When you work with React app, it normally needs some data from the server to store it for immediate use (e.g., show it on the page). If the app works with some complex relational database the work than may be a bit challenging. In this article I am going to describe the issues with organizing the data, which we faced during our work on one of DashBouquet projects, and I will also speak about our solutions.

To make it easier, we will have a look at an example. Let's think of an app with multiple pages where each page needs certain amount of data from the server. In case there are enough pages, the amount of data would become too massive to be fetched at once while loading the app. So the data is requested for every page load. And for every page we most probably have a partition where we can save page-related data, so it would be a good idea to put there fetched data too.

In our example the app has few pages. First page would be list of teachers in a university and second page would have a list of students. When the user clicks on the teacher name, they will get to a page of a certain teacher with lists of the students and courses. While clicking on the student, the user will get to his page with list of teachers and courses.

The ER diagram would look like this:

We can decide what data we need on which page:

To describe my example I will use the stack that we deployed in our project. It includes Loopback for back-end, React, Redux and Redux-saga for front-end and Axios for interaction with the server.

It is clear that same data may be required for different pages. For example we will have all teachers on Teachers page and some of them on a page of a single student. Where do we store them? Maybe we don't always need to fetch the teachers on Single student page? But if we decide not to fetch the teachers, where do we take the data? If we decide to fetch on both pages where do we put the data to avoid duplication? When the data is spread across the whole store it becomes very confusing, especially if the app is big (more than four pages) and has great amount of data.

Keeping all the data in one place may help you a lot. But there is a question: how would you fetch it? For sure we don't want to fetch the entities separately, because this way we would easily reach the point of 30-50 requests per page and this would make page loading awfully slow. We want to try and get as much data as possible in one request (e.g. using an include filter in case of Loopback). In the response for teachers we get something like this:

[
{
students: [
{
id,
name,
studentCourses: [
{
studentId,
courseId,
grande,
},
...
],
},
...
],
courses: [
{
id,
name,
},
...
],
},
...
]

In case you use Loopback as well, have a look at this doc where you can read about how to combine data in queries here.

In this case our actions would spare us two requests but this is not very useful if we speak about storing on front-end. First of all, what would happen if something changes in the database (like adding new courses for a teacher)? Instead of refetching the courses, we would have to refetch both courses and teachers. Secondly, we might need courses inside of a student entity instance, but when we make a query to the server, we don't include these courses to avoid duplication.

To deal with these issues we can start with Normalizr - a utility that normalizes data with nested objects, just like in our case. No need to describe it in detail: you can find all information here. The point is that after applying some simple manipulations to the result of Normalizr's work we get data that we can keep in store.

We need to define a couple of sagas. If you haven't used redux-saga in your projects yet, I think this should convince you to do so.

The first saga will fetch data:

const urls = {
teachers: '/teachers,
students: '/students’,
courses: '/courses',
};

export function* fetchModel(model, ids, include) {
const url = urls[model];
const where = {id: {inq: ids}};
const filter = {where, include};
const params = {filter};
return yield get({url, params});
}

The second will store the data:

export function* queryModels(modelName, ids, include]) {
const singleModelSchema = schema[modelName];
const denormalizedData = yield fetchModel(modelName, ids, include);
const normalizedData = normalize(denormalizedData, [singleModelSchema]);

const {entities} = normalizedData;
yield put(addEntities(entities));
}

And a reducer will add new pieces of data to the already fetched ones:

case ADD_ENTITIES: {
const models = action.entities;
const newState = cloneDeep(state);
return mergeWith(newState, models);
}

Having done all that we can query the data from the server in this manner:

export function* fetchTeachers() {
yield queryModels('teachers', ids, [
{
relation: 'students',
scope: {
include: ['studentCourses'],
}
},
{
relation: 'courses',
}
]);
}

Normalizr uses a normalization schema as described here.

Eventually we end up with the state that looks like this:

state: {
...
models: {
teachers: {...},
sudents: {...},
courses: {...},
studentCourses: {...},
},
...
}

This is actually a pretty little copy of a part of our database in the store. There is no need to dispatch plenty of actions to put fetched data to different sections of the store and to remember where every piece of data should be stored. It is done in queryModels saga and we always know where the fetched data is going to be put.

After that we can use it in any page of the app combing it in selectors as required.

In our case, if needed, we can get an object for teacher as complicated as this:

{
students: [
{
id,
name,
studentCourses: [...],
courses: [...],
},
...
],
courses: [
{
id,
name,
sudents: [...],
teachers: [...],
},
...
],
}

There is another way as well. We can describe an all-purpose API to denormalize the data before using it. The problem here is that we need an API to denormalize data, because the denormalize function that comes along with Normalizr package would only denormalize data to a shape it was when it came from the server, which is not exactly what we want. As described above, we got courses only within teacher entities, though we might need them anywhere else. In case of larger projects I believe you may want to spend time on a custom denormalization function. However, this is a different topic and we may cover it in the next article.

For me this approach became quite a relief. The main advantage here is that you always know where to find what you need. And in case you decide that it would be better to aggregate data in another way, you don't need to mess with the queries again, you just change a selector a little. In general, managing data in store requires much less effort with this approach.

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