This is pretty amazing. It shows there is a level of intelligence that exists above what these 5 can achieve collectively. Scientists have effectively created life when something can make decisions for itself and outsmart humans.
Human intelligence has reached a ceiling and machines will get smarter and smarter. In hundreds or thousands of years, AI will be smart enough to control us. This Go game will manifest itself into reality - it will be a strategic game of humans vs machines.
For those doubting the interview, I was asked many of these same questions for a mid-level role by the recruiter just a couple of months ago. It's real. Passed the phone screen, but decided not to continue.
How are you getting accurate pricing for AWS components? I know Amazon's Simple Calculator uses an undocumented pricing endpoint, but that endpoint could change and become inaccurate.
Pages like https://aws.amazon.com/ec2/pricing use a JSONP request to get a big messy chunk of pricing data. I have parsed that, and serve it with a custom API that does the calculations from the Cloudcraft backend to the React-based frontend.
However, AWS just announced a proper pricing API! Check out https://aws.amazon.com/blogs/aws/new-aws-price-list-api
This will make life much easier, it does however not yet cover all services, for example ElastiCache is still missing while Cloudcraft already serves pricing for it.
You could sell it now, and couch the sale in terms of having an unaffected, EPA compliant, car. If depreciation is your primary concern, then you can sell now and buy something with lower potential depreciation.
Is there any really way to design your application to handle S3 failures like this? S3's SLA has 99.99% availability, but is there a way to handle the 1% so your application is not affected? Options I can think of:
1. Using a CDN to serve files can help in some cases
2. On-prem systems may be able to use gateway-cached volumes and use the local disk cache vs S3
Slightly OT, but there's an interesting phenomenon at work now that so much of the internet depends on Amazon's infrastructure. When it goes down you might not even need to worry about it that much, as so many sites/apps will be broken that most users will just assume that the internet is broken.
It happened a few times before. And no Internet was not broken and not many sites are using AWS as you would assume. A lot of services still run on their datacenters or on-premise servers, or maintain a hot backup that they can switch to immediately.
>> When it goes down you might not even need to worry about it that much
I'm afraid this is pretty much getting the entire cloud thing wrong.
lot of services still run on their datacenters or on-premise servers, or maintain a hot backup that they can switch to immediately.
I'm not an idiot, I'm well aware of that. My point is that when a large number of consumer-facing sites go down, users (who aren't aware of Amazon cloud servers) simply assume something is wrong with the internet.
Obviously if you have a mission critical service this isn't acceptable. But for a lot of average sites/apps it might not be worth the investment in time/effort to cover relatively small outages such as these.
I'm afraid this is pretty much getting the entire cloud thing wrong.
Not really. It's the utility of the cloud - if there's an outage there are already a lot of people working to fix it. If you're self-hosted, that's on you.
> I'm afraid this is pretty much getting the entire cloud thing wrong.
This was in reference to your comment "most users will assume the internet is broken". In the context of "it's OK because the customer thinks everything is down" the comment would be completely appropriate. It's being done wrong if this is the way we approach things.
Similarly, a lot of users will call tech support to complain that "the Internet is down" when it's merely their email provider, or Facebook, or League of Legends, and everything else is fine... :-)
Which works if you fail to read-only, and simply switch to reading from the non-failed region. More complicated is attempting to continue writing to a non-failed region and then attempting to bring everything consistent once the failed region is available again.
EDIT: If you were using hashing/uuids/guids for objects, this should be possible using a background task that'll scan various buckets in multiple regions and move objects when/where necessary to return to a consistent state.
S3 SLA is actually for only three nines (99.9%) or 8.76 hours / year or 43.8 minutes / month of downtime: https://aws.amazon.com/s3/sla/
CloudFront offers the same availability. Many CDNs offer no more than three nines. Some claim 100%, but there will eventually be faults. Most do really well to not have recognized outages, but I nonetheless think they offer 100% to guarantee so that you always get credit for any downtime rather than guaranteeing they are never down.
Understand that there isn't 'An S3 service'. There are multiple S3 services in multiple Regions within AWS, and they're all operated independently of each other (this goes for all other AWS services too) so that cascading failures/etc. don't occur between regions.
So, use 2-3 different S3 regions, or some other multi-cloud solution...
Second this, I would love to hear how companies handle S3 outages. Although one correction, it's 0.01% that you need to handle (if they deliver on their availability promise). That's less than an hour a year.
I wonder, what's the yearly downtime of Amazon? If S3 were up 99.99% of the time then the remainder .001% is only 5.256 minutes per year. S3 is actually down more than that. But how much exactly? It's impossible to discuss mitigation strategies if we don't even know what the exact issue we're mitigating is!
The way it works is that they are promising 99.99%. If it is lower you will be reimbursed for that month (if I remember correctly), but only when you complain to them about it.
Django REST Framework is the the most popular way to build APIs with Django. Lot of neat features out of the box such as throttling and permissions make it easy to build API endpoints.
It also looks similar to a goodly proportion of the results of a Google image search for "cute ghost"... which I think is inevitably what happens when you use a generically recognisable device like that.
I'm in the middle of this process, but from an even older version.
Since 1.7 introduced migrations, I recommend upgrading to 1.6 first and getting your South migrations caught up with third party apps. Some may have data and schema migrations e.g. django-photologue. Then jump to 1.7 or 1.8.
You'll likely have to update your requirements.txt with the latest packages, so you'll want to see if they're compatible with the version you're upgrading to.
I recommend keeping it up-to-date with every release if possible. Much easier to make incremental updates than spend months on upgrading 4 major versions.
See my comment above, pertaining supported versions, etc.
Do try out migrations in 1.8, btw. When Andrew Godwin migrated them into Django, a lot of things were changed, and now migrations are truly great to work with.
