Performing load tests on my app, I noticed that the Instances dashboard graph shows a pretty big difference between the number of active and billed instances:
What do active and total mean?
Also, after spending the day running load tests, here's what I see:
In the first peak, the number of billed instances pretty much matches the number of total instances. Then, on subsequent loads, the bumber of billed instances sits in between total and active.
Update 2013-02-21: I did another batch of load tests today, and I'm still seeing variance in where the billed instances stand relative to total and _active:
How are these numbers calculated? How should interpret them, considering that I'm trying to forecast our operational costs based on these numbers?
It seems (I believe) that if you have F2 instance in application settings each F2 active instance is counted as 2 billing instances. If you set F4 instances it counted as 4 billing instances. And so forth.
Total instances is number of instantiated but not billed instances - kind of "gift" from Google. If there would be more requests that need more instances GAE would not need to start a new instance but would use 1 from those "non-active". When the load is raising GAE start new instances but when the load is going down GAE would keep instances for a while but would not charge you for them. But they would be shut down eventually if load did not raise back.
Related
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.
My Java app runs on the Standard Google App Engine (GAE) and is configured to have 1 minimum instance and 1 maximum instance. It is also configured to have 1 minimum idle instance which allows the single instance to run non-stop. I ran a timer for 1 hour and then checked how many instance hours have elapsed. It indicates slightly over 2 hours. How is this possible when only a single instance is running?
From your configuration you should actually be having 2 instances running:
one resident instance, due to the minimum idle instance configuration. This serves only sudden transient traffic peaks while GAE spins up the necessary dynamic instances, see min-idle-instance on GAE/J and Why do more requests go to new (dynamic) instances than to resident instance?
one dynamic instance, due to the min/max 1 instance configs, handling the regular traffic
Note: the instance class also matters (but probably it's not your case here). From Standard environment instances:
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.
I have a relatively low traffic app that can easily be handled by a single instance more than 95% of the time. Occasionally, having more than one instance running would be helpful to provide a better user experience.
It seems that GAE should be able to automatically scale in this way, but I can't get GAE to keep only a single instance when traffic is low. This is what I have tried:
Set min instances to 1
Set max instances to 3
Set min pending latency to 1 second
Set max pending latency to automatic (and also 1 second)
With this configuration, GAE will just about always run two instances even though one is sufficient.
I know I can set max instances to one, but I want to be able to automatically scale when I need it.
Is it possible to do what I want?
Note that the min/max property that you are setting are for IDLE instances.
Set min instances to 1 means that you will ALWAYS have at least one instance running, even when there are no requests for over 15 minutes. This could be set to 0 if you have low traffic AND your app launches quickly, i.e. under 1-2 seconds, otherwise the users will have bad experience with very slow response on their first request.
Set max instances to 3 means that it's OK for GAE to keep up to three instances running at any time, even when there are only few requests. This could be set to 1 to save some costs but would make some requests slow (time it takes to start new instance + time to launch your app) when traffic increases.
The max-idle-instances does not limit the number of instances in the event of a traffic spike, your app will always scale and new instances will keep launching if needed. The min/max settings are only there to help handle a sudden increase in traffic and there is no way to limit the number of instances that can be launched.
Take a look at this article for some more details: Setting the Number of Idle Instances
Regarding your question, you could try decreasing the max-idle-instances to 1 and see if that helps. You don't have to worry about scaling, new instances will still launch if needed, just keep in mind that the experience might not be as smooth for your users. If you decreased the number of max-idle-isntances and you still see more than 1 instance running on very low traffic, then your app might need to be optimized and multi-threading might need to be enabled if it wasn't.
I am terribly worried why my Google App Engine Application consumes super fast to its Front End Instance Hours. It's like 1 hour a day and then my Instance hour is reach its quota. Why I am experiencing this? I already read some articles regarding on this but it seems not solved. What is the right value of Idle Instance and Pending Latency? Thanks for helping guys.
In your Application Dashboard, go to Application Settings
Under performance, check the Frontend Instance Class - An F1 will cost you one instance hour and hour, F2 will be 2, etc. You probably want it set to F1.
Set pending and idle instances to automatic-automatic - this means appengine will scale down your instances to the minimum required.
Assuming you have low volume and no particular memory or CPU requirements, these settings will allow you to run all day for free.
If you are running any backends (check under the Main -> Backends ), these will consume instance hours as well based on the type (B1, B2 etc). You can make these more cost effective by making them dynamic.
My guess is that your instances are staying active for the default 12 hours after the last activity, which, for a Cloud SQL instance in a test environment, causes a lot of extra charges. I haven't yet determined how to programmatically shutdown instances, but you can change the default idle time before shutdown in the appengine-web.xml file (for Java), or the app.yaml file (for Python). I changed my ".xml" file so that my instances shut down after five minutes of inactivity by adding the following lines immediately before the final </appengine-web-app> line:
<basic-scaling>
<idle-timeout>5m</idle-timeout>
</basic-scaling>
I found this information on the following page: https://developers.google.com/appengine/docs/java/modules/
The Python information can be found here:
https://developers.google.com/appengine/docs/python/modules/
1-2 days ago startup time increase from 1-3 second to 10-30 seconds with same code.
I use python 2.7 multithreading.
Code in this request read one value from memcache and return it to user. If memcache empty it read and render simple html-template from template in local filesystem. Both use equal cpu_ms.
Same code work fine in test application. Startup time in test application about 1-2 second.
I send production issue yesterday in night, but don't receive answer.
I try change instance type from F1 to F4, startup time for F4 8-10 seconds.
AppID of my app: f1f2ru
Log record before problem:
Log record at start problem:
Log record now:
Log record in test app:
Without logs etc it's hard to know. But who knows, perhaps Google is running low on resources and your free apps are paying the price.
https://developers.google.com/appengine/docs/adminconsole/instances#Loading_Requests
Currently I get about a 5 second startup time from cold on one of my very simple apps. 30 seconds does seem a long time however.
https://developers.google.com/appengine/docs/python/config/appconfig#Warmup_Requests
App Engine frequently needs to load application code into a fresh instance. This happens when you redeploy the application, when the load pattern has increased beyond the capacity of the current instances, or simply due to maintenance or repairs of the underlying infrastructure or physical hardware.
Or just pay:
https://developers.google.com/appengine/docs/adminconsole/performancesettings?hl=en
The Idle Instances sliders control the minimum and maximum number of idle instances available to your application at any given time.
The upper slider sets the minimum number of idle instances:
Note: In order to specify the minimum number of idle instances, you must have a paid app.
As usual, the more you pay the better the service.
Are the libraries the same from before or after? Adding libraries to the source code can and often does increase start up time (AppEngine needs to load them). It is something easily overlooked, and some of them are really really bloated.
I don't know reason of first increase startup time.
Problem of increase operations time is my mistake:
my application do query:
object.date < datetime.datetime.now()
and my datastore have no objects with date < datetime.now(), but it have objects with date = None (null). I forget about null it is normal, indexed value and null < any value.