They ask for years of experience, skills that no Jr would know since, well, it is a Jr and the process to apply for jobs are surreal. Thousands of tests, interviews that goes nowhere and lots of ghosting. And the pay is not that good. No wonder after 2 or 3 years of experience a lot of develpers starts working for companies outside of Brazil.

Last one to contact me sent me a test to do it in 1 week. I went above and beyond and learned a lot of things. Before this, i had some small projects in Go and Python. Now i needed to learn Docker, tests, github actions, Postgresql and other things. Not everything was mandatory, but i did my best and did it all. I finished in 5 days since i have a day job.

Here is the result: https://github.com/crdpa/conservice

Showing the data in the browser was not necessary, but i think it was a nice touch and well made. If this does not land me a job as a junior developer i don't know what else could.

I'm glad i already have a job in another area, but me and my SO are separated by a 4 hour drive and i'm tired. I want to work from home to be near her and our dog. Paying rent in two places is becoming a burden.

I would be happy if you guys could test the application i made. It only needs docker.

And do you guys have any tips from now on?

Hey folks.

May someone recommend a good web dev community out there for quality discussions?

Right now I'm using Vue for a project and I'm wrestling with architectural decisions. I'd love for a place where I can discuss different approaches' trade-offs and merits.

Many thanks. :)

So I wanna purchase a domain for my personal website (just a WordPress thing), and I wanna pay for three years in advance (I have my reasons). Which domain sellers are reasonably priced, trustworthy, and more likely to assist a less technical, non-developer user like myself?

Thanks!

This below is a summary of some real-world performance investigation I recently went through. The tools I used are installed on all linux systems, but I know some people don't know them and would straight up jump to heavyweight log analysis services and what not, or writing their own solution.

Let's say you have request log sampling in a bunch of log files that contain lines like these:

127.0.0.1 [2021-05-27 23:28:34.460] "GET /static/images/flags/2/54@3x.webp HTTP/2" 200 1806 TLSv1.3 HIT-CLUSTER SessionID:(null) Cache:max-age=31536000
127.0.0.1 [2021-05-27 23:51:22.019] "GET /pl/player/123456/changelog/ HTTP/1.1" 200 16524 TLSv1.2 MISS-CLUSTER SessionID:(null) Cache:

You might recognize Fastly logs there (IP anonymized). Now, there's a lot you might care about in this log file, but in my case, I wanted to get a breakdown of hits vs misses by URL.

So, first step, let's concatenate all the log files with cat *.log > all.txt, so we can work off a single file.

Then, let's split the file in two: hits and misses. There are a few different values for them, the majority are covered by either HIT-CLUSTER or MISS-CLUSTER. We can do this by just grepping for them like so:

grep HIT-CLUSTER all.txt > hits.txt; grep MISS-CLUSTER all.txt > misses.txt

However, we only care about url and whether it's a hit or a miss. So let's clean up those hits and misses with cut. The way cut works, it takes a delimiter (-d) and cuts the input based on that; you then give it a range of "fields" (-f) that you want.

In our case, if we cut based on spaces, we end up with for example: 127.0.0.1 [2021-05-27 23:28:34.460] "GET /static/images/flags/2/54@3x.webp HTTP/2" 200 1806 TLSv1.3 HIT-CLUSTER SessionID:(null) Cache:max-age=31536000.

We care about the 5th value only. So let's do: cut -d" " -f5 to get that. We will also sort the result, because future operations will require us to work on a sorted list of values.

cut -d" " -f5 hits.txt | sort > hits-sorted.txt; cut -d" " -f5 misses.txt | sort > misses-sorted.txt

Now we can start doing some neat stuff. wc (wordcount) is an awesome utility, it lets you count characters, words or lines very easily. wc -l counts lines in an input, since we're operating with one value per line we can easily count our hits and misses already:

$ wc -l hits-sorted.txt misses-sorted.txt
  132523 hits-sorted.txt
  220779 misses-sorted.txt
  353302 total

220779 / 132523 is a 1:1.66 ratio of hits to misses. That's not great…

Alright, now I'm also interested in how many unique URLs are hit versus missed. uniq tool deduplicates immediate sequences, so the input has to be sorted in order to deduplicate our entire file. We already did that. We can now count our urls with uniq < hits-sorted.txt | wc -l; uniq < misses-sorted.txt | wc -l. We get 49778 and 201178, respectively. It's to be expected that most of our cache misses would be in "rarer" urls; this gives us a 1:4 ratio of cached to uncached URL.

Let's say we want to dig down further into which URLs are most often hitting the cache, specifically. We can add -c to uniq in order to get a duplicate count in front of our URLs. To get the top ones at the top, we can then use sort, in reverse sort mode (-r), and it also needs to be numeric sort, not alphabetic (-n). head lets us get the top 10.

$ uniq -c < hits-sorted.txt | sort -nr | head
    815 /static/app/webfonts/fa-solid-900.woff2?d720146f1999
    793 /static/app/images/1.png
    786 /static/app/fonts/nunito-v9-latin-ext_latin-regular.woff2?d720146f1999
    760 /static/CACHE/js/output.cee5c4089626.js
    758 /static/images/crest/3/light/notfound.png
    757 /static/CACHE/css/output.4f2b59394c83.css
    756 /static/app/webfonts/fa-regular-400.woff2?d720146f1999
    754 /static/app/css/images/loading.gif?d720146f1999
    750 /static/app/css/images/prev.png?d720146f1999
    745 /static/app/css/images/next.png?d720146f1999

And same for misses:

$ uniq -c < misses-sorted.txt | sort -nr | head
     56 /
     14 /player/237678/
     13 /players/
     12 /teams/
     11 /players/top/
<snip>

So far this tells us static files are most often hit, and for misses it also tells us… something, but we can't quite track it down yet (and we won't, not in this post). We're not adjusting for how often the page is hit as a whole, this is still just high-level analysis.

One last thing I want to show you! Let's take everything we learned and analyze those URLs by prefix instead. We can cut our URLs again by slash with cut -d"/". If we want the first prefix, we can do -f1-2, or -f1-3 for the first two prefixes. Let's look!

cut -d'/' -f1-2 < hits-sorted.txt | uniq -c | sort -nr | head
 100189 /static
   5948 /es
   3069 /player
   2480 /fr
   2476 /es-mx
   2295 /pt-br
   2094 /tr
   1939 /it
   1692 /ru
   1626 /de

cut -d'/' -f1-2 < misses-sorted.txt | uniq -c | sort -nr | head
  66132 /static
  18578 /es
  17448 /player
  17064 /tr
  11379 /fr
   9624 /pt-br
   8730 /es-mx
   7993 /ru
   7689 /zh-hant
   7441 /it

This gives us hit-miss ratios by prefix. Neat, huh?