Welcome!

Python Authors: Pat Romanski, Matt Davis, AppDynamics Blog, Donald Meyer, Liz McMillan

Related Topics: Java IoT, Microservices Expo, Open Source Cloud, Machine Learning , Ruby-On-Rails, Python

Java IoT: Article

The Taming of the Queue

Measuring the Impact of Request Queueing

A few weeks back webserver request queueing came under heightened scrutiny as rapgenius blasted Heroku for not using as much autotune as promised in their “intelligent load balancing”. If you somehow missed the write-up (or response), check it out for its great simulations of load balancing strategies on Heroku.

What if you’re not running on Heroku? Well, the same wisdom still applies – know your application’s load balancing and concurrency and measure its performance. Let’s explore how request queueing affects applications in the non-PaaS world and what you can do about it.

Full-stack apps have full-stack problems
Rapgenius had been monitoring server-side request latency as only the time the request spent being processed in the app layer – leading to large discrepancies between what their APM tools were reporting and what the actual user experience was. The missing latency was attributable to queueing happening just before the application processed each request, which was outside the visibility of the tools being used to monitor the site.

If your application processes requests at a constant speed but receives an increasing volume of requests (generally a good problem to have), you’ll start to face request queueing.

What does this queueing look like?
(I’ll be using nginx and gunicorn as examples here because that’s what we use, but the same reasoning and analysis principles apply no matter what stack you’re running.)

To visualize this problem, let’s look at a simple test stack running nginx in front of a Python app with eight worker processes. In our case, it’s actually intelligently load balanced by gunicorn because there’s a single queue that knows which workers are busy (unlike Heroku at scale). However, we can still run into plenty of problems.

I’ve instrumented it so we watch the latency of requests moving through the full stack, starting at the load balancer:queueing

In this image, orange represents time spent queued in a webserver, while the other colors represent the components of the application (app, DB, cache).

As you can see, the application performs admirably, slowing a bit under load but never getting slower than 150 ms to process a response. If that’s all you were looking at, you’d be delighted! But the slow buildup of queue depth results in and increased amount of time spent in each request, which is shown in orange. Yikes!

Mind your Ps and Queues
In your application, there’s likely to be queueing anywhere you distribute request load over multiple backends. In the simplest app, this might be happening between your webserver and application layer, as above. Dynamic requests must be handled by the app, and if all the app workers are busy, requests will have to wait. Here’s what that might look like for a single Heroku dyno, or an app you stand up on a development server:

queueing

In fact, a common problem we see is that an app is underprovisioning app workers in production, even if the nodes they’re running on aren’t working very hard. If you see request queueing with low server load, consider running more app worker processes:

queueing

This has the great property of helping you get the most out of your frontend node, but assuming that your local app server can do intelligent load balancing like gunicorn, it also has some beneficial load distribution properties. We’ll get to those in a second.

Third scenario: you’re running single application workers on multiple frontend nodes. This is your Thin app running on a number of Heroku dynos. It will look more like this:queueing

The challenge now is that unless the remote load balancer is keeping track of which workers are busy, it will have to distribute load less intelligently.

What’s wrong with random balancing?
Random assignment sounds pretty good intuitively. Let’s say I’m going to route 100 requests to two app workers, with a 50% probability of choosing each worker each time. At the end, you’d expect me to have around 50 processed by each. Sounds fair, right?

The problem is that at any given time during the handling of those 50 requests, one node might be two or three deep while the other is empty, which is a problem for latency. Compounding this is the possibility that different requests take different amounts of time to process.

For a mathematical analysis, check out this blog post. For a simulation, I’ll cite this cool animated gif from the rapgenius analysis:

So, it seems like we want to have at least some level of intelligence in our load balancing.

Alleviating the pain of scale
Heroku’s response is that it can be difficult to keep track of which workers are busy and which are free when you’re at scale – that’s why their routing mesh degrades to semi-random behavior.  This is definitely not an easy problem, because their “load balancer” is actually a distributed system.  However, even without tackling this omnipotence problem at the top level, local intelligence under a random umbrella can be very effective.

There’s a lot of app servers that support this. For instance, if you’re running Unicorn for Ruby, or gunicorn for Python, each app server has a pool of workers which have a local queue and are routed to intelligently. So, your setup looks more like this:

queueing

This actually makes a big impact on performance. If you replace each single-worker dyno with a two-worker intelligently-routed app server, you get much-improved performance.

However, that assumes evented workers, where the cost of adding a second worker to a node is minimal. What if you’re using non-evented threads or processes, so you care about the total CPU and memory consumption of your workers?

To answer that question, and to try out R for the first time, I modified the rapgenius simulations to look at the effects of scaling the overall number of workers and workers-per-node, on request queueing:

queueing

(source on github)

Queueing performance improves quite well with the number of workers on each naively-balanced endpoint. (The shelf in the eight-worker line is due to the fact that 10 and 15 are both < 16). You can see that, in fact, two naively-routed pairs of eight-worker (intelligently-routed) nodes are better than 100 naively-routed one-worker nodes. See the pattern? The lines are converging on a single, fully intelligently-routed cluster.

This is possible with app worker processes or threads on each node, but if you’re running evented workers, each individual worker is capable of handling quite a number of requests simultaneously!

How do I know if I have this problem?
Okay, so it’s an interesting problem to think about, but really the practical question is, “Is queuing affecting my application’s responsiveness?” Monitoring the full stack is the best way to stay on top of performance problems – webserver queueing among many others.

You can usually get an isolated look at webserver queueing from your load balancer and/or app server. For instance, if you’re running FCGI on lighttpd, you can check the queue depth of each worker.

But the ultimate determinant of the success or failure of your load balancing is the impact on latency and concurrency. Check out this 3-minute video on understanding webserver queueing.

Related Articles

More Stories By Dan Kuebrich

Dan Kuebrich is a web performance geek, currently working on Application Performance Management at AppNeta. He was previously a founder of Tracelytics (acquired by AppNeta), and before that worked on AmieStreet/Songza.com.

IoT & Smart Cities Stories
Dynatrace is an application performance management software company with products for the information technology departments and digital business owners of medium and large businesses. Building the Future of Monitoring with Artificial Intelligence. Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more busine...
The challenges of aggregating data from consumer-oriented devices, such as wearable technologies and smart thermostats, are fairly well-understood. However, there are a new set of challenges for IoT devices that generate megabytes or gigabytes of data per second. Certainly, the infrastructure will have to change, as those volumes of data will likely overwhelm the available bandwidth for aggregating the data into a central repository. Ochandarena discusses a whole new way to think about your next...
CloudEXPO | DevOpsSUMMIT | DXWorldEXPO are the world's most influential, independent events where Cloud Computing was coined and where technology buyers and vendors meet to experience and discuss the big picture of Digital Transformation and all of the strategies, tactics, and tools they need to realize their goals. Sponsors of DXWorldEXPO | CloudEXPO benefit from unmatched branding, profile building and lead generation opportunities.
All in Mobile is a place where we continually maximize their impact by fostering understanding, empathy, insights, creativity and joy. They believe that a truly useful and desirable mobile app doesn't need the brightest idea or the most advanced technology. A great product begins with understanding people. It's easy to think that customers will love your app, but can you justify it? They make sure your final app is something that users truly want and need. The only way to do this is by ...
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
DXWorldEXPO LLC announced today that Big Data Federation to Exhibit at the 22nd International CloudEXPO, colocated with DevOpsSUMMIT and DXWorldEXPO, November 12-13, 2018 in New York City. Big Data Federation, Inc. develops and applies artificial intelligence to predict financial and economic events that matter. The company uncovers patterns and precise drivers of performance and outcomes with the aid of machine-learning algorithms, big data, and fundamental analysis. Their products are deployed...
Cell networks have the advantage of long-range communications, reaching an estimated 90% of the world. But cell networks such as 2G, 3G and LTE consume lots of power and were designed for connecting people. They are not optimized for low- or battery-powered devices or for IoT applications with infrequently transmitted data. Cell IoT modules that support narrow-band IoT and 4G cell networks will enable cell connectivity, device management, and app enablement for low-power wide-area network IoT. B...
The hierarchical architecture that distributes "compute" within the network specially at the edge can enable new services by harnessing emerging technologies. But Edge-Compute comes at increased cost that needs to be managed and potentially augmented by creative architecture solutions as there will always a catching-up with the capacity demands. Processing power in smartphones has enhanced YoY and there is increasingly spare compute capacity that can be potentially pooled. Uber has successfully ...
SYS-CON Events announced today that CrowdReviews.com has been named “Media Sponsor” of SYS-CON's 22nd International Cloud Expo, which will take place on June 5–7, 2018, at the Javits Center in New York City, NY. CrowdReviews.com is a transparent online platform for determining which products and services are the best based on the opinion of the crowd. The crowd consists of Internet users that have experienced products and services first-hand and have an interest in letting other potential buye...
When talking IoT we often focus on the devices, the sensors, the hardware itself. The new smart appliances, the new smart or self-driving cars (which are amalgamations of many ‘things'). When we are looking at the world of IoT, we should take a step back, look at the big picture. What value are these devices providing. IoT is not about the devices, its about the data consumed and generated. The devices are tools, mechanisms, conduits. This paper discusses the considerations when dealing with the...