Brave New Geek Introspections of a software engineer

tag

design patterns

  1. #91 17 min

    Microservice Observability, Part 2: Evolutionary Patterns for Solving Observability Problems

    In part one of this series, I described the difference between monitoring and observability and why the latter starts to become more important when dealing with microservices. Next, we’ll discuss some strategies and patterns for implementing better observability. Specifically, we’ll look at the idea of an observability pipeline and how we can start to iteratively improve observability in our systems. To recap, observability can be described simply as the ability to ask questions of your systems without knowing those questions in advance. This requires capturing a variety of signals such as logs, metrics, and traces as well as tools for interpreting those signals like log analysis, SIEM, data warehouses, and time-series databases. A number of challenges surface as a result of this. Clint Sharp does a great job discussing the key problems, which I’ll summarize below along with some of my own observations.

  2. #80 12 min

    The Observability Pipeline

    The rise of cloud and containers has led to systems that are much more distributed and dynamic in nature. Highly elastic microservice and serverless architectures mean containers spin up on demand and scale to zero when that demand goes away. In this world, servers are very much cattle, not pets. This shift has exposed deficiencies in some of the tools and practices we used in the world of servers-as-pets. It has also led to new tools and services created to help us support our systems.

  3. #23 6 min

    Iris Decentralized Cloud Messaging

    A couple weeks ago, I published a rather extensive analysis of numerous message queues, both brokered and brokerless. Brokerless messaging is really just another name for peer-to-peer communication. As we saw, the difference in message latency and throughput between peer-to-peer systems and brokered ones is several orders of magnitude. ZeroMQ and nanomsg are able to reliably transmit millions of messages per second at the expense of guaranteed delivery. Peer-to-peer messaging is decentralized, scalable, and fast, but it brings with it an inherent complexity. There is a dichotomy between how brokerless messaging is conceptualized and how distributed systems are actually built. Distributed systems are composed of services like applications, databases, caches, etc. Services are composed of instances or nodes—individually addressable hosts, either physical or virtual. The key observation is that, conceptually, the unit of interaction lies at the service level, not the instance level. We don’t care about which database server we interact with, we just want to talk to a database server (or perhaps multiple). We’re concerned with logical groups of nodes.

  4. #7 6 min

    Modularizing Infinitum: A Postmortem

    In addition to getting the code migrated from Google Code to GitHub, one of my projects over the holidays was to modularize the Infinitum Android framework I’ve been working on for the past year. Infinitum began as a SQLite ORM and quickly grew to include a REST ORM implementation, REST client, logging wrapper, DI framework, AOP module, and, of course, all of the framework tools needed to support these various functionalities. It evolved as I added more and more features in a semi-haphazard way. In my defense, the code was organized. It was logical. It made sense. There was no method, but there also was no madness. Everything was in an appropriately named package. Everything was coded to an interface. There was no duplicated code. However, modularity — in terms of minimizing framework dependencies — wasn’t really in mind at the time, and the code was all in a single project.

  5. #6 7 min

    The Importance of Being Idle

    “Practice not-doing and everything will fall into place.” It’s good to be lazy. Sometimes, in programming, it can also be hard to be lazy. It’s this paradox that I will explore today — The Art of Being Lazy. Specifically, I’m going to dive into a design pattern known as lazy loading by discussing why it’s used, the different flavors it comes in, and how it can be implemented. Lazy loading is a pretty simple concept: don’t load something until you really need it. However, the philosophy can be generalized further: don’t do something until you need to do it. It’s this line of thinking that has helped lead to processes like Kanban and lean software development (and also probably got you through high school). Notwithstanding, this tenet goes beyond the organizational level. It’s about optimizing efficiency and minimizing waste. There’s a lot to be said about optimizing efficiency in a computer program, which is why The Art of Being Lazy is an exceedingly relevant principle.