Category: Uncategorized

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:

http://ubuntuforums.org/showthread.php?t=261366

After my previous raves about Sphinx in general and Thinking Sphinx in particular, I was excited to get my hands on the new Sphinx 0.9.9 release that was finally made available at the beginning of December via the Sphinx Search site.

Given that our Sphinx usage is what I think would fall the “advanced cases” heading, I expected probably a day or two of upgrade headaches before we’d be back on track. Worth it, said I, for the potential to get working index merge, which could set the stage for indexes that happen more often than once every four hours (our current index takes about 3 hours, plus time to transfer files between the building Sphinx machine and the search daemon machines).

Alas, our upgrade did not go according to plan.

This Monkey Patches Going to Heaven

Given how prompt Pat Allen (creator of Thinking Sphinx) has been in addressing and fixing bugs in the past, I don’t doubt that many of our upgrade headaches from the TS side will probably be fixed soon (if not already, since I emailed him most of our issues). That said, we required about five monkey patches to get the most recent version of TS with 0.9.9 working the same as our previous TS with 0.9.8 did. The patches ranged from patching the total_entries method (if search can’t be completed it throws exception) to real time updates not working (via client#update), to searches that used passed a string to TS where it expected an int throwing an exception.

This does not include “expected” differences, such as the fact that search is now lazily evaluated, so if you previously wrapped your search statements in a begin-rescue block to catch possible errors, your paradigm needs to shift.

It also appears that the after_commit plugin bundled with TS 0.9.8 has been modified such that it does not available to models in our project by default. Never figured out a fix for that bug, since by the time I noticed it, I had also become aware of an even bigger 0.9.9 detriment: overall performance. Reviewing our New Relic stats since we updated to 0.9.9, we have found an across-the-board decrease of about 50% to our Sphinx calls. I parsed the Sphinx logs to try to ascertain if the slowness was spawning from Sphinx or TS, and it appears to be Sphinx as the main culprit.

Performance

TS 0.9.8

Considering 290227 searches.
Average time is 0.0318751770166445.
Median time is 0.005.
Top 10% average is 0.193613017710702 across 29022 queries

TS 0.9.9

Considering 843569 searches.
Average time is 0.0430074540435297.
Median time is 0.006.
Top 10% average is 0.286621461425376 across 84356 queries

Many of our queries take 0.00 or 0.01, so the median doesn’t look too much different between the two, but the average time (which is what New Relic picks up on) is 35% slower in Sphinx alone, and about 50% slower once all is said and done. An action on our site that does a Sphinx search for similar items (and nothing else) consistently averaged 200 ms for weeks before our upgrade, and has averaged almost exactly 300 ms for the week since the upgrade.

Conclusion: Proceed with Caution

Would be nice if I had more time to debug why this slowness has come about, but the bottom line for us is that, after spending about 3 days patching TS to get it to work in the first place, and with the “after_commit” anomaly still on our plate (not to mention overall memory usage increasing by about 20%), I have ultimately decided to return to TS 0.9.8 until such time that a release of Sphinx is made available that speaks directly to its performance compared to previous versions. I think the Sphinx team is doing a great job, but amongst juggling the numerous new features, it seems that performance testing relative to 0.9.8 didn’t make the final cut?

Or there could always be some terrible misconfiguration on our part. But given that we changed our configuration as little as possible in moving from 0.9.8->0.9.9, if we are screwing up, I would say it is for perfectly understandable reasons.

Alright, enough with the 10 page tutorials from 2006 describing detailed browser-specific implementation tricks to get sound playing. Google needs a refresh. In my experience in the 2010’s, here’s all you need to do to make a simple sound play in your web page:

Step one: add an element to your page

Step two: play a sound via that element

document.getElementById("sound_element").innerHTML= 
"";

Or, if you’re using jQuery:

$('#sound_element').html(
"");

I’ve tried it in Firefox, Chrome and IE and it works like a charm for me. I’d imagine that your user has to have some basic sound software installed in their computer, but at this point, I’d reckon 99% of users do.

Feel free to add to the comments if you find any embellishments necessary to get this working.