Issue #21: 10 Reads, A Handcrafted Weekly Newsletter For Humans

The time to read this newsletter is 165 minutes.

Quote

He who learns but does not think, is lost! He who thinks but does not learn is in great danger — Confucius

1. Stop Learning Frameworks: 15 mins read. This post makes a great point that we should focus more on gaining deep understanding of software development fundamentals than learning new frameworks. Frameworks come and go and most of the time knowledge you gain is not portable. Focussing on core software development topics like algorithms, unit testing, design patterns, HTTP, DDD, etc will give you better return for your time. If you encounter a new technology at work then you will learn it anyhow.
   Another post on similar lines that I read this week said Stack is irrelevant. Author writes,
   I’ve started with almost no knowledge about the stack and been up to speed in less than 3 months or so. This is why I always tell people to pick up technology and language agnostic problem solving skills because those are the only skills transferable across stacks.
2. Analyzing Hacker News book suggestions in Python: 15 mins read. A quick tutorial that will teach you how to do simple text processing in Python. This tutorial covers how to find top book recommendations in an HN thread. The approach used in simple and most programmers can easily follow it.
3. The Major Features in Postgres 11: 20 mins read. This is 24 page PDF slidedeck covering important features introduced in Postgres 11. My favourite features from the list are:
   1. Partitioning table by hash
   2. Parallel hash joins
   3. Finer-Grained access control
4. Materialized views vs. Rollup tables in Postgres: 15 mins read. I loved this post for being clear and to the point. It teaches when to use Materialized view and rollup tables. I could see myself using it in near future. Great post!
5. How to Get Over Productivity Guilt: 10 mins read. Just the kind of post that you need to end your year. I feel productivity guilt on many days. I am trying to be better at it. Else, it kills all your happiness and you end up doing nothing. Prioritize the essentials tasks that you need to do and get them done. Life will be good. Don’t over stress yourself.
6. The business case for serverless: 10 mins read. I spent few months on building an application using Serverless. I uses AWS stack to build the application.I enjoyed Serverless experience but I found that pace of development goes down as you are not able to do end-to-end testing on your local machine. This post does a good job in making a business case for Serverless. The main point author makes is that Serverless increases developer velocity. Author concludes the post by writing
   One day, complexity will grow past a breaking point and development velocity will begin to decline irreversibly, and so the ultimate job of the founder is to push that day off as long as humanly possible. The best way to do that is to keep your ball of mud to the minimum possible size— serverless is the most powerful tool ever developed to do exactly that.
   Another interesting post on Serverless that I read this week is by Amazon ‘s Tim Bray — Serverless Everything. He proposed an interesting way to look at Serverless using data plane and control plane analogy. Give it a read, it is not that long.
   The Amazon MQ service, which is a managed version of the ex­cel­lent Apache Ac­tiveMQ open-source message broker. To make this us­able by AWS cus­tomer­s, we had to write a bunch of soft­ware to cre­ate, de­ploy, con­fig­ure, start, stop, and delete mes­sage bro­ker­s. In this sort of sce­nar­i­o, Ac­tiveMQ it­self is called the “data plane” and the man­age­ment soft­ware we wrote is called the “control plane”. The con­trol plane’s APIs are REST­ful and, in Ama­zon MQ, its im­ple­men­ta­tion is en­tire­ly server­less, based on Lamb­da, API Gate­way, and Dy­namoDB.
7. Benchmark PostgreSQL With Linux HugePages: 15 mins read. PostgreSQL is my first choice when it comes to RDBMS. This post covers how we can configure Linux HugePages configuration to improve performance of Postgres database. Author concludes the post by writing
   One of my key recommendations is that we must keep Transparent HugePages off. You will see the biggest performance gains when the database fits into the shared buffer with HugePages enabled. Deciding on the size of huge page to use requires a bit of trial and error, but this can potentially lead to a significant TPS gain where the database size is large but remains small enough to fit in the shared buffer.
8. How we built Globoplay’s API Gateway using GraphQL: 15 mins read. This blog start with the reason why Globo’s team decided to use GraphQL instead of REST for building API. The main reason outlined in the post for choosing GraphQL is the ease with which you can support different requirements for different devices. After covering the why GraphQL, the post talks about how you can get started with it.
   In mid 2018, we had two backends for frontends (BFF) doing very similar tasks: One for web, and another for iOS, android and TV. As much as I love the “backend for frontend” idea (and how cool it sounds), we could not keep the current architecture. Not only because of the reasons I just said, but because each BFF was serving slightly different content to its clients while the business team started to ask for something new: Ubiquity among all clients. the more I reviewed everything we needed to support, the more GraphQL started to make sense. While TVs need a big program poster, mobiles need a small one. We need to show exactly the same video duration among all clients. TVs should provide detailed information about each program, but iOS and android could show only a poster + program title.
9. Envoy Proxy at Reddit: 20 mins read. The post goes into depth on why Reddit moved to Envoy for service to service communication. What I like in this post is how they incorporated a new technology in a step by step manner rather than going the Big Bang approach. It is a well written post so you will end up learning a lot about how big sites like Reddit introduce new technologies in their ecosystem and how they architecture evolve over time. The post outlines three requirements Reddit team had from their service mesh choice. Envoy and its ecosystem fit all of these requirements.
   1. Performance: Avoid adding a performance bottleneck at all costs. Any performance losses at the proxy level need to be offset by considerable feature gains. The two biggest considerations here were resource utilization and latency impact. Our mesh approach accounts for a sidecar proxy on every host, so we wanted the solution to be one that we were comfortable running on every host and at every hop in the network.
   2. Features: The biggest differentiator among the options was the possibility of L7 Thrift support in the proxy. Thrift is our main inter-service RPC protocol and without first-class support for the behavior control we want in a service mesh, it wouldn’t make sense to switch to something that would just be providing the same basic TCP load balancing we’re getting out of HAProxy. We’ll address this in the next section.
   3. Integrations and Extensibility: Being able to contribute or request integrations and possibly extend out-of-the-box functionality was also a core requirement. The network proxy needed to be able to evolve with Reddit’s service needs and developer feature requests.
10. Going Head-to-Head: Scylla vs Amazon DynamoDB: 30 mins read. This post compares ScyllaDB with Amazon DynamoDB. Author writes, Scylla is a drop-in replacement for Cassandra, implemented from scratch in C++. Cassandra itself was a reimplementation of concepts from the Dynamo paper. So, in a way, Scylla is the “granddaughter” of Dynamo. That means this is a family fight, where a younger generation rises to challenge an older one. It was inevitable for us to compare ourselves against our “grandfather,” and perfectly in keeping with the traditions of Greek mythology behind our name.
    The conclusion from the post says it all
    1. DynamoDB failed to achieve the required SLA multiple times, especially during the population phase.
    2. DynamoDB has 3x-4x the latency of Scylla, even under ideal conditions
    3. DynamoDB is 7x more expensive than Scylla
    4. Dynamo was extremely inefficient in a real-life Zipfian distribution. You’d have to buy 3x your capacity, making it 20x more expensive than Scylla
    5. Scylla demonstrated up to 20x better throughput in the hot-partition test with better latency numbers
    6. Last but not least, Scylla provides you freedom of choice with no cloud vendor lock-in (as Scylla can be run on various cloud vendors, or even on-premises).

I will end this newsletter with a short video — The Electronic Coach. This short video shows how Donald Knuth build a mainframe program that helped a basketball team win 11 out of 14 matches. This is an early example of computer used in data driven decision making. In case you don’t know Donald Knuth, he is one of the greatest computer scientist. His book The Art of Computer Programming is included by American Scientist on its list of books that shaped the last century of science.