Creating a Heavy-Duty Balancer with Nginx and Lua

Creating a Heavy-Duty Balancer With Nginx and Lua

Or How Amazon Helped Us Replace an Expensive ELB And Still Process Many Thousands of Requests per Second

Creating a Heavy-Duty Balancer with Nginx and Lua

Picture credit: George E. Curtis

ELBs Can Be Too Expensive

Amazon Balancers (or Elastic Load Balancers, in their terminology) are awesome things: they are able to process many thousands of requests per second without issues for weeks on end. If you have used Amazon AWS with some kind of high availability setup, you have probably used an ELB.

At MediaSmart Mobile we have had peaks of over 300 krps (300 thousand requests per second), and all of them went through an ELB. Without a hitch. Continuously (at night, traffic never goes below 100 krps). But they were increasing our costs too much: it got to the point where 25% of our total Amazon bill went to pay the ELBs.

How We Got Here

Our business at MediaSmart Mobile is very peculiar: we receive an insane amount of traffic from several ad exchanges, and use about 1% of it to make bids for the marketing campaigns of our clients, using the OpenRTB protocol . The remaining 99% is discarded. We need this amount of unused traffic because our customers tend to have peculiar campaigns: campaigns marketed at a smallish number of user IDs, geolocalized to several areas of a country, or trying to get clicks and downloads from targeted users. Our customers value having that much traffic available. We use increasingly sophisticated algorithms to sift through those bid requests and find where our campaigns work best.

About a year ago we were using 40 frontend servers (large c3.2xlarge machines with 8 processors), and the cost was killing us. So we came up with a clever solution: write an Erlang filter which decides which requests are more likely to be used by the frontends, and filters out the rest.

This resulted in a dual architecture with two ELBs: one to receive traffic from exchanges and deliver it to the Erlang filters, and another to receive the filtered traffic and send it to the frontends.

Creating a Heavy-Duty Balancer with Nginx and Lua

MediaSmart systems architecture overview.

ELB Costs Out of Control

This was working great for a long time, until our costs started to skyrocket. The good folks at Amazon helped us analyze our monthly bill, with a surprising result: we were being charged twice for our ELBs!

Amazon helpfully states :

Data processed by Amazon Elastic Load Balancing will incur charges in addition to Amazon EC2 data transfer charges.

Let us see what that really means.

The first charge is assigned to the ELB itself: currently it is $0.008 per GB of data processed in US East. The second can be found inside Data Transfer : $0.01 per GB received. Why is this small charge of less than two cents per GB relevant?

See, while traffic out of EC2 costs a small amount, Amazon advertises that traffic into EC2 is free. It is usually true, but not when you are using an ELB: in that case you are charged one cent per GB transferred into EC2. This small detail becomes crucial when you are receiving a lot of requests per second. The OpenRTB protocol is optimized to minimize outgoing traffic when you do not bid: an HTTP response of 204 No Content is usually enough, which means sending just a few bytes. But requests are largish (a request size of 1.5 KB is typical), and combined with about 18 billion requests per day it results in over 27 TB per day of incoming traffic, or more than 0.8 petabytes per month. At $0.018 per GB the total cost is about $15k, which can be a substantial portion of our AWS costs. Going from free to $15k just in ELB-related traffic costs does not look good.

The solution was obvious: remove the first ELB handling the majority of the traffic, and connect the exchanges directly with our traffic filters. So we got approval from our boss to go for it. This time for real!

Previous Experiences

This was not the first time that we had tried removing the first ELB; in fact I had wanted to do it for a very long time. You know, ELBs are awesome in what they do, but you are a bit blind about what they are actually doing. For starters, it is not easy to get access logs; you can request that they be sent to S3, but it is cumbersome and you have little control about the log format.

A nice feature of ELBs is that you get a lot of shiny graphs for traffic, latency, etcetera in Cloudwatch. They are distributed by HTTP status code . However, there is not enough granularity: there is only 2XX, 3XX, 4XX and 5XX. In our business a 200 OK is very different from a 204 No Content: the first means we are bidding, the second that we pass on that bid offer. Therefore we did not know how many bids we were making unless we looked it up in the exchanges themselves, or did a lot of fudging around. Same for 400 Bad Request, 401 Unauthorized or 404 Not Found: they have very different semantics, and are the symptoms of disparate problems.

Creating a Heavy-Duty Balancer with Nginx and Lua

2XX Requests to Frontends. Note the micro-interruption at 7:30 when we reboot the service.

There are other minor annoyances with Cloudwatch: for instance, you cannot mix latency and number of requests in the same graph. The reason is that you cannot show the sum of one metric mixed with the average of another. Default behavior is to show the average of each metric, which is not helpful with number of requests since they need to be summed. Graph data cannot be exported directly, and so on. But these are minor annoyances compared to the sensation of losing control of your own systems.

MediaSmart CTO Guillermo Fernández and myself had done a few experiments to remove the main ELB, with mixed success. The immediate question is: how do you balance traffic without an ELB? The trick is to have an entry in the DNS registry that points to a set of IP addresses that correspond to your servers. The DNS registry will reorder them randomly so that every client sees an arbitrary server first. Since each client is supposed to contact the first IP address in the list, the load is distributed between your servers. This is known as Round-robin DNS and is very easy to do with Route53 (or indeed any DNS provider): just enter a list of IP addresses as an A record. Combined with a short TTL (time to live) of a minute, exchanges should start sending traffic to the filters quite fast.

That part was easy. The hard part was getting the filter servers to handle the load directly, without an intermediate ELB. Apparently the ELB was doing some kind of "smoothing" with the connections, and dealing with some misbehaving exchanges that opened and closed connections very fast. Our Erlang filters would handle the load for a few seconds, then start losing traffic and finally collapse altogether.

To smooth the traffic we set up an HAProxy in each filter server. It is an amazing product that works very well for some large Internet services such as Stack Overflow. It would receive the traffic, then redirect it to the Erlang server. This scheme was an improvement, although not completely successful: the filter servers worked for a minute or two before collapsing in a miserable pile.

We increased the number of open files/sockets, enabled TIME_WAIT connection reuse and a few other tricks, to no avail. To this day, Guillermo thinks that the problem is due to some Linux configuration knob for TCP that eludes us. It is really an odd scenario: exchanges open an indeterminate number of connections, which can be idle for many seconds. To improve the situation, Guillermo (who by now is an Erlang wizard) even implemented a connection pool, in case the servers were running out of some unknown low level resource. Nothing worked.

Whatever the cause, we were getting nowhere. Also, tests were a bit disruptive since we had to run them directly on production. At the time we just waited to have a bigger motivation. That moment had now come.

Nginx to the Rescue

This time we had a new strategy: use Nginx as a reverse proxy.

About Nginx

Nginx needs little presentation. According to the Netcraft web server survey it is the second most popular web server for active sites, and it may eventually overtake Apache in the top million busiest sites.

What many people ignore is that it is also a top-notch reverse proxy : a server which redirects requests to other servers, or to other ports inside the same server. It handles HTTPS beautifully (something which HAProxy did not do until recently), and the configuration is even simpler.

Experimenting Live

We furnished our filter servers with a snazzy Nginx along our custom Erlang code. We could reuse our existing Erlang code with just a small change: instead of Erlang listening on port 80 directly, Nginx would listen on port 80 and redirect to Erlang listening on a different port.

One issue with our experiments is that ELBs need to be "pre-warmed" when receiving a lot of traffic. Otherwise an unsuspecting ELB can stutter and reject most of the requests, and this can go on indefinitely under heavy load. If you have many krps you need to ask AWS to "pre-warm" the ELB . Our fear was that the ELB would "cool down" if we diverted all requests for a long time. Our AWS technical contacts assured us that our ELBs would not "cool down" at least for five minutes, so this is the time frame that we had for our experiments. After that time it would be hard to revert the setup. Not much, but it would have to do.

Each individual experiment went as follows:

  • change the DNS,
  • wait for some seconds until exchanges start sending traffic,
  • see if it breaks,
  • revert the DNS to point to the ELB.
  • Then start hunting in the logs to find out where it broke and why.

We had to fine-tune a few parameters in Nginx because we were running out of file descriptors so fast: we set worker_rlimit_nofile to about 10k, and worker_connections to 9k. Then we had to set multi_accept to on ; and make sure to use . epoll .

But still our Erlang servers were dying after a few minutes. The final touch was enabling the old connection pool in Erlang; after that point everything was running smoothly. Success! We now had our own balancer capable of handling 300 krps and more, at a fraction of the cost. But there were a few details that needed some polishing.


Amazon offers Auto Scaling Groups to create or destroy servers as needed. With a custom DNS-based load balancer you will need to orchestrate your servers yourself. Fortunately we have been using a custom orchestrator for a few years now. The reason is that the default Auto Scaling algorithm is not good for our needs.

Auto Scaling decides whether to create or destroy instances based solely on their current load: if the average load is above the high watermark (e.g. 90%) then a new instance is created, and if it is below the low watermark (e.g. 80%) then an existing instance is destroyed. Nice, right? The problem is that, when removing instances, we do not want to use their current load, but the projected load that they would have with one less instance to hold the traffic. Otherwise the remaining instances may go above the high watermark instantly, so we would need to create a new one, which would again set the load below the low watermark, and so on. This kind of ping-pong is very bad for service stability (and, when you pay servers by the hour, for the bottom line).

Example: we have two servers at 60% load. Removing one would leave us with a 120% load for just one server, which means that it will reject requests until a new server is created. With two servers, after a while the load goes back to 60%, and the cycle is restarted.

This is assuming that servers are perfectly linear, which is often not the case, so the situation can be a bit better. Still, the only solution is to set a ridiculously low watermark such as 40%. With 20 servers, going as low as 40% means that we could do the job with just half of them at 80% load. The result is a lot of wasted CPU time.

Our custom orchestrator reads the instances present in an ELB, checks their load and decides if it has to create a new server or destroy one. First the median server load is computed, which by the way works better than the average load. If this median load is above the high watermark, an instance is created and then added to the ELB. Otherwise the median server load is computed with one less server than currently present . If this projected load is below the low watermark the last instance is destroyed, which automatically removes it from the ELB.

Example: now we have three servers at 65% load. The orchestrator tries to share a load of 3*65%=195% between two servers, predicts that each would need to handle a 97.5% load which is above the low watermark, and decides against destroying one.

This simple algorithm allows us to use a low watermark of 80% and a high watermark of 90%, making sure we make good use of our servers.

Since we already have a custom orchestrator, adjusting it to work with the new Round-robin DNS balancer was easy: we just had to read the instances from the DNS instead of from the ELB, add the IPs of any new instances to the DNS registry, and remove the IPs of terminated instances.

Nice Graphs

Along the path we had lost those nice Cloudwatch graphs. Even if we had the statistics for each server, and we could show them on a nice graph, we now had a variable number of servers. But we did not even have statistics for each server. So there were in fact three challenges:

  • get statistics for each server,
  • aggregate them across all servers,
  • and show them on a nice graph.

Lua Logging

The first part can be done with a bit of Lua magic. To use Lua with Nginx you can install the OpenResty full package, which is path recommended by most people; since we are using Ubuntu I chose instead to use the package nginx-extras , which is a version of Nginx with all plugins compiled in.

I then used this three-year-old post by Matthieu Tourne (then at CloudFlare) as a guide, modifying it heavily. For every HTTP status code Nginx now reports the number of requests it has received and the sum of the time it took to answer all those requests.

In Nginx a bit of Lua code needs to be added to log everything programmatically. With a few more lines we can compose a page that shows the aggregated results as a JSON document. It is a good idea to add a random nonce to the logging URL to obfuscate it slightly, although there is probably no harm in exposing it. In our case this results page is log_2l8J2yjy1ofgZQOj . You can create your own nonce on Unix with this simple command:

$ head -c 12 /dev/urandom | base64

And repeat until the result has only alphanumerical characters. On Linux you can use /dev/random for extra security. This simple technique can be used for moderately private pages, but do not use it if you need to keep stuff secret.

The relevant excerpts of the Nginx config file site.conf are shown here commented:

# declare the filter server upstream filter {         server max_fails=3 fail_timeout=1s;         keepalive 1024; } server {     # listen on port 80     listen 80 default_server;      # do not use the regular log     access_log off;      # send json as text, also in Lua     default_type text/plain;     lua_use_default_type on;      location / {         # send traffic to the filter server         proxy_pass http://filter;         # log using Lua         log_by_lua '             local logging = require("logging")             local request_time = ngx.now() - ngx.req.start_time()             local status = ngx.status             # log status code and time taken by the request             logging.add_plot(ngx.shared.log_dict, status, request_time)             ';     }     location /log_2l8J2yjy1ofgZQOj {         error_log /var/log/nginx/lua.log warn;         content_by_lua '             local logging = require("logging")             ngx.say("{")             # get all status codes: count and sum of request times             local all = logging.get_all(ngx.shared.log_dict)             for key, value in pairs(all) do                 ngx.say("  //"", key, "//": ", value, ",")             end             ngx.say("  //"end//": 0")             ngx.say("}")             ';     } }

For attentive readers, that last "end": 0 is just added to have a valid JSON without having to remove the trailing comma.

And the logging library used here is an adaptation of Matthieu Tourne’s ; it is improved by keeping track of the count of requests and the total time taken to serve them, for all HTTP status codes received. You can find our modified libraryhere.

The result of accessing the server at http://[ip]/log_2l8J2yjy1ofgZQOj is something like this:

{   "200-count": 798846388,   "200-sum": 35589095.191413,   "204-count": 104633411097,   "204-sum": 570651966.49768,   "400-count": 10821,   "400-sum": 2471.0660040379,   "408-count": 14363,   "408-sum": 862349.73800206,   "499-count": 323868637,   "499-sum": 83989644.311747,   "500-count": 16746,   "500-sum": 183.11798548698,   "502-count": 36341442,   "502-sum": 4577069.404109,   "504-count": 102240,   "504-sum": 6162582.6290374,   "end": 0 }

This particular server has served up to now about 104 billion requests, most of them resulting in 204 No Content as expected (shown here as 204-count ). Given that the server has been up for 55 days, it has served almost two billion requests per day. Just the 204 requests have collectively taken 570 million seconds to process (shown as 204-sum ), or 18.1 years; the average is a little over 5 ms per request.

Note that these timings are for latency , not for total processing time. The filter server consumes almost no CPU time itself on each request, or Nginx would not be able to serve so many requests. The same happens with the eventual frontend servers: that is the magic of event-driven processing! The Erlang filter however uses a different paradigm for concurrent programming: message passing , which is completely different but also very efficient.

With logging in place, the load on our filters goes about 30% to Nginx and 70% to the filter. This means that we are using about 43% more filter servers than with the ELB. It is not a bad tradeoff: it represents much less than $2k. for a functionality that used to cost about $15k.


Then we had to aggregate data across all servers. No problem: just take the list of IPs from the DNS registry using the Route53 API, then invoke each one and get the number of requests per status code, and aggregate them. With a little Node.js code it was done in a breeze; it is the kind of problem that we give to our candidates during the hiring process.

The next challenge was storing the data somewhere to graph it. There was really no reason to ditch Cloudwatch: it is a very reasonable time-series database with nice graphing capabilities, and as long as you do not access it too often it is not expensive. We could just aggregate the stats from all servers every minute and write them to Cloudwatch, just adding a couple of lines of Node.js code to the existing aggregation code.

We are careful to send all metrics at once, to minimize costs. The cost of making one call to PutMetric per minute is about $0.43 per month. Very reasonable.

One advantage of aggregating number of requests and latency every minute is that now we can show both on a graph at the same time. Combined with the new Cloudwatch dashboards we now get this nice page.

Creating a Heavy-Duty Balancer with Nginx and Lua

Traffic dashboard shows traffic and HTTP responses.

Apart from global monitoring, all servers have to be individually monitored. In this case we had to check if they were still alive and otherwise remove them from balancing. A good way is to publish a NOP ( No OPeration ) URL that can be called from the outside and returns a 200 OK if the server is 100% operative. If the server does not answer in time, it is removed from the DNS registry. In our case we are already accessing all servers every minute, so we just need to invoke the DNS registry removal code from the orchestrator and terminate the instance if a server does not answer in time.

Other Modifications

We have also had to move our HTTPS certificates to Nginx, which by the way deals with encryption beautifully.

We used to have a very nice report emailed to us every morning that detailed the number of requests that we were receiving. It took the data from the Cloudwatch ELB info, and we still have not got around to modifying it to read from the new data.

Cost Reduction

With all these modifications we reduced our costs about 20%. So Amazon has lost a chunk of cash; you might think that our account manager would be sad. But in reality he has helped us a lot in the process, and our technical contacts at Amazon have followed through.

You see, when costs are out of control you usually start shopping around if some other provider can sell you the same stuff cheaper. It is not as if Amazon AWS is the only game in town. A happy customer is much less likely to look at the alternatives.

This cost reduction has allowed us to open more traffic in the US and grow our business, which is what a healthy company usually does. So our boss is happy, and we are happy.


ELBs are amazing. If you are handling a moderate-to-high amount of traffic, they are easy to set up and great to operate.

But Nginx is equally amazing, very configurable and cheap to operate. After a point the cost difference matters. If you are willing to replicate some components, the result may be even more impressive than the original ELB.


Carlos Sanchiz ( Amazon ), Claudio Piras ( Amazon ) have helped us pour through our costs and locate possible improvements.

Guillermo Fernández ( MediaSmart Mobile ),Alfredo López Moltó ( MediaSmart Mobile ), Matthieu Tourne have reviewed this article and offered valuable suggestions that have helped me improve it.

My gratitude goes to them all.

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