Data stores, which come from the
@orbit/store package, have been discussed at
length throughout this guide already. Stores implement the
Syncable interfaces, and are the primary interface through
which developers will interact with an Orbit application.
Every store keeps its data in memory in a
Cache, which is accessible via the
Caches use immutable data maps from
@orbit/immutable to store their actual
data. This makes it incredibly efficient to clone caches, and thus stores,
which has benefits we’ll discuss shortly.
The use of immutable data structures does not extend into the records themselves, which are stored as simple POJOs. Therefore, it’s not necessary to use immutable access methods when working with records. There is some performance tradeoff involved here, because individual records must be cloned on mutation.
Every change to a cache is observed by several “operation processors”, whose job it is to ensure that the cache maintains its integrity and continues to conform to its associated schema.
A single transform applied to a store may result in many changes being made by operation processors. For instance, when a record is removed, it must be removed from all of its associated relationships. When a relationship with an inverse is removed, that inverse relationship must also be removed.
Typically you should not be applying changes directly to a cache. It’s far
preferable to apply changes to the associated store through its
However, caches can be modified via a
patch method, that takes an
or array of
PatchResult that’s returned has the following signature:
All changes to a cache will be emitted as
patch events. These events include
Operation that was applied as well as any data returned.
Its important to recognize that
patch events will be emitted for EVERY
change, including those made by operation processors. Therefore, if you need
a high fidelity log of changes to a store, observe its cache’s
As has been discussed, the contents of a cache can be queried directly and synchronously, using the same query expressions that can be applied to other sources.
store.query is asynchronous and thus returns results wrapped in a
store.cache.query is synchronous and returns results directly. For
By querying the cache instead of the store, you’re not allowing other sources to participate in the fulfillment of the query. If you want to coordinate queries across multiple sources, it’s critical to make requests directly on the store.
Because caches store their data in immutable structures, cloning them is incredibly “cheap”. It’s possible to “fork” a store quickly, modify its data in isolation from its parent, and optionally “merge” those changes back.
Let’s look at an example of store forking and merging:
It’s important to note a few things about store forking and merging:
Once a store has been forked, the original and forked stores’ data can diverge independently.
A store fork can simply be abandoned without cost.
Merging a fork will gather the transforms applied since the fork point, coalesce the operations in those transforms into a single new transform, and then update the original store.
Want to experiment with store forking and merging?