Overcoming querying limitations in Couchbase - mapreduce

We recently made a shift from relational (MySQL) to NoSQL (couchbase). Basically its a back-end for social mobile game. We were facing a lot of problems scaling our backend to handle increasing number of users. When using MySQL loading a user took a lot of time as there were a lot of joins between multiple tables. We saw a huge improvement after moving to couchbase specially when loading data as most of it is kept in a single document.
On the downside, couchbase also seems to have a lot of limitations as far as querying is concerned. Couchbase alternative to SQL query is views. While we managed to handle most of our queries using map-reduce, we are really having a hard time figuring out how to handle time based queries. e.g. we need to filter users based on timestamp attribute. We only need a user in view if time is less than current time:
if(user.time < new Date().getTime() / 1000)
What happens is that once a user's time is set to some future time, it gets exempted from this view which is the desired behavior but it never gets added back to view unless we update it - a document only gets re-indexed in view when its updated.
Our solution right now is to load first x user documents and then check time in our application. Sorting is done on user.time attribute so we get those users who's time is less than or near to current time. But I am not sure if this is actually going to work in live environment. Ideally we would like to avoid these type of checks at application level.
Also there are times e.g. match making when we need to check multiple time based attributes. Our current strategy doesn't work in such cases and we frequently get documents from view which do not pass these checks when done in application. I would really appreciate if someone who has already tackled similar problems could share their experiences. Thanks in advance.
Update:
We tried using range queries which works for only one key. Like I said in most cases we have multiple time based keys meaning multiple ranges which does not work.

If you use Date().getTime() inside a view function, you'll always get the time when that view was indexed, just as you said "it never gets added back to view unless we update it".
There are two ways:
Bad way (don't do this in production). Query views with stale=false param. That will cause view to update before it will return results. But view indexing is slow process, especially if you have > 1 milllion records.
Good way. Use range requests. You just need to emit your date in map function as a key or a part of complex key and use that range request. You can see one example here or here (also if you want to use DateTime in couchbase this example will be more usefull). Or just look to my example below:
I.e. you will have docs like:
doc = {
"id"=1,
"type"="doctype",
"timestamp"=123456, //document update or creation time
"data"="lalala"
}
For those docs map function will look like:
map = function(){
if (doc.type === "doctype"){
emit(doc.timestamp,null);
}
}
And now to get recently "updated" docs you need to query this view with params:
startKey="dateTimeNowFromApp"
endKey="{}"
descending=true
Note that startKey and endKey are swapped, because I used descending order. Here is also a link to documnetation about key types that couchbase supports.
Also I've found a link to a question that can also help.

Related

Feed Algorithm + Database: Either too many rows or too slow retrieval

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

Reducing the number of calls to MongoDB with mongoengine

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

Design pattern for caching dynamic user content (in django)

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.

How can I persist a single value in Django?

My Django application retrieves an RSS feed every day. I would like to persist the time the feed was last updated somewhere in the app. I'm only retrieving one feed, it will never grow to be multiple feeds. How can I persist the last updated time?
My ideas so far
Create a model and add a datetime field to it. This seems like overkill as it adds another table to the database, in which there will only ever be one row. Other than that, it's the most obvious and straight-forward solution.
Create a settings object which just stores key/value mappings. The last updated date would just be row in this database. This is essentially a generic version of the previous solution.
Use dbsettings/django-values, which allows you to store settings in the database. The last updated date would just be a 'setting'.
Any other ideas that I'm missing?
In spite of the fact databases regularly store many rows in any given table, having a table with only one row is not especially costly, so long as you don't have (m)any indexes, which would waste space. In fact most databases create many single row tables to implement some features, like monotonic sequences used for generating primary keys. I encourage you to create a regular model for this.
RAM is volatile, thus not persistent: memcached is not what you asked for.
XML it is not the right technology to store a single value.
RDMS is not the right technology to store a single value.
Django cache framework will answer your question if CACHE_BACKEND is set to anything else than file://...
The filesystem is the right technology to "persist a single value".
In settings.py:
RSS_FETCH_DATETIME_PATH=os.path.join(
os.path.abspath(os.path.dirname(__file__)),
'rss_fetch_datetime'
)
In your rss fetch script:
from django.conf import settings
handler = open(RSS_FETCH_DATETIME_PATH, 'w+')
handler.write(int(time.time()))
handler.close()
Wherever you need to read it:
from django.conf import settings
handler = open(RSS_FETCH_DATETIME_PATH, 'r+')
timestamp = int(handler.read())
handler.close()
But cron is the right tool if you want to "run a command every day", for example at 5AM:
0 5 * * * /path/to/manage.py runscript /path/to/retreive/script
Of course, you can still write the last update timestamp in a file at the end of the retreive script, and use it somewhere else, if that makes sense to you.
Concluding by quoting Ken Thompson:
One of my most productive days was
throwing away 1000 lines of code.
One solution I've used in the past is to use Django's cache feature. You set a value to True with an expiration time of one day (in your case.) If the value is not set, you fetch the feed, otherwise you don't do anything.
You can see my solution here: Importing your Flickr photos with Django
If you need it only for caching purposes, why not store it in the memcached?
On the other hand, if you use this data for other purposes (e.g. display it on the page, or to make some calculation, etc.), then I would store it in a new model - in Django, all persistence is built on top of the database, via models, and I would not try to use other "clever" solutions.
One thing I used to do when I was deving with PHP, was to store the xml somewhere, but with a new tag inserted to hold the timestamp of the latest retrieval. It wasn't great, but it was quick and simple.
Keeping it simple would lead to the idea of just storing it in the file system ... why can't you do that? You could, for example, have a siteconfig module in one of your apps which held these sorts of data. This could load up data from a specific file, which could be text, JSON, ConfigParser, pickle or any suitable format. Just import siteconfig somewhere, and it can load the data and make it available to the other modules in your site. You could easily extend this to hold a dict-like object with a number of settings (e.g., if you ever have multiple feeds, but don't want to have a model just for 2-3 rows, you could easily hold the last-retrieved time for each feed in a dict keyed by feed URL).
Create a session key, which persists forever and update the feed timestamp every time you access it.

Memcache db models to make search more efficient

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