Jane Street's Incremental: Engineering Self-Adjusting Computations for the Real World

The Core Problem: Recomputing the World

Imagine you're building a real-time financial dashboard. A single trade happens, and you now need to update every single view: the trader's P&L, the firm's aggregate risk, the margin calculations, and a dozen live charts. The naive approach—recomputing the entire state from scratch on every event—quickly becomes a performance catastrophe as your system scales.

This is the problem Jane Street's engineering team faced. In a high-frequency trading environment, the gap between an event and an updated view is measured in microseconds. Recomputing a billion-dollar portfolio's risk metrics from scratch for every price tick isn't just slow; it's architecturally bankrupt.

The common band-aid is ad-hoc caching and manual invalidation. You write custom logic to say, "If price X changes, only recompute these three things." This works for small systems but is a nightmare to maintain. It's brittle, error-prone, and creates a tangled web of dependencies that silently produces stale data when a developer forgets to wire a new dependency correctly.

Jane Street open-sourced their answer to this: a library called Incremental. It’s not just a caching layer. It’s an entire framework for building computations that understand their own dependency graph and can update themselves with minimal work when inputs change. The source is available at github.com/janestreet/incremental.

What Incremental Actually Does: A DAG That Edits Itself

At its core, Incremental is an engine for building and maintaining a dynamic Directed Acyclic Graph (DAG). You, the programmer, write your business logic as pure functions that transform data. Incremental then automatically wires these functions into a dependency graph.

When an input variable—an Incr.Var—changes, the engine doesn't re-run your whole program. It propagates the change through the graph, determines exactly which intermediate nodes are "dirty," and recomputes only those nodes. This isn't a simple cache-hit check. It's a self-adjusting computation.

The magic is in the stabilization step. After a change, Incremental performs a topological sort on the dirty nodes and recomputes them in dependency order. Crucially, it uses a technique called "diffing" or "change propagation" where it compares the new output of a node to its old output. If the output hasn't changed, the propagation stops dead in its tracks, preventing an avalanche of unnecessary downstream computations.

This model turns a static computation into a living system. You write code that looks like it generates the entire world from scratch, but the runtime efficiently maintains the world for you.

The Architecture: Nodes, Steps, and the Diff Algorithm

Let's break down the key players in the Incremental architecture:

Incr.Var: The mutable input cells. These are the only source of change. You set a new value, and the engine takes over.

Incr.map: The static dependency. Given a value a, apply a pure function f to produce b. This is the workhorse. If a changes, f is called. If the result of f is physically equal (==) to the previous result, the downstream nodes are not invalidated.

Incr.bind: The dynamic dependency. This is where Incremental gets truly powerful and dangerous. bind allows you to dynamically create a sub-graph based on the current value. If you have a value that represents a "choice of strategy," bind lets you swap out an entire computation sub-tree. The engine handles tearing down the old sub-graph and building a new one, reusing nodes where possible.

The Stabilization Engine: The scheduler that runs after a variable is set. It maintains a heap of dirty nodes, recomputes them in order, and uses physical equality to cut off propagation. This is the secret sauce that makes it fast.

Why This Matters for Engineers (Even If You Don't Use OCaml)

You might be thinking, "I write Python and TypeScript. Why should I care about an OCaml library from a trading firm?"

The answer is that Incremental is the purest expression of a design pattern that is desperately needed in modern data-intensive applications. The pattern of a self-adjusting DAG is the logical conclusion of reactive programming. It’s what React’s Virtual DOM diffing does for the browser DOM, but for arbitrary application state.

For a Forward Deployed Engineer (FDE) or a full-stack engineer, the concepts translate directly:

1. Complex Dashboard Logic: You are often tasked with building internal tools that ingest streams of data and produce live views. Manually tracking which API response invalidates which chart is a bug farm. An incrementalized model lets you declaratively define the view as a function of all inputs and let the engine optimize the updates. This is similar to how you might build a personal finance categorizer where new CSV rows only update affected monthly aggregates, not the entire spreadsheet. For a deeper dive into that pattern, see our guide on building a Personal Finance Categorizer Over Bank CSV Exports with Gemini.

2. Automation Pipelines: Consider a system that scrapes competitor websites and generates alerts. You could write a script that re-scrapes and re-diffs everything every hour. Or, you could model the pipeline as an incremental computation where the "fetch" node is an input, and downstream "diff" and "alert" nodes only fire when the fetched content actually changes from the previous run. This is the exact principle behind our tutorial on building a Competitor Monitoring Agent That Alerts on Site Changes Using Playwright.

3. Code Generation and Transformation: Imagine an agent that turns UI screenshots into code. If the user tweaks a single element in the design, you don't want to re-invoke the entire vision model pipeline on the whole image. An incremental architecture could detect which sub-component changed and only re-generate that module. We explore similar agentic workflows in our piece on turning UI Screenshots into Production Code with a Free Vision Model on Hugging Face.

How to Try It Today: A Practical Walkthrough

Let's walk through a minimal example in OCaml. The goal is to feel the paradigm, not become an OCaml expert. First, you'll need to set up an OCaml environment with opam and install the library:

opam install incremental

Now, let's model a simple financial calculation. We have a price and a quantity, and we want to compute the total value and a "large order" flag.

open Core
open Incremental

(* 1. Create mutable input variables *)
let price_var = Incr.Var.create 100.0
let qty_var = Incr.Var.create 1000

(* 2. Derive incremental values from the inputs *)
let price = Incr.Var.watch price_var
let qty = Incr.Var.watch qty_var

(* 3. Build the computation graph using map *)
(* This node recomputes only when price or qty changes *)
let total_value =
  Incr.map2 price qty ~f:(fun p q -> p *. Float.of_int q)

(* A downstream node depends on total_value *)
let is_large_order =
  Incr.map total_value ~f:(fun v -> v > 50_000.0)

(* 4. Create an observer to see the results *)
let obs =
  Incr.observe is_large_order ~f:(fun flag ->
    printf "Is large order: %b\n" flag)

(* 5. Stabilize to run the initial computation *)
let () = Incr.stabilize ()

(* 6. Change an input and stabilize again *)
let () =
  Incr.Var.set price_var 60.0;
  Incr.stabilize ();
  (* Output: Is large order: true *)

  Incr.Var.set qty_var 500;
  Incr.stabilize ();
  (* Output: Is large order: false *)

This code is deceptively simple. What happens under the hood is profound. When we change qty_var to 500, the engine marks total_value as dirty. It recomputes total_value, sees the new value 30000.0 is not physically equal to the old value 60000.0, and therefore marks is_large_order as dirty. is_large_order recomputes, sees the boolean changed, and fires the observer. If we had set price_var to 50.0 and qty_var to 1000, total_value would recompute to 50000.0, but is_large_order would still be false. The engine would stop propagation at the is_large_order node, and the observer would never fire.

A Balanced Take: The Sharp Edges

The library is not a silver bullet. Its power comes with significant constraints that can bite you if you're not prepared.

The Physical Equality Trap: The reliance on physical equality (==) for cutting propagation is a double-edged sword. It's incredibly fast, but it means you must be meticulous about constructing new values. If your map function returns a structurally identical but physically new object (e.g., a new list with the same elements), the engine will think the value changed and will uselessly propagate to downstream nodes. You must learn to use features like Incr_map or persistent data structures that share physical storage when unchanged.

The bind Complexity Cliff: Incr.bind is the "break glass in case of emergency" feature. It lets you dynamically change the graph's shape, but it makes performance reasoning extremely difficult. A naive bind can cause the engine to destroy and recreate large sub-graphs on every input tick, destroying the very efficiency you sought. Jane Street's own guidance is to use it sparingly and prefer map whenever possible.

Language Lock-In: The library is deeply tied to OCaml's runtime and type system. While the concepts are universal, there isn't a direct, equally mature port in Python or TypeScript. Libraries like JAX or React's state management echo parts of the philosophy, but none provide the full, generic self-adjusting DAG. As an engineer, you’re more likely to borrow the architecture than the library itself unless you’re in an OCaml shop.

Debugging Nightmares: When a node doesn't update, you have to debug a graph, not a call stack. Understanding why propagation stopped requires mental modeling of the DAG and the exact points of physical equality. Standard debugging tools are not built for this time-traveling, dependency-based execution model.

FAQ: Is This Just Caching?

Q: How is this different from memoization or a simple cache?

A: Memoization caches the output of a function for a given input to avoid recomputing it later. Incremental is about propagating changes through a network of functions. It not only caches the final output but also the intermediate states. When an input changes, it doesn't just check if it has seen that exact input before; it recalculates the minimal slice of the dependency graph that is affected by the change. It's "push-based" reactivity rather than "pull-based" caching.

Q: Can I use this pattern in my Python backend?

A: You can implement the pattern, but you won't find a library as mature as Jane Street's Incremental. You could build a simple version using a DAG library and event emitters, but handling dynamic dependencies (bind) and ensuring physical equality checks for efficient cut-off is a significant engineering undertaking. The closest you might get in the Python ecosystem is combining something like networkx with a custom event loop, but you'll be building most of the engine yourself.

Q: Is this only for financial systems?

A: Absolutely not. Any system with a complex web of derived data that needs to react to a stream of small changes is a candidate. Think of a build system (like Bazel or Buck), a UI framework (like React), a real-time game engine, or a CI/CD pipeline that re-runs only affected tests. The financial industry was just the first to feel the acute pain of the problem.

Q: What's the first step to learning this if I'm not an OCaml developer?

A: Start by applying the mental model to your current stack. The next time you're writing a complex data pipeline, sketch out the dependency graph on a whiteboard. Identify your Vars (inputs) and your maps (transformations). Ask yourself: if this one input changes, what is the absolute minimum set of functions that must re-run? If you find yourself writing manual invalidation logic, you've found a place where the Incremental pattern would have saved you. And if you're looking to build a career solving exactly these kinds of high-impact engineering problems, the mindset of decomposing systems into reactive, efficient graphs is exactly what we cultivate in Forward Deployed Engineering. For a real-world view of how these skills apply daily, check out our breakdown of What a Forward Deployed Engineer Actually Does in a Week: A Time Audit.