Fusion: Perfecting `state:modified` & Core Migrations

Hey everyone! Let's dive into the exciting progress we're making on ensuring state:modified works seamlessly in dbt Fusion and how we're tackling migrations from dbt Core. This is a crucial step in making the transition to Fusion as smooth as possible, so let's get into the details.

Understanding the Challenge: state:modified in Fusion

The state:modified functionality in dbt is super useful, right? It helps us identify which models have changed since the last run, so we only rebuild what's necessary. This saves time and resources, which is always a win. However, getting state:modified to work perfectly in dbt Fusion, especially when migrating from dbt Core, presents some interesting challenges.

When we talk about state:modified, we're essentially comparing the current state of our dbt project with its previous state. This involves checking a whole bunch of attributes, checksums, and other details to determine if a node (like a model or a test) has been modified. The goal is to be accurate: we want to identify actual changes while avoiding false positives, where dbt thinks something has changed when it really hasn't.

Now, here's where things get a bit tricky with Fusion and Core migrations. dbt Core and dbt Fusion, while sharing the same core principles, have some internal differences in how they represent and manage these states. This means that a direct, attribute-for-attribute comparison between a dbt Core manifest (the record of your project's state) and a dbt Fusion manifest might not always be perfect. We've already decided that we won't be aiming for a 100% attribute-for-attribute conformity between Core v12 and Fusion v20 manifests. It's just not feasible, and honestly, it's not the most efficient way to tackle this. However, we are committed to making this transition as smooth as possible for you guys.

The main issue we're addressing is this: if Fusion thinks a node has changed when it hasn't, it leads to unnecessary rebuilds. Imagine running dbt and seeing a bunch of models being rebuilt even though you didn't actually change them – that's exactly what we want to avoid! This is why we're putting in the work to minimize these false positives. Think of it like this: we're striving for a system where Fusion is a super-reliable detective, only flagging changes that are truly there.

Our Strategy: Balancing Compatibility and Progress

So, how are we tackling this? Our approach is to strike a balance between ensuring forward-compatibility for critical features and making pragmatic choices about where to focus our efforts. Let's break down the key aspects of our strategy:

1. Forward-Compatibility for Deferral: A Top Priority

Deferral is a powerful feature in dbt that allows you to compare your current project against a previous run, often in a production environment. This is incredibly useful for ensuring that changes you're making in development won't break things in production. To make this work seamlessly when migrating to Fusion, we're prioritizing forward-compatibility for deferral. This means that dbt Fusion needs to be able to successfully read and interpret dbt Core manifests (specifically v12 manifests).

Essentially, Fusion needs to understand the key information within a Core manifest to perform deferral correctly. This doesn't mean Fusion needs to understand every single attribute, but it does need to be able to extract the necessary data points. We're focusing on ensuring that Fusion can deserialize these older manifests and read the specific attributes required for deferral. This is a critical step because it allows you to start using Fusion in your development environment while still deferring to your existing dbt Core production environment. This gives you a safe and controlled way to adopt Fusion without disrupting your production workflows. Think of it as building a bridge between your old dbt Core world and your new dbt Fusion world – you can start using the new features of Fusion while still relying on the stability of your Core setup for production runs.

2. Best Effort for state:modified: Minimizing False Positives

While we're prioritizing deferral, we're also putting in a best effort to make state:modified work as accurately as possible in Fusion when migrating from Core. As we discussed earlier, achieving 100% perfect accuracy is a very high bar, especially given the internal differences between Core and Fusion. However, we're committed to minimizing false positives – instances where Fusion incorrectly identifies a node as modified.

Our approach here is to carefully analyze the key attributes and checksums that state:modified relies on and ensure that Fusion handles them in a way that's as compatible as possible with Core. This involves digging into the nitty-gritty details of how manifests are generated and compared in both Core and Fusion. We're identifying potential areas where discrepancies might arise and implementing strategies to mitigate them. For example, this might involve normalizing certain data formats or adjusting how checksums are calculated. The goal is to make the comparison process as robust as possible, even when dealing with manifests from different versions of dbt.

It's important to remember that our primary goal here is to avoid unnecessary rebuilds. We'd rather err on the side of Fusion thinking something has changed when it hasn't (a false positive) than the other way around (a false negative, where Fusion misses an actual change). A false positive might lead to a slightly longer run time, but a false negative could lead to inconsistencies in your data. So, we're focusing on making Fusion a bit overcautious in this regard, at least during the initial migration phase. This ensures data integrity and gives you peace of mind as you transition to Fusion. We're essentially building a safety net to catch any potential issues during the migration process.

3. The Long-Term Solution: Full Fusion Adoption

It's worth noting that the