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+---
+title: What I've learned developing ad server
+permalink: /what-i-ve-learned-developing-ad-server.html
+date: 2017-04-17T12:00:00+02:00
+layout: post
+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.