Almost everyone hates Java’s checked exceptions. Even those that still use them will admit that they lead to a lot of boilerplate. Everyone has code along the following lines: Continue reading “A small tweak to checked exceptions”
If you need an unguessable random string (for a session cookie or access token, for example), it can be tempting to reach for a random UUID, which looks like this:
This is a 128-bit value formatted as 36 hexadecimal digits separated by hyphens. In Java and most other programming languages, these are very simple to generate:
import java.util.UUID; String id = UUID.randomUUID().toString();
Under the hood this uses a cryptographically secure pseudorandom number generator (CSPRNG), so the IDs generated are pretty unique. However, there are some downsides to using random UUIDs that make them less useful than they first appear. In this note I will describe them and what I suggest using instead.
I’m seeing a large spike in attempted comment spam, so I’ve turned off all commenting for now.
Updated 20th July 2017 to clarify notation for the point of infinity. A previous version used the symbol 0 (zero) rather than O, which may have been confusing.
In the wake of the recent critical security vulnerabilities in some JOSE/JWT libraries around ECDH public key validation, a number of implementations scrambled to implement specific validation of public keys to eliminate these attacks. But how do we know whether these checks are sufficient? Is there any guidance on what checks should be performed? The answer is yes, but it can be a bit hard tracking down exactly what validation needs to be done in which cases. For modern elliptic curve schemes like X25519 and Ed25519, there is some debate over whether validation should be performed at all in the basic primitive implementations, as the curve eliminates some of the issues while high-level protocols can be designed to eliminate others. However, for the NIST standard curves used in JOSE, the question is more clear cut: it is absolutely critical that public keys are correctly validated, as evidenced by the linked security alert.
I am sometimes asked whether doing a PhD was worth it, given that I left academia and research to become a full-time software developer. My answer is an unequivocal “yes”, despite the fact that my thesis is about as relevant to what I do now as a book on the sex lives of giraffes.
By far the most important skill I learnt during that time was not any particular technical knowledge, but rather a general approach to critical thinking—how to evaluate evidence and make rational choices. In a profession such as software engineering, where we are constantly bombarded with new technologies, products and architectural styles, it is absolutely essential to be able to step back and evaluate the pros and cons to form sensible technology choices. In this post I’ll try and summarise the approach I take to making these decisions.
A new month a new job. I’m now working as a Java Application Developer for a large software consultancy. I’ll be developing Java EE applications for this job, which will be an interesting experience. I’m keen to really get my hands dirty with what is currently the de facto standard for building large business apps.
My first challenge is to get to grips with the Java Persistence API (JPA), which provides a standardised object-relational mapping (ORM) for Java applications. Basically, it takes a bunch of annotated Java classes and determines how best to map these to a relational database, taking care of foreign key constraints, inheritance, joins, and so forth. On the surface, this is all very good. Current best practice in developing these kinds of apps is to separate out the data model of the domain into a layer of simple Java beans (i.e., objects with properties and little custom behaviour), so something like JPA makes this much easier to knock together than using JDBC directly. (Although, it’s not that hard to use JDBC). Continue reading “Relations ain’t “flat””