Implementing dynamically updating upvote/downvote - mysql

How to implement dynamically updating vote count similar to quora:- Whenever a user upvotes an answer its reflected automatically for every one who is viewing that page.
I am looking for an answer that address following:
Do we have to keep polling for upvote counts for every answer, If yes
then how to manage the server load arising because of so many users
polling for upvotes.
Or to use websockits/push notifications, how scalable are these?
How to store the upvote/downvote count in databases/inmemory to support this. How do they control the number of read/writes. My backend database is mysql
The answer I am looking for may not be exactly how quora is doing it, but may be how this can be done using available opensource technologies.

It's not the back-end system details that you need to worry about but the front end. Having connection being open all the time is impractical at any real scale. Instead you want the opposite - to be able to serve and close connection from back-end as fast as you can.
Websockets is a sexy technology, but again, in real world there are issues with proxies, if you are developing something that should work on a variety of screens (desktop, tablet, mobile) it might became a concern to you. Even good-old long polls might not work through firewalls and proxies.
Here is a good news: I think
"keep polling for upvote counts for every answer"
is a totally good solution in this case. Consider the following:
your use-case does not need any real real-time updates. There is little harm to see the counter updated a bit later
for very popular topics you would like to squash multiple up-votes/down-votes into one anyway
most of the topics will see no up-vote/down-vote traffic at all for days/weeks, so keeping a connection open, waiting for an event that never comes is a waste
most of the user will never up-vote/down-vote that just came to read a topic, so your read/write ration of topics stats will be greatly skewed toward reads
network latencies varies hugely across clients, you will see horrible transfer rates for a 100B http responses, while this sluggish client is fetching his response byte-by-byte your precious server connection and what is more importantly - thread on a back end server is busy
Here is what I'd start with:
have browsers periodically poll for a new topic stat, after the main page loads
keep your MySQL, keep counters there. Every time there is an up/down vote update the DB
put Memcached in front of the DB as a write-through cache i.e. every time there is an up/down vote update cache, then update DB. Set explicit expire time for a counter there to be 10-15 minutes . Every time counter is updated expire time is prolongated automatically.
design these polling http calls to be cacheable by http proxies, set expire and ttl http headers to be 60 sec
put a reverse proxy(Varnish, nginx) in front of your front end servers, have this proxy do the caching of the said polling calls. These takes care of the second level cache and help free up backend servers threads quicker, see network latencies concern above
set-up your reverse proxy component to talk to memcached servers directly without making a call to the backend server, yes if your can do it with both Varnish and nginx.
there is no fancy schema for storing such data, it's a simple inc()/dec() operation in memcached, note that it's safe from the race condition point of view. It's also a safe atomic operation in MySQL UPDATE table SET field = field + 1 WHERE [...]
Aggressive multi level caching covers your read path: in Memcached and in all http caches along the way, note that these http poll requests will be cached on the edges as well.
To take care of the long tail of unpopular topic - make http ttl for such responses reverse proportional to popularity.
A read request will only infrequently gets to the front end server, when http cache expired and memcached does not have it either. If that is still a problem, add memecached servers and increase expire time in memcached across the board.
After you done with that you have all the reads taken care of. The only problem you might still have, depending on the scale, is high rate of writes i.e. flow of up/down votes. This is where your single MySQL instance might start showing some lags. Fear not - proceed along the old beaten path of sharding your instances, or adding a NoSQL storage just for counters.
Do not use any messaging system unless absolutely necessary or you want an excuse to play with it.

Websockets, Server Sent Events (I think that's what you meant by 'push notifications') and AJAX long polling have the same drawback - they keep underlying TCP connection open for a long time.
So the question is how many open TCP connections can a server handle.
Basically, it depends on its OS, number of file descriptors (a config parameter) and available memory (each open connection reserves a read/write buffers).
Here's more on that.
We once tested a possibility to keep 1 million websocket connections open on a single server (Windows 7 x64 with 16Gb of RAM, JVM 1.7 with 8Gb of heap, using Undertow beta to serve Web requests).
Surprisingly, the hardest part was to generate the load on the server )
It managed to hold 1M. But again the server didn't do something useful, just received requests, went through protocol upgrade and kept those connections open.
There was also some number of lost connections, for whatever reason. We didn't investigate. But in production you would also have to ping the server and handle reconnection.
Apart from that, Websockets seem like an overkill here, SSE still aren't widely adopted.
So I would go with good old AJAX polling, but optimize it as much as possible.
Works everywhere, simple to implement and tweak, no reliance on an external system (I had bad experience with that several times), possibilities for optimization.
For instance, you could group updates for all open articles in a single browser, or adjust update interval according to how popular the article is.
After all it doesn't seem like you need real-time notifications here.

sounds like you might be able to use a messaging system like Kafka, or RabbitMQ, or ActiveMQ. Your front end would sent votes to a message channel and receive them with a listener, and you could have a server side piece persist the votes to the db periodically.
You could also accomplish your task by polling your database, and by incre/decre menting a number related to a post via a stored proc... there are a bunch of options here and it depends on how much concurrency you may be facing.

Related

Does Symfony3 / Doctrine open one MySQL connection per visitor?

So I have developped this website with Symfony3 and Doctrine. I have one major concern about performance with MySQL and more specifically the number of simultaneous open connexions.
For the moment, one to five users are online on the website. What happens if, let's say, 1,500 users connect within one minute? Does Symfony3 or Doctrine handle this kind of situations? How can I be sure the website doesn't go down providing me with the Too many connections MySQL error?
And if I go up to 5,000? And 10,000? The server has 4GB of RAM and a 2.40Ghz mono-core processor but I wouldn't worry about the hardware as I'm more concerned about MySQL.
These situations already happened in the past but I was running the website with Wordpress and W3 Total Cache plugin. Should I consider using a cache manager such as memcached or else?
In short, I'm concerned about the website becoming unavailable in case of sudden high trafic (and thought of the MysQL Too many connections error in first but I might be missing something even more important).
Thanks for lightening me out on this one as I'm not fully aware about performance issues with Symfony.
I believe it does open one connection per visitor. Regardless of whether it does or not however neither Symfony or Doctrine has a magic bullet to handle every load/connection scenario.
Why don't you use a load testing tool (there are many) and see how it actually pans out? In my experience predicting a bottleneck is useless, as they will always crop up where you least expect it.
For example, the MySQL connection limit is only one part of the optimisation puzzle. It's no good just worrying about connection limits, you need to respond to web requests as quickly and efficiently as possible to free up MySQL connection resources (and other resources your app is using). So if your server is slow you will run out of connections (or some other resource) almost immediately under significant load, regardless of MySQL connection limits.
That said, those server specifications seem a little low for 5-10k users per minute. I wouldn't expect a machine like that to handle that kind of load without some serious optimisation/caching/etc.
The symfony performance page is a good starter, and there is also a good article on caching - there's a ton of available material on the subject. Good luck! :)
If you use php-fpm it depends on pm.max_children in fpm/pool.d/www.conf.
pm.max_children refers to the maximum number of concurrent PHP-FPM processes allowed to exist in such a pool. If the volume of incoming requests requires the creation of more PHP-FPM processes than the number allowed by the max_children limit, those additional requests are backlogged in a queue to await service.
So when pm.max_children > max_connections (my.cnf) and active users > max_connections you will get "Too many connections".

Web request performance really bad under stress

I wrote a web application using python and Flask framework, and set it up on Apache with mod_wsgi.
Today I use JMeter to perform some load testing on this application.
For one web URL:
when I set only 1 thread to send request, the response time is 200ms
when I set 20 concurrent threads to send requests, the response time increases to more than 4000ms(4s). THIS IS UNACCEPTABLE!
I am trying to find the problem, so I recorded the time in before_request and teardown_request methods of flask. And it turns out the time taken to process the request is just over 10ms.
In this URL handler, the app just performs some SQL queries (about 10) in Mysql database, nothing special.
To test if the problem is with web server or framework configuration, I wrote another method Hello in the same flask application, which just returns a string. It performs perfectly under load, the response time is 13ms with 20-thread concurrency.
And when doing the load test, I execute 'top' on my server, there are about 10 apache threads, but the CPU is mostly idle.
I am at my wit's end now. Even if the request are performed serially, the performance should not drop so drastically... My guess is that there is some queuing somewhere that I am unaware of, and there must be overhead besides handling the request.
If you have experience in tuning performance of web applications, please help!
EDIT
About apache configuration, I used MPM worker mode, the configuration:
<IfModule mpm_worker_module>
StartServers 4
MinSpareThreads 25
MaxSpareThreads 75
ThreadLimit 64
ThreadsPerChild 50
MaxClients 200
MaxRequestsPerChild 0
</IfModule>
As for mod_wsgi, I tried turning WSGIDaemonProcess on and off (by commenting the following line out), the performance looks the same.
# WSGIDaemonProcess tqt processes=3 threads=15 display-name=TQTSERVER
Congratulations! You found the performance problem - not your users!
Analysing performance problems on web applications is usually hard, because there are so many moving parts, and it's hard to see inside the application while it's running.
The behaviour you describe is usually associated with a bottleneck resource - this happens when there's a particular resource that can't keep up, so queues requests, which tends to lead to a "hockey stick" curve with response times - once you hit the point where this resource can't keep up, the response time goes up very quickly.
20 concurrent threads seems low for that to happen, unless you're doing a lot of very heavy lifting on the page.
First place to start is TOP - while CPU is low, what's memory, disk access etc. doing? Is your database running on the same machine? If not, what does TOP say on the database server?
Assuming it's not some silly hardware thing, the next most likely problem is the database access on that page. It may be that one query is returning literally the entire database when all you want is one record (this is a fairly common anti pattern with ORM solutions); that could lead to the behaviour you describe. I would use the Flask logging framework to record your database calls (start, end, number of records returned), and look for anomalies there.
If the database is performing well under load, it's either the framework or the application code. Again, use logging statements in the code to trace the execution time of individual blocks of code, and keep hunting...
It's not glamorous, and can be really tedious - but it's a lot better that you found this before going live!
Look at using New Relic to identify where the bottleneck is. See overview of it and discussion of identifying bottlenecks in my talk:
http://lanyrd.com/2012/pycon/spcdg/
Also edit your original question and add the mod_wsgi configuration you are using, plus whether you are using Apache prefork or worker MPM as you could be doing something non optimal there.

Prevent 'too many connections'(ConnectionPool is not the answer, looking for mysql server side solution)

A few weeks ago, I post a question about queuing database access request to prevent 'too many connection' error when massive concurrent db requests happen. People told me ConnectionPool is the right way to go which I agreed at that time. However, I finally realized this is not the solution especially when there are a lot of different clients accessing mysql server through network, because connection pool is at client side it can not prevent the sum of connections of all clients from exceeding the max connection number of mysql server.
I think there should be some middleware on the mysql server working as a queue or pool, is anybody familiar with this? Thank you.
I know this question is widely asked, I am also surprised as if there is no total solution for it.
HAProxy should perform TCP-level queueing for you purpose. Though, would it be better to build an application server in the middle, to handle incoming flow at more conscious level than TCP. This could require rewriting of both server and clients, but could give you more control over what's happening.
What you ask is actually a pretty complicated problem.
First of all you need to decide whether mis-alignments in data are acceptable, for example: if you store in the database the number of Likes received, and you ask this number at 12:00:00, and the number in the DB is 500, and someone posts a LIKE at 12:00:01, and you query it again at 12:00:02; is it OK to receive "500" again, even if the correct number should be 501, provided that in a little time the answer "501" does come out?
If this is acceptable (the infamous "301 bug" in YouTube), then you might start caching some SELECT responses.
You might even cache them in middleware, i.e. have a special process running continuously and hogging ONE connection to MySQL, and answering requests in a queue. You might run it internally in the server as a Web server on port 8001 and have an Apache ReverseProxy, HAproxy, pound, or NginX location to proxy it outside.
You can do the same for special UPDATE/DELETE queries even if it's trickier.
It would be best to cache queries running asynchronously through AJAX first, if any, because serializing queries with a proxy is liable to perceptibly slow down the application.
You have a threefold target:
run queries on MySQL as fast as possible (look into indexing and MySQL caching) in order to free the ConnectionPool and keep it as lightly loaded as possible.
refactor the application in order to extract all information from queries (e.g., the number of rows with a certain property AND those rows as data are often retrieved using TWO queries, but with proper management you need only one and a SQLNumRows() call. Also, quite often similar queries with different informations are run, when a single query might have returned all information at one go: typically, one query to check user/password, another to fetch the complete user profile).
divert the most calls possible to something not at all (NginX, middleware) or lightly (queuing process) bound to MySQL; in the latter case, using a known number of connections in order to run predictably.
Unfortunately there's no easy "magic bullet" to solve this problem (except of course increasing the number of connections, maybe replicating the DB on several hosts running as master-slave. While not really a magic bullet, it is easier to design and implement).

How To Interpret Siege and or Apache Bench Results

We have a MySQL driven site that will occasionally get 100K users in the space of 48 hours, all logging into the site and making purchases.
We are attempting to simulate this kind of load using tools like Apache Bench and Siege.
While the key metric seems to me number of concurrent users, and we've got our report results, we still feel like we're in the dark.
What I want to ask is: What kinds of things should we be testing to anticipate this kind of traffic?
50 concurrent users 1000 Times? 500 concurrent users 10 times?
We're looking at DB errors, apache timeouts, and response times. What else should we be looking at?
This is a vague question and I know there is no "right" answer, we're just looking for some general thoughts on how to determine what our infrastructure can realistically handle.
Thanks in advance!
Simultaneous users is certainly one of the key factors - especially as that applies to DB connection pools, etc. But you will also want to verify that the page rate (pages/sec) of your tests is also in the range you expect. If the the think-time in your testcases is off by much, you can accidentally simulate a much higher (or lower) page rate than your real-world traffic. Think time is the amount of time the user spends between page requests - reading the page, filling out a form, etc.
Depending on what other information you have on hand, this might help you calculate the number of simultaneous users to simulate:
Virtual User Calculators
The complete page load time seen by the end-user is usually the most important metric to evaluate system performance. You'll also want to look for failure rates on all transactions. You should also be on the lookout for transactions that never complete. Some testing tools do not report these very well, allowing simulated users to hang indefinitely when the server doesn't respond...and not reporting this condition. Look for tools that report the number of users waiting on a given page or transaction and the average amount of time those users are waiting.
As for the server-side metrics to look for, what other technologies is your app built on? You'll want to look at different things for a .NET app vs. a PHP app.
Lastly, we have found it very valuable to look at how the system responds to increasing load, rather than looking at just a single level of load. This article goes into more detail.
Ideally you are going to want to model your usage to the user, but creating simulated concurrent sessions for 100k users is usually not easily accomplished.
The best source would be to check out your logs for the busiest hour and try and figure out a way to model that load level.
The database is usually a critical piece of infrastructure, so I would look at recording the number and length of lock waits as well as the number and duration of db statements.
Another key item to look at is disk queue lengths.
Mostly the process is to look for slow responses either in across the whole site or for specific pages and then hone in on the cause.
The biggest problem for load testing is that is quite hard to test your network and if you have (as most public sites do) a limited bandwidth through your ISP, that may create a performance issue that is not reflected in the load tests.

Scaling up from 1 Web Server + 1 DB Server

We are Web 2.0 company that built a hosted Content Management solution from the ground up using LAMP. In short, people log into our backend to manage their website content and then use our API to extract that content. This API gets plugged into templates that can be hosted anywhere on the interwebs.
Scaling for us has progressed as follows:
Shared hosting (1and1)
Dedicated single server hosting (Rackspace)
1 Web Server, 1 DB Server (Rackspace)
1 Backend Web Server, 1 API Web Server, 1 DB Server
Memcache, caching, caching, caching.
The question is, what's next for us? Every time one of our sites are dugg or mentioned in a popular website, our API server gets crushed with too many connections. Or every time our DB server gets overrun with queries, our Web server requests back up.
This is obviously the 'next problem' for any company like ours and I was wondering if you could point me in some directions.
I am currently attracted to the virtualization solutions (like EC2) but need some pointers on what to consider.
What/where/how to scale is dependent on what your issues are. Since you've been hit a few times, and you know it's the API server, you need to identify what's actually causing the issue.
Is it DB lookup times?
A volume of requests that the web server just can't handle even though they're shortlived?
API requests take too long to process? (independent of DB lookups, e.g., does the code take a bit to run)?
Once you identify WHAT the problem is, you should have a pretty clear picture of what you need to do. If it's just volume of requests, and it's the API server, you just need more web servers (and code changes to allow horizontal scaling) or a beefier web server. If it's API requests taking too long, you're looking at code optimizations. There's never a 1-shot fix when it comes to scalability.
The most common scaling issues have to do with slow (2-3 seconds) execution of the actual code for each request, which in turn leads to more web servers, which leads to more database interactions (for cross-server sessions, etc.) which leads to database performance issues. High performance, server independent code with memcache (I actually prefer a wrapper around memcache so the application doesn't know/care where it gets the data from, just that it gets it and the translation layer handles DB/memcache lookups as well as populating memcache).
Depends really if your bottleneck is reads or writes. Scaling writes is much harder than reads.
It also depends on how much data you have in the database.
If your database is small, but cannot cope with the read load, you can deploy enough ram that it fits in ram. If it still cannot cope, you can add read-replicas, possibly on the same box as your web servers, this will give you good read-scalability - the number of slaves from one MySQL master is quite high and will depend chiefly on the write workload.
If you need to scale writes, that's a totally different game. To do that you'll need to split your data out, either horizontally (partitioning / sharding) or vertically (functional partitioning etc) so that you can spread the workload over several write servers which do not need to do each others' work.
I'm not sure what EC2 can do for you, it essentially offers slow, high latency machines with nonpersistent discs and low IO performance on the end of a more-or-less nonexistent SLA. I guess it might be useful in your case as you can provision them relatively quickly - provided you're just using them as read-replicas and you don't have too much data (remember they have nonpersistent discs and sucky IO)
What is the level of scaling you are looking for? Is it a stop-gap solution e.g. scale vertically? If it is a more strategic scaling project, does your current architecture support scaling horizontally?