Last week we looked at how compilers worked in general. We saw that they were mostly composed of two parts, the front end and back end. The front end being the compiler from programming source code to intermediate representation and the back end being the runtime. In today’s post we will look specifically into how Python gets compiled and interpreted with the default implementation, CPython.
With the recent introduction of Apple M1 chip, we have seen the start of the adoption of the ARM processor architecture for Apple Macbook line. ARM being a different family of instruction set architecture than x86 (most common as of today), this means that any application built for x86 (for Mac with Intel or AMD chips) needs to be recompiled to run on ARM (M1 chip). The process of compilation is a multistep process starting taking code written in programming languages and transforming it into machine code understood by the processor. In today’s post we will look at the different part involved in the compilation process.
When using a branching mechanism like GitFlow or Mainline, we usually create pull request from short lived branches to long lived branches (e.g.
master). The pull request diff page gives a view of the changes that were brought by the branch. By default all repositories managers like GitHub, GitLab or Bitbucket uses “triple dot” diff to show diff. In this post we’ll look at the difference between “triple dot” and “double dot” diff.
Git Rebase is a command used to reapply commits on top of another base tip. The base tip could be either another branch or the current branch. Rebase is useful in scenarios where we need to keep branches up to date with long lived branches or scenarios where we want to rework the history of a feature branch. In this week blog post we will look at different scenarios where Git Rebase is useful and understand the usage of the command with its parameters.
Kakfa supports multiple log cleanup policy,
compact. When set to
delete, log segments will be deleted when the size or time limit is reached. When
compact is set, Kafka will ensure to keep at least the latest value of messages per message key. With log compaction setup on a Kafka topic, the topic becomes a database where messages are rows in database term, mutation of rows are done via messages where the last message received represents the latest state. In this post we will see how we can setup compaction and the different settings that affect its behaviour.
Offsets are a big part of Kafka. They indicate the position of a log in the partition of a topic, allowing consumers to know what to read, and from where to start. In today’s post we will look into how consumers manage the offset, store and commit them, and how brokers maintain them to allow failure to happen on a consumer group.