I'm working on a project where i want to do a mysql query from time to time. The query is too long, and actually it's done when the user does a request.
I'm afraid if many users does the request, the application will be too slow to respond. So, I want to do the query and load it with the query response from time to time, and then, on a request, the action from the controller will use this variable, instead of doing the query again and again.
How can I do that using Whenever?
on the schedule.rb
every 5.minutes do
runner "variable = Model.method"
end
and on the controller
def some_action
"the variable should be loaded here"
end
I agree with Damien Roche, you need to cache the results of the query. But, I don't think the example he gives is the best answer for you because you don't want for a user to wait for the query when it isn't cached, at the times when the cache is expired, even if this is a rare occurrence.
So you need to combine the periodic query with whenever, like you suggested, with a caching mechanism to store your query result, and retrieve it from the cache in your controller. since the runner is a different process, you will have to use a cache that is available to both the runner and your app. I recommend you look into Redis. it should be very simple to get it to work so that the runner runs the query and when it finishes writes a result set to the Redis cahce. The controller will then read the result set from the cache.
Related
We are working on a data visualization problem right now. Our customer wants us to show the last 6 months data for a honeybee hive on a graph.
Clearly it's gonna be a huge dataset. Adding indexes we overcame the database slowness problem in loading data though we still have problem in visualizing data on a graph.
Here is the related code:
def self.prepare_single_hive_messages_for_datatable_dygraph(messages, us_metric_enabled)
data = []
messages.each do |message|
record = []
record << message.occurance_time.to_s(:dygraph_format)
record << weight_according_to_metric(message.weight, us_metric_enabled)
record << temperature_according_to_metric(message.temperature, us_metric_enabled)
record << (message.humidity.nil? ? nil : message.humidity.to_f)
data << record
end
return data
end
The problem is that messages.each is very slow and takes more than 30 seconds. Is there any solution to overcome this?
Project Specification:
Rails Version: 4.1.9
Graph Library: Dygraph
Database: Postgres
There are two ways to attack a performance problem like this.
Find and correct the performance bottle neck
Break it into smaller pieces
Finding Performance issues
First, get a dataset large enough to reproduce the problem setup on your dev system. Then look at the logs so you can see how long the transaction is taking. You should be looking for a line like this:
Completed 200 OK in 432.1ms (Views: 367.7ms | ActiveRecord: 61.4ms)
Rerun the task a couple times since caching can cause variations. Write down your different times. Then remove everything in the loop and run it with just the loop. Do the numbers go back to looking reasonable? If that is the case then you know the problem is the work you are doing inside the loop. Next, add each line in the loop back on its own (or one at a time if they depend on each other). Figure out which line causes those numbers to jump the most.
This is the point where you should try to performance tune your code. Check for queries that could be smarter. Make sure you aren't querying the same data over and over. If you have a function in a model that computes something and you call it multiple times to get the same answer then use this to only compute once:
def something
return #savedvalue if #savedvalue
#savedvalue = really complex calculation
end
The goal is to find the worse offender so you can make changes that have the biggest impact. However, if you are working with a LOT of data this may only get you so far. It may be impossible to performance tune enough for all the data. In that case there is option 2.
Break it into smaller pieces
Write a second rails action who's only job is to render a single record on a graph. It will do the inner part of your loop but only on the message who's id was passed to it.
Call your original function to setup the view and pass the list of messages to the view. In the view loop through the list of messages to setup jquery ajax code to call the above action once for each message. Have this run in on document ready.
Then, the page will load with an empty graph... but as soon as it is up the individual processed records will be fed to it and appear one at a time on the page. It will still take just ask long (or even a little longer because of overhead) to complete the graph... but it will no longer time out. Each ajax call will be its own quick hit to the server instead of one big long hit.
I just used this very technique to load a rather long report on a site I work on. Ideally we'd like to fix any underlying performance issues... but what we really wanted was to have a report working right away and then fix the performance issues as we had time.
Ok you said every person sees the same set of data, which is great, means we can cache without worrying about who's logged in, first here's your method, with tiny improvements
def self.prepare_single_hive_messages_for_datatable_dygraph(messages, us_metric_enabled)
messages.inject([]) do |records, message|
records << [].tap do |record|
record << message.occurance_time.to_s(:dygraph_format)
record << weight_according_to_metric(message.weight, us_metric_enabled)
record << temperature_according_to_metric(message.temperature, us_metric_enabled)
record << (message.humidity.nil? ? nil : message.humidity.to_f)
end
end
end
Then create a caching function, that runs this method and caches it
# some class constants
CACHE_KEY = 'some_cache_key'
EXPIRY_TIME = 15.minutes
# the methods
def self.write_single_hive_messages_to_cache(messages, us_metric_enabled)
Rails.cache.write CACHE_KEY,
self.class.prepare_single_hive_messages_for_datatable_dygraph(messages, us_metric_enabled),
expires_in: EXPIRY_TIME
end
And a simple cache reading method
self.read_single_hive_messages_from_cache
Rails.cache.read CACHE_KEY
end
Then create a rake task that just fetches these messages and call the caching method, and rails will write the cache.
Create a cron job that calls this rake task, set the cron job to 5 minutes or so, the expiry time is longer just in case for some reason the cron job didn't run, the data will still be available for the next run.
This way your processing is run in the background, every 5 ( or whatever time you choose ) minutes, the page load should happen normally with no delay at all, since the array data will be loaded from the pre-calculated cache.
In case the cron stops working, the data will expire in the 15 minutes I've set, and then the read cache method will return nil, you could avoid this and set the data to never expire, but then the data will become stale and the old data will keep getting returned.
Another way to handle this is to tell the cache reading method how to generate the cache it self, so if it finds the cache empty it generates one and caches it itself before returning the data, the method would look like this
def self.read_single_hive_messages_from_cache(messages, us_metric_enabled)
Rails.cache.fetch CACHE_KEY, expires_in: EXPIRY_TIME do
self.class.write_single_hive_messages_to_cache(messages, us_metric_enabled)
end
end
But then make sure that messages is an ActiveRecord::Relation and not a processed array, because you don't want to query for 1+ million records and then find the cache already ready, if it's an ActiveRecord::Relation it will not touch the database until the array is started ( inside the caching block ), if the cache exists it will be returned before you enter the block and thus the data won't get fetched, saving you that huge query.
I know the answer got long, if you need more help tell me.
I have a class method (placed in /app/lib/) which performs some heavy calculations and sub-http requests until a result is received.
The result isn't too dynamic, and requested by multiple users accessing a specific view in the app.
So, I want to schedule a periodic run of the method (using cron and Whenever gem), store the results somewhere in the server using JSON format and, by demand, read the results alone to the view.
How can this be achieved? what would be the correct way of doing that?
What I currently have:
def heavyMethod
response = {}
# some calculations, eventually building the response
File.open(File.expand_path('../../../tmp/cache/tests_queue.json', __FILE__), "w") do |f|
f.write(response.to_json)
end
end
and also a corresponding method to read this file.
I searched but couldn't find an example of achieving this using Rails cache convention (and not some private code that I wrote), on data which isn't related with ActiveRecord.
Thanks!
Your solution should work fine, but using Rails.cache should be cleaner and a bit faster. Rails guides provides enough information about Rails.cache and how to get it to work with memcached, let me summarize how I would use it in your case
Heavy method
def heavyMethod
response = {}
# some calculations, eventually building the response
Rails.cache.write("heavy_method_response", response)
end
Request
response = Rails.cache.fetch("heavy_method_response")
The only problem here is that when ur server starts for the first time, the cache will be empty. Also if/when memcache restarts.
One advantage is that somewhere on the flow, the data u pass in is marshalled into storage, and then unmartialled on the way out. Meaning u can pass in complex datastructures, and dont need to serialize to json manually.
Edit: memcached will clear your item if it runs out of memory. Will be very rare since its using a LRU (i think) algoritm to expire things, and I presume you will use this often.
To prevent this,
set expires_in larger than your cron period,
change your fetch code to call the heavy_method if ur fetch fails (like Rails.cache.fetch("heavy_method_response") {heavy_method}, and change heavy_method to just return the object.
Use something like redis which will not delete items.
I am looking to find information on how the caching mechanism in Rails 4 prevents against multiple users trying to regenerate cache keys at once, aka a cache stampede: http://en.wikipedia.org/wiki/Cache_stampede
I've not been able to find out much information via Googling. If I look at other systems (such as Drupal) cache stampede prevention is implemented via a semaphores table in the database.
Rails does not have a built-in mechanism to prevent cache stampedes.
According to the README for atomic_mem_cache_store (a replacement for ActiveSupport::Cache::MemCacheStore that mitigates cache stampedes):
Rails (and any framework relying on active support cache store) does
not offer any built-in solution to this problem
Unfortunately, I'm guessing that this gem won't solve your problem either. It supports fragment caching, but it only works with time-based expiration.
Read more about it here:
https://github.com/nel/atomic_mem_cache_store
Update and possible solution:
I thought about this a bit more and came up with what seems to me to be a plausible solution. I haven't verified that this works, and there are probably better ways to do it, but I was trying to think of the smallest change that would mitigate the majority of the problem.
I assume you're doing something like cache model do in your templates as described by DHH (http://37signals.com/svn/posts/3113-how-key-based-cache-expiration-works). The problem is that when the model's updated_at column changes, the cache_key likewise changes, and all your servers try to re-create the template at the same time. In order to prevent the servers from stampeding, you would need to retain the old cache_key for a brief time.
You might be able to do this by (dum da dum) caching the cache_key of the object with a short expiration (say, 1 second) and a race_condition_ttl.
You could create a module like this and include it in your models:
module StampedeAvoider
def cache_key
orig_cache_key = super
Rails.cache.fetch("/cache-keys/#{self.class.table_name}/#{self.id}", expires_in: 1, race_condition_ttl: 2) { orig_cache_key }
end
end
Let's review what would happen. There are a bunch of servers calling cache model. If your model includes StampedeAvoider, then its cache_key will now be fetching /cache-keys/models/1, and returning something like /models/1-111 (where 111 is the timestamp), which cache will use to fetch the compiled template fragment.
When you update the model, model.cache_key will begin returning /models/1-222 (assuming 222 is the new timestamp), but for the first second after that, cache will keep seeing /models/1-111, since that is what is returned by cache_key. Once 1 second passes, all of the servers will get a cache-miss on /cache-keys/models/1 and will try to regenerate it. If they all recreated it immediately, it would defeat the point of overriding cache_key. But because we set race_condition_ttl to 2, all of the servers except for the first will be delayed for 2 seconds, during which time they will continue to fetch the old cached template based on the old cache key. Once the 2 seconds have passed, fetch will begin returning the new cache key (which will have been updated by the first thread which tried to read/update /cache-keys/models/1) and they will get a cache hit, returning the template compiled by that first thread.
Ta-da! Stampede averted.
Note that if you did this, you would be doing twice as many cache reads, but depending on how common stampedes are, it could be worth it.
I haven't tested this. If you try it, please let me know how it goes :)
The :race_condition_ttl setting in ActiveSupport::Cache::Store#fetch should help avoid this problem. As the documentation says:
Setting :race_condition_ttl is very useful in situations where a cache entry is used very frequently and is under heavy load. If a cache expires and due to heavy load seven different processes will try to read data natively and then they all will try to write to cache. To avoid that case the first process to find an expired cache entry will bump the cache expiration time by the value set in :race_condition_ttl. Yes, this process is extending the time for a stale value by another few seconds. Because of extended life of the previous cache, other processes will continue to use slightly stale data for a just a bit longer. In the meantime that first process will go ahead and will write into cache the new value. After that all the processes will start getting new value. The key is to keep :race_condition_ttl small.
Great question. A partial answer that applies to single multi-threaded Rails servers but not multiprocess(or) environments (thanks to Nick Urban for drawing this distinction) is that the ActionView template compilation code blocks on a mutex that is per template. See line 230 in template.rb here. Notice there is a check for completed compilation both before grabbing the lock and after.
The effect is to serialize attempts to compile the same template, where only the first will actually do the compilation and the rest will get the already completed result.
Very interesting question. I searched on google (you get more results if you search for "dog pile" instead of "stampede") but like you, did I not get any answers, except this one blog post: protecting from dogpile using memcache.
Basically does it store you fragment in two keys: key:timestamp (where timestamp would be updated_at for active record objects) and key:last.
def custom_write_dogpile(key, timestamp, fragment, options)
Rails.cache.write(key + ':' + timestamp.to_s, fragment)
Rails.cache.write(key + ':last', fragment)
Rails.cache.delete(key + ':refresh-thread')
fragment
end
Now when reading from the cache, and trying to fetch a non existing cache, will it instead try to fecth the key:last fragment instead:
def custom_read_dogpile(key, timestamp, options)
result = Rails.cache.read(timestamp_key(name, timestamp))
if result.blank?
Rails.cache.write(name + ':refresh-thread', 0, raw: true, unless_exist: true, expires_in: 5.seconds)
if Rails.cache.increment(name + ':refresh-thread') == 1
# The cache didn't exists
result = nil
else
# Fetch the last cache, as the new one has not been created yet
result = Rails.cache.read(name + ':last')
end
end
result
end
This is a simplified summary of the by Moshe Bergman that i linked to before, or you can find here.
There is no protection against memcache stampedes. This is a real problem when multiple machines are involved and multiple processes on those multiple machines. -Ouch-.
The problem is compounded when one of the key processes has "died" leaving any "locking" ... locked.
In order to prevent stampedes you have to re-compute the data before it expires. So, if your data is valid for 10 minutes, you need to regenerate again at the 5th minute and re-set the data with a new expiration for 10 more minutes. Thus you don't wait until the data expires to set it again.
Should also not allow your data to expire at the 10 minute mark, but re-compute it every 5 minutes, and it should never expire. :)
You can use wget & cron to periodically call the code.
I recommend using redis, which will allow you to save the data and reload it in the advent of a crash.
-daniel
A reasonable strategy would be to:
use a :race_condition_ttl with at least the expected time it takes to refresh the resource. Setting it to less time than expected to perform a refresh is not advisable as the angry mob will end up trying to refresh it, resulting in a stampede.
use an :expires_in time calculated as the maximum acceptable expiry time minus the :race_condition_ttl to allow for refreshing the resource by a single worker and avoiding a stampede.
Using the above strategy will ensure that you don't exceed your expiry/staleness deadline and also avoid a stampede. It works because only one worker gets through to refresh, whilst the angry mob are held off using the cache value with the race_condition_ttl extension time right up to the originally intended expiry time.
I want to run NOn_cached queries using rails. Any help?
Rails is only going to cache the query result DURING the request-response cycle, so it's only going to live for the duration of the request.
You should be able to use the uncached command
uncached do
find(blah)
end
at least within a controller action.
Another (BAD!) way to do it is add a query attribute that busts the cache
where("name= ? OR 0= ?", 'smith', rand(100))
or similar, so that you get OR '0=0.3567' which is always false AND different for each request.
You really want to use uncached though :)
Rails has a nice set of filters (before_validation, before_create, after_save, etc) as well as support for observers, but I'm faced with a situation in which relying on a filter or observer is far too computationally expensive. I need an alternative.
The problem: I'm logging web server hits to a large number of pages. What I need is a trigger that will perform an action (say, send an email) when a given page has been viewed more than X times. Due to the huge number of pages and hits, using a filter or observer will result in a lot of wasted time because, 99% of the time, the condition it tests will be false. The email does not have to be sent out right away (i.e. a 5-10 minute delay is acceptable).
What I am instead considering is implementing some kind of process that sweeps the database every 5 minutes or so and checks to see which pages have been hit more than X times, recording that state in a new DB table, then sending out a corresponding email. It's not exactly elegant, but it will work.
Does anyone else have a better idea?
Rake tasks are nice! But you will end up writing more custom code for each background job you add. Check out the Delayed Job plugin http://blog.leetsoft.com/2008/2/17/delayed-job-dj
DJ is an asynchronous priority queue that relies on one simple database table. According to the DJ website you can create a job using Delayed::Job.enqueue() method shown below.
class NewsletterJob < Struct.new(:text, :emails)
def perform
emails.each { |e| NewsletterMailer.deliver_text_to_email(text, e) }
end
end
Delayed::Job.enqueue( NewsletterJob.new("blah blah", Customers.find(:all).collect(&:email)) )
I was once part of a team that wrote a custom ad server, which has the same requirements: monitor the number of hits per document, and do something once they reach a certain threshold. This server was going to be powering an existing very large site with a lot of traffic, and scalability was a real concern. My company hired two Doubleclick consultants to pick their brains.
Their opinion was: The fastest way to persist any information is to write it in a custom Apache log directive. So we built a site where every time someone would hit a document (ad, page, all the same), the server that handled the request would write a SQL statement to the log: "INSERT INTO impressions (timestamp, page, ip, etc) VALUES (x, 'path/to/doc', y, etc);" -- all output dynamically with data from the webserver. Every 5 minutes, we would gather these files from the web servers, and then dump them all in the master database one at a time. Then, at our leisure, we could parse that data to do anything we well pleased with it.
Depending on your exact requirements and deployment setup, you could do something similar. The computational requirement to check if you're past a certain threshold is still probably even smaller (guessing here) than executing the SQL to increment a value or insert a row. You could get rid of both bits of overhead by logging hits (special format or not), and then periodically gather them, parse them, input them to the database, and do whatever you want with them.
When saving your Hit model, update a redundant column in your Page model that stores a running total of hits, this costs you 2 extra queries, so maybe each hit takes twice as long to process, but you can decide if you need to send the email with a simple if.
Your original solution isn't bad either.
I have to write something here so that stackoverflow code-highlights the first line.
class ApplicationController < ActionController::Base
before_filter :increment_fancy_counter
private
def increment_fancy_counter
# somehow increment the counter here
end
end
# lib/tasks/fancy_counter.rake
namespace :fancy_counter do
task :process do
# somehow process the counter here
end
end
Have a cron job run rake fancy_counter:process however often you want it to run.