The Worker Pools pattern maintains a fixed number of workers that process jobs from a shared queue. This pattern is essential for controlling resource usage, handling bursty workloads, and providing predictable performance in concurrent systems.
NOTE: For other posts on concurrency patterns, check out the index post to this series of concurrency patterns.
The Fan-out/Fan-in pattern is a concurrency pattern that distributes work across multiple workers (fan-out) and then collects results from all workers (fan-in). This pattern is essential for parallel processing of independent tasks, load balancing, and improving throughput for CPU-intensive operations.
NOTE: For other posts on concurrency patterns, check out the index post to this series of concurrency patterns.
The Pipeline pattern is a fundamental concurrency pattern that connects multiple stages of processing where each stage processes data and passes it to the next stage. This pattern is particularly useful for data transformation workflows, stream processing, and multi-step computations.
NOTE: For other posts on concurrency patterns, check out the index post to this series of concurrency patterns.
I’ve been diving deep into implementing Datadog in iOS applications and wanted to share my experience with both SwiftUI and UIKit approaches and the related elements of the work. Let’s break this down into what works, what doesn’t, and why you might choose various options when using Datadog (or deciding not to use Datadog).
First things first, you’ll need these Datadog SDK packages:
dependencies: [
.package(url: "https://github.com/DataDog/dd-sdk-ios", from: "2.27.0")
]For next steps of why I set up TimeScale DB up for local dev, and being able to just do things, I need two new data generators over on Data Diluvium. One for humidity, which will be this post, and one for temperature, which will be next.
When working with TimeScale DB for time-series data, having realistic environmental data is crucial. I’ve found that humidity is a particularly important parameter that affects everything from agriculture to HVAC systems. Having realistic humidity data is essential for:
I created a humidity generator that produces realistic values based on typical Earth conditions. Here’s what I considered:
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