Today, I was working with a Spring Boot application that does local JVM cache warming on the server start up. Application was calling a global Redis cache and storing state that does not change often in an in-memory JVM cache. It is a common pattern that many applications use. In our case, application not only just warm the cache but it also first process some data and then cache the result in the local JVM cache.
Many times junior developers forget to start redis or any other depending service and then application fails to start on their local machine. Then, they need to spend few minutes reading the long Java stack trace to find the problem. These stack trace can be quite long. And, it is difficult to find needle in this haystack.
Recently, I learnt about a Spring Boot feature called FailureAnalyzer. FailureAnalyzer allows you to intercept exceptions that occur at the start-up of an application causing an application startup failure. Using FailureAnalyzer you can replace the stack trace of the exception with a more human readable message. The best example of this is when your code has cyclic dependencies. A common example of cyclic dependency is a bean A depending on bean B and vice versa as shown below.
The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge. – Stephen Hawking
System design hack: Postgres is a great pub/sub & job server: 10 mins read. I have read multiple times that people are using Postgres as a job queue or as a pub/sub solution. It does require you to mess with SQL and write PSQL functions but I think it could be a good solution if you don’t want to manage some other pub/sub server.
Head In The Clouds: 15 min read. This articles covers how folks at FreeAgent planned their cloud migration journey. The key points from the post are:
Co-locating has been a terrific win for us over the years, providing us with a cost-effective, high performance compute platform that has allowed us to scale to over 95,000 customers with close to 5 9’s reliability.
Growth often acts as a forcing function with regards to infrastructure. Head count has doubled. Customer count is growing quickly.
Desire for new features is another forcing function. They wanted more datacenters to increase resilience. They were reaching hardware limitations. The ops team was pressed and it was challenging to find ops engineers with the right skills. They were experimenting with ML. Serverless was becoming a go to for production. They wanted to improve deployment. And scaling the database was a challenge.
Experiments were run to research moving to AWS: Granted, any infrastructure migration would be expensive, the project complex and it would come with many challenges, but the advantages and opportunities that a full cloud migration would open up in the future were undeniable.
The decision was made to migrate to AWS!
Early on in the R&D phase we became customers of Gruntwork.io and have relied heavily on their Infrastructure as Code library and training to accelerate the project.
We built network isolation for 1,500 services to make Monzo more secure: 20 mins read. In the Security team at Monzo, one of our goals is to move towards a completely zero trust platform. This means that in theory, we’d be able to run malicious code inside our platform with no risk – the code wouldn’t be able to interact with anything dangerous without the security team granting special access.
Scaling in the presence of errors—don’t ignore them: 20 mins read. The secret to error handling at scale isn’t giving up, ignoring the problem, or even it trying again—it is structuring a program for recovery, making errors stand out, allowing other parts of the program to make decisions. Techniques like fail-fast, crash-only-software, process supervision, but also things like clever use of version numbers, and occasionally the odd bit of statelessness or idempotence. What these all have in common is that they’re all methods of recovery. Recovery is the secret to handling errors. Especially at scale. Giving up early so other things have a chance, continuing on so other things can catch up, restarting from a clean state to try again, saving progress so that things do not have to be repeated. That, or put it off for a while. Buy a lot of disks, hire a few SREs, and add another graph to the dashboard.
Modern Data Practice and the SQL Tradition: 15 mins read. Over the last one year I have read multiple posts suggesting we should start with relational database route. SQL is becoming the defacto language for all things data. Most developers start looking at alternatives too early in the cycle before understanding pros and cons of using a technology. The key points from the post are:
The more I work with existing NoSQL deployments however, the more I believe that their schemaless nature has become an excuse for sloppiness and unwillingness to dwell on a project’s data model beforehand.
One can now model the “known” part of his data model in a typical relational manner and dump his “raw and unstructured” data into JSON columns. No need to “denormalize all the things” just because some element of the domain is “unstructured”.
The good thing with this approach is that one can have a single database for both their structured and unstructured data without sacrificing ACID-compliance.
SQL and relational databases have come a long way and nowadays offer almost any function a data scientist could ask.
Relational databases usually make more sense financially too. Distributed systems like MongoDB and ElasticSearch are money-hungry beasts and can kill your technology and human resources budget; unless you are absolutely certain and have run the numbers and decided that they do really make sense for your case.
Performance and stability with relational databases can be better out of the box
Hash join in MySQL 8: 10 mins read. You should read this blog if you want to learn how hash joins are implemented by databases. It will give you a good and detailed understanding on the subject.
Managing a Go monorepo with Bazel: 10 mins read. I don’t think we still have a winner between monorepo and multiple repo approach when building Microservices. We have big organisations like Google and Facebook that prefer Monorepo approach and then we have organizations like Netflix that recommend multi repo approach. This post covers how you can manage a Go monorepo using Bazel build tool. I have not used Bazel so far but I am seriously considering it for my personal projects.
The Value in Go’s Simplicity: 10 mins read. Go is one language that I really want to spend more time on. It is a popular language used almost everywhere these days. In this blog, author makes the case for Go’s simplicity. As author mentioned, Go core development team has take simplicity to another level. To keep language simple they are not allowing many good features like Generics implemented in Go.
When XML beats JSON: UI layouts: 5 mins read. UI layouts are represented as component trees. And XML is ideal for representing tree structures. It’s a match made in heaven! In fact, the most popular UI frameworks in the world (HTML and Android) use XML syntax to define layouts.
Daily Stand-up Injection of Guilt: 10 mins read. Yegor writes, “Only weak managers need daily stand-up meetings to coordinate the team, while strong ones use more formal instruments to organize the flow of information. However, as someone noted, morning meetings are not supposed to be used by managers to coordinate anyone, but “to discuss progress, impediments and to plan.” I’m not buying it.”