Situation:
A user with a TB worth of files on our Azure blob storage and gigabytes of storage in our Azure databases decides to leave our services. At this point, we need to export all his data into 2GB packages and deposit them on the blob storage for a short period (two weeks or so).
This should happen very rarely, and we're trying to cut costs. Where would it be optimal to implement a task that over the course of a day or two downloads the corresponding user's blobs (240 KB files) and zips them into the packages?
I've looked at a separate webapp running a dedicated continuous webjob, but webjobs seem to shut down when the app unloads, and I need this to hibernate and not use resources when not up and running, so "Always on" is out. Plus, I can't seem to find a complete tutorial on how to implement the interface, so that I may cancel the running task and such.
Our last resort is abandoning webapps (three of them) and running it all on a virtual machine, but this comes up to greater costs. Is there a method I've missed that could get the job done?
This sounds like a job for a serverless model on Azure Functions to me. You get the compute scale you need without paying for idle resources.
I don't believe that there are any time limits on running the function (unlike AWS Lambda), but even so you'll probably want to implement something to split the job up first so it can be processed in parallel (and to provide some resilience to failures). Queue these tasks up and trigger the function off the queue.
It's worth noting that they're still in 'preview' at the moment though.
Edit - have just noticed your comment on file size... that might be a problem, but in theory you should be able to use local storage rather than doing it all in memory.
Related
I'm trying to find the optimal cloud architecture to host a software on Microsoft Azure.
The scenario is the following:
A (containerised) REST API is exposed to the users through which they can submit POST and GET requests. POST requests trigger a backend that needs a robust configuration to operate properly and GET requests are sent to fetch the result of the backend, if any. This component of the solution is currently hosted on an Azure Web App Service which does the job perfectly.
The (containerised) backend (triggered by POST requests) perform heavy calculations during a short amount of time (typically 5-10 minutes are allotted for the calculation). This backend needs (at least) 4 cores and 16 Gb RAM, but the more the better.
The current configuration consists in the backend hosted together with the REST API on the App Service with a plan that accommodates the backend's requirements. This is clearly not very cost-efficient, as the backend is idle ~90% of the time. On top of that it's not really scalable despite an automatic scaling rule to spawn new instances based on the CPU use: it's indeed possible that if several POST requests come at the same time, they are handled by the same instance and make it crash due to a lack of memory.
Azure Functions doesn't seem to be an option: the serverless (consumption plan) solution they propose is restricted to 1.5 Gb RAM and doesn't have Docker support.
Azure Container Instances neither, because first the max number of CPUs is 4 (which is really few for the needs here, although acceptable) and second there are cold starts of approximately 2 minutes (I imagine due to the creation of the container group, pull of the image, and so on). Despite the process is async from a user perspective, a high latency is not allowed as the result is expected within 5-10 minutes, so cold starts are a problem.
Azure Batch, which at first glance appears to be a perfect fit (beefy configurations available, made for hpc, cost effective, made for time limited tasks, ...) seems to be slow too (it takes a couple of minutes to create a pool and jobs don't run immediately when submitted).
Do you have any idea what I could use?
Thanks in advance!
Azure Functions
You could look at Azure Functions Elastic Premium plan. EP3 has 4 cores, 14GB of RAM and 250GB of storage.
Premium plan hosting provides the following benefits to your functions:
Avoid cold starts with perpetually warm instances
Virtual network connectivity.
Unlimited execution duration, with 60 minutes guaranteed.
Premium instance sizes: one core, two core, and four core instances.
More predictable pricing, compared with the Consumption plan.
High-density app allocation for plans with multiple function apps.
https://learn.microsoft.com/en-us/azure/azure-functions/functions-premium-plan?tabs=portal
Batch Considerations
When designing an application that uses Batch, you must consider the possibility of Batch not being available in a region. It's possible to encounter a rare situation where there is a problem with the region as a whole, the entire Batch service in the region, or your specific Batch account.
If the application or solution using Batch always needs to be available, then it should be designed to either failover to another region or always have the workload split between two or more regions. Both approaches require at least two Batch accounts, with each account located in a different region.
https://learn.microsoft.com/en-us/azure/batch/high-availability-disaster-recovery
We have a service running as an Azure function (Event and Service bus triggers) that we feel would be better served by a different model because it takes a few minutes to run and loads a lot of objects in memory and it feels like it loads it every time it gets called instead of keeping in memory and thus performing better.
What is the best Azure service to move to with the following goals in mind.
Easy to move and doesn't need too many code changes.
We have long term goals of being able to run this on-prem (kubernetes might help us here)
Appreciate your help.
To achieve first goal:
Move your Azure function code inside a continuous running Webjob. It has no max execution time and it can run continuously caching objects in its context.
To achieve second goal (On-premise):
You need to explain this better, but a webjob can be run as a console program on-premise, also you can wrap it into a docker container to move it from on-premise to any cloud but if you need to consume messages from an Azure Service Bus you will need an On-Premise-Azure approach connecting your local server to the cloud with a VPN or expressroute.
Regards.
There are a couple of ways to solve the said issue, each with slightly higher amount of change from where you are.
If you are just trying to separate out the heavy initial load, then you can do it once in a Redis Cache instance and then reference it from there.
If you are concerned about how long your worker can run, then Webjobs (as explained above) can work, however, that is something I'd suggest avoiding since its not where Microsoft is putting its resources. Rather look at durable functions. Here an orchestrator function can drive a worker function. (Even here be careful, that since durable functions retain history after running for very very very long times, the history tables might get too large - so probably program in something like, restart the orchestrator after say 50,000 runs (obviously the number will vary based on your case)). Also see this.
If you want to add to this, the constrain of portability then you can run this function in a docker image that can be run in an AKS cluster in Azure. This might not work well for durable functions (try it out, who knows :) ), but will surely work for the worker functions (which would cost you the most compute anyways)
If you want to bring the workloads completely on-prem then Azure functions might not be a good choice. You can create an HTTP server using the platform of your choice (Node, Python, C#...) and have that invoke the worker routine. Then you can run this whole setup inside an image on an AKS cluster on prem and to the user it looks just like a load balanced web-server :) - You can decide if you want to keep the data on Azure or bring it down on prem as well, but beware of egress costs if you decide to move it out once you've moved it up.
It appears that the functions are affected by cold starts:
Serverless cold starts within Azure
Upgrading to the Premium plan would move your functions to pre-warmed instances, which should counter the problem you are experiencing:
Pre-warmed instances for Azure Functions
However, if you potentially want to deploy your function/triggers to on-prem, you should spin them out as microservices and deploy them with containers.
Currently, the fastest way would probably be to deploy the containerized triggers via Azure Container Instances if you don't already have a Kubernetes Cluster running. With some tweaking, you can deploy them on-prem later on.
There are few options:
Move your function app on to premium. But it will not help u a lot at the time of heavy load and scale out.
Issue: In that case u will start facing cold startup issues and problem will be persist in heavy load.
Redis Cache, it will resolve your most of the issues as the main concern is heavy loading.
Issue: If your system is multitenant system then your Cache become heavy during the time.
Create small micro durable functions. It will be not the answer of your Q as u don't want lots of changes but it will resolve your most of the issues.
I have a C# console application which extracts 15GB FireBird database file on a server location to multiple files and loads the data from files to SQLServer database. The console application uses System.Threading.Tasks.Parallel class to perform parallel execution of the dataload from files to sqlserver database.
It is a weekly process and it takes 6 hours to complete.
What is best option to move this (console application) process to azure cloud - WebJob or WorkerRole or Any other cloud service ?
How to reduce the execution time (6 hrs) after moving to cloud ?
How to implement the suggested option ? Please provide pointers or code samples etc.
Your help in detail comments is very much appreciated.
Thanks
Bhanu.
let me give some thought on this question of yours
"What is best option to move this (console application) process to
azure cloud - WebJob or WorkerRole or Any other cloud service ?"
First you can achieve the task with both WebJob and WorkerRole, but i would suggest you to go with WebJob.
PROS about WebJob is:
Deployment time is quicker, you can turn your console app without any change into a continues running webjob within mintues (https://azure.microsoft.com/en-us/documentation/articles/web-sites-create-web-jobs/)
Build in timer support, where WorkerRole you will need to handle on your own
Fault tolerant, when your WebJob fail, there is built-in resume logic
You might want to check out Azure Functions. You pay only for the processing time you use and there doesn't appear to be a maximum run time (unlike AWS Lambda).
They can be set up on a schedule or kicked off from other events.
If you are already doing work in parallel you could break out some of the parallel tasks into separate azure functions. Aside from that, how to speed things up would require specific knowledge of what you are trying to accomplish.
In the past when I've tried to speed up work like this, I would start by spitting out log messages during the processing that contain the current time or that calculate the duration (using the StopWatch class). Then find out which areas can be improved. The slowness may also be due to slowdown on the SQL Server side. More investigation would be needed on your part. But the first step is always capturing metrics.
Since Azure Functions can scale out horizontally, you might want to first break out the data from the files into smaller chunks and let the functions handle each chunk. Then spin up multiple parallel processing of those chunks. Be sure not to spin up more than your SQL Server can handle.
At the moment we are running our application on an AWS Beanstalk but are trying to determine the suitablilty of Azure.
Our biggest issue is the amount of wasted CPU time we are paying for but not using. We are running on t2.small instances as these have the min amount of RAM we need but we never use even the base amount of CPU time allotted. (20% for a t2.small ) We need lots of CPU power during short bursts of the day and bringing more instances on line in advance of this is the only way we can handle it.
AWS Lambda looks a good solution for us but we have dependencies on Windows components like SAPI so we have to run inside of Windows VMs.
Looking at Azure cloud services we thought using a Web role would be best fit for our app but it seems a Web role is nothing more than a Win 2012 VM with IIS enabled. So as the app scales it just brings on more of these VMs which is exactly what we have at the moment. Does Azure have a service similar to Lambda where you just pay for the CPU processing time you use?
The reason for our inefficient use of CPU resources is that our speech generation app uses lost of 3rd party voices but can only run single threaded when calling into SAPI because the voice engine is prone to crashing when multithreading. We have no control over this voice engine. It must have access to a system registry and Windows SAPI so the ideal solution is to somehow wrap all dependencies is a package and deploy this onto Azure and then kick off multiple instances of this. What "this" is I have no Idea
Microsoft just announced a new serverless compute service as an alternative to AWS Lambda, called Azure Functions:
https://azure.microsoft.com/en-us/services/functions/
http://www.zdnet.com/article/microsoft-releases-preview-of-new-azure-serverless-compute-service-to-take-on-aws-lambda/
With Azure Functions you only pay for what you use with compute metered to the nearest 100ms at Per/GB price based on the time your function runs and the memory size of the function space you choose. Function space size can range from 128mb to 1536mb. With the first 400k GB/Sec free.
Azure Function requests are charged per million requests, with the first 1 million requests free.
Based on the documentation on Azure website here: https://azure.microsoft.com/en-in/campaigns/azure-vs-aws/mapping/, the services equivalent to AWS Lambda are Web Jobs and Logic Apps.
The most direct equivalent of Lambda on Azure is Azure Automation which does a lot of what Lambda does except it runs Powershell instead of Node etc. It isn't as tightly integrated into other services like Lambda is, but it has the same model. i.e. you write a script, and it is executed on demand.
I presume by SAPI you are refering to the speech API? If so you can create Powershell modules for Azure, and they can include dll files. In which case you could create a module to wrap around the SAPI dll, and that should do what you are looking for.
If you want a full compute environment, without the complexity of multiple machines when you run. You could use Azure Batch which would be the Azure recommended way of running what you are looking for.
The cost benefit you need to evaluate would be how much quicker your solution would run against a native .net stack (in batch), and if performance is significantly degraded when run from Powershell.
Personally I would give Automation a try, it is surprisingly powerful.
There is something called "Cloud Service" in azure which allows you to run code on a pure VM. Scaling options on these include such things as CPU%, queue size, etc. If you can schedule your needs, Azure allows you to easily set up a scheduled scaler, i.e. 4 VM's from 8AM until 08:10AM, and of course, in Azure, you pay by the minute, so it could be a feasible solution.
I'd say more, but the documentation in Azure is really so great that I'd be offending them by offering my "translation" here. Checkout azure.com for more info :)
I have a continuous Webjob running on my Azure Website. It is responsible for doing some work after retrieving items from a QueueTrigger. I am attempting to increase the rate in which the items are processed off the Queue. As I scale out my App Service Plan, the processing rate increases as expected.
My concern is that it seems wasteful to pay for additional VMs just to run additional instances of my Webjob. I am looking for options/best practices to run multiple instances of the same Webjob on a single server.
I've tried starting multiple JobHosts in individual threads within Main(), but either that doesn't work or I was doing something wrong... the Webjob would fail to run due to what looks like each thread trying to access 'WebJobSdk.marker'. My current solution is to publish my Webjob multiple times, each time modifying 'webJobName' slightly in 'webjob-publish-settings.json' so that the same project is considered a different Webjob at publish time. This works great so far, expect that it creates a lot of additional work each time I need to make any update.
Ultimately, I'm looking for some advice on what the recommended way of accomplishing this would be. Ideally, I would like to get the multiple instances running via code, and only have to publish once when I need to update the code.
Any thoughts out there?
You can use the JobHostConfiguration.QueuesConfiguration.BatchSize and NewBatchThreshold settings to control the concurrency level of your queue processing. The latter NewBatchThreshold setting is new in the current in progress beta1 release. However, by enabling "prerelease" packages in your Nuget package manager, you'll see the new release if you'd like to try it. Raising the NewBatchThreshold setting increases the concurrency level - e.g. setting it to 100 means that once the number of currently running queue functions drops below 100, a new batch of messages will be fetched for concurrent processing.
The marker file bug was fixed in this commit a while back, and again is part of the current in progress v1.1.0 release.