at GitHub removed the last traces of conditionals based on the user agent header in January 2020, and recorded an internal ADR explicitly disallowing this pattern due to how hard it is to maintain code that relies on user agent header parsing.
With that said, we still need to ensure some compatibility for user agents, which do not fall into the neat box of evergreen browsers. Universal access is important, and 1% of 73 million users is still 730,000 users.
Older browsers
When looking at the remaining 4% of browser traffic, we not only see very old versions of the most popular browsers, but also a diverse array of other branded user agents. Alongside older versions of Chrome (80-89 make up 1%, and 70-79 make up 0.2%), there are also Chromium forks, like QQ Browser (0.085%), Naver Whale (0.065%), and Silk (0.003%). Alongside older versions of Firefox (80-89 make up 0.12%, and 70-79 at 0.09%) there are Firefox forks, like IceWeasel (0.0002%) and SeaMonkey (0.0004%). The data also contains lots of esoteric user agents too, such as those from TVs, e-readers, and even refrigerators. In total, we’ve seen close to 20 million unique user agent strings visiting GitHub in 2022 alone.
Another vector we look at is logged-in vs. logged-out usage. As a whole, around 20% of the visits to GitHub come from browsers with logged-out sessions, but when looking at older browsers, the proportion of logged-out visits is much higher. For example, requests coming from Amazon Silk make up around 0.003% of all visits, but 80% of those visits are with logged-out sessions. Meaning, the number of logged-in visits on Silk is closer to 0.0006%. Users making requests with forked browsers also tend to make requests from evergreen browsers. For example, users making requests with SeaMonkey do so for 37% of their usage, while the other 63% come from Chrome or Firefox user agents.
We consider logged-in vs. logged-out, and main vs. a secondary browser to be important distinctions, because the usage patterns are quite different. Actions that a logged-out user takes (reading issues and pull requests, cloning repositories, and browsing files) are quite different to the actions a logged-in user takes (replying to issues and reviewing pull requests, starring repositories, editing files, and looking at their dashboard). Logged-out activities tend to be more “read only” actions, which means they hit very few paths that require JavaScript to run. Whereas logged-in users tend to perform the kind of rich interactions that require JavaScript.
With JavaScript disabled, you’re still able to log in, comment on issues and pull requests (although our rich markdown toolbar won’t work), browse source code (with syntax highlighting), search for repositories, and even star, watch, or fork them. Popover menus even work, thanks to the clever use of the HTML <details> element.
How we engineer for older browsers
With such a multitude of browsers, with various levels of standards compliance, we cannot expect our engineers to know the intricacies of each. We also don’t have the resources to test on the hundreds of browsers across thousands of operating system and version combinations we see, while 0.0002% of you are using your Tesla to merge pull requests, a Model 3 won’t fit into our testing budget! 
Instead, we use a few industry standard practices, like linting and polyfills, to make sure we’re delivering a good baseline experience:
Static code analysis (linting) to catch browser compatibility issues:
We love ESLint. It’s great at catching classes of bugs, as well as enforcing style, for which we have extensive configurations, but it can also be useful for catching cross-browser bugs. We use amilajack/eslint-plugin-compat for guarding against use of features that aren’t well supported, and we’re not prepared to polyfill (for example, ResizeObserver). We also use keithamus/eslint-plugin-escompat for catching use of syntax that browsers do not support, and we don’t polyfill or transpile. These plugins are incredibly useful for catching quirks, for example, older versions of Edge supported destructuring, but in some instances these caused a SyntaxError. By linting for this corner case, we were able to ship native destructuring syntax to all browsers with a lint check to prevent engineers from hitting SyntaxErrors. Shipping native destructuring syntax allowed us to remove multiple kilobytes of transpiled code and helper functions, while linting kept code stable for older versions of Edge.
Polyfills to patch browsers with modern features
Past iterations of our codebase made liberal use of polyfills, such as mdn-polyfills, es6-promise, template-polyfill, and custom-event-polyfill, to name a few. Managing polyfills was burdensome and in some cases hurt performance. We were restricted in certain ways. For example, we postponed adoption of ShadowDOM due to the poor performance of polyfills available at the time.
More recently, our strategy has been to maintain a small list of polyfills for code features that are easy enough to polyfill with low impact. These polyfills are open sourced in our browser-support repository. In this repository, we also maintain a function that checks if a browser has a base set of functionality necessary to run GitHub’s JavaScript. This check expects variables, like Blob, globalThis, and MutationObserver to exist. If a browser doesn’t pass this check, JavaScript still executes, but any uncaught exceptions will not be reported to our error reporting library that we call failbot. By preventing browsers that don’t meet our minimum requirements, we reduce the amount of noise in our error reporting systems, which increases the value of error reporting software dramatically. Here’s some relevant code from failbot.ts:
import {isSupported} from '@github/browser-support' const extensions = /(chrome|moz|safari)-extension:\/\// // Does this stack trace contain frames from browser extensions? function isExtensionError(stack: PlatformStackframe[]): boolean { return stack.some(frame => extensions.test(frame.filename) || extensions.test(frame.function)) } let errorsReported = 0 function reportable() { return errorsReported < 10 && isSupported() } export async function report(context: PlatformReportBrowserErrorInput) { if (!reportable()) return if (isExtensionError()) return errorsReported++ // ... }
In order to help us quickly determine which browsers meet our minimum requirements, and which browsers require which polyfills, our browser-support repository even has its own publicly-visible compatibility table!
Shipping changes and validating data
When it comes to making a change, like shipping native optional chaining syntax, one tool we reach for is an internal CLI that lets us quickly generate Markdown tables that can be added to pull requests introducing new native syntax or features that require polyfilling. This internal CLI tool uses mdn/browser-compat-data and combines it with the data we have to generate a Can I Use-style feature table, but tailored to our usage data and the requested feature. For example:
browser-support-cli $ ./browsers.js optional chaining
#### [javascript operators optional_chaining](https://developer.mozilla.org/docs/Web/JavaScript/Reference/Operators/Optional_chaining)
| Browser | Supported Since | Latest Version | % Supported | % Unsupported |
| :---------------------- | --------------: | -------------: | ----------: | ------------: |
| chrome | 80 | 101 | 73.482 | 0.090 |
| edge | 80 | 100 | 6.691 | 0.001 |
| firefox | 74 | 100 | 12.655 | 0.014 |
| firefox_android | 79 | 100 | 0.127 | 0.001 |
| ie | Not Supported | 11 | 0.000 | 0.078 |
| opera | 67 | 86 | 1.267 | 0.000 |
| safari | 13.1 | 15.4 | 4.630 | 0.013 |
| safari_ios | 13.4 | 15.4 | 0.505 | 0.006 |
| samsunginternet_android | 13.0 | 16.0 | 0.020 | 0.000 |
| webview_android | 80 | 101 | 0.001 | 0.008 |
| **Total:** | | | **99.378** | **0.211** |
We can then take this table and paste it into a pull request description to help provide data at the fingertips of whoever is reviewing the pull request, to ensure that we’re making decisions that are inline with our principles.
This CLI tool has a few more features. It actually generated all the tables in this post, which we could then easily generate graphs with. For quick glances at feature tables, it also allows for exporting of our analytics table into a JSON format that we can import into Can I Use.
browser-support-cli $ ./browsers.js
Usage:
node browsers.js <query>
Examples:
node browsers.js --stats [--csv] # Show usage stats by browser+version
node browsers.js --last-ten [--csv] # Show usage stats of the last 10 major versions, by vendor
node browsers.js --cadence [--csv] # Show release cadence stats
node browsers.js --caniuse # Output a `simple.json` for import into caniuse.com
node browsers.js --html <query> # Output html for github.github.io/browser-support
Wrap-up
This is how GitHub thinks about our users and the browsers they use. We back up our principles with tooling and data to make sure we’re delivering a fast and reliable service to as many users as possible.
Concepts like progressive enhancement allow us to deliver the best experience possible to the majority of customers, while delivering a useful experience to those using older browsers.
Metasploit Weekly Wrap-Up
Post Syndicated from Brendan Watters original https://blog.rapid7.com/2022/06/10/metasploit-weekly-wrap-up-161/
A Confluence of High-Profile Modules
This release features modules covering the Confluence remote code execution bug CVE-2022-26134 and the hotly-debated CVE-2022-30190, a file format vulnerability in the Windows Operating System accessible through malicious documents. Both have been all over the news, and we’re very happy to bring them to you so that you can verify mitigations and patches in your infrastructure. If you’d like to read more about these vulnerabilities, Rapid7 has AttackerKB analyses and blogs covering both Confluence CVE-2022-26134 (AttackerKB, Rapid7 Blog)and Windows CVE-2022-30190 (AttackKB, Rapid7 Blog).
Metasploit 6.2
While we release new content weekly (or in real-time if you are using github), we track milestones as well. This week, we released Metasploit 6.2, and it has a whole host of new functionality, exploits, and fixes
New module content (2)
- Atlassian Confluence Namespace OGNL Injection by Spencer McIntyre, Unknown, bturner-r7, and jbaines-r7, which exploits CVE-2022-26134 – This module exploits an OGNL injection in Atlassian Confluence servers (CVE-2022-26134). A specially crafted URI can be used to evaluate an OGNL expression resulting in OS command execution.
- Microsoft Office Word MSDTJS by mekhalleh (RAMELLA Sébastien) and nao sec, which exploits CVE-2022-30190 – This PR adds a module supporting CVE-2022-30190 (AKA Follina), a Windows file format vulnerability.
Enhancements and features (2)
- #16651 from red0xff – The
test_vulnerablemethods in the various SQL injection libraries have been updated so that they will now use the specified encoder if one is specified, ensuring that characters are appropriately encoded as needed. - #16661 from dismantl – The impersonate_ssl module has been enhanced to allow it to add Subject Alternative Names (SAN) fields to the generated SSL certificate.
Bugs fixed (4)
- #16615 from NikitaKovaljov – A bug has been fixed in the IPv6 library when creating solicited-multicast addresses by finding leading zeros in last 16 bits of link-local address and removing them.
- #16630 from zeroSteiner – The
auxiliary/server/capture/smbmodule no longer stores duplicate Net-NTLM hashes in the database. - #16643 from ojasookert – The
exploits/multi/http/php_fpm_rcemodule has been updated to be compatible with Ruby 3.0 changes. - #16653 from adfoster-r7 – :
This PR fixes an issue where named pipe pivots failed to establish the named pipes in intermediate connections.
Get it
As always, you can update to the latest Metasploit Framework with msfupdate
and you can get more details on the changes since the last blog post from
GitHub:
If you are a git user, you can clone the Metasploit Framework repo (master branch) for the latest.
To install fresh without using git, you can use the open-source-only Nightly Installers or the
binary installers (which also include the commercial edition).
Use an AD FS user and Tableau to securely query data in AWS Lake Formation
Post Syndicated from Jason Nicholls original https://aws.amazon.com/blogs/big-data/use-an-ad-fs-user-and-tableau-to-securely-query-data-in-aws-lake-formation/
Security-conscious customers often adopt a Zero Trust security architecture. Zero Trust is a security model centered on the idea that access to data shouldn’t be solely based on network location, but rather require users and systems to prove their identities and trustworthiness and enforce fine-grained identity-based authorization rules before granting access to applications, data, and other systems.
Some customers rely on third-party identity providers (IdPs) like Active Directory Federated Services (AD FS) as a system to manage credentials and prove identities and trustworthiness. Users can use their AD FS credentials to authenticate to various related yet independent systems, including the AWS Management Console (for more information, see Enabling SAML 2.0 federated users to access the AWS Management Console).
In the context of analytics, some customers extend Zero Trust to data stored in data lakes, which includes the various business intelligence (BI) tools used to access that data. A common data lake pattern is to store data in Amazon Simple Storage Service (Amazon S3) and query the data using Amazon Athena.
AWS Lake Formation allows you to define and enforce access policies at the database, table, and column level when using Athena queries to read data stored in Amazon S3. Lake Formation supports Active Directory and Security Assertion Markup Language (SAML) identity providers such as OKTA and Auth0. Furthermore, Lake Formation securely integrates with the AWS BI service Amazon QuickSight. QuickSight allows you to effortlessly create and publish interactive BI dashboards, and supports authentication via Active Directory. However, if you use alternative BI tools like Tableau, you may want to use your Active Directory credentials to access data stored in Lake Formation.
In this post, we show you how you can use AD FS credentials with Tableau to implement a Zero Trust architecture and securely query data in Amazon S3 and Lake Formation.
Solution overview
In this architecture, user credentials are managed by Active Directory, and not Amazon Identity and Access Management (IAM). Although Tableau provides a connector to connect Tableau to Athena, the connector requires an AWS access key ID and an AWS secret access key normally used for programmatic access. Creating an IAM user with programmatic access for use by Tableau is a potential solution, however some customers have made an architectural decision that access to AWS accounts is done via a federated process using Active Directory, and not an IAM user.
In this post, we show you how you can use the Athena ODBC driver in conjunction with AD FS credentials to query sample data in a newly created data lake. We simulate the environment by enabling federation to AWS using AD FS 3.0 and SAML 2.0. Then we guide you through setting up a data lake using Lake Formation. Finally, we show you how you can configure an ODBC driver for Tableau to securely query your data in the lake data using your AD FS credentials.
Prerequisites
The following prerequisites are required to complete this walkthrough:
- An understanding of IAM roles and concepts
- A basic understanding of Lake Formation and Athena
- A copy of Tableau with a 14-day trail or fully licensed software
- An understanding of the concepts of Active Directory, and how to join a computer to an Active Directory domain
- An understanding of configuring ODBC components on a Windows machine
Create your environment
To simulate the production environment, we created a standard VPC in Amazon Virtual Private Cloud (Amazon VPC) with one private subnet and one public subnet. You can do the same using the VPC wizard. Our Amazon Elastic Compute Cloud (Amazon EC2) instance running the Tableau client is located in a private subnet and accessible via an EC2 bastion host. For simplicity, connecting out to Amazon S3, AWS Glue, and Athena is done via the NAT gateway and internet gateway set up by the VPC wizard. Optionally, you can replace the NAT gateway with AWS PrivateLink endpoints (AWS Security Token Service (AWS STS), Amazon S3, Athena, and AWS Glue endpoints are required) to make sure traffic remains within the AWS network.
The following diagram illustrates our environment architecture.
After you create your VPC with its private and public subnets, you can continue to build out the other requirements, such as Active Directory and Lake Formation. Let’s begin with Active Directory.
Enable federation to AWS using AD FS 3.0 and SAML 2.0
AD FS 3.0, a component of Windows Server, supports SAML 2.0 and is integrated with IAM. This integration allows Active Directory users to federate to AWS using corporate directory credentials, such as a user name and password from Active Directory. Before you can complete this section, AD FS must be configured and running.
To set up AD FS, follow the instructions in Setting up trust between AD FS and AWS and using Active Directory credentials to connect to Amazon Athena with ODBC driver. The first section of the post explains in detail how to set up AD FS and establish the trust between AD FS and Active Directory. The post ends with setting up an ODBC driver for Athena, which you can skip. The post creates a group name called ArunADFSTest. This group relates to a role in your AWS account, which you use later.
When you have successfully verified that you can log in using your IdP, you’re ready to configure your Windows environment ODBC driver to connect to Athena.
Set up a data lake using Lake Formation
Lake Formation is a fully managed service that makes it easy for you to build, secure, and manage data lakes. Lake Formation provides its own permissions model that augments the IAM permissions model. This centrally defined permissions model enables fine-grained access to data stored in data lakes through a simple grant/revoke mechanism. We use this permissions model to grant access to the AD FS role we created earlier.
- On the Lake Formation console, you’re prompted with a welcome box the first time you access Lake formation.
The box asks you to select the initial administrative user and roles. - Choose Add myself and choose Get Started.
We use the sample database provided by Lake Formation, but you’re welcome to use your own dataset. For instructions on loading your own dataset, see Getting Started with Lake Formation.With Lake Formation configured, we must grant read access to the AD FS role (ArunADFSTest) we created in the previous step. - In the navigation pane, choose Databases.
- Select the database
sampledb. - On the Actions menu, choose Grant.
We grant theSamlOdbcAccessrole access tosampledb. - For Principals, select IAM users and roles.
- For IAM users and roles, choose the role
ArunADFSTest. - Select Named data catalog resources.
- For Databases, choose the database
sampledb. - For Tables¸ choose All tables.
- Set the table permissions to Select and Describe.
- For Data permissions, select All data access.
- Choose Grant.
Our AD FS user assumes the roleArunADFSTest, which has been granted access tosampledbby Lake Formation. However, theArunADFSTestrole requires access to Lake Formation, Athena, AWS Glue, and Amazon S3. Following the practice of least privilege, AWS defines policies for specific Lake Formation personas. Our user fits the Data Analyst persona, which requires enough permissions to run queries. - Add the
AmazonAthenaFullAccessmanaged policy (for instructions, see Adding and removing IAM identity permissions) and the following inline policy to theArunADFSTestrole:Each time Athena runs a query, it stores the results in an S3 bucket, which is configured as the query result location in Athena.
- Create an S3 bucket, and in this new bucket create a new folder called
athena_results. - Update the settings on the Athena console to use your newly created folder.
Tableau uses Athena to run the query and read the results from Amazon S3, which means that theArunADFSTestrole requires access to your newly created S3 folder. - Attach the following inline policy to the
ArunADFSTestrole:
Our AD FS user can now assume a role that has enough privileges to query the sample database. The next step is to configure the ODBC driver on the client.
Configure an Athena ODBC driver
Athena is a managed serverless and interactive query service that allows you to analyze your data in Amazon S3 using standard Structured Query Language (SQL). You can use Athena to directly query data that is located in Amazon S3 or data that is registered with Lake Formation. Athena provides you with ODBC and JDBC drivers to effortlessly integrate with your data analytics tools (such as Microsoft Power BI, Tableau, or SQL Workbench) to seamlessly gain insights about your data in minutes.
To connect to our Lake Formation environment, we first need to install and configure the Athena ODBC driver on our Windows environment.
- Download the Athena ODBC driver relevant to your Windows environment.
- Install the driver by choosing the driver file you downloaded (in our case,
Simba+Athena+1.1+64-bit.msi).
- Choose Next on the welcome page.
- Read the End-User License Agreement, and if you agree to it, select I Accept the terms in the License Agreement and choose Next.
- Leave the default installation location for the ODBC driver and choose Next.
- Choose Install to begin the installation.
- If the User Access Control pop-up appears, choose Yes to allow the driver installation to continue.
- When the driver installation is complete, choose Finish to close the installer.
- Open the Windows ODBC configuration application by selecting the Start bar and searching for ODBC.
- Open the version corresponding to the Athena ODBC version you installed, in our case 64 bit.
- On the User DSN tab, choose Add.
- Choose the Simba Athena ODBC driver and choose Finish.
Configure the ODBC driver to AD FS authentication
We now need to configure the driver.
- Choose the driver on the Driver configuration page.
- For Data Source Name, enter
sampledb. - For Description, enter
Lake Formation Sample Database. - For AWS Region, enter
eu-west-1or the Region you used when configuring Lake Formation. - For Metadata Retrieval Method, choose Auto.
- For S3 Output Location, enter
s3://[BUCKET_NAME]/athena_results/. - For Encryption Options, choose NOT_SET.
- Clear the rest of the options.
- Choose Authentication Options.
- For Authentication Type, choose ADFS.
- For User, enter
[DOMAIN]\[USERNAME]. - For Password, enter your domain user password.
- For Preferred Role, enter
aws:iam::[ACCOUNT NUMBER]:role/ArunADFSTest.
The preferred role is the same role configured in the previous section (ArunADFSTest). - For IdP Host, enter the AD federation URL you configured during AD FS setup.
- For IdP Port, enter
443. - Select SSL Insecure.
- Choose OK.
- Choose Test on the initial configuration page to test the connection.
- When you see a success confirmation, choose OK.
We can now connect to our Lake Formation sample database from our desktop environment using the Athena ODBC driver. The next step is to use Tableau to query our data using the ODBC connection.
Connect to your data using Tableau
To connect to your data, complete the following steps:
- Open your Tableau Desktop edition.
- Under To a Server, choose More.
- On the list of available Tableau installed connectors, choose Other Databases (ODBC).
- Choose the ODBC database you created earlier.
- Choose Connect.
- Choose Sign In.
When the Tableau workbook opens, select the database, schema, and table that you want to query. - For Database, choose the database as listed in the ODBC setup (for this post,
AwsDataCatalog). - For Schema, choose your schema (
sampledb). - For Table, search for and choose your table (
elb_logs). - Drag the table to the work area to start your query and further report development.
Clean up
AWS Lake Formation provides database-, table-, column-, and tag-based access controls, and cross-account sharing at no charge. Lake Formation charges a fee for transaction requests and for metadata storage. In addition to providing a consistent view of data and enforcing row-level and cell-level security, the Lake Formation Storage API scans data in Amazon S3 and applies row and cell filters before returning results to applications. There is a fee for this filtering. To make sure that you’re not charged for any of the services that you no longer need, stop any EC2 instances that you created. Remove any objects in Amazon S3 you no longer require, because you pay for objects stored in S3 buckets.
Lastly, delete any Active Directory instances you may have created.
Conclusion
Lake Formation makes it simple to set up a secure data lake and then use the data lake with your choice of analytics and machine learning services, including Tableau. In this post, we showed you how you can connect to your data lake using AD FS credentials in a simple and secure way by using the Athena ODBC driver. Your AD FS user is configured within the ODBC driver, which then assumes a role in AWS. This role is granted access to only the data you require via Lake Formation.
To learn more about Lake Formation, see the Lake Formation Developer Guide or follow the Lake Formation workshop.
About the Authors
Jason Nicholls is an Enterprise Solutions Architect at AWS. He’s passionate about building scalable web and mobile applications on AWS. He started coding on a Commodore VIC 20, which lead to a career in software development. Jason holds a MSc in Computer Science with specialization in coevolved genetic programming. He is based in Johannesburg, South Africa.
Francois van Rensburg is a Partner Management Solutions Architect at AWS. He has spent the last decade helping enterprise organizations successfully migrate to the cloud. He is passionate about networking and all things cloud. He started as a Cobol programmer and has built everything from software to data centers. He is based in Denver, Colorado.
Introducing a new AWS whitepaper: Does data localization cause more problems than it solves?
Post Syndicated from Jana Kay original https://aws.amazon.com/blogs/security/introducing-a-new-aws-whitepaper-does-data-localization-cause-more-problems-than-it-solves/
Amazon Web Services (AWS) recently released a new whitepaper, Does data localization cause more problems than it solves?, as part of the AWS Innovating Securely briefing series. The whitepaper draws on research from Emily Wu’s paper Sovereignty and Data Localization, published by Harvard University’s Belfer Center, and describes how countries can realize similar data localization objectives through AWS services without incurring the unintended effects highlighted by Wu.
Wu’s research analyzes the intent of data localization policies, and compares that to the reality of the policies’ effects, concluding that data localization policies are often counterproductive to their intended goals of data security, economic competitiveness, and protecting national values.
The new whitepaper explains how you can use the security capabilities of AWS to take advantage of up-to-date technology and help meet your data localization requirements while maintaining full control over the physical location of where your data is stored.
AWS offers robust privacy and security services and features that let you implement your own controls. AWS uses lessons learned around the globe and applies them at the local level for improved cybersecurity against security events. As an AWS customer, after you pick a geographic location to store your data, the cloud infrastructure provides you greater resiliency and availability than you can achieve by using on-prem infrastructure. When you choose an AWS Region, you maintain full control to determine the physical location of where your data is stored. AWS also provides you with resources through the AWS compliance program, to help you understand the robust controls in place at AWS to maintain security and compliance in the cloud.
An important finding of Wu’s research is that localization constraints can deter innovation and hurt local economies because they limit which services are available, or increase costs because there are a smaller number of service providers to choose from. Wu concludes that data localization can “raise the barriers [to entrepreneurs] for market entry, which suppresses entrepreneurial activity and reduces the ability for an economy to compete globally.” Data localization policies are especially challenging for companies that trade across national borders. International trade used to be the remit of only big corporations. Current data-driven efficiencies in shipping and logistics mean that international trade is open to companies of all sizes. There has been particular growth for small and medium enterprises involved in services trade (of which cross-border data flows are a key element). In a 2016 worldwide survey conducted by McKinsey, 86 percent of tech-based startups had at least one cross-border activity. The same report showed that cross-border data flows added some US$2.8 trillion to world GDP in 2014.
However, the availability of cloud services supports secure and efficient cross-border data flows, which in turn can contribute to national economic competitiveness. Deloitte Consulting’s report, The cloud imperative: Asia Pacific’s unmissable opportunity, estimates that by 2024, the cloud will contribute $260 billion to GDP across eight regional markets, with more benefit possible in the future. The World Trade Organization’s World Trade Report 2018 estimates that digital technologies, which includes advanced cloud services, will account for a 34 percent increase in global trade by 2030.
Wu also cites a link between national data governance policies and a government’s concerns that movement of data outside national borders can diminish their control. However, the technology, storage capacity, and compute power provided by hyperscale cloud service providers like AWS, can empower local entrepreneurs.
AWS continually updates practices to meet the evolving needs and expectations of both customers and regulators. This allows AWS customers to use effective tools for processing data, which can help them meet stringent local standards to protect national values and citizens’ rights.
Wu’s research concludes that “data localization is proving ineffective” for meeting intended national goals, and offers practical alternatives for policymakers to consider. Wu has several recommendations, such as continuing to invest in cybersecurity, supporting industry-led initiatives to develop shared standards and protocols, and promoting international cooperation around privacy and innovation. Despite the continued existence of data localization policies, countries can currently realize similar objectives through cloud services. AWS implements rigorous contractual, technical, and organizational measures to protect the confidentiality, integrity, and availability of customer data, regardless of which AWS Region you select to store their data. As an AWS customer, this means you can take advantage of the economic benefits and the support for innovation provided by cloud computing, while improving your ability to meet your core security and compliance requirements.
For more information, see the whitepaper Does data localization cause more problems than it solves?, or contact AWS.
If you have feedback about this post, submit comments in the Comments section below.
sex robot 2
Post Syndicated from turnoff.us - geek comic site original http://turnoff.us/geek/sex-robot-2
readme
Post Syndicated from turnoff.us - geek comic site original http://turnoff.us/geek/readme
Identification of replication bottlenecks when using AWS Application Migration Service
Post Syndicated from Tobias Reekers original https://aws.amazon.com/blogs/architecture/identification-of-replication-bottlenecks-when-using-aws-application-migration-service/
Enterprises frequently begin their journey by re-hosting (lift-and-shift) their on-premises workloads into AWS and running Amazon Elastic Compute Cloud (Amazon EC2) instances. A simpler way to re-host is by using AWS Application Migration Service (Application Migration Service), a cloud-native migration service.
To streamline and expedite migrations, automate reusable migration patterns that work for a wide range of applications. Application Migration Service is the recommended migration service to lift-and-shift your applications to AWS.
In this blog post, we explore key variables that contribute to server replication speed when using Application Migration Service. We will also look at tests you can run to identify these bottlenecks and, where appropriate, include remediation steps.
Overview of migration using Application Migration Service
Figure 1 depicts the end-to-end data replication flow from source servers to a target machine hosted on AWS. The diagram is designed to help visualize potential bottlenecks within the data flow, which are denoted by a black diamond.
Figure 1. Data flow when using AWS Application Migration Service (black diamonds denote potential points of contention)
Baseline testing
To determine a baseline replication speed, we recommend performing a control test between your target AWS Region and the nearest Region to your source workloads. For example, if your source workloads are in a data center in Rome and your target Region is Paris, run a test between eu-south-1 (Milan) and eu-west-3 (Paris). This will give a theoretical upper bandwidth limit, as replication will occur over the AWS backbone. If the target Region is already the closest Region to your source workloads, run the test from within the same Region.
Network connectivity
There are several ways to establish connectivity between your on-premises location and AWS Region:
- Public internet
- VPN
- AWS Direct Connect
This section pertains to options 1 and 2. If facing replication speed issues, the first place to look is at network bandwidth. From a source machine within your internal network, run a speed test to calculate your bandwidth out to the internet; common test providers include Cloudflare, Ookla, and Google. This is your bandwidth to the internet, not to AWS.
Next, to confirm the data flow from within your data center, run a traceroute (Windows) or tracert (Linux). Identify any network hops that are unusual or potentially throttling bandwidth (due to hardware limitations or configuration).
To measure the maximum bandwidth between your data center and the AWS subnet that is being used for data replication, while accounting for Security Sockets Layer (SSL) encapsulation, use the CloudEndure SSL bandwidth tool (refer to Figure 1).
Source storage I/O
The next area to look for replication bottlenecks is source storage. The underlying storage for servers can be a point of contention. If the storage is maxing-out its read speeds, this will impact the data-replication rate. If your storage I/O is heavily utilized, it can impact block replication by Application Migration Service. In order to measure storage speeds, you can use the following tools:
- Windows: WinSat (or other third-party tooling, like AS SSD Benchmark)
- Linux: hdparm
We suggest reducing read/write operations on your source storage when starting your migration using Application Migration Service.
Application Migration Service EC2 replication instance size
The size of the EC2 replication server instance can also have an impact on the replication speed. Although it is recommended to keep the default instance size (t3.small), it can be increased if there are business requirements, like to speed up the initial data sync. Note: using a larger instance can lead to increased compute costs.
Common replication instance changes include:
- Servers with <26 disks: change the instance type to m5.large. Increase the instance type to m5.xlarge or higher, as needed.
- Servers with <26 disks (or servers in AWS Regions that do not support m5 instance types): change the instance type to m4.large. Increase to m4.xlarge or higher, as needed.
Note: Changing the replication server instance type will not affect data replication. Data replication will automatically pick up where it left off, using the new instance type you selected.
Application Migration Service Elastic Block Store replication volume
You can customize the Amazon Elastic Block Store (Amazon EBS) volume type used by each disk within each source server in that source server’s settings (change staging disk type).
By default, disks <500GiB use Magnetic HDD volumes. AWS best practice suggests not change the default Amazon EBS volume type, unless there is a business need for doing so. However, as we aim to speed up the replication, we actively change the default EBS volume type.
There are two options to choose from:
- The lower cost, Throughput Optimized HDD (st1) option utilizes slower, less expensive disks.
-
- Consider this option if you(r):
- Want to keep costs low
- Large disks do not change frequently
- Are not concerned with how long the initial sync process will take
- Consider this option if you(r):
- The faster, General Purpose SSD (gp2) option utilizes faster, but more expensive disks.
-
- Consider this option if you(r):
- Source server has disks with a high write rate, or if you need faster performance in general
- Want to speed up the initial sync process
- Are willing to pay more for speed
- Consider this option if you(r):
Source server CPU
The Application Migration Service agent that is installed on the source machine for data replication uses a single core in most cases (agent threads can be scheduled to multiple cores). If core utilization reaches a maximum, this can be a limitation for replication speed. In order to check the core utilization:
- Windows: Launch the Task Manger application within Windows, and click on the “CPU” tab. Right click on the CPU graph (this is currently showing an average of cores) > select “Change graph to” > “Logical processors”. This will show individual cores and their current utilization (Figure 2).
Figure 2. Logical processor CPU utilization
Linux: Install htop and run from the terminal. The htop command will display the Application Migration Service/CE process and indicate the CPU and memory utilization percentage (this is of the entire machine). You can check the CPU bars to determine if a CPU is being maxed-out (Figure 3).
Figure 3. AWS Application Migration Service/CE process to assess CPU utilization
Conclusion
In this post, we explored several key variables that contribute to server replication speed when using Application Migration Service. We encourage you to explore these key areas during your migration to determine if your replication speed can be optimized.
Related information
Tala Bashmi | MENA’s Top Female Chef | Talks at Google
Post Syndicated from Talks at Google original https://www.youtube.com/watch?v=Y9s_zlTcbcQ
[VIDEO] An Inside Look at the RSA 2022 Experience From the Rapid7 Team
Post Syndicated from Jesse Mack original https://blog.rapid7.com/2022/06/10/video-an-inside-look-at-the-rsa-2022-experience-from-the-rapid7-team/
![[VIDEO] An Inside Look at the RSA 2022 Experience From the Rapid7 Team](https://blog.rapid7.com/content/images/2022/06/RSAC-2022-experience.jpg)
The two years since the last RSA Conference have been pretty uneventful. Sure, COVID-19 sent us all to work from home for a little while, but it’s not as though we’ve seen any supply-chain-shattering breaches, headline-grabbing ransomware attacks, internet-inferno vulnerabilities, or anything like that. We’ve mostly just been baking sourdough bread and doing woodworking in between Zoom meetings.
OK, just kidding on basically all of that (although I, for one, have continued to hone my sourdough game).
The reality has been quite the opposite. Whether it’s because an unprecedented number of crazy things have happened since March 2020 or because pandemic-era uncertainty has made all of our experiences feel a little more heightened, the past 24 months have been a lot. And now that restrictions on gatherings are largely lifted in most places, many of us are feeling like we need a chance to get together and debrief on what we’ve all been through.
Given that context, what better timing could there have been for RSAC 2022? This past week, a crew of Rapid7 team members gathered in San Francisco to sync up with the greater cybersecurity community and take stock of how we can all stay ahead of attackers and ready for the future in the months to come. We asked four of them — Jeffrey Gardner, Practice Advisor – Detection & Response; Tod Beardsley, Director of Research; Kelly Allen, Social Media Manager; and Erick Galinkin, Principal Artificial Intelligence Researcher — to tell us a little bit about their RSAC 2022 experience. Here’s a look at what they had to say — and a glimpse into the excitement and energy of this year’s RSA Conference.
What’s it been like returning to full-scale in-person events after 2 years?
![[VIDEO] An Inside Look at the RSA 2022 Experience From the Rapid7 Team](https://play.vidyard.com/H2GfDP6w1PPGWrcrbR6cDW.jpg)
What was your favorite session or speaker of the week? What made them stand out?
![[VIDEO] An Inside Look at the RSA 2022 Experience From the Rapid7 Team](https://play.vidyard.com/rjqC7aBPwPEp682bGvm3Pt.jpg)
What was your biggest takeaway from the conference? How will it shape the way you think about and practice cybersecurity in the months to come?
![[VIDEO] An Inside Look at the RSA 2022 Experience From the Rapid7 Team](https://play.vidyard.com/FBbwA1HVwbrsC9gNEPC7h5.jpg)
Want to relive the RSA experience for yourself? Check out our replays of Rapid7 speakers’ sessions from the week.
Additional reading:
[$] Vetting the cargo
Post Syndicated from original https://lwn.net/Articles/897435/
Modern language environments make it easy to discover and incorporate
externally written libraries into a program. These same mechanisms can
also make it easy to inadvertently incorporate security vulnerabilities or
overtly malicious code, which is rather less gratifying. The stream of
resulting vulnerabilities seems like it will
never end, and it afflicts relatively safe
languages like Rust just as much as any other language. In an effort
to avoid the embarrassment that comes with shipping vulnerabilities (or
worse) by way of its dependencies, the Mozilla project has come up with a new supply-chain management tool known as
“cargo vet“.
Three Weird Computer Mice: Cakes, Cursors, & Balls
Post Syndicated from LGR original https://www.youtube.com/watch?v=JgDh3ZRtzJg
Security updates for Friday
Post Syndicated from original https://lwn.net/Articles/897518/
Security updates have been issued by Debian (python-bottle), Fedora (grub2 and kernel), Mageia (python-pypdf2, python-ujson, and vim), and SUSE (fribidi, grub2, mozilla-nss, and webkit2gtk3).
Spanish Canada: The Nootka Crisis
Post Syndicated from The History Guy: History Deserves to Be Remembered original https://www.youtube.com/watch?v=D4r-HcgaLTA
I belong in computer science
Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/i-belong-in-computer-science-isaac-computer-science/
At the Raspberry Pi Foundation, we believe everyone belongs in computer science, and that it is a much more varied field than is commonly assumed. One of the ways we want to promote inclusivity and highlight the variety of skills and interests needed in computer science is through our ‘I belong’ campaign. We do this because the tech sector lacks diversity. Similarly, in schools, there is underrepresentation of students in computing along the axes of gender, ethnicity, and economic situation. (See how researchers describe data from England, and data from the USA.)
The ‘I belong’ campaign is part of our work on Isaac Computer Science, our free online learning platform for GCSE and A level students (ages 14 to 18) and their teachers, funded by the Department for Education. The campaign celebrates young computer scientists and how they came to love the subject, what their career journey has been so far, and what their thoughts are about inclusivity and belonging in their chosen field.
These people are role models who demonstrate that everyone belongs in computer science, and that everyone can bring their interests and skills to bear in the field. In this way, we want to show young people that they can do much more with computing than they might think, and to inspire them to consider how computing could be part of their own life and career path.
Meet Salome
Salome is studying Computer Science with Digital Technology Solutions at the University of Leeds and doing a degree apprenticeship with PricewaterhouseCoopers (PwC).
“I was quite lucky, as growing up I saw a lot about women in STEM which inspired me to take this path. I think to improve the online community, we need to keep challenging stereotypes and getting more and more people to join, thereby improving the diversity. This way, a larger number of people can have role models and identify themselves with someone currently there.”
“Another thing is the assumption that computer science is just coding and not a wide and diverse field. I still have to explain to my friends what computer science involves and can become, and then they will say, ‘Wow, that’s really interesting, I didn’t know that.’”
Meet Devyani
Devyani is a third-year degree apprentice at Cisco.
“It was at A level where I developed my programming skills, and it was more practical rather than theoretical. I managed to complete a programming project where I utilised PHP, JavaScript, and phpMyAdmin (which is a database). It was after this that I started looking around and applying for degree apprenticeships. I thought that university wasn’t for me, because I wanted a more practical and hands-on approach, as I learn better that way.”
“At the moment, I’m currently doing a product owner role, which is where I hope to graduate into. It’s a mix between both a business role and a technical role. I have to stay up to speed with the current technologies we are using and developing for our clients and customers, but also I have to understand business needs and ensure that the team is able to develop and deliver on time to meet those needs.”
Meet Omar
Omar is a Mexican palaeontologist who uses computer science to study dinosaur bones.
“I try to bring aspects that are very well developed in computer science and apply them in palaeontology. For instance, when digitising the vertebrae, I use a lot of information theory. I also use a lot of data science and integrity to make sure that what we have captured is comparable with what other people have found.”
“What drove me to computers was the fact you are always learning. That’s what keeps me interested in science: that I can keep growing, learn from others, and I can teach people. That’s the other thing that makes me feel like I belong, which is when I am able to communicate the things I know to someone else and I can see the face of the other person when they start to grasp a theory.”
Meet Tasnima
Tasnima is a computer science graduate from Queen Mary University of London, and is currently working as a software engineer at Credit Suisse.
“During the pandemic, one of the good things to come out of it is that I could work from home, and that means working with people all over the world, bringing together every race, religion, gender, etc. Even though we are all very different, the one thing we all have in common is that we’re passionate about technology and computer science. Another thing is being able to work in technology in the real world. It has allowed me to work in an environment that is highly collaborative. I always feel like you’re involved from the get-go.”
“I think we need to also break the image that computer science is all about coding. I’ve had friends that have stayed away from any tech jobs because they think that they don’t want to code, but there’s so many other roles within technology and jobs that actually require no coding whatsoever.”
Meet Aleena
Aleena is a software engineer who works at a health tech startup in London and is also studying for a master’s degree in AI ethics at the University of Cambridge.
“I do quite a lot of different things as an engineer. It’s not just coding, which is part of it but it is a relatively small percentage, compared to a lot of other things. […] There’s a lot of collaborative time and I would say a quarter or third of the week is me by myself writing code. The other time is spent collaborating and working with other people and making sure that we’re all aligned on what we are working on.”
“I think it’s actually a very diverse field of tech to work in, once you actually end up in the industry. When studying STEM subjects at a college or university level it is often not very diverse. The industry is the opposite. A lot of people come from self-taught or bootcamp backgrounds, there’s a lot of ways to get into tech and software engineering, and I really like that aspect of it. Computer science isn’t the only way to go about it.”
Meet Alice
Alice is a final-year undergraduate student of Computer Science with Artificial Intelligence at the University of Brighton. She is also the winner of the Global Challenges COVID-19 Research Scholarship offered by Santander Universities.
“[W]e need to advertise computer science as more than just a room full of computers, and to advertise computer sciences as highly collaborative. It’s very creative. If you’re on a team of developers, there’s a lot of communication involved.”
“There’s something about computer science that I think is so special: the fact that it is a skill anybody can learn, regardless of who they are. With the right idea, anybody can build anything.”
Share these stories to inspire
Help us spread the message that everyone belongs in computer science: share this blog with schools, teachers, STEM clubs, parents, and young people you want to inspire.
You can learn computer science with us
Whether you’re studying or teaching computer science GCSE or A levels in the UK (or thinking about doing so!), or you’re a teacher or student in another part of the world, Isaac Computer Science is here to help you achieve your computer science goals. Our high-quality learning platform is free to use and open to all. As a student, you can register to keep track of your progress. As a teacher, you can sign up to guide your students’ learning.
And for younger learners, we have lots of fun project guides to try out coding and creating with digital technologies.
The post I belong in computer science appeared first on Raspberry Pi.
AAE-1 & SMW5 cable cuts impact millions of users across multiple countries
Post Syndicated from David Belson original https://blog.cloudflare.com/aae-1-smw5-cable-cuts/
Just after 1200 UTC on Tuesday, June 7, the Africa-Asia-Europe-1 (AAE-1) and SEA-ME-WE-5 (SMW-5) submarine cables suffered cable cuts. The damage reportedly occurred in Egypt, and impacted Internet connectivity for millions of Internet users across multiple countries in the Middle East and Africa, as well as thousands of miles away in Asia. In addition, Google Cloud Platform and OVHcloud reported connectivity issues due to these cable cuts.
The impact
Data from Cloudflare Radar showed significant drops in traffic across the impacted countries as the cable damage occurred, recovering approximately four hours later as the cables were repaired.
It appears that Saudi Arabia may have also been affected by the cable cut(s), but the impact was much less significant, and traffic recovered almost immediately.
In the graphs above, we show that Ethiopia was one of the impacted countries. However, as it is landlocked, there are obviously no submarine cable landing points within the country. The Afterfibre map from the Network Startup Resource Center (NSRC) shows that that fiber in Ethiopia connects to fiber in Somalia, which experienced an impact. In addition, Ethio Telecom also routes traffic through network providers in Kenya and Djibouti. Djibouti Telecom, one of these providers, in turn peers with larger global providers like Telecom Italia (TI) Sparkle, which is one of the owners of SMW5.
In addition to impacting end-user connectivity in the impacted countries, the cable cuts also reportedly impacted cloud providers including Google Cloud Platform and OVHcloud. In their incident report, Google Cloud noted “Google Cloud Networking experienced increased packet loss for egress traffic from Google to the Middle East, and elevated latency between our Europe and Asia Regions as a result, for 3 hours and 12 minutes, affecting several related products including Cloud NAT, Hybrid Connectivity and Virtual Private Cloud (VPC). From preliminary analysis, the root cause of the issue was a capacity shortage following two simultaneous fiber-cuts.” OVHcloud noted that “Backbone links between Marseille and Singapore are currently down” and that “Upon further investigation, our Network OPERATION teams advised that the fault was related to our partner fiber cuts.”
When concurrent disruptions like those highlighted above are observed across multiple countries in one or more geographic areas, the culprit is often a submarine cable that connects the impacted countries to the global Internet. The impact of such cable cuts will vary across countries, largely due to the levels of redundancy that they may have in place. That is, are these countries solely dependent on an impacted cable for global Internet connectivity, or do they have redundant connectivity across other submarine or terrestrial cables? Additionally, the location of the country relative to the cable cut will also impact how connectivity in a given country may be affected. Due to these factors, we didn’t see a similar impact across all of the countries connected to the AAE-1 and SMW5 cables.
What happened?
Specific details are sparse, but as noted above, the cable damage reportedly occurred in Egypt – both of the impacted cables land in Abu Talat and Zafarana, which also serve as landing points for a number of other submarine cables. According to a 2021 article in Middle East Eye, “There are 10 cable landing stations on Egypt’s Mediterranean and Red Sea coastlines, and some 15 terrestrial crossing routes across the country.” Alan Mauldin, research director at telecommunications research firm TeleGeography, notes that routing cables between Europe and the Middle East to India is done via Egypt, because there is the least amount of land to cross. This places the country in a unique position as a choke point for international Internet connectivity, with damage to infrastructure locally impacting the ability of millions of people thousands of miles away to access websites and applications, as well as impacting connectivity for leading cloud platform providers.
As the graphs above show, traffic returned to normal levels within a matter of hours, with tweets from telecommunications authorities in Pakistan and Oman also noting that Internet services had returned to their countries. Such rapid repairs to submarine cable infrastructure are unusual, as repair timeframes are often measured in days or weeks, as we saw with the cables damaged by the volcanic eruption in Tonga earlier this year. This is due to the need to locate the fault, send repair ships to the appropriate location, and then retrieve the cable and repair it. Given this, the damage to these cables likely occurred on land, after they came ashore.
Keeping content available
By deploying in data centers close to end users, Cloudflare helps to keep traffic local, which can mitigate the impact of catastrophic events like cable cuts, while improving performance, availability, and security. Being able to deliver content from our network generally requires first retrieving it from an origin, and with end users around the world, Cloudflare needs to be able to reach origins from multiple points around the world at the same time. However, a customer origin may be reachable from some networks but not from others, due to a cable cut or some other network disruption.
In September 2021, Cloudflare announced Orpheus, which provides reachability benefits for customers by finding unreachable paths on the Internet in real time, and guiding traffic away from those paths, ensuring that Cloudflare will always be able to reach an origin no matter what is happening on the Internet.
Conclusion
Because the Internet is an interconnected network of networks, an event such as a cable cut can have a ripple effect across the whole Internet, impacting connectivity for users thousands of miles away from where the incident occurred. Users may be unable to access content or applications, or the content/applications may suffer from reduced performance. Additionally, the providers of those applications may experience problems within their own network infrastructure due to such an event.
For network providers, the impact of such events can be mitigated through the use of multiple upstream providers/peers, and diverse physical paths for critical infrastructure like submarine cables. Cloudflare’s globally deployed network can help content and application providers ensure that their content and applications remain available and performant in the face of network disruptions.
What’s Up, Home? – Don’t Forget the Facial Cream
Post Syndicated from Janne Pikkarainen original https://blog.zabbix.com/whats-up-home-dont-forget-the-facial-cream/21063/
Can you monitor the regular use of facial cream with Zabbix? Of course, you can! Here’s how. This same method could be very useful for monitoring if the elderly remember to take their meds or so.
What the heck?
A little background story. My forehead has a tendency for dry skin, so I should be using facial cream daily. Of course, as a man, I can guarantee you that 100% of the days I remember to use the cream, I apply it, so in practice, this means about 40-50% hit ratio.
As lately I have been adding more monitored targets to my home Zabbix, one night my wife probably thought she was being snarky or funny when she said “One monitor I could happily receive data about would be how often you remember to use your facial cream.”
A monitoring nerd does not take such ideas lightly.
Howdy door sensor, would you like to do some work?
I found a spare magnetic door sensor and a handy box where to store the cream.
You can see where this is going. This totally beautiful prototype of my Facial Cream Smart Storage Box is now deployed to test. If I open or close the box, the door sensor status changes, thus the facial cream mercy countdown timer resets.
How does it work? And does it really work?
Cozify smart IoT hub is keeping an eye on the magnetic door sensor’s last status change. And look, that awesome brown tape does not bother the magnets at all, Cozify reported the status as changed.
Now that I got the Cozify part working, my Zabbix can then receive the last change time as in Unix time.
On my Grafana, there’s now this absolutely gorgeous new panel, converting the Unix time to the “How long ago the last event happened?” indicator.
So the dashboard part is now working. But that is not all we need to do.
Alerting and escalation
Dashboards and monitoring are not useful at all if proper alerts are not being sent out. I now have this new alert trigger action rule in place.
In other words, if I forget to apply the facial cream, I have a one-hour time window to apply it, or otherwise, the alert gets escalated to my wife.
Will this method work? Is my prototype box reliable? I will tell you next time.
I have been working at Forcepoint since 2014 and never get tired of finding out new areas to monitor. — Janne Pikkarainen
The post What’s Up, Home? – Don’t Forget the Facial Cream appeared first on Zabbix Blog.
Какво е манипулативно съдържание?
Post Syndicated from Йоанна Елми original https://toest.bg/kakvo-e-manipulativno-sudurzhanie/
През последните две години сме свидетели на инфодемия, свързана с пандемията от COVID-19. Днес инфодемията продължава, но фокусирана върху войната в Украйна.
Какво представлява манипулативното съдържание?
Манипулативното съдържание може да включва в себе си неверни твърдения или да се базира на вярна информация, която е извадена от контекст или изкривена така, че да пасне на определена теза. Манипулативното съдържание често се описва със съкращението МДМ – мисинформация, дезинформация и малинформация.
Дезинформацията означава напълно невярна информация, която обикновено е разпространена преднамерено, понякога координирано и с цел да подведе потребителя. Пример за такъв тип информация е твърдение на сайта The Bulgarian Times как редица лаборатории са установили, че коронавирусът не съществува. Като източник е посочен американски сайт, посветен на борбата с флуора, в чиято оригинална статия откриваме сканиран документ от стандартна лабораторна процедура със спорадични подчертавания, без да е ясна връзката им с аргумента. Българският сайт, известен със системното разпространяване на невярна информация, цитира новината като потвърждение от 187 лаборатории, че коронавирусът не съществува. В твърдението няма никаква истина.
Нещо повече – в оригиналната статия се твърди, че научните лаборатории крият истината, тоест лъжат, докато българската статия ги използва с легитимираща цел, тоест като подкрепа за аргумента, че коронавирусът не съществува, в пълно противоречие с оригинала, който е цитиран. Вътрешните противоречия са чест сигнал за манипулирана информация. В допълнение, когато данните не са експлицитно обяснени, а просто се цитират множество числа, имена и институции, това често пъти има за цел да обърка читателя, което спомага за постигане на определено внушение, тъй като се губи логическата последователност между събитията и представените данни.
Мисинформацията също не е истина, но не се разпространява умишлено – често мисинформацията започва като дезинформация, която е споделена от нищо неподозиращи потребители. За разлика от първите два типа, в които става въпрос за лъжи, малинформацията се базира на истината, но се използва, за да навреди някому – личност, организация, държава. Такава информация може да е вярна, но да има премълчан контекст или умишлено превратно тълкуване на определени събития – всъщност по-голямата част от невярната информация е именно такава, а не директни лъжи.
Пример откриваме отново в същия сайт, където легитимни данни от доклад на Агенцията по здравна сигурност на Обединеното кралство се цитират погрешно и с превратна интерпретация. Първо, в материала на сайта се твърди, че става дума за „необработени данни“, без да се дефинира казаното – данните в доклада са обработени и представени в таблици с необходимите статистически интервали и пояснение за разчитане на резултатите. В статията се описва, че „общо 1 086 434 случая на СОVID-19 са регистрирани при ваксинирани лица, което представлява 73% от всички случаи през този период“. Това обаче е непълна информация и създава невярно внушение: в доклада изрично се посочва ефективността на ваксините при превенция от хоспитализация, тежко протичане на болестта и смърт.
В статията се премълчава и основната бележка за интерпретиране на данните – при население с много висок процент на ваксинално покритие е напълно нормално да има висок процент заразени ваксинирани просто защото мнозинството от хората са ваксинирани. Въпросът е при колко от тях заболяването протича тежко – леталният край продължава да е в пъти по-вероятен при неваксинираните, което в материала липсва. Вместо това се поднасят множество разбъркани данни от най-различни места, като отново целта е объркване на читателя. В рамките на няколко дни статията е споделена в множество Facebook групи.
Голяма част от съвременната пропаганда е именно малинформация. Пропагандата се отличава от горните понятия по това, че информацията се разпространява от заинтересована страна, която цели да обрисува свой опонент в изключително негативна светлина, както и да култивира определени идеи и доктрини. Пропагандата може да бъде държавна, партийна или друга и често си служи с всякакъв вид манипулативно съдържание.
Информационно замърсяване
На първо време изобилието от манипулативно съдържание – иначе казано, информационно замърсяване – се подпомага от това, че голяма част от новинарското съдържание онлайн е безплатно и лесносмилаемо. Множество сайтове се издържат от приходи за реклами – тяхната цел е да задържат потребителя максимално дълго на страницата, а това често става с публикуване на сензационно и екстремистко съдържание, което е семпло и не отегчава. Разбира се, има и сайтове, които са инструменти за пропаганда и част от координирани дезинформационни кампании. Невярната онлайн информация е и по-достъпна от вярната, която често е по-сложна и изисква повече усилия както за създаване, проверка и корекция, така и за осмисляне.
Манипулативната информация разчита основно на социалните мрежи, където всеки потребител може да усили разпространението на съдържание, което незадължително отговаря на професионалните стандарти. В допълнение на това социалните мрежи дават предимство на чувствата пред разума, а манипулативното съдържание се заиграва именно с тези психологически и емоционални лостове. Журналистите също могат да имат роля в създаването и разпространението на невярна информация, когато им липсват високи професионални стандарти или имат тежки политически зависимости и предразсъдъци – какъвто, за съжаление, нерядко е случаят в България.
Също така не е достатъчно просто да посочим, че определена медия или автор са ненадеждни – необходими са аргументи защо. Това е обаче един от парадоксите на невярната информация: тя е количествено много повече от вярната и опровергаването на всяка неистина е трудно и времеемко, а журналистите просто нямат такъв ресурс. Регулацията на медиите пък е сложна и трудно осъществима от държавата, ако искаме да избегнем цензурата. Към момента решението остава в ръцете на медийния потребител, който трябва да се научи да разпознава манипулацията. В противен случай невярната информация постига най-голямата си цел: публиката спира да вярва на всякаква информация, включително на добрата журналистика, което пък е пагубно за демокрацията и свободата.
Имената имат значение
Ако при липса на авторство, непрозрачна собственост на медията и крещящи заглавия трябва да ни светва лампичка, това невинаги е напълно достатъчно, за да сме сигурни, че четем манипулативна информация. Основно правило е, че когато статията е подписана от журналист, а медията има прозрачна собственост, етичен кодекс и адрес за кореспонденция, това осигурява по-добра отчетност за качеството на информацията и предоставя на читателя механизми за контакт с редакцията и журналиста в случай на грешки.
Авторите и източниците на дадена информация имат огромно значение. Един от основните разпространители на невярна информация в разгара на пандемията – Венцислав Ангелов – Чикагото, на когото дължим невярното твърдение, че „самолетите пръскат с коронавирус“ – днес е свързан с огромния транспарант пред сградата на правителството, който гласи, че „войната в Украйна е истинското лице на глобалния сатанизъм“, пред което България трябва да запази „пълен неутралитет“. Клиповете на Чикагото се разпространяват в десетки Facebook групи от профил на име Антон Желязков. Той пък на свой ред системно споделя статии от сайта vsekidenbg.eu, чиито първи публикации са от края на април т.г. и са с насочено антиправителствено и проруско съдържание.
На страницата за контакт на vsekidenbg.eu намираме шаблонно съдържание, недействителни имейли и телефонни номера, а като локация се посочва щатът Юта, САЩ. Това не пречи статия от сайта, в която Путин обещава да накаже виновниците за пандемията, да бъде споделена в проруска Facebook група и в рамките на по-малко от ден да генерира 45 коментара и 66 последващи споделяния. Седмица по-късно съдържанието се разпространява от множество потребители онлайн, независимо дали сериозно, с цел порицаване или със сатиричен коментар. Сайтът публикува и фотографии и видеа, без да се посочва дали това съдържание е минало през проверка на произхода и фактите, както обичайно се прави от професионалните журналисти.
Друг пример за важността на източниците е Facebook групата БРОД, в която членуват над 18 000 души и чиято хронология проследява ясно маркирана дезинформационна траектория – от Стратегията за детето, през антиковид съдържание, до изключително проруски и антиукраински информационни материали в настоящето. В този случай не е ясно кое е първо – яйцето или кокошката, тъй като е напълно възможно потребителите на БРОД да се информират от едни и същи източници, които преливат от една дезинформационна линия в друга. Тогава би ставало дума за дезинформация или малинформация, преминала в мисинформация.
Политическа поляризация
Проблемът с адресирането на манипулативното съдържание се влошава и от нарастващата политическа поляризация. Това е още по-затруднено от емпирично доказания факт, че определени политически партии и идеолози разчитат системно повече на дезинформацията. Така опровергаването на дадена теза може да се възприеме като директна атака срещу политическите пристрастия на даден човек, откъдето да се провокира много силна реактивност и резистентност към опровергаването ѝ – и с това се затваря порочният кръг. Погрешните схващания, които са обвързани с политически пристрастия, най-малко се поддават на промяна, тъй като човек трудно подлага на преоценка вече изградени възгледи. Хората, занимаващи се с дезинформация, разчитат именно на това.
Политическата поляризация захранва идеята, че съществува медиен, академичен или институционален заговор, който крие някаква истина от обикновения човек. Тези тенденции се поддържат от нарастващото недоверие в институциите, което не е напълно неоснователно поради редица хронични проблеми. Но манипулативната информация рядко предлага по-добри решения – тя по-скоро цели да експлоатира слабости, да настрои и разгневи, да уплаши. Парадоксално е, че голяма част от антиковидната или антиукраинската реторика е насочена съответно срещу организации като СЗО или ООН, но това не пречи те да бъдат цитирани избирателно, когато подкрепят определени тези. Така се получава, че медиите и институциите са „честни“ именно тогава, когато критикуват Запада или подкрепят Русия например, но не и когато критикуват руския президент. Подобни противоречия може да бъдат червен флаг за потребителя.
Избирателно цитиране, премълчаване на контекст и фалшиви автори
Макар че оспорват легитимността на големите медии, дезинформационните сайтове се стараят да приличат на професионална новинарска организация и често цитират реномирани медии, на които би трябвало да са „алтернатива“. Пример за това е сайтът durjavnik.bg, който също публикува проруско и антизападно съдържание, но и други съкратени препечатки. Сайтът разчита на истинска информация, която препубликува избирателно и без контекст – от изказвания на Кадиров и Путин до интервю с професорката от престижния Лондонски икономически университет Кристина Шпор, която се цитира така, че да се рамкира инвазията в Украйна като устояване на заплаха от Запада вместо като война за териториално и културно надмощие.
Статиите в durjavnik.bg се подписват от Георги Александров, но снимката, която стои до името му, се използва и в други сайтове под други имена. В същото време не откриваме данни за репортер с такова име, с изключение на починалия преди две години варненски журналист. Facebook страницата „Голата истина“, в чието описание е отбелязано „Държавник – водещият онлайн портал за новини“ и в която се споделят изключително и само статии на durjavnik.bg, е следвана от над 116 000 потребители (за разлика от официалната Facebook страница на сайта с под 7000 последователи). Основната разлика между durjavnik.bg и vsekidenbg.eu е, че първият препечатва реални новини без коментар, докато вторият залага повече на превратно и избирателно тълкуване. И двата сайта са популярни във Facebook групи, в които доскоро се разпространяваше антиваксърско съдържание, преливащо в момента в проруско.
В такава ситуация потребителят би могъл да разбере дали става дума за манипулативно съдържание, ако обърне внимание на два фактора: позиции и предоставяне на контекст. Ако статиите представят само едностранчива позиция и критикуват системно едни, а възхваляват други, вероятно става въпрос за малинформация или дезинформация. Професионалните журналисти отразяват не определени страни, а конкретни проблеми и събития.
Проруският сайт pogled.info например цитира избирателно реномирани чуждестранни медии, като „Гардиън“ и „Ню Йорк Таймс“, за да подкрепи материал, който отбелязва победата на Русия в „Азовстал“, без да споменава цялостния контекст на информацията. Pogled.info е пореден пример за „алтернативна“ медия, която цитира легитимни източници, когато е угодно. Същият метод беше използван от сайта при отразяването на пандемията. Подобни сайтове често цитират критични към Запада статии от реномирани медии, но хулят същите медии, когато те са критични към Кремъл или когато заявяват различна ценностна позиция.
Попитахме читателите на „Тоест“
Обърнахме се и към нашите читатели с въпроса какво според тях представлява манипулативното съдържание. Отговорите им обобщават обяснените вече признаци: подвеждащи и емоционални заглавия, липса на източници, дата и автор, както и използване на език, който цели да повлияе на читателя и да му внуши определени изводи, вместо да го остави сам да формира мнение на база на фактите.
„Невероятните твърдения изискват невероятни доказателства“, коментира една читателка. На друга ѝ правят впечатление внушения от заглавието дали един политик е лош, или не, на базата на негово изказване, без да е добавен контекст и история, или пък радикални заглавия за вдигане на цените например, които обаче не обясняват икономически и социални фактори, допринасящи за тези скокове.
Друг читател посочва тактиката да се транскрибират думи и понятия от определен език, и то по такъв начин, че да звучат чуждо и плашещо. „Ювенална юстиция“ е чудесен пример за нещо, за което си има български превод – „детско правосъдие“. Но пък така пренесено (по руски тертип), звучи непознато. И тук е лесно да се направи логическото заключение (в главата на читателя), че някой отвън се опитва да ни налага нещо несвойствено за нас, казва той.
Инфографики и заглавна илюстрация: © Ива Тошкова / „Тоест“
В настоящата поредица Йоанна Елми подробно изследва темата за манипулативното съдържание и отговаря на пет въпроса: какво представлява то и как, от кого, кога и защо се разпространява. Поредицата се осъществява с подкрепата на международната програма за научна журналистика Science+ на Free Press Unlimited и Free Press for Eastern Europe.
Exercise Progression
Post Syndicated from original https://xkcd.com/2631/
New – Amazon EC2 R6id Instances with NVMe Local Instance Storage of up to 7.6 TB
Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/new-amazon-ec2-r6id-instances/
In November 2021, we launched the memory-optimized Amazon EC2 R6i instances, our sixth-generation x86-based offering powered by 3rd Generation Intel Xeon Scalable processors (code named Ice Lake).
Today I am excited to announce a disk variant of the R6i instance: the Amazon EC2 R6id instances with non-volatile memory express (NVMe) SSD local instance storage. The R6id instances are designed to power applications that require low storage latency or require temporary swap space.
Customers with workloads that require access to high-speed, low-latency storage, including those that need temporary storage for scratch space, temporary files, and caches, have the option to choose the R6id instances with NVMe local instance storage of up to 7.6 TB. The new instances are also available as bare-metal instances to support workloads that benefit from direct access to physical resources.
Here’s some background on what led to the development of the sixth-generation instances. Our customers who are currently using fifth-generation instances are looking for the following:
- Higher Compute Performance – Higher CPU performance to improve latency and processing time for their workloads
- Improved Price Performance – Customers are very sensitive to price performance to optimize costs
- Larger Sizes – Customers require larger sizes to scale their enterprise databases
- Higher Amazon EBS Performance – Customers have requested higher Amazon EBS throughput (“at least double”) to improve response times for their analytics applications
- Local Storage – Large customers have expressed a need for more local storage per vCPU
Sixth-generation instances address these requirements by offering generational improvement across the board, including 15 percent increase in price performance, 33 percent more vCPUs, up to 1 TB memory, 2x networking performance, 2x EBS performance, and global availability.
Compared to R5d instances, the R6id instances offer:
- Larger instance size (.32xlarge) with 128 vCPUs and 1024 GiB of memory, enabling customers to consolidate their workloads and scale up applications.
- Up to 15 percent improvement in compute price performance and 20 percent higher memory bandwidth.
- Up to 58 percent higher storage per vCPU and 34 percent lower cost per TB.
- Up to 50 Gbps network bandwidth and up to 40 Gbps EBS bandwidth; EBS burst bandwidth support for sizes up to .4xlarge.
- Always-on memory encryption.
- Support for new Intel Advanced Vector Extensions (AVX 512) instructions such as VAES, VCLMUL, VPCLMULQDQ, and GFNI for faster execution of cryptographic algorithms such as those used in IPSec and TLS implementations.
The detailed specifications of the R6id instances are as follows:
|
Instance Name |
vCPUs | RAM (GiB) |
Local NVMe SSD Storage (GB) |
EBS Throughput (Gbps) |
Network Bandwidth (Gbps) |
| r6id.large | 2 | 16 | 1 x 118 | Up to 10 | Up to 12.5 |
| r6id.xlarge | 4 | 32 | 1 x 237 | Up to 10 | Up to 12.5 |
| r6id.2xlarge | 8 | 64 | 1 x 474 | Up to 10 | Up to 12.5 |
| r6id.4xlarge | 16 | 128 | 1 x 950 | Up to 10 | Up to 12.5 |
| r6id.8xlarge | 32 | 256 | 1 x 1900 | 10 | 12.5 |
| r6id.12xlarge | 48 | 384 | 2 x 1425 | 15 | 18.75 |
| r6id.16xlarge | 64 | 512 | 2 x 1900 | 20 | 25 |
| r6id.24xlarge | 96 | 768 | 4 x 1425 | 30 | 37.5 |
| r6id.32xlarge | 128 | 1024 | 4 x 1900 | 40 | 50 |
| r6id.metal | 128 | 1024 | 4 x 1900 | 40 | 50 |
Now available
The R6id instances are available today in the AWS US East (Ohio), US East (N.Virginia), US West (Oregon), and Europe (Ireland) Regions as On-Demand, Spot, and Reserved Instances or as part of a Savings Plan. As usual, with EC2, you pay for what you use. For more information, see the Amazon EC2 pricing page.
To learn more, visit our Amazon EC2 R6i instances page, and please send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.
– Veliswa x