Title: Sams Teach Yourself Java in 21 Days, 7th Edition
Author(s): Rogers Cadenhead
I had three premises while searching for a next read: 1. I needed to learn some Java, because I know some, but university level 2. I needed to target at minimum Java 8 3. I wanted something that covered a broad spectrum of the language, while not being way too entry-level
I decided to go with this book because it covers Java 8, it starts simple but covers everything regarding language fundamentals, and it goes into more "advanced" topics like threading, sockets and web requests. It also strikes a good balance between teaching you about a topic without becoming a reference book that lists every single method, parameter and overload of every class available (which can be useful, but not for initial learning).
I must confess I skimmed through the chapters about Swing and Java2D, RSS and Android. I want to do backend-related development so I don't need Swing (and it looked similar to back when I learned it) and the other topics were interesting examples but not of my interest right now.
I was pleased to see that Java now has channels, generics and lambdas, and the book does a good job of explaining them simply and clearly. I had fun writing some of the book examples and altering them a bit, or doing some tiny refactors here and there.
If I had to complain about something, it'd be that the code sometimes can be improved: There are too many examples in which all (sometimes really heavy) logic is done inside the class constructor, constants are mentioned but never used afterwards, variables are created a bit chaotically, sometimes inside loops and the like, and variable namings are reminiscent of old C days (
pw, ...). But even with that the code purposes are nice and easily understood.
During last April I've had to work via my phone's 4G data tethering. I did quite a few code pairing sessions with colleagues each week, and after feeling that Google Meet wasn't working good enough I decided to try Skype (which has quite a few years of focused product building behind). These are my findings.
As I was on 4G, I was probably under a very limited output bandwidth. I had no issues on the reception/input side (nor from the video-conferencing technology at least), but output was poor and the main the reason why I tested alternatives and kind-of measured them.
Most sessions were 1h non-stop meetings, of 2 participants + shared desktop (VS Code, Pycharm, OSX terminal, so not so many moving pixels). I always had OSX Activity Monitor open with the Network Info tab and, while I didn't wrote down the exact numbers of every session, I had plenty of sessions to compare both approaches until I had a decent internet connection again.
~200 MB input data,
~50 MB output data.
[50,100] MB input data,
[75, 100] MB output data.
Google Meet: Good input, unstable output, sometimes ok, sometimes poor.
Skype: Good input, good output.
Google Meet: Initially unstable input, but "settles down" and remains good meanwhile there's no application switching (e.g. between terminal and IDE). With movement clearly appreciable compression blocks. Same with output.
Skype: Excellent input (almost no compression noticed), good output.
Google Meet: Lowering the video quality to 360p only works if there's not much screen movement, else it feels we're back in the nineties with blocky compressed MPEG videos. Skype: There are no options as Skype handles video quality, resolution, etcetera automatically, and for once it simply works.
Meet is very convenient and good for normal meetings, presentations (without animations/videos, else is awful), team dailies and the like.
Skype has amazing visual quality and overall smaller data consumption without perceived data loss.
Still, whenever possible I prefer to use Live Share for VS Code so there's no need to stream a whole screen.
Title: Site Reliability Engineering
Author(s): Betsy Beyer, Chris Jones, Jennifer Petoff, Niall Richard Murphy
There are some resources that you know you must read, even if it is a steep ascent. Considering that this title is freely available, that it contains so much good ideas, tips, and practices that many people won't ever put half of them in practice, and that it is a big book (around 500 pages), it is hard to complain about it. But still, I'd like to start with my only complain: Most of the book feels like a bunch of academic papers grouped together, often under a common topic, but sometimes even overlapping in the contents despite being at different chapters and book parts. It's not been an easy book to read because it can get dull at times, up to the point that I stopped reading it halfway, and retook it recently and decided to finish it.
That said, everything else is so interesting. Even if sometimes we're not exactly told how their internal systems work, Google SRE teams authors explain enough techniques, procedures, ideas and advices to create tickets for years of work at any medium or large sized company that has more than a few services in production. From recommending monitoring everything, placing retries and circuit breakers, or explaining their "production readiness requirements" SRE guidelines, to less often heard concepts like "given enough requests per second, a simple random load balancer strategy can perform better than a round robin one", or how Google in some services employs an initially counter-intuitive practice of, in case of high latency, discarding the newer requests instead of the older ones (while also always discarding those that have been waiting for more than X seconds). Tons if ideas of how, what and when to monitor, log, alert, automate, improve, fix, and a myriad of related actions you can take related with services, tasks, teams, scenarios... Just take a look at the table of contents to grasp the sheer amount of topics it covers.
I marked some internal highlights but if you want some really hardcore and useful notes, I can only encourage you to check the in-depth review at danluu.com/google-sre-book/. Every chapter except the final SRE team management and integration ones is summarized.
But even the management final chapters are useful, explaining why interruptions are bad, how to deal with them and mitigate them, how to collaborate between teams, how, when and why to meet...
As I mentioned before, not the easiest book to read but definitely one that most engineers, SREs or not, should read.
It's been quite a while since I started working in tech. I've been in tiny startups, medium companies, and a few big companies; I've done both in-house development and consulting services, the later sometimes outsourced to clients for long periods; I've both seen from within workforces grow and adapt to change, and landed into already well-oiled machinery (theoretically at least).
You learn to recognize some people patterns, some archetypes, that typically either are already present or appear sooner or later. The superstars known in the community, the quick learners, the slower but methodical thinkers, the still-junior-but-will-be-awesome profiles... Some of these people will step forward and also help with the technical outreach, often by becoming speakers and/or participating in local user groups. But there will always be a subset, maybe just shy, maybe just not interested in public spotlight, that in any case won't shine so publicly (and sometimes even internally if the company is big enough), and yet get work done and sometimes achieve great things not expecting any ego boosting public recognition.
They don't speak at conferences, they might not have a blog, Twitter or any "social presence", or if they do they use it rarely... They just work, just focus on doing things the best they can. They usually excel and get great evaluations, and any time you ask about them to a colleague, the answer will always be positive. And if you speak with or, even better, directly work alongside them, you will always learn something new.
Those are the heroes who save the tech world, one company at a time. Those are the people I'm trying to emulate as of late.
Taking advantage of the
#FreeApril Pluralsight initiative, and considering I wanted to learn the fundamentals about BigQuery, I took the Data Analytics on Google Cloud path, which consists on four courses and in total 12-something hours of content.
Grabbed from the official page, a summarized description reads: "This path teaches how to derive insights through data analysis and visualization using the Google Cloud Platform. Feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. Cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.".
Exploring and Preparing your Data with BigQuery: Beginner course (4.5h) about SQL and BigQuery and some CloudDataPrep, which makes sense as data ingestion is the first step.
Creating New BigQuery Datasets and Visualizing Insights: Intermediate course (2.5h), heavily focused on BigQuery and data ingestion from external sources (online, and local files), SQL JOINs and UNIONs, and an introduction to Google Data Studio.
Achieving Advanced Insights with BigQuery: Advanced course (3.5h), now getting into advanced SQL and BigQuery features, including using arrays and structs to nest and unnest data, performance optimizations and an intro to Cloud Datalab (Google's Jupyter Notbook solution)
Applying Machine Learning to your Data with GCP: Advanced course (~3h), about how to do Machine Learning with BigQuery data, and inside BigQuery (this is the awesome part), by creating models, training and validating them.
The labs allow you to practice on your own, but you also get "fully guided" videos solving them, which is good for both cases of wanting to do them or preferring to just watch how they're done.
The courses are great, and my only complaint is that volume is not normalized among the videos, some are really low while others are fine, so you need to take care of the volume between them. Otherwise the production quality is very professional, you can clearly notice it is "official" Google built content.