From cd6644ea4ddc78597934ab0ef5ba50e3c3daa927 Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Sat, 8 Jul 2023 23:25:41 +0200 Subject: Moved to a simpler SSG --- public/what-i-ve-learned-developing-ad-server.html | 126 +++++++++++++++++++++ 1 file changed, 126 insertions(+) create mode 100755 public/what-i-ve-learned-developing-ad-server.html (limited to 'public/what-i-ve-learned-developing-ad-server.html') diff --git a/public/what-i-ve-learned-developing-ad-server.html b/public/what-i-ve-learned-developing-ad-server.html new file mode 100755 index 0000000..44e8d61 --- /dev/null +++ b/public/what-i-ve-learned-developing-ad-server.html @@ -0,0 +1,126 @@ +What I've learned developing ad server

What I've learned developing ad server

Apr 17, 2017

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 for full-text +search but was later replaced by Elasticsearch for +better CPU utilization and better search performance. This provided us with many +amazing functionalities of Elasticsearch. 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 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 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 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

{
+  "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 +and then visualizing with KCachegrind. +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 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 ➜ /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 ➜ 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. 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. 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 over +Memcached 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.

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