I would like to setup server for 'image processing' activity on my website. What is the comparable power in GAE if I use 'n1-standard-1' instance in GCE? It is because I miscalculated it, or the price difference is substantial between the two for the same power?
App Engine instances are more expensive than Compute Engine instances on a per hour basis. If you have a constant load, it's cheaper to keep a GCE instance running.
App Engine has an advantage that it can scale down to zero instances after 15 minutes of inactivity. If you have no load during long stretches of time (night, weekends) or few requests that come less frequently than ~ once per hour, App Engine instance maybe a more efficient solution.
Also, take a look at Google Cloud Functions. This is a Node.js runtime that is priced to 100 milliseconds. There are no instances to start or shutdown, no ongoing costs at all - you pay only when you process a request. And you get a free daily quota. The only limitation is that an individual request should be completed in 9 minutes.
Related
I have a backend for an iOS app that I've built on App Engine and I'm looking to do potentially long running background tasks to add records to my Cloud SQL database. Is this possible without Compute Engine? I've seen Cloud Tasks can do asynchronous work and you can set the dispatchDeadline to basically anything you want, but I've also read in the documentation
For App Engine tasks, 0 indicates that the request has the default deadline. The default deadline depends on the scaling type of the service: 10 minutes for standard apps with automatic scaling, 24 hours for standard apps with manual and basic scaling, and 60 minutes for flex apps. If the request deadline is set, it must be in the interval [15 seconds, 24 hours 15 seconds]. Regardless of the task's dispatchDeadline, the app handler will not run for longer than than the service's timeout. We recommend setting the dispatchDeadline to at most a few seconds more than the app handler's timeout. For more information see Timeouts.
I don't particularly need the App Engine instance to care if the task completes or not... so I'm not sure why the recommendation is at most a few seconds more than the app handler's timeout ... can anyone shed any light on this? What am I missing? Adding a Compute Engine for these relatively simple tasks that will take at most a few ours to complete seems like a lot of overhead and I don't want this to dictate which scaling options I choose.
Thanks for your time.
The recommendation is only for logging purpose. If your task timeout is shorter that your app timeout, you never know if there is an error from your app, because you don't have the return.
If you have longer timeout on Cloud Task, you can catch and trace in Cloud Task logs the app return code and thus gracefully track the errors.
App Engine with a basic scaling mode is a great solution.
You have 9H free per days (B instance type)
App Engine scale to 0 automatically after a period of inactivity (that you can define in the basic scaling: idle_timeout parameter)
You have a regional available service. Not a zonal like a compute engine, or you need to have 9 computes engine, to cover the regional High Availability (3 per zone, over 3 zones)
You don't have server to manage: no update, no patching, no network/ip/firewall rule...
If you ask me about the overhead, I will anser Compute Engine and not App Engine (even if you need few configuration)
I want to host an application in the google engine, the purpose of my application is to get data from a different server once in a day. So I don't want my application to work full time in day(As it is costlier). I just want to run the application for an hour in a day and pay only on that basis. Is it possible to do it in that way?
All depend of your needs. App Engine standard has 28H free per day of F1 instance type, and 9H of B1 instance type (depend of your scaling type).
App Engine flexible doesn't have free quota -> This is expensive for some hours per days.
If App Engine standard matches your need, take care of the timeout. Each request can last more than 60 secondes. You can use Cloud Task to defer background task. By the way, a task can last up to 24h.
If App Engine standard doesn't match, because of language limitation, third party library/binary limitation and you need to have a container and that's why you need to run on App Engine Flexible, you can consider Cloud Run. However, the request are limited to 15 minutes and you have a generous free tier.
So, provide more inputs if you want more advice.
Situation:
My project are mostly automated tasks.
My GAE (standard environment) app has 40 crons job like this, all run on default module (frontend):
- description: My cron job Nth
url: /mycronjob_n/ ###### Please note n is the nth cron job.
schedule: every 1 minutes
Each of cron jobs
#app.route('/mycronjob_n/')
def mycronjob_n():
for i in (0,100):
pram = prams[i]
options = TaskRetryOptions(task_retry_limit=0,task_age_limit=0)
deferred.defer(mytask,pram)
Where mytask is
def mytask(pram):
#Do some loops, read and write datastore, call api, which I guesss taking less than 30 seconds.
return 'Task finish'
Problem:
As title of the question, i am running out of RAM. Frontend instance hours are increasing to 100 hours.
My wrong thought?
defer task runs on background because it is not something that user sends request when visit the website. Therefore, they will not be considered as a request.
I break my cronjobs_n into small different tasks because i think it can help to reduce the running time each cronjobs_n so that REDUCE instance's ram consumption.
My question: (purpose: keep the frontend/backend instance hours as low as possible, and I accept latency)
Is defer task counted as request?
How many request do I have in 1 mintues?
40 request of mycronjob_n
or
40 requests of mycronjob_n x 100 mytask = 4000
If 3-4 instances can not handle 4000 requests, why doesnt GAE add 10 to 20 F1 instances more and then shut down if idle? I set autoscale in app.yaml. I dont see the meaning of autoscale of GAE here as advertised.
What is the best way to optimize my app?
If defer task is counted as request, it is meaningless to slit mycronjob_n into different small tasks, right? I mean, my current method is as same as:
#app.route('/mycronjob_n/')
def mycronjob_n():
for i in (0,100):
pram = prams[i]
options = TaskRetryOptions(task_retry_limit=0,task_age_limit=0)
mytask(pram) #Call function mytask
Here, will my app has 40 requests per minute, each request runs for 100 x 30s = 3000s? So will this approach also return out of memory?
Should I create a backend service running on F1 instance and put all cron jobs on that backend service? I heard that a request can run for 24 hours.
If I change default service instance from F1 to F2,F3, will I still get 28 hours free? I heard free tier apply to F1 only. And will my backend service get 9 hours free if it runs on B2 instead of B1?
My regret:
- I am quite regret that I choose GAE for this project. I choosed it because it has free tier. But I realized that free tier is just for hobby/testing purpose. If I run a real app, the cost will increase very fast that it make me think GAE is expensive. The datastore reading/writing are so expensive even though I tried my best to optimize them. The frontend hours are also always high. I am paying 40 usd per month for GAE. With 40 usd per month, maybe I can get better server if I choose Heroku, Digital Ocean? Do you think so?
Yes, task queue requests (deferred included) are also requests, they just can run longer than user requests. And they need instances to serve them, which count as instance hours. Since you have at least one cron job running every minute - you won't have any 15 minute idle interval allowing your instances to shut down - so you'll need at least one instance running at all times. If you use any instance class other than F1/B1 - you'll exceed the free instance hours quota. See Standard environment instances billing.
You seem to be under the impression that the number of requests is what's driving your costs up. It's not, at least not directly. The culprit is most likely the number of instances running.
If 3-4 instances can not handle 4000 requests, why doesnt GAE add 10
to 20 F1 instances more and then shut down if idle?
Most likely GAE does exactly that - spawns several instances. But you keep pumping requests every minute, they don't reach an idle state long enough, so they don't shut down. Which drives your instance hours up.
There are 2 things you can do about it:
stagger your deferred tasks so they don't hit need to be handled at the same time. Fewer instance (maybe even a single one?) may be necessary to handle them in such case. See Combine cron jobs to reduce number of instances and Preventing Google App Engine Cron jobs from creating multiple instances (and thus burning through all my instance hours)
tune your app's scaling configuration (the range is limited though). See Scaling elements.
You should also carefully read How Instances are Managed.
Yes, you only pay for exceeds the free quota, regardless of the instance class. Billing is in F1/B1 units anyways - from the above billing link:
Important: When you are billed for instance hours, you will not see any instance classes in your billing line items. Instead, you will
see the appropriate multiple of instance hours. For example, if you
use an F4 instance for one hour, you do not see "F4" listed, but you
see billing for four instance hours at the F1 rate.
About the RAM usage, splitting the cron job in multiple tasks isn't necessarily helping, see App Engine Deferred: Tracking Down Memory Leaks
Finally, cost comparing GAE with Heroku, Digital Ocean isn't an apples-to-apples comparison: GAE is PaaS, not IaaS, it's IMHO expected to be more expensive. Choosing one or the other is really up to you.
I'm redesigning a monolith application into a MicroServices architecture and am hoping to use Google Cloud Platform (GCP) to host the entire solution. I'm having a very hard time understanding their costing breakdown, and am concerned that my costs will be uncontrollable after I build it. This is for a personal project but I'm hoping will have many users after I launch so I want to get the underlying architecture right and at the same time have reasonable costs initially when I launch.
Here is my architecture:
MicroServices 1 - 4 (Total 4 API Services):
Runs on App Engine
Exposes a REST API and saves data to DataStore
Initially each API should get hit around 200 times a day
MicroService 5 (Events triggered API Service):
Runs on App Engine
Listens for PubSub events and saves to DataStore (basically I have a sensor that pushes data to this Service for storage)
Initially the PubSub should receive events around 200 times a day
MicroService 6-7 (Total 2 UI Services):
Runs on App Engine
These are UIs so people can login and use the systems. The UIs are lightweight frond end apps that use the REST Services above to populate user data in a nice way.
Each UI Service should be used around 3 hours a day
So in Total I have 7 MicroServices with each running as AppEngine "Services" in a single GCP "Project". A DataStore is shared between these APIs within this Project.
As I have 7 App Engine instances running, and they only need to be operational for a short period of time per day, how does the pricing work?
I want to use App Engine because it's completely Managed, which is one of my design requirements. But I'm hoping AppEngine has some kind of Sleep Mode, so that when there is no usage it does not bill?
Any help in understanding what my monthly costs would be would be appreciated.
Thanks very much.
Update 8/2/2017
I've decided to stay out of GCP for now. As I hope to have 7 App Engines Services running in Flex (as they are node.js) I don't seem to get access to a free tier or the ability to scale idle services to 0 instances.
This means I'll be paying full price for these services. (i.e. 7 X Full App Engine VM Cost per Monthly :O )
This is an expense I cant have just for a POC of a proper MicroService design. Instead I'm going to continue with my MicroService design but use a 10$ DigitalOcean box and Dokku to containerise my Services. If this works well and I have a need I will migrate this design to GCP (or AWS)
The full outline of App Engine instance handling is available at https://cloud.google.com/appengine/docs/python/how-instances-are-managed .
In short, your best bet is to enable automatic scaling and set
max_idle_instances = 0
in your app.yaml.
That means that your app will autoscale to handle traffic as needed and shut down the instances afterwards. Also
When settling back to normal levels after a load spike, the number of idle instances can temporarily exceed your specified maximum. However, you will not be charged for more instances than the maximum number you've specified.
Later - when load time becomes more important you can set min_idle_instances to a more suitable number - this allows for responsive apps.
am concerned that my costs will be uncontrollable after I build it
You should be aware that automatically scalable GAE apps always have cost components dependent on the external user request patterns which are not controllable.
For example, in the standard GAE env, the way those 200 requests/day are distributed matters significantly:
if they are evenly distributed they will come in less than 15 min apart - the minimum billed time per instance lifetime, so the respective service will be billed for minimum 24 instance hours per day (very close to the daily 28 free instance-hours/day for billed apps, only a single-service app using the smallest instance class can fit in it).
if they are all received within a 15 minutes interval the service will be billed for 0.5 instance hours daily (which can easily fit in the free daily quota even with multiple services and/or with more powerfull instance classes).
The actual scalability configuration of each service can matter as well. See, for example,
The only way to keep costs under strict control is via the daily budget configuration (but hitting that limit means your app's functionality will be temporarily crippled).
All other usage-based costs being equal due to the functionality being performed you have some (potentially significant) control over costs via:
the GAE environment type selected for each service:
the standard env is billed by instance hours and includes a free daily quota
the flex env has no free daily quota.
the number of services: you could start with fewer services by combining their functionalities (you can still keep them modularized for later split). The expected initial load you describe can easily fit within the free daily budget with just a single standard env service.
Once the app usage picks up and the free daily quotas percentage in the total costs become neglijible you can gradually split the app into multiple services as needed. In general this can be a relatively simple task if the app is properly modularized.
I'm doing a prototype backend and in the near future I expect little traffic but while testing I consumed all my 300$ free trail.
How can I configure my app to consume the least possible resources? I need things like limiting the number of instances to 1, using a cheap machine, sleep whenever possible, I've read something about Client vs Backend intances.
With time I'll learn the config that best suits me, but now I need the CHEAPEST config to get going.
BTW: I am using managed-vms with Dart.
EDIT
I've been recommended to configure my app.yaml file, what options would you recommend to confront this issue?
There are two train of thought for your issue.
1) Optimization of code: This is very difficult for us as we are not privy to your App's usage and client-base and architecture. In general, it depends on what Google App Engine product you use the most, for example: Datastore API call (fetch, write, delete... etc...), BigQuery and Cloud SQL. Even after optimization, you can still incur a lot of cost depending on traffic.
2) Enforcing cheap operation: This is easier and I think this is what you want. You can manually enforce a daily budget (in your billing setup page) so the App never cost more than a certain amount per day. You can also artificially lower the maximum amount of idling instances to 0 and use the smallest instance possible (F1 for frontend).
For pricing details see this article - https://cloud.google.com/appengine/pricing#Billable_Resource_Unit_Costs
If you use managed VM -- you'll be billed for Compute Engine Instance prices, not for App Engine Instances, and, as I know, the minimum possible instance to use as Managed VM is "g1-small" which costs you $0.023 per hour full sustained usage (if it will be turned on all month), so you minimum bill will be 0.023 * 24 * 30 = $16.56 only for instance hours. Excluding disk and traffic. With minimum amount of datastore operations you may stay on free quota.
Every application consumes resources differently. To minimize your cost, you need to know what resources used the majority of your expenses and go from there.
If it is spent on extra instances that were just sitting there - then trim the number of instances to the minimum required and use a lower class instance. If you are seeing a lot of expense on datastore calls - then look at optimizing your entities and take advantage of memcache.
Lowest Cost for a simple app:
Use App Engine Standard. It scales to zero instances, so will not cost anything if there is no traffic. With App Engine Flex you will pay for the instance hours and the Flex (GCE) instances are bigger.
Use autoscaling with max instances, F1 instance class:
With autoscaling you do not need to guess how many instances you need. F1 are the smallest instances. Set the max instances in case you get DoS'd or more traffic than you can afford.
Stop Instances:
You can stop the App Engine versions when you do not expect the app to be used. The will be no charge for instance hours for either Standard or Flex. For Flex there will be disk charges. The app will be ready to go when you need it again.
App Engine Version Cleanup:
Versions are easy to create and harder to remove. Here is a post on project cleanup. See this post on App Engine cleanup
https://medium.com/google-cloud/app-engine-project-cleanup-9647296e796a