A few tiny notes from the land of Linux touchpad improvements:
- If you’d like to ensure you’re notified when I have substantive updates on the touchpad driver, I’ve setup a subscription list here: https://tinyletter.com/inedibill. It will be used only for sending updates about the touchpad driver, so probably no more than one email every couplefew months
- Matt Mayfield has been doing hero’s work on ensuring that Linux touchpad driver can successfully discard thumb input. His commits to this end are in this branch. In using the libinput debug tool, it’s apparent that Matt’s branch is excellent at allowing a second finger to rest on the touchpad while still taking the active finger input as cursor movement, whereas the latest libinput release tends to interpret any cursor movement with an extra finger down as scrolling. We’re working to make it possible to download an installable version of Matt’s changes soon.
- I’ve begun the process of attempting to enumerate the acceleration differences between Linux and Mac here. If anyone else wants to take a stab at concisely describing how acceleration differs on macOS vs Linux touchpad, feel free to drop me a line at bill -at- staticobject.com and I’ll aim to incorporate your findings.
Hoping to have a more robust update on this in the next couple months, or when we get an installable driver available (if sooner).
The title of this blog came up recently as we continue to optimize our Google Shopping Adwords bidding tool, and I wanted to share my learnings with future web searchers. Our situation is that we use Google Shopping PLAs to drive traffic to Bonanza, and our bid amounts are specified via Adwords labels that we apply to each item. Most items will have multiple Adwords labels, where each Adwords label corresponds to an Adwords auto target. My question to Google was: when there is one product that has multiple Adwords labels, and those labels correspond to auto targets that have the same bid, which auto target gets credit for the impression (and subsequent click/conversion)?
The answer, straight from Google, makes a lot of sense:
So the one that enters the auction will be attributed with the clicks and impressions and this is dependent on the performance history associated with that auto target. The one with the stronger performance history – clicks and CTR attributed to it, will enter the auction and hence get the impressions.
Additionally if they are from different ad groups – the past ad group performance history and ad group level CTR would also matter.
Thus the answer: whichever auto target performs best has the best chance of being shown in Google’s Adwords “auction” (the name they give to the process of choosing which Adword or GS products to show).
For my own reference, this is a piece o cake:
So you’ve upgraded from Rails 2.x to Rails 3 and you’re not happy with the response times you’re seeing? Or you are considering the upgrade, but don’t want to close your eyes and step into the great unknown without having some idea what to expect from a performance standpoint? Well, here’s what.
Prior to upgrading, this action (one of the most commonly called in our application) averaged 225-250 ms.
One our first day after upgrading, we found the same unmodified action averaging 480ms. Not cool.
Thus began the investigative project. Since New Relic was not working with Rails 3 views this week (seriously. that’s a whole different story), I (sigh) headed into the production logs, which I was happy to discover actually broke out execution times by partial. But there seemed to be an annoyingly inconsistent blip every time we called this action, where one of the partials (which varied from action to action) would have would have something like 250-300 ms allocated to it.
I casually mentioned this annoyance to Mr. Awesome (aka Jordan), who speculated that it could have something to do with garbage collection. I’d heard of Ruby GC issues from time to time in the past, but never paid them much mind since I assumed that, since we were already using Ruby Enterprise Edition, the defaults would likely be fine enough. But given my lack of other options, I decided to start the investigation. That’s when I discovered this document from those otherworldly documenters at Phusion, describing the memory settings that “Twitter uses” (quoted because it is probably years old) to run their app. Running low on alternatives, I gave it a shot. Here were our results:
New average time: 280ms. Now that is change we can believe in! Compared with the default REE, we were running more than 40% faster, practically back to our 2.2 levels. (Bored of reading this post and want to go implement these changes immediately yourself? This is a great blog describing how. Thanks, random internet dude with broken commenting system!)
That was good. But it inspired Jordan to start tracking our garbage collection time from directly within New Relic (I don’t have a link to help ya there, but Google it — I assure you it’s possible, and awesome), and we discovered that even with these changes, we were still spending a good 25-30% of our time collecting garbage in our app (an improvement over the 50-60% from when we initially launched, but still). I wondered if we could get rid of GC altogether by pushing our garbage collection to happen between requests rather than during them?
Because every director will tell you that audiences love a good cliffhanger, I’ll leave that question for the reader to consider. Hint: after exploring the possibility, our action is now faster in Rails 3 than it had been in Rails 2. It’s all about the garbage, baby.