Add fields
Forest Admin allows creating new fields on any collection, either computationally, by fetching data on an external API or based on other data that is available on the connected data sources.
By default, the fields that you create will be read-only, but you can make them filterable, sortable, and writable by using the relevant methods.
How does it work?
When creating a new field you will need to provide:
columnType
Type of the new field which can be any primitive or composite type
dependencies
List of fields that you need from the source records and linked records in order to run the handler
getValues
Handler which computes the new value for a batch of records
enumValues (optional)
When columnType is Enum
, you must specify the values that the field will support
Examples
Adding a field by concatenating other fields
This example adds a user.displayName
field, which is computed by concatenating the first and last names, and then another which capitalize it.
// User collection has the following structure: { id, firstName, lastName }
agent.customizeCollection('user', collection => {
collection.addField('displayName', {
// Type of the new field
columnType: 'String',
// Dependencies which are needed to compute the new field (must not be empty)
dependencies: ['firstName', 'lastName'],
// Compute function for the new field
// Note that the function computes the new values in batches: the return value must be
// an array which contains the new values in the same order than the provided records.
getValues: (records, context) => records.map(r => `${r.firstName} ${r.lastName}`),
});
});
Adding a field that depends on another computed field
This example adds a user.displayName
field, which is computed by concatenating the first and last names.
// User collection has the following structure: { id, firstName, lastName }
agent.customizeCollection('user', collection => {
collection
// Create a first field which is computed by concatenating the first and last names
.addField('displayName', {
columnType: 'String',
dependencies: ['firstName', 'lastName'],
getValues: (records, context) => records.map(r => `${r.firstName} ${r.lastName}`),
})
// Create a second field which is computed by uppercasing the first field
.addField('displayNameCaps', {
columnType: 'String',
dependencies: ['displayName'], // It is legal to depend on another computed field
getValues: (records, context) => records.map(r => r.displayName.toUpperCase()),
});
});
Adding a field that depends on a many-to-one relationship
We can improve the previous example by adding the city of the user to the display name.
// Structure:
// User { id, addressId, firstName, lastName }
// Address { id, city }
agent.customizeCollection('user', collection => {
collection.addField('displayName', {
columnType: 'String',
// We added 'address:city' in the list of dependencies,
// which tells forest to fetch the related record
dependencies: ['firstName', 'lastName', 'address:city'],
// The address is now available in the parameters
getValues: (records, context) =>
records.map(r => `${r.firstName} ${r.lastName} (from ${r.address.city})`),
});
});
Adding a field that depends on a one-to-many relationship
Let's now add a user.totalSpending
field by summing the amount of all orders
.
// Structure
// User { id }
// Order { id, customer_id, amount }
agent.customizeCollection('user', collection => {
collection.addField('totalSpending', {
columnType: 'Number',
dependencies: ['id'],
getValues: async (records, context) => {
const recordIds = records.map(r => r.id);
// We're using Forest Admin's query interface (you can use an ORM or a plain SQL query)
const filter = { conditionTree: { field: 'customer_id', operator: 'In', value: recordIds } };
const aggregation = { operation: 'Sum', field: 'amount', groups: [{ field: 'customer_id' }] };
const rows = await context.dataSource.getCollection('order').aggregate(filter, aggregation);
return records.map(record => {
const row = rows.find(r => r.group.customer_id === record.id);
return row?.value ?? 0;
});
},
});
});
Adding a field fetching data from an API
Let's imagine that we want to check if the email address of our users is deliverable. We can use a verification API to perform that work.
// Fictional verification API.
const emailVerificationClient = require('@sendchimplio/client');
client.setApiKey(process.env.SENDCHIMPLIO_API_KEY);
// User collection has the following structure: { id, email }
agent.customizeCollection('user', collection => {
collection.addField('emailDeliverable', {
columnType: 'Boolean',
dependencies: ['email'],
getValues: async (records, context) => {
// Structure of the response is
// {
// '[email protected]': { usernameChecked: false, usernameValid: null, domainValid: true },
// '[email protected]': { usernameChecked: true, usernameValid: true, domainValid: true },
// '[email protected]': { usernameChecked: true, usernameValid: true, domainValid: true },
// }
const response = await emailVerificationClient.verifyEmails(records.map(r => r.email));
// Always return values in the same order than the source records
return records.map(r => {
const check = response[r.email];
return check.domainValid && (!usernameChecked || usernameValid);
});
},
});
});
Performance
When adding many fields, keep in mind that:
You should refrain from making queries to external services
Use relationships in the
dependencies
array when that is possibleUse batch APIs instead of performing queries inside of the
records.map()
handler.
Only add fields you need in the
dependencies
listThis will reduce the pressure on your data sources (fewer columns to fetch)
And increase the probability of reducing the number of records that will be passed to your handler (records are deduplicated).
Do not duplicate code between handlers of different fields: fields can depend on each other (no cycles allowed).
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