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How Relay Enables Optimal Data Fetching

· 5 min read

Relay’s approach to application authorship enables a unique combination of optimal runtime performance and application maintainability. In this post I’ll describe the tradeoffs most apps are forced to make with their data fetching and then describe how Relay’s approach allows you to sidestep these tradeoffs and achieve an optimal outcome across multiple tradeoff dimensions.


In component-based UI systems such as React, one important decision to make is where in your UI tree you fetch data. While data fetching can be done at any point in the UI tree, in order to understand the tradeoffs at play, let’s consider the two extremes:

  • Leaf node: Fetch data directly within each component that uses data
  • Root node: Fetch all data at the root of your UI and thread it down to leaf nodes using prop drilling

Where in the UI tree you fetch data impacts multiple dimensions of the performance and maintainability of your application. Unfortunately, with naive data fetching, neither extreme is optimal for all dimensions. Let’s look at these dimensions and consider which improve as you move data fetching closer to the leaves, vs. which improve as you move data fetching closer to the root.

Loading experience

  • 🚫 Leaf node: If individual nodes fetch data, you will end up with request cascades where your UI needs to make multiple request roundtrips in series (waterfalls) since each layer of the UI is blocked on its parent layer rendering. Additionally, if multiple components happen to use the same data, you will end up fetching the same data multiple times
  • ✅ Root node: If all your data is fetched at the root, you will make single request and render the whole UI without any duplicate data or cascading requests

Suspense cascades

  • 🚫 Leaf node: If each individual component needs to fetch data separately, each component will suspend on initial render. With the current implementation of React, unsuspending results in rerendering from the nearest parent suspense boundary. This means you will have to reevaluate product component code O(n) times during initial load, where n is the depth of the tree.
  • ✅ Root node: If all your data is fetched at the root, you will suspend a single time and evaluate product component code only once.

Composability

  • ✅ Leaf node: Using an existing component in a new place is as easy as rendering it. Removing a component is as simple as not-rendering it. Similarly adding/removing data dependencies can be done fully locally.
  • 🚫 Root node: Adding an existing component as a child of another component requires updating every query that includes that component to fetch the new data and then threading the new data through all intermediate layers. Similarly, removing a component requires tracing those data dependencies back to each root component and determining if the component you removed was that data’s last remaining consumer. The same dynamics apply to adding/removing new data to an existing component.

Granular updates

  • ✅ Leaf node: When data changes, each component reading that data can individually rerender, avoiding the need to rerender unaffected components.
  • 🚫 Root node: Since all data originates at the root, when any data updates it always forces the root component to update forcing an expensive rerender of the entire component tree.

Relay

Relay leverages GraphQL fragments and a compiler build step to offer a more optimal alternative. In an app that uses Relay, each component defines a GraphQL fragment which declares the data that it needs. This includes both the concrete values the component will render as well as the fragments (referenced by name) of each direct child component it will render.

At build time, the Relay compiler collects these fragments and builds a single query for each root node in your application. Let’s look at how this approach plays out for each of the dimensions described above:

  • ✅ Loading experience - The compiler generated query fetches all data needed for the surface in a single roundtrip
  • ✅ Suspense cascades - Since all data is fetched in a single request, we only suspend once, and it’s right at the root of the tree
  • ✅ Composability - Adding/removing data from a component, including the fragment data needed to render a child component, can be done locally within a single component. The compiler takes care of updating all impacted root queries
  • ✅ Granular updates - Because each component defines a fragment, Relay knows exactly which data is consumed by each component. This lets relay perform optimal updates where the minimal set of components are rerendered when data changes

Summary

As you can see, Relay’s use of a declarative composable data fetching language (GraphQL), combined a compiler step, allows us to achieve optimal outcomes across all of the tradeoff dimensions outlined above:

Leaf nodeRoot nodeGraphQL/Relay
Loading experience🚫
Suspense cascades🚫
Composability🚫
Granular updates🚫