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Black Canyon 100K 2021 Race Recap

“Net down­hill” is some­what syn­ony­mous with “fast”. In road run­ning, there are reg­u­la­tions in place that lim­it how much drop a point-­to-­point course can have be­fore it be­comes in­valid for record pur­pos­es. Boston, a gen­er­al­ly fast marathon—­most­ly due to its qual­i­fi­ca­tion pur­pos­es—is aid­ed in its rel­a­tive speed due to a –447’ net el­e­va­tion. It’s too down­hill for records pur­pos­es. When Ge­of­frey Mu­tai ran 2:03:02 in 2011 it was the fastest marathon at the time, but not a world record.

Trail run­ning is a dif­fer­ent sto­ry all to­geth­er. While trails vary wide­ly, they gen­er­al­ly fa­vor the up­hill. Black Canyon 100K is not like that.

I un­der­es­ti­mat­ed it. Don’t make that same mis­take.

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Using Elastic APM to visualize asyncio behavior

A few weeks ago, Chris Wellon­s’ blog “La­ten­cy in Asyn­chro­nous Python” was shared on our in­ter­nal #python chan­nel at Elas­tic, and as some­one look­ing in­to the per­for­mance char­ac­ter­is­tics of a re­cent asyn­cio-re­lat­ed change, I gave it a read and you should too. It was a time­ly post as it made me think ahead about a few things, and co­in­cid­ed nice­ly with our re­cent us­age of Elas­tic’s Ap­pli­ca­tion Per­for­mance Mon­i­tor­ing so­lu­tion.

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Use glom

Dictionaries are really cool. They’re very powerful and a large part of Python: so much stuff is built on the dict type. They’re generally quite easy to work with, but what about when you have nested dictionaries that contain lists that contain dictionaries that contain certain keys only in some cases and sometimes there’s another list involved?

Use glom.

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