Jackson CSV Serialization and Deserialization from the Ground Up

While there are many examples of Jackson serialization to JSON, there are comparatively few resources of Jackson serialization to CSV. Following is an example of working with a TSV-formatted dataset from the ground up, starting with creating the model object, building code samples for parsing CSV to Java objects using Jackson, writing Java objects to CSV using Jackson, and ending with code to a full round-trip test for serialization.

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Generating Java record classes with Jackson Annotations to map JSON using ChatGPT

There’s a lot of discussion about how to use ChatGPT to generate tests for code. Another interesting use case I’ve seen fairly little coverage of is generating DTOs from JSON. Here is an example with the prompt I’ve put together applied to JSON from the manifest of a Distributed Map Run.

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AWS SageMaker Object Detection Training Gotchas

As part of updates to arachn.io, I’ve started tinkering with object detection machine learning models. During my experiments on AWS SageMaker, I found that AutoPilot does not support object detection models, so I had train using notebooks. As a result, I hit some “gotchas” fine-tuning TensorFlow Object Detection models. While this notebook works a treat on its own training data (at least when run through SageMaker studio), this discussion will focus on things I learned while trying to run it on my own data on August 31, 2024.

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Efficient Image Metadata Extraction with Java

Java has a rich set of tools for processing images built in to the standard library. However, it’s not always clear how to use that library to perform even simple tasks. There are already lots of great guides out there for working with images once they’re loaded… but what can Java do without ever loading the image into memory at all?

When working with images from untrusted sources — for example, images discovered during a web crawl — it’s best to treat data defensively. This article will show how to perform some useful tasks on images without ever loading their pixel data into memory.

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Yes, Java ThreadLocalRandom is Safe to Use with Virtual Threads

Because Java 21 virtual threads are very cheap — per JEP Cafe, they are about 1,000 times faster to launch than a platform thread, and use about 1,000 times less memory than a platform thread — they should never be pooled. As a result, ThreadLocal variables are unlikely to be useful in applications that use virtual threads for concurrent processing. If each task lives in its own dedicated thread, then each call to ThreadLocal ends up returning a new value, and it ends up being a factory instead of a pool! So using ThreadLocal in virtual threads is an anti-pattern. Developers should use another technique for pooling objects, such as structured concurrency, instead.

But what about ThreadLocalRandom? Is this dedicated class for providing sources of randomness to threads safe to use with virtual threads?

The short answer is: Yes, ThreadLocalRandom is safe to use with virtual threads. ThreadLocalRandom is tightly integrated with the Thread class to ensure that it remains efficient, even for virtual threads. For more information, keep reading.

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Example JWT with Private JWK

When writing code or testing, it’s useful to have valid examples of whatever you’re working with for sanity checks. I need some example JWTs with accompanying private JWK and couldn’t find any, so I minted a few. I’m posting them here in the hopes they’re useful to others, too.

These keys were created using a fresh RS256 key. No other keys have been created using this key. Because the private part of this key pair is public — it’s literally right below, in plain text — remember that these keys should only ever be used for sanity checking and testing. In particular, never use these keys for production use cases!

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Reverse Engineering with ChatGPT: An Example

I’m porting Jonas Wagner‘s excellent smartcrop.js, which analyzes an image to recommend a good crop, from JavaScript to Java.

An example crop with diagnostic info, based on N. Feans

When porting code, I always try to understand it along the way so that if (when) there are bugs, I’ll have an idea of where the bug might be. I used ChatGPT 4 to untangle a particularly terse bit of code, and was — frankly — shocked at how well it did.

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