i want to run a program continiously on appengine.This program will automatically crawl some website continiously and store the data into its database.Is it possible for the program to
continiously keep doing it on appengine?Or will appengine kill the process?
Note:The website which will be crawled is not stored on appengine
i want to run a program continiously
on appengine.
Can't.
The closest you can get is background-running scheduled tasks that last no more than 30 seconds:
Notably, this means that the lifetime
of a single task's execution is
limited to 30 seconds. If your task's
execution nears the 30 second limit,
App Engine will raise an exception
which you may catch and then quickly
save your work or log process.
A friend of mine suggested following
Create a task queue
Start the queue by passing some data.
Use an Exception handler and handle DeadlineExceededException.
In your handler create a new queue for same purpose.
You can run your job infinitely. You only need to consider used CPU Time and storage.
You might want to consider Backends introduced in the newer version of GAE.
These run continuous processes
Is Possible Yes, I have already build a solution on Appengine - wowprice
Sharing all details here will make my answer lengthy,
Problem - Suppose I want to crawl walmart.com, As i known that I cant crawl in one shot(millions products)
Solution - I have designed my spider to break the task in smaller task.
Step 1 : I input job for walmart.com, Job scheduler will create a task.
Step 2 : My spider will pick the job and its notice that Its index page, now my spider will create more jobs as starting page as categories page, Now its enters 20 more tasks
Step 3 : now spider make more smaller jobs for subcategories, and its will go till it gets product list page and create task for it.
Step 4 : for product list pages, its get the product and make call to to stores the product data and in case of next page It ll make one task to crawl them.
Advantages -
We can crawl without breaking 30 seconds rules, and speed of crawling will depends backend machine, It will provide parallel crawling for single target.
they fixed it for you.
you can run background threads on a manual scaled instance.
check https://developers.google.com/appengine/docs/python/modules/#Python_Background_threads
You cannot literally run one continuous process for more than 30 seconds. However, you can use the Task Queue to have one process call another in a continuous chain. Alternatively you can schedule jobs to run with the Cron service.
Use a cron job to periodically check for pages which have not been scraped in the past n hours/days/whatever, and put scraping tasks for some subset of these pages onto a task queue. This way your processes don't get killed for taking too long, and you don't hammer the server you're scraping with excessive bursts of traffic.
I've done this, and it works pretty well. Watch out for task timeouts; if things take too long, split them into multiple phases and be sure to use memcached liberally.
Try this:
on appengine run any program. You connect from browser, click for start url during ajax. Ajax call server, download some data from internet and return you (your browser) next url. This is not one request, each url is one diferent request. You mast only resolve in JS how ajax is calling url un cycle.
You can using lasted GAE service called backends . Check this http://code.google.com/appengine/docs/java/backends/
Backends are special App Engine instances that have no request deadlines, higher memory and CPU limits, and persistent state across requests. They are started automatically by App Engine and can run continously for long periods. Each backend instance has a unique URL to use for requests, and you can load-balance requests across multiple instances.
Related
i would like to write a script that schedules various things throughout the day. unfortunately it will do > 100 different tasks a day, closer to 500 and could be up to 10,000 in the future.
All the tasks are independent in that you can think of my script as a service for end users who sign up and want me to schedule a task for them. so if 5 ppl sign up and person A wants me to send them an email at 9 am, this will be different than person B who might want me to query an api at 10:30 pm etc.
now, conceptually I plan to have a database that tells me what each persons task will be and what time they asked to schedule that task and the frequency. once a day I will get this data from my database so I have an up-to-date record of all the tasks that need to be executed in the day
running them through a loop I can create channels that can execute timers or tickers for each task.
the question I have is how does this get deployed in production to, for example google app engine? since those platforms are for Web servers I'm not sure how this would work...Or am I supposed to use Google Compute Engine and have it act as a computation for 24 hours? Can google compute engine even make http calls?
also if I have to have say 500 channels in go open 24 hrs a day, does that count as 500 containers in google app engine? I imagine that will get very costly quickly, despite what is essentially a very low cost product.
so again the question comes back to, how does a cron script get deployed in production?
any help or guidance will be greatly appreciated as I have done a lot of googling and unfortunately everything leads back to a cron scheduler that has a limit of 100 tasks in google app engine...
Details about cron operation on GAE can be found here.
The tricky portion from your prospective is that updating the cron configuration is done from outside the application, so it's at least difficult (if not impossible) to customize the cron jobs based on your app user's actions.
It is however possible to just run a generic cron job (once a minute, for example) and have that job's handler read the users' custom job configs and further generate tasks accordingly to handle them. Running ~10K tasks per day is usually not an issue, they might even fit inside the free app quotas (depending on what the tasks are actually doing).
The same technique can be applied on a regular Linux OS (including on a GCE VM). I didn't yet use GCE, so I can't tell exactly if/how would a dynamically updated cron be possible with it.
You only need one cron job for your requirements. This cron job can run every 30 minutes - or once per day. It will see what has to be done over the next period of time, create tasks to do it, and add these tasks to the queue.
It can all be done by a single App Engine instance. The number of instances you need to execute your tasks depends, of course, on how long each task runs. You have a lot of control over running the task queue.
I would like to try out dropwizard-metrics + graphite.
For order to this to work out i need to run a job regular (e.g. each 5:th second) that sends metrics from the instance to the graphite server.
Is this even possible?
The java documentation and python documentation for Cron in App Engine says that the minimal interval in App Engine is configurable in 'minutes'. Thus the simple answer would be: No you cannot schedule a job every 5 seconds.
However...
Knowing that tasks queue tasks can run up to 10 Minutes (see deadlines) you could manually schedule a task every (let's say) 5 minutes and handle the 5 second interval yourself in your servlet (or whatever it is called in python).
I'm just saying it is possible to use suche short intervals. You should really avoid a crutch like that. This kind of behaviour will eat through your quota and make your app expensive really fast.
Edit:
Since the title of the question asks for running jobs in specific instances:
As Dmitry pointed out and as documented here it is possible to address specific instances when using manual or basic scaling with modules. Instances are anonymous when using automatic scaling and thus cannot be addressed. It seems this feature is only documented and available for app engine modules.
I'd like to make a Google App Engine app that sends a Facebook message to a user a fixed time (e.g. one day) after they click a button in the app. It's not scalable to use cron or the task queue for potentially millions of tiny jobs. I've also considered implementing my own queue using a background thread, but that's only available using the Backends API as far as I know, which is designed for much larger usage and is not free.
Is there a scalable way for a free Google App Engine app to execute a large number of small tasks after a fixed period of time?
For starters, if you're looking to do millions of tiny jobs, you're going to blow past the free quota very quickly, any way you look at it. The free quota's meant for testing.
It depends on the granularity of your tasks. If you're executing a lot of tasks once per day, cron hooked up to a mapreduce operation (which essentially sends out a bunch of tasks on task queues) works fine. You'll basically issue a datastore query to find the tasks that need to be run, and send them out on the mapreduce.
If you execute this task thousands of times a day (every minute), it may start getting expensive because you're issuing many queries. Note that if most of those queries return nothing, the cost is still minimal.
The other option is to store your tasks in memory rather than in the datastore, that's where you'd want to start using backends. But backends are expensive to maintain. Look into using Google Compute Engine, which gives much cheaper VMs.
EDIT:
If you go the cron/datastore route, you'd store a new entity whenever a user wants to send a deferred message. Most importantly, it'd have a queryable timestamp for when the message should be sent, probably rounded to the nearest minute or the nearest 5 minutes, whatever you decide your granularity should be.
You would then have a cron job that runs at the set interval, say every minute. On each run it would build a query for all the cron jobs it needs to send for the given minute.
If you really do have hundreds of thousands of messages to send each minute, you're not going to want to do it from the cron task. You'd want the cron task to spawn a mapreduce job that will fan out the query and spawn tasks to send your messages.
I'm using the Python 2.5 runtime on Google App Engine. Needless to say I'm a bit worried about the new costs so I want to get a better idea of what kind of traffic volume I will experience.
If 10 users simultaneously access my application at myapplication.appspot.com, will that spawn 10 instances?
If no, how many users in an instance? Is it even measured that way?
I've already looked at http://code.google.com/appengine/docs/adminconsole/instances.html but I just wanted to make sure that my interpretation is correct.
"Users" is a fairly meaningless term from an HTTP point of view. What's important is how many requests you can serve in a given time interval. This depends primarily on how long your app takes to serve a given request. Obviously, if it takes 200 milliseconds for you to serve a request, then one instance can serve at most 5 requests per second.
When a request is handled by App Engine, it is added to a queue. Any time an instance is available to do work, it takes the oldest item from the queue and serves that request. If the time that a request has been waiting in the queue ('pending latency') is more than the threshold you set in your admin console, the scheduler will start up another instance and start sending requests to it.
This is grossly simplified, obviously, but gives you a broad idea how the scheduler works.
First, no.
An instance per user is unreasonable and doesn't happen.
So you're asking how does my app scale to more instances? Depends on the load.
If you have much much requests per second then you'll get (automatically) another instance so the load is distributed.
That's the core idea behind App Engine.
I'm trying to construct a non-trivial GAE app and I'm not sure if a cron job, tasks, backends or a mix of all is what I need to use based on the request time-out limit that GAE has for HTTP requests.
The distinct steps I need to do are:
1) I have upwards of 15,000 sites I need to pull data from at a regular schedule and without any user interaction. The total number of sites isn't going to static but they're all saved in the datastore [Table0] along side the interval at which they're read at. The interval may vary as regular as every day to every 30 days.
2) For each site from step #1 that fits the "pull" schedule criteria, I need to fetch data from it via HTTP GET (again, it might be all of them or as few as 2 or 3 sites). Once I get the response back from the site, parse the result and save this data into the datastore as [Table1].
3) For all of the data that was recently put into the datastore in [Table1] (they'll have a special flag), I need to issue additional HTTP request to a 3rd party site to do some additional processing. As soon as I receive data from this site, I store all of the relevant info into another table [Table2] in the datastore.
4) As soon as data is available and ready from step #3, I need to take all of it and perform some additional transformation and update the original table [Table1] in the datastore.
I'm not certain which of the different components I need to use to ensure that I can complete each piece of the work without exceeding the response deadline that's placed on the web requests of GAE. For requests initiated by cron jobs and tasks, I believe you're allowed 10 mins to complete it, whereas typical user-driven requests are allowed 30 seconds.
Task queues are the best way to do this in general, but you might want to check out the App Engine Pipeline API, which is designed for exactly the sort of workflow you're talking about.
GAE is a tough platform for your use-case. But, out of extreme masochism, I am attempting something similar. So here are my two cents, based on my experience so far:
Backends -- Use them for any long-running, I/O intensive tasks you may have (Web-Crawling is a good example, assuming you can defer compute-intensive processing for later).
Mapreduce API -- excellent for compute-intensive/parallel jobs such as stats collection, indexing etc. Until recently, this library only had a mapper implementation, but recently Google also released an in-memory Shuffler that is good for jobs that fit in about 100MB.
Task Queues -- For when everything else fails :-).
Cron -- mostly to kick off periodic tasks -- which context you execute them in, is up to you.
It might be a good idea to design your backend tasks so that they can be scheduled (manually, or perhaps by querying your current quota usage) in the "Frontend" context using task queues, if you have spare Frontend CPU cycles.
I abandoned GAE before Backends came out, so can't comment on that. But, what I did a few times was:
Cron scheduled to kick off process
Cron handler invokes a task URL
task grabs first item (URL) from datastore, executes HTTP request, operates on data, updates the URL record as having worked on it and the invokes the task URL again.
So cron is basically waking up taskqueue periodically and taskqueue runs recursively until it reaches some stopping point.
You can see it in action one of my public GAE apps - https://github.com/mavenn/watchbots-gae-python.