What do you mean by Microsoft-specific stuff? I run a bunch of stuff there. When I started using these things their PaaS offerings were much more mature than the AWS counterpart (since they focused more on IaaS).
We run all PaaS except for two very special case VMs. It is an effin' delight not having to worry about security, maintenance, updates. I just deploy my code (app services) and query my data (azure table storage, azure sql). Shit just works. Every time I need to deal with the VMs I cringe.
yes people use Azure to host all sort of services, OSes, programming languages etc. Companies run Microsoft stuff on Google cloud and AWS too, surprising eh?
> What factors other than some specific affinity to Microsoft makes a company choose Azure over AWS?
Azure has a decent offering even if you don't have any "specific affinity to Microsoft". You can use Azure to develop products that don't use any Microsoft technologies. Although from my own experience, Azure tends to be a bit rough around the edges especially when used with non-Microsoft tools and languages.
Cool, I didn't realize that. Thanks for the correction.
I should mention that this was back in September 2014, where IBM was the only one offering anything in Canada (hence the migration).
Once we'd done all the work to decouple from AWS, it was fairly straightforward when Azure came knocking and offered us a ton of compute credits from their startup program. IBM had also rebooted a couple of critical VMs with no prior warning which left us wanting a cloud provider that took its tenants seriously. This was in ~May 2016 and we hadn't heard anything about a Canadian AWS datacentre, which apparently launched in Dec 2016.
We still use SES to this day because we haven't found a good alternative, so it's not like we hate AWS or anything. Just offering a (dated) perspective on why someone might choose Azure.
In the serverless space - a few deciding factors we see people weigh (disclosure: Functions PM in Azure)-
* Dev tooling. VS Code and Visual Studio 1st class tooling, including local debugging/breakpoints
* Pricing. No API Gateway layer so these types of high load scenarios become significantly less expensive
* Platform. Azure serverless is much more than just functions. Also serverless workflows (Logic Apps) and Events (Event Grid). Also enable multiple hosting models (serverless, dedicated, on-premises)
We have no Microsoft affinity but have moved our large SaaS application from AWS to Azure. Our tech stack is 80% linux / OSS and 20% .NET. And we're deprecating the MSFT portion as quickly as possible.
For us, Azure was a big win for security compliance and data sovereignty. Those factors triggered the switch.
After the switch, we came to realize that Azure is much stronger in support and in the ability to interface with the Azure product teams to help guide the direction of the platform.
I've seen some recent reports that Azure is growing twice as fast as AWS so I wouldn't count them out by any means.
What factors other than some specific affinity to Microsoft makes a company choose Azure over AWS?
More locations, tho' every major cloud is opening more all the time. A much better hybrid story, tho' this may change once (if) AWS get their alliance with VMware working properly. Better layered applications (X-as-a-service) but that might be a matter of taste - AWS is more about giving you building blocks to build your things, Azure has the blocks but is better at giving you integrated "things" out of the box (e.g. IaaS vs PaaS). Per-second billing, generally better budget control.
I've a bit of experience with both Azure and AWS. There's nothing I could build in one that I couldn't build in the other, tho' depending on what that is, it might be more effort in one of them. For my own personal stuff, I use Azure, it's more fun :-)
I hope they figure out how to productise Python properly, because these improvements on the margin while great don't really fix 30 second import times for a single library which is the current state of Python function apps. That's a Microsoft library too (pydocumentdb).
We have some really cool stuff in the works in this area right now. An entirely new and revamped Python worker. Send me a DM on Twitter and happy to help (@jeffhollan)
I'm in the middle of trying to move a very simple ETL-light script to Google (want to use bigquery over redshift). The idea was to use Cloud Functions.
I have ran into scale problems very fast at Google and now am having to use App Engine and add more complication which I don't have personal engineering skill/capacity.
Google support first bumped me up to 12000 max queries per 100 seconds and said that's the limit, but that's not close to enough. We have super bursty traffic (webhooks that aren't concatenated from source).
Google then increased to 100,000 per 100 seconds after they claimed 12k was maximum.
It's still not enough and I think their sampling might be funky because when I look at the invocation/traffic graphs it doesn't align with the stated maximum (still throws quota errors).
Kind of frustrating. The idea of stateless functions without worrying about engineering is a dream! I don't get why Google can't get 'google scale' here. In the past we've used Snowplow on AWS when we had engineering help but that's a huge amount of work/engineering time. The actual accept POST, store to cloud storage, insert to DB is less than 100 rows.
Response time is also pretty bad in the extreme cases. Even though I end the response before processing data I still see 95%/99% spikes when bursting on the far less bursty data source (as in only bursting to 10/second). Just today for this function I got 3764ms 99%!
I don't know your flow, but how about using a queue system to prevent the bursts of data?
We're internally using pubsub (and nats) sending billions of messages every day. We don't use the push mode as Spotify [0] but pull from the queue instead which allows us to run on our pace.
We do write into bigquery (and citus) and each components is doing it on their own pace based on what they're capable of process at the moment.
We use nats mostly for synchronous tasks (where the response has to be returned to the exact client initializing the request) and pubsub for everything else.
We’re slowly transitioning to nats as we have a better control over what’s happening there. Also we had no issues with stability which I can’t say about pubsub
To be clearer it's same quota problem using cloud functions. Moving to something without the same quota/scale problems like Compute Engine or just DIY containers might make sense but then I'm back to a complicated system (Snowplow) that we have in AWS that's too complicated for me alone
Understood. I think there is a bit simpler setup that you could utilize, but it does require a custom deployment on GCE.
Request -> load balancer -> auto scaled pubsub writer -> pubsub -> client (write to BQ, ...)
If it doesn't have to be a post request, then you could just write into pubsub directly. Pubsub has a wide support of libraries [0] so you could do it from any language. Pubsub also scales well. We're sending in around 300k messages per sec and have very few problems with it.
Yup that's pretty much where I'm going although without pub/sub ideally. it's marketing analytics so if a few inserts fail it doesn't matter. But might not be able to get the 0 -> 1000s instant working without Google's abstraction messaging in front (similar setup on AWS w. kinesis but it's too complicated for me)
I did think about doing something like transforming the POST in AWS Lambda (the emitter data is hosted there too) and transform into something I can ingest direct into pub/sub.
I can't control the emitting data. if I did it would be a much easier problem, each POST request contains very little data if there were multiple entries per single POST it would solve most of my problems, instead of sending 5k tiny JSON posts /sec lol
My feeling is that Google cloud will always be hit and miss because their biggest customer by far is Google. So they'll do unbelievable scale on the stuff they use, and just passable on everything else.
It's also why I think that Facebook never joined the rat race. Most of what they do has no relevance to the average company.
Amazon, an online retailer, was really the most natural place to start an outsourcing of banal technologies for the largest market segment. And has grown from there.
According to [1], the standard quota is 1,000,000 invocations per 100 seconds and can be increased. Maybe try asking again for increased quota? If the documentation is incorrect, please file an issue [2].
Counter-intuitively, ending the response before processing the data may actually be hurting your p99 response time. According to [3], instances which are not currently handling a request get very little CPU and generally don't make any progress. This means that this work will still be waiting to be done when the next request comes in and can slow it down. You may even end up with an instance trying to process several requests' data and also trying to handle another request on top of that. This last request is going to get an overloaded instance and likely be very slow. Further, the function isn't guaranteed to ever be run again, so that deferred work might not even ever happen.
It probably will cause you to use more of your invocations per 100 seconds quota, but you really shouldn't start background processing tasks in a Google Cloud Function that continue after completing a request.
I have paid support. It's socket connections we're hitting super quick.
Interesting and thanks I had no idea on the end response CPU time. It's just storing the json to Google Cloud Storage but it does take 100-300ms it seems!
33 comments
[ 3.6 ms ] story [ 77.0 ms ] threadWhat factors other than some specific affinity to Microsoft makes a company choose Azure over AWS?
What do you mean by Microsoft-specific stuff? I run a bunch of stuff there. When I started using these things their PaaS offerings were much more mature than the AWS counterpart (since they focused more on IaaS).
Yes.
> What factors other than some specific affinity to Microsoft makes a company choose Azure over AWS?
Azure has a decent offering even if you don't have any "specific affinity to Microsoft". You can use Azure to develop products that don't use any Microsoft technologies. Although from my own experience, Azure tends to be a bit rough around the edges especially when used with non-Microsoft tools and languages.
See marketing page: https://azure.microsoft.com/en-us/overview/azure-vs-aws/
We have data residency requirements to keep things in Canada. This actually forced us to migrate off AWS, first to IBM, then eventually to Azure.
https://aws.amazon.com/blogs/aws/now-open-aws-canada-central...
I should mention that this was back in September 2014, where IBM was the only one offering anything in Canada (hence the migration).
Once we'd done all the work to decouple from AWS, it was fairly straightforward when Azure came knocking and offered us a ton of compute credits from their startup program. IBM had also rebooted a couple of critical VMs with no prior warning which left us wanting a cloud provider that took its tenants seriously. This was in ~May 2016 and we hadn't heard anything about a Canadian AWS datacentre, which apparently launched in Dec 2016.
We still use SES to this day because we haven't found a good alternative, so it's not like we hate AWS or anything. Just offering a (dated) perspective on why someone might choose Azure.
For us, Azure was a big win for security compliance and data sovereignty. Those factors triggered the switch.
After the switch, we came to realize that Azure is much stronger in support and in the ability to interface with the Azure product teams to help guide the direction of the platform.
I've seen some recent reports that Azure is growing twice as fast as AWS so I wouldn't count them out by any means.
CosmosDB was the difference for us.
More locations, tho' every major cloud is opening more all the time. A much better hybrid story, tho' this may change once (if) AWS get their alliance with VMware working properly. Better layered applications (X-as-a-service) but that might be a matter of taste - AWS is more about giving you building blocks to build your things, Azure has the blocks but is better at giving you integrated "things" out of the box (e.g. IaaS vs PaaS). Per-second billing, generally better budget control.
I've a bit of experience with both Azure and AWS. There's nothing I could build in one that I couldn't build in the other, tho' depending on what that is, it might be more effort in one of them. For my own personal stuff, I use Azure, it's more fun :-)
I have ran into scale problems very fast at Google and now am having to use App Engine and add more complication which I don't have personal engineering skill/capacity.
Google support first bumped me up to 12000 max queries per 100 seconds and said that's the limit, but that's not close to enough. We have super bursty traffic (webhooks that aren't concatenated from source).
Google then increased to 100,000 per 100 seconds after they claimed 12k was maximum.
It's still not enough and I think their sampling might be funky because when I look at the invocation/traffic graphs it doesn't align with the stated maximum (still throws quota errors).
Kind of frustrating. The idea of stateless functions without worrying about engineering is a dream! I don't get why Google can't get 'google scale' here. In the past we've used Snowplow on AWS when we had engineering help but that's a huge amount of work/engineering time. The actual accept POST, store to cloud storage, insert to DB is less than 100 rows.
Response time is also pretty bad in the extreme cases. Even though I end the response before processing data I still see 95%/99% spikes when bursting on the far less bursty data source (as in only bursting to 10/second). Just today for this function I got 3764ms 99%!
We're internally using pubsub (and nats) sending billions of messages every day. We don't use the push mode as Spotify [0] but pull from the queue instead which allows us to run on our pace.
We do write into bigquery (and citus) and each components is doing it on their own pace based on what they're capable of process at the moment.
[0] https://labs.spotify.com/2017/11/20/autoscaling-pub-sub-cons...
We’re slowly transitioning to nats as we have a better control over what’s happening there. Also we had no issues with stability which I can’t say about pubsub
Request -> load balancer -> auto scaled pubsub writer -> pubsub -> client (write to BQ, ...)
If it doesn't have to be a post request, then you could just write into pubsub directly. Pubsub has a wide support of libraries [0] so you could do it from any language. Pubsub also scales well. We're sending in around 300k messages per sec and have very few problems with it.
[0] https://cloud.google.com/pubsub/docs/reference/libraries
Request -> gae -> pubsub -> dataflow to bq template.
https://cloud.google.com/dataflow/docs/templates/provided-te...
I did think about doing something like transforming the POST in AWS Lambda (the emitter data is hosted there too) and transform into something I can ingest direct into pub/sub.
I can't control the emitting data. if I did it would be a much easier problem, each POST request contains very little data if there were multiple entries per single POST it would solve most of my problems, instead of sending 5k tiny JSON posts /sec lol
It's also why I think that Facebook never joined the rat race. Most of what they do has no relevance to the average company.
Amazon, an online retailer, was really the most natural place to start an outsourcing of banal technologies for the largest market segment. And has grown from there.
Counter-intuitively, ending the response before processing the data may actually be hurting your p99 response time. According to [3], instances which are not currently handling a request get very little CPU and generally don't make any progress. This means that this work will still be waiting to be done when the next request comes in and can slow it down. You may even end up with an instance trying to process several requests' data and also trying to handle another request on top of that. This last request is going to get an overloaded instance and likely be very slow. Further, the function isn't guaranteed to ever be run again, so that deferred work might not even ever happen.
It probably will cause you to use more of your invocations per 100 seconds quota, but you really shouldn't start background processing tasks in a Google Cloud Function that continue after completing a request.
[1] https://cloud.google.com/functions/quotas [2] https://issuetracker.google.com/issues/new?component=187195&... [3] https://cloud.google.com/functions/docs/bestpractices/tips
Interesting and thanks I had no idea on the end response CPU time. It's just storing the json to Google Cloud Storage but it does take 100-300ms it seems!