code « Narf

I talk about restoring backups often recently. This is because the disk on my trusty bare-metal server died. This gave me the opportunity to reassess my hosting choices, and do the ground work to move from where it was to where I want it to be.

One of those changes is moving static website hosting away from a Apache HTTPd, running on an OS I administrate (read: “frequently broke”), to a more focused and hands-off system in the cloud, AWS S3 with a CloudFront CDN (more on this in a later post).

Unfortunately, decades of running Apache have left me with a number of static sites using some on-the-fly templating by relying on Server-side Includes (SSI). Headers, footers, geeky IPv6 and last-modified tags, … none of those work with a truly static host. I needed a solution to render those snippets into full pages.

At first, I thought I’d just write a simple parser in Python. I quickly gave up on the idea, however, when I realised I used included templates with parameters. Pretty nifty stuff, but also not trivial to write a parser for.

Then I realised I already had the perfect parser: Apache. All I needed was to let it render all the pages one last time, and publish those instead! This was packed quickly with a relatively simple Docker container, and the trusty wget. The busy person can find a Gist of the Dockerfile here.

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We’ve been having some fun with Click and Python decorators at work.

We had a situation where we wanted to

transform any Exception to a click.ClickException, so they would be rendered nicely, and
catch one particular exception, and retry the function that raised it with a different parameter value as a fallback.

We got the first behaviour quickly into a decorator. We then realised that the second could also be done nicely with a decorator, too.

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If you have a Python string that contains the representation of escaped characters (say, a backslash \, an x, and two hexadecimal digits), and you want to decode those escapes back to the actual character they represent, you can use codecs.escape_decode.

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GitHub now allows to expand/collapse all files in a PR diff at once (pressing Alt while clicking one of the toggles). Unfortunately, there is no similar feature to mark all files as viewed. This is handy after having reviewed meaningful changes to file, and automatically modified/generated files can be ignored.

So here goes a one-liner for the JS console.

Array.from(document.getElementsByClassName('js-reviewed-toggle')).forEach(c => c.getElementsByTagName('input')[0].checked || c.click())

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I wrote this article for the Learnosity blog, where it originally appeared. I repost it here, with permission, for archival. With thanks, again, to Micheál Heffernan for countless editing passes.

In this series, I look at how we load test our platform to ensure platform stability during periods of heavy user traffic. For Learnosity, that’s typically during the back-to-school period. The year was different though, as COVID caused a dramatic global pivot to online assessment in education. Here is what the result of that looked like in terms of traffic.

Weekly Learnosity users comparison 2012--2020

We expect major growth every year but that kind of hockey stick curve is not something you can easily predict. But, because scalability is one of the cornerstones of our product offering, we were well-equipped to handle it.

This article series reveals how we prepared for that.

In part one (which was, incidentally, pre-COVID), I detailed how we actually created the load by writing a script using Locust. In this post, I’ll go through the process of running the load test. I’ll also look at some system bottlenecks it helped us find.

Let’s kick things off by looking at some important things a good load-testing method should do. Namely, it should

Apply a realistic load, starting from known-supported levels.
Determine whether the behaviour under load matches the requirements.
- If the behaviour is not as desired, you need to identify errors and fix them. These could be in
  - the load-test code (not realistic enough)
  - the load-test environment (unable to generate enough load)
  - the system parameters
  - the application code
- If the behaviour is as desired, then ramp up the load exponentially.

We used two separate tools for steps 1 above (as described in the first part of this series) and tracked the outcomes of step 2 in a spreadsheet.

TL;DR

We used Locust to create the load, and a custom application to verify correct behaviour.
We found a number of configuration-level issues, mainly around limits on file descriptors and open connections.
Stuff we learned along the way:
- Record all parameters and their values, change one at a time;
- Be conscious of system limits, particularly on the allowed number of open files and sockets.

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Every now and then, some spurious peaks show up on munin graphs. The peaks are order of magnitude higher than the expected range of the data. This particularly happens with DERIVE plugins, that are notably used for network interfaces.

One way to fix this, as suggested by Steve Schnepp (and in the faq), is to set the maximum straight into the RRD database, and then let it reprocess the data to honour this maximum.

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I wrote this article for the Learnosity blog, where it originally appeared. I repost it here, with permission, for archival. With thanks to Micheál Heffernan for countless editing passes.

The dramatic increase in Learnosity users during the back-to-school period each year challenges our engineering teams to find new approaches to ensuring rock-solid reliability at all times.

Stability is a core part of Learnosity’s offering. Prior to back-to-school (known as “BTS” internally) we load-test our system to handle a 5x to 10x increase on current usage. That might sound excessive, but it accounts for the surge of first-time users that new customers bring to the fold as well as the additional users that existing customers bring.

Since the BTS traffic spike occurs from mid-August to mid-October, we start preparing in March. We test our infrastructure and apps to find and remove any bottlenecks.

Last year, a larger client ramped up their testing. This created a 3x usage increase of our Events API. In the process, several of our monitoring thresholds were breached and the message delivery latency increased to an unacceptable level.

As a result, we poured resources into testing and ensuring our system was stable even under exceptional stress. To detail the process, I’ve broken the post into two parts:

Creating the load with Locust (this piece)
Running the load test (in part two, coming soon).

TL;DR

Here’s a snapshot of what I cover in this post:

Our target metrics.
How we wrote a Locust script to generate load for a Publish/Subscribe system.
Our observations that:
- The load test must reflect real user behaviours and interactions
- Load testing alone doesn’t validate system behaviour against target metrics. It’s better to measure this separately while the system is under load.

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Sometimes, one just needs an OAuth access token (for the client credential authentication flow).

export ACCESS_TOKEN=$(curl -X POST \
 --user "${CLIENT_ID}:${CLIENT_SECRET}" \
 --data-urlencode "grant_type=client_credentials" \
 --data-urlencode "scope=${SCOPE}" \
 ${TOKEN_URL} \
 | jp -u access_token)

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I finally mastered the shell (beit bash or zsh, but really, this is readline)’s history with command replacement. It took me 19 years and my entire family fortune to gather enough wits to read that part of the manual with enough attention and will as to learn to use it.

Essentially, you can recall previous commands from the history with !number. You can then change some content of the previous command programmatically before running it by adding :s/PATTERN/REPLACEMENT/ or :gs/PATTERN/REPLACEMENT/ (the first one will replace the first occurrence, the second one will replace them all).

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I’ve been using Kodi (then XBMC) for more than a decade now (yup, “XB” did stand for X Box alright, but now LibreELEC on a WeTek Core). I’ve also had the library in MySQL for more than half of it. Across migrations, it had developed some quirky content, such as duplicate albums, and some rarities, such as this version of 21, by Adèle, where the description reminds us that her previous album, Ixnay on the Hombre, was only moderately successful on launch; go figure…

As suggested, pretty much everywhere, as the solution for duplicate content in Kodi, I first tried cleaning the library, repeatedly, to no avail. The duplicate albums were still there. One of their noticeable characteristics, though, was that there was always some copy of the album (and in Adèle’s case, the one following Ixnay), that did not have any associated tracks. This felt like it could be a good angle to help me clear those up. Enter some SQL.

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Narf

The blagosphere got me…

Category Archives: code

Render Apache Server-side Includes one last time, with Docker

Fun with Python decorators

Resolve escapes in Python strings

GitHub Pull Request: mark all files as viewed

Scaling in the time of COVID: How we run load tests at Learnosity

TL;DR

Removing spurious peaks from munin graphs

How we manipulated Locust to test system performance under pressure

TL;DR

Getting an OAuth access token with cURL

Shell history and modifiers

Clearing persistent duplicates in the Kodi music library