From 2417a6b7603524dc5cd30d29b153f91024b9443d Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Wed, 1 Nov 2023 22:54:27 +0100 Subject: Move to Jekyll --- ...04-17-what-i-ve-learned-developing-ad-server.md | 199 --------------------- 1 file changed, 199 deletions(-) delete mode 100644 content/posts/2017-04-17-what-i-ve-learned-developing-ad-server.md (limited to 'content/posts/2017-04-17-what-i-ve-learned-developing-ad-server.md') diff --git a/content/posts/2017-04-17-what-i-ve-learned-developing-ad-server.md b/content/posts/2017-04-17-what-i-ve-learned-developing-ad-server.md deleted file mode 100644 index 3a6410f..0000000 --- a/content/posts/2017-04-17-what-i-ve-learned-developing-ad-server.md +++ /dev/null @@ -1,199 +0,0 @@ ---- -title: What I've learned developing ad server -url: what-i-ve-learned-developing-ad-server.html -date: 2017-04-17T12:00:00+02:00 -type: post -draft: false ---- - -For the past year and half I have been developing native advertising server that -contextually matches ads and displays them in different template forms on -variety of websites. This project grew from serving thousands of ads per day to -millions. - -The system is made from couple of core components: - -- API for serving ads, -- Utils - cronjobs and queue management tools, -- Dashboard UI. - -Initial release was using [MongoDB](https://www.mongodb.com/) for full-text -search but was later replaced by [Elasticsearch](https://www.elastic.co/) for -better CPU utilization and better search performance. This provided us with many -amazing functionalities of [Elasticsearch](https://www.elastic.co/). You should -check it out if you do any search related operations. - -Because the premise of the server is to provide native ad experience, they are -rendered on the client side via simple templating engine. This ensures that ads -can be displayed number of different ways based on the visual style of the -page. And this makes JavaScript client library quite complex. - -So now that you know basic information about the product lets get into the -lessons we learned. - -## Aggregate everything - -After beta version was released everything (impressions, clicks, etc) was -written in nanosecond resolution in the database. At that time we were using -[PostgreSQL](https://www.postgresql.org/) and database quickly grew way above -200GB in disk space. And that was problematic. Statistics took disturbingly long -time to aggregate. Also using indexes on stats table in database was no help -after we reached 500 million datapoints. - -> There is a marketing product information and there is real life experience. -And the tend to be quite the opposite. - -This was the reason that now everything is aggregated on daily basis and this -data is then fed to Elastic in form of daily summary. With this we achieved we -can now track many more dimensions such as zone, channel and platform -information. And with this information we can now adapt occurrences of ads on -specific places more precisely. - -We have also adapted [Redis](https://redis.io/) as a full-time citizen in our -stack. Because Redis also stores information on a local disk we have some sort -of backup if server would accidentally suffer some failure. - -All the real-time statistics for ad serving and redirecting is presented as -counters in Redis instance and daily extracted and pushed to Elastic. - -## Measure everything - -The thing about software is that we really don't know how well it is performing -under load until such load is presented. When testing locally everything is fine -but when on production things tend to fall apart. - -As a solution for this we are measuring everything we can. Function execution -time (by encapsulating functions with timers), server performance (cpu, memory, -disk, etc), Nginx and [uWSGI](https://uwsgi-docs.readthedocs.io/) performance. -We sacrifice a bit of performance for the sake of this information. And we store -all this information for later analysis. - -**Example of function execution time** - -```json -{ - "get_final_filtered_ads": { - "counter": 1931250, - "avg": 0.0066143431, - "elapsed": 12773.9500310003 - }, - "store_keywords_statistics": { - "counter": 1931011, - "avg": 0.0004605267, - "elapsed": 889.2821669996 - }, - "match_by_context": { - "counter": 1931011, - "avg": 0.0055960716, - "elapsed": 10806.0758889999 - }, - "match_by_high_performance": { - "counter": 262, - "avg": 0.0152770229, - "elapsed": 4.00258 - }, - "store_impression_stats": { - "counter": 1931250, - "avg": 0.0006189991, - "elapsed": 1195.4419869999 - } -} -``` - -We have also started profiling with [cProfile](https://pymotw.com/2/profile/) -and then visualizing with [KCachegrind](http://kcachegrind.sourceforge.net/). -This provides much more detailed look into code execution. - -## Cache control is your friend - -Because we use Javascript library for rendering ads we rely on this script -extensively and when in need we need to be able to change behavior of the script -quickly. - -In our case we can not simply replace javascript url in html code. It usually -takes a day or two for the guys who maintain sites to change code or add -?ver=xxx attribute. And this makes rapid deployment and testing very difficult -and time consuming. There is a limitation of how much you can test locally. - -We are now in the process of integrating [Google Tag -Manager](https://www.google.com/analytics/tag-manager/) but couple of websites -are developed on ASP.net platform that have some problems with tag manager. With -a solution below we are certain that we are serving latest version of the -script. - -And it only takes one mistake and users have the script cached and in case of -caching it for 1 year you probably know where the problem is. - -```nginx -# nginx ➜ /etc/nginx/sites-available/default -location /static/ { - alias /path-to-static-content/; - autoindex off; - charset utf-8; - gzip on; - gzip_types text/plain application/javascript application/x-javascript text/javascript text/xml text/css; - location ~* \.(ico|gif|jpeg|jpg|png|woff|ttf|otf|svg|woff2|eot)$ { - expires 1y; - add_header Pragma public; - add_header Cache-Control "public"; - } - location ~* \.(css|js|txt)$ { - expires 3600s; - add_header Pragma public; - add_header Cache-Control "public, must-revalidate"; - } -} -``` - -Also be careful when redirecting to url in your python code. We noticed that if -we didn't precisely setup cache control and expire headers in response we didn't -get the request on the server and therefore couldn't measure clicks. So when -redirecting do as follows and there will be no problems. - -```python -# python ➜ bottlepy web micro-framework -response = bottle.HTTPResponse(status=302) -response.set_header("Cache-Control", "no-store, no-cache, must-revalidate") -response.set_header("Expires", "Thu, 01 Jan 1970 00:00:00 GMT") -response.set_header("Location", url) -return response -``` - -> Cache control in browsers is quite aggressive and you need to be precise to -avoid future problems. We learned that lesson the hard way. - -## Learn NGINX - -When deciding on a web server we went with Nginx as a reverse proxy for our -applications. We adapted micro-service oriented architecture early in the -project to ensure when we scale we can easily add additional servers to our -cluster. And Nginx was crucial to perform load balancing and static content -delivery. - -At first our config file was quite simple and later grew larger. After patching -and adding new settings I sat down and learned more about the guts of Nginx. -This proved to be very useful and we were able to squeeze much more out of our -setup. So I advise you to take your time and read through the -[documentation](https://nginx.org/en/docs/). This saved us a lot of headache. -Googling for solutions only goes so far. - -## Use Redis/Memcached - -As explained above we are using caching basically for everything. It is the -corner stone of our services. At first we were very careful about the quantity -of things we stored in [Redis](https://redis.io/). But we later found out that -the memory footprint is very low even when storing large amount of data in it. - -So we gradually increased our usage to caching whole HTML outputs of dashboard. -This improved our performance in order of magnitude. And by using native TTL -support this goes hand in hand with our needs. - -The reason why we choose [Redis](https://redis.io/) over -[Memcached](https://memcached.org/) was the nature of scalability of Redis out -of the box. But all this can be achieved with Memcached. - -## Conclusion - -There are a lot more details that could have been written and every single topic -in here deserves it's own post but you probably got the idea about the problems -we faced. -- cgit v1.2.3