Recently, a project I worked on was considering using Electron as a fallback technology for an initial Progressive Web Application. At the time, the assumption was that since Electron uses Chromium, a browser, it should allow application developers to not only use the features of a PWA but also gain native access to technologies, such … Read More
If you want to run OS X Sonoma, but can’t dedicate a computer to it, you could always install it on the UTM virtual machine engine. This allows you to test out Beta OS X features without taking over your primary machine’s OS. Pre-requisites: A Mac with Apple Silicon running OS X Ventura An Apple … Read More
In a perfect world, there would never be a need to connect to your resources running on AWS. In the real world, it’s sometimes necessary to get your hands dirty and look at what’s happening on the actual machine, especially during development. This post dives into a few ways to connect your workstation to resources running inside a VPC. It started out as a how-to for using bastion hosts, but quickly expanded to look beyond the bastion.
Decision support databases have a number of quirks that are not obvious to the casual user, particularly someone coming from an OLTP background. In this post I look at how unbalanced distributions can impact your query performance, how you can identify imbalances, and what you can do to fix them.
It’s possible to analyze your Glue jobs using just the logs they produce. Possible. But it’s not a pleasant task: your log messages are buried in messages from the framework, and in the case of a distributed PySpark job they’ll be spread amongst multiple CloudWatch log streams. In this post I look at an alternative: AWS X-Ray, which captures and aggregates “trace segments” that monitor specific sections of your code. With X-Ray, you can easily see where your jobs are spending their time, and compare different runs.
Several years ago I wrote CloudFormation Tips and Tricks, in which I gave the advice to “use outputs lavishly, exports sparingly.” The reason is that when you export a value from one stack and import it into another you bind those stacks tightly together, and can’t change that exported value. For example, you might create … Read More
JavaScript exports can be a bit tricky. They are either named or the default, and how you pull them in has to match exactly. Find out how making a mistake in importing the wrong default caused me half a day of debugging confusion in my React/Next.js application.
Amazon Athena is a service that lets you run SQL queries against structured data files stored in S3. It takes a “divide and conquer” approach, spinning up parallel query execution engines that each examine only a portion of your data. The performance of these queries, however, depends on how you consolidate and partition your data. In this post I compare query times for a moderately large dataset, looking for the “sweet spot” between number of files and individual file size.
Different numbers of availability zones are appropriate for different workloads. This post helps you pick an appropriate number for your needs.
“Traditional” deployment patterns separate the application from its infrastructure. Lambda deployments turn this model on its head, binding the infrastructure tightly to the running code. This can be a challenge, especially when developing in a team: it is all too easy for one developer to accidentally overwrite another’s work. In this post I look at several deployment options, and how they impact a development team.
