On my website I'm going to provide points for some activities, similarly to stackoverflow. I would like to calculate value basing on many factors so each computation for each user will take for instance 10 SQL queries.
I was thinking about caching it:
in memcache,
in user's row in database (so that wherever I need to get user from base I easly show the points)
Storing in database seems easy but on other hand it's redundant information and I decided to ask, since maybe there is easier and prettier solution which I missed.
I'd highly recommend this app for storing the calculated values in the model: https://github.com/initcrash/django-denorm
Memcache is faster than the db... but if you already have to retrieve the record from the db anyway, having the calculated values cached in the rows you're retrieving (as a 'denormalised' field) is even faster, plus it's persistent.
Related
I have a view which returns data associated with a randomly-chosen row from one of my models. I'm aware of order_by('?') and its performance problems, and I want to avoid using order_by('?') in my view.
Because the data in my model changes very rarely (if at all), I'm considering the approach of caching the entire model in memory between requests. I know how many records I'm dealing with, and I'm comfortable taking the memory hit. If the model does change somehow, I could regenerate the cache at that moment.
Is my strategy reasonable? If so, how do I implement it? If not, how can I quickly select a random row from a model that changes very rarely if at all?
If you know the ids of your object, and its range you can randomize over the ids, and then query the database
A better approach might be to keep the number of objects in your cache, and simply retrieve a random one when you need it:
item_number = random.randint(MODEL_COUNT)
MyModel.objects.all()[item_number]
Say I have a general website that allows someone to download their feed in a small amount of time. A user can be subscribed to many different pages, and the user's feed must be returned to the user from the server with only N of the most recent posts between all of the pages subscribed to. Originally when a user queried the server for a feed, the algorithm was as follows:
look at all of the pages a user subscribed to
getting the N most recent posts from each page
sorting all of the posts
return the N most recent posts to the user as their feed
As it turns out, doing this EVERY TIME a user tried to refresh a feed was really slow. Thus, I changed the database to have a table of feedposts, which simply has a foreign key to a user and a foreign key to the post. Every time a page makes a new post, it creates a feed post for each of its subscribing followers. That way, when a user wants their feed, it is already created and does not have to be created upon retrieval.
The way I am doing this is creating far too many rows and simply does not seem scalable. For instance, if a single page makes 1 post & has 1,000,000 followers, we just created 1,000,000 new rows in our feedpost table.
Please help!
How do companies such as facebook handle this problem? Do they generate the feed upon request? Are my database relationships terrible?
It's not that the original schema itself would be inherently wrong, at least not based on the high-level description you have provided. The slowness stems from the fact that you're not accessing the database in a way relational databases should be accessed.
In general, when querying a relational database, you should use JOINs and in-database ordering where possible, instead of fetching a bunch of data, and then trying to connect related objects and sort them in your code. If you let the database do all this for you, it will be much faster, because it can take advantage of indices, and only access those objects that are actually needed.
As a rule of thumb, if you need to sort the results of a QuerySet in your Python code, or loop through multiple querysets and combine them somehow, you're most likely doing something wrong and you should figure out how to let the database do it for you. Of course, it's not true every single time, but certainly often enough.
Let me try to illustrate with a simple piece of code. Assume you have the following models:
class Page(models.Model):
name = models.CharField(max_length=47)
followers = models.ManyToManyField('auth.User', related_name='followed_pages')
class Post(models.Model):
title = models.CharField(max_length=147)
page = models.ForeignKey(Page, related_name='posts')
content = models.TextField()
time_published = models.DateTimeField(auto_now_add=True)
You could, for example, get the list of the last 20 posts posted to pages followed by the currently logged in user with the following single line of code:
latest_posts = Post.objects.filter(page__followers=request.user).order_by('-time_published')[:20]
This runs a single SQL query against your database, which only returns the (up to) 20 results that match, and nothing else. And since you're joining on primary keys of all tables involved, it will conveniently use indices for all joins, making it really fast. In fact, this is exactly the kind of operation relational databases were designed to perform efficiently.
Caching will be the solution here.
You will have to reduce the database reads, which are much slower as compared to cache reads.
You can use something like Redis to cache the post.
Here is an amazing answer for better understanding
Is Redis just a cache
Each page can be assigned a key, and you can pull all of the posts for that page under that key.
you need not to cache everything , just cache resent M posts, where M>>N and safe enough to reduce the database calls.Now if in case user requests for posts beyond the latesd M ones, then they can be directly fetched from the DB.
Now when you have to generate the feed you can make a DB call to get all of the subscribed pages(or you can put in the cache as well) and then just get the required number of post's from the cache.
The problem here would be keeping the cache up-to date.
For that you can use something like django-signals. Whenever a new post is added, add it to the cache as well using the signal.
So for each DB write you will have to write to cache as well.
But then you will not have to read from DB and as Redis is a in memory datastore it is pretty fast as compared to standard relational databases.
Edit:
These are a few more articles which can help for better understanding
Does Stack Exchange use caching and if so, how
How Twitter Uses Redis to Scale - 105TB RAM, 39MM QPS, 10,000+ Instances
I'm working to optimize a Django application that's (mainly) backed by MongoDB. It's dying under load testing. On the current problematic page, New Relic shows over 700 calls to pymongo.collection:Collection.find. Much of the code was written by junior coders and normally I would look for places to add indicies, make smarter joins and remove loops to reduce query calls, but joins aren't an option here. What I have done (after adding indicies based on EXPLAINs) is tried to reduce the cost in loops by making a general query and then filtering that smaller set in the loops*. While I've gotten the number down from 900 queries, 700 still seems insane even with the intense amount of work being done on the page. I thought perhaps find was called even when filtering an existing queryset, but the code suggests it's always a database query.
I've added some logging to mongoengine to see where the queries come from and to look at EXPLAIN statements, but I'm not having a ton of luck sifting through the wall of info. mongoengine itself seems to be part of the performance problem: I switched to mongomallard as a test and got a 50% performance improvement on the page. Unfortunately, I got errors on a bunch of other pages (as best I can tell it appears Mallard doesn't do well when filtering an existing queryset; the error complains about a call to deepcopy that's happening in a generator, which you can't do-- I hit a brick wall there). While Mallard doesn't seem like a workable replacement for us, it does suggest a lot of the proessing time is spent converting objects to and from Python in mongoengine.
What can I do to further reduce the calls? Or am I focusing on the wrong thing and should be attacking the problem somewhere else?
EDIT: providing some code/ models
The page in question displays the syllabus for a course, showing all the modules in the course, their lessons and the concepts under the lessons. For each concept, the user's progress in the concept is also shown. So there's a lot of looping to get the hierarchy teased out (and it's not stored according to any of the patterns the Mongo docs suggest).
class CourseVersion(Document):
...
course_instances = ListField(ReferenceField('CourseInstance'))
courseware_containers = ListField(EmbeddedDocumentField('CoursewareContainer'))
class CoursewareContainer(EmbeddedDocument):
id = UUIDField(required=True, binary=False, default=uuid.uuid4)
....
courseware_containers = ListField(EmbeddedDocumentField('self'))
teaching_element_instances = ListField(StringField())
The course's modules, lessons and concepts are stored in courseware_containers; we need to get all of the concepts so we can get the list of ids in teaching_element_instances to find the most recent one the user has worked on (if any) for that concept and then look up their progress.
* Just to be clear, I am using a profiler and looking at times and doings things The Right Way as best I know, not simply changing things and hoping for the best.
The code sample isn't bad per-sae but there are a number of areas that should be considered and may help improve performance.
class CourseVersion(Document):
...
course_instances = ListField(ReferenceField('CourseInstance'))
courseware_containers = ListField(EmbeddedDocumentField('CoursewareContainer'))
class CoursewareContainer(EmbeddedDocument):
id = UUIDField(required=True, binary=False, default=uuid.uuid4)
....
courseware_containers = ListField(EmbeddedDocumentField('self'))
teaching_element_instances = ListField(StringField())
Review
Unbounded lists.
course_instances, courseware_containers, teaching_element_instances
If these fields are unbounded and continuously grow then the document will move on disk as it grows, causing disk contention on heavily loaded systems. There are two patterns to help minimise this:
a) Turn on Power of two sizes. This will cost disk space but should lower the amount of io churn as the document grows
b) Initial Padding - custom pad the document on insert so it gets put into a larger extent and then remove the padding. Really an anti pattern but it may give you some mileage.
The final barrier is the maximum document size - 16MB you can't grow your data bigger than that.
Lists of ReferenceFields - course_instances
MongoDB doesn't have joins so it costs an extra query to look up a ReferenceField - essentially they are an in app join. Which isn't bad per-sae but its important to understand the tradeoff. By default mongoengine won't automatically dereference the field only doing course_version.course_instances will it do another query and then populate the whole list of references. So it can cost you another query - if you don't need the data then exclude() it from the query to stop any leaking queries.
EmbeddedFields
These fields are part of the document, so there is no cost for them, other than the wire costs of transmitting and loading the data. **As they are part of the document, you don't need select_related to get this data.
teaching_element_instances
Are these a list of id's? It says its a StringField in the code sample above. Either way, if you don't need to dereference the whole list then storing the _ids as a StringField and manually dereferencing may be more efficient if coded correctly - especially if you just need the latest (last?) id.
Model complexity
The CoursewareContainer is complex. For any given CourseVersion you have n CoursewareContainers with themselves have a list of n containers and those each have n containers and on...
Finding the most recent instances
We need to get all of the concepts so we can get the list of ids in
teaching_element_instances to find the most recent one the user has
worked on (if any) for that concept and then look up their progress.
I'm unsure if there is a single instance you are after or one per Container or one per Course. Either way - the logic for querying the data should be examined. If its a single instance you are after - then that could be stored against the user so to simplify the logic of looking this up. If its per course or container then to improve performance ensure you minimise the number of queries - if possible collect all the ids and then at the end issue a single $in query, rather than doing a query per container.
Mongoengine costs
Currently, there is a performance cost to loading the data into Mongoengine classes - if you don't need the classes and are happy to work with simple dictionaries then either issue a raw pymongo query or use as_pymongo.
Schema design
The schema looks logical enough but is it suitable for the use case - in essence is it using MongoDB's strengths or is it putting a relational peg in a document database shaped hole? I can't answer than for you but I do know the way to the happy path with MongoDB is design the schema based on its use case. With relational databases schema design from the outset is simple - you normalise, with document databases how the data is used is a primary factor.
MongoDB best practices
There are many other best practices and mongodb have a guide which might be of interest: MongoDB Operations Best Practices.
Feel free to contact me via the Mongoengine mailing list to discuss further and if needs be discuss in private.
Ross
I have a couple hundred of image thumbnails, 15k each. I want to display 20 or so on each page.
Would django.core.paginator suffice for the pagination of these pages? I.e., will it return only those images displayed on the current page? (And if not, what would be a good way to do this?) Thank you.
Depends, because there is one big limitation from the RDBMS (which affects all databases, including MySQL, Postgres, etc.).
django.core.paginator takes a QuerySet which represent any kind of SQL query and adds a LIMIT clause to just get a couple of entries from the database. This approach works well for many kinds of applications, but might become a serious problem if you have a lot of entries. The particular problem is, that whenever you access the 800th page, the database will actually fetch 801*20 entries and then drop the first 800*20 entries again to return the last twenty.
Unfortunately, there is no easy way to solve this problem. In a lot of cases, a next/prev button might be enough so you can write your own pagination which does operate on after-keys instead of page numbers. For example, if the last entry currently displayed by the user has the key "D" you show a next button which links to /next?after=D and then use a SQL query like SELECT * FROM objects WHERE key >DORDER BY key LIMIT 20. The advantage of this approach is, that you can add an index on objects.key which speed up things significantly.
The other approach requires, that you add an additional, indexed (!) column page_num to your table. Then you can perform SQL queries like SELECT * FROM objects WHERE page_num=800 ORDER BY key. With that approach, you can still access all pages randomly, but you have to maintain the page_num column. This might be easy if data is mostly appended at the end and is more complicated if you want to delete/insert elements from the middle efficiently.
So, I would start with django.core.paginator because it's just about 1 line of code. But keep an eye on the response times of your paginated views and the slowquery log from your database. If your database server can't handle the load anymore, you will have to choose one of the techniques mentioned above. Choose solution 2 if random page access is an requirement and solution 1 otherwise (because it's much simpler).
PS: And yes, django.core.paginator will work correctly. :)
I need to set up some kind of e-store with search functionality.
For every search request I got to query structure like this:
product:
-name
-tags
--tag
-ingredients
--ingredient
---tags
----tag
---options
----option
-----option details
-variants
--variant
---tags
----tag
---options
----option measure
----value
---price
Now imagine number of queries... Database is normalized (2nd level I guess).
It seems to me that one obvious solution here is to store each fetched model result set (product set, ingredient set, attribute set, tag set etc.) in memory for a very long time (products and its attributes updated not so often and only by admin) and make query from there.
So what do you think? Is there a better way to reduce db queries count?
Another option I thought about is to use sphinx, but I don't need full-text search at all, just exact matches with tag-like fields.
Thank you in advance!
On my Google App Engine app I normally move things from the datastore to memcache and work with them there since querying for the data can take a lot of time. MemCache, in my case, returns the data and has less load on CPU than accessing the data which can go through a number of queries until it gets what it is looking for.
I would recommend setting a long timeout on your memcache so that memcache doesnt flush it more often than you are expecting. I think the maximum timout is up to 1 month but normally setting it for a couple days will suffice.
You can always add code to flush the memcache if the data for a product has been updated so that you do the DB hit again but only once this time