I'm currently evaluating the use of zod in my application and have a small problem when having to parse an object that can contain optional keys. I'm using .passthrough to allow the keys to stay in the object but would like to custom validate the keys or at least make sure that the key names and types are valid. The .catchall only allows to specify a type of all optional keys but I would require to custom validate each optional key.

import {z} from 'zod';

// mandatory user information
const user = z.object({
    id: z.number(),
    name: z.string(),
});

// additional keys like:
// string: key in the format /^add_\d{3}_s$/
// number: key in the format /^add_\d{3}_n$/ 

add_001_s: z.string()
add_002_s: z.string()
add_003_n: z.number()
add_004_n: z.number()

I'm currently evaluating the use of zod in my application and have a small problem when having to parse an object that can contain optional keys. I'm using .passthrough to allow the keys to stay in the object but would like to custom validate the keys or at least make sure that the key names and types are valid. The .catchall only allows to specify a type of all optional keys but I would require to custom validate each optional key.

import {z} from 'zod';

// mandatory user information
const user = z.object({
    id: z.number(),
    name: z.string(),
});

// additional keys like:
// string: key in the format /^add_\d{3}_s$/
// number: key in the format /^add_\d{3}_n$/ 

add_001_s: z.string()
add_002_s: z.string()
add_003_n: z.number()
add_004_n: z.number()

• javascript
• typescript
• zod

• Could you provide an example of the code you're working with as well as the shape of the data you are trying to validate? I'm not sure I fully understand what you're trying to do. – Souperman Commented Aug 23, 2022 at 13:27
• 1 @Souperman I have added an example that might help to better understand. There are mandatory keys and an arbitrary number of optional keys and I would like to validate the names/types of the optional keys. – doberkofler Commented Aug 23, 2022 at 16:42
• I see, the arbitrary nature of the optionals was what I was not understanding. I was thinking you just wanted optional at first. – Souperman Commented Aug 23, 2022 at 22:34
• Note to help with search indexing. This problem is mon when dealing with form data that uses the array notation. For example if you have a <form> with <input name=`items[${i}]` value="x" /> and submit data, the FormData will look like {'items[0]': 'x', 'items[1]': 'x'} if reading the entries as an object or [['items[0]', 'x'], ['items[1]', 'x']] if reading as an array. E.g. SvelteKit is an opinionated framework that encourages form submissions to your backend over traditional API endpoints, so you may need to make similar schemas when using that. – Dennis Commented Feb 17, 2023 at 2:19

1 Answer 1

The way I would go about this is with a bination of three schemas as follows:

import { z } from "zod";

const mandatoryFields = z.object({
  id: z.number(),
  name: z.string()
});

const stringRegex = /^add_\d{3}_s$/;
const optionalStringFields = z.record(
  z.string().regex(stringRegex),
  z.string()
);

const numberRegex = /^add_\d{3}_n$/;
const optionalNumberFields = z.record(
  z.string().regex(numberRegex),
  z.number()
);

These three schemas make up the core of the type you want to parse out, but there isn't a great way to bine them using and because the record types will conflict and the mandatory fields also aren't parsable as part of either record. I think it would be difficult to define a type in vanilla TypeScript for data you're receiving without a massive enumerated type.

My solution to this these three base schemas to preprocess the input into a new object that breaks out each of the three pieces of the schema. It doesn't do any validations on what's there it just pulls those fields out to pass them into the final schema that will do the more specific validations:

const schema = z.preprocess(
  (args) => {
    const unknownRecord = z.record(z.string(), z.unknown()).safeParse(args);
    if (!unknownRecord.success) {
      // In the event that what was passed in wasn't an unknown record
      // this skips the rest of the preprocessing and lets the schema
      // fail with a better error message.
      return args;
    }
    const entries = Object.entries(unknownRecord.data);
    // Pulls out just stuff that looks like optional number fields
    const numbers = Object.fromEntries(
      entries.filter(
        ([k, v]): [string, unknown] | null => k.match(numberRegex) && [k, v]
      )
    );
    // pulls out just stuff that looks like optional string fields
    const strings = Object.fromEntries(
      entries.filter(
        ([k, v]): [string, unknown] | null => k.match(stringRegex) && [k, v]
      )
    );
    // The types here are all unknowns but now the pieces of the data
    // have been grouped in a way that those three core schemas can parse them
    return {
      mandatory: args,
      numbers,
      strings
    };
  },
  z.object({
    mandatory: mandatoryFields,
    numbers: optionalNumberFields,
    strings: optionalStringFields
  })
);

So now, if you pass in something like:

const test = schema.parse({
  id: 11,
  name: "steve",
  add_101_s: "cat",
  add_123_n: 43,
  dont_care: "something"
});

console.log(test);
/* Logs:
mandatory: Object
  id: 11
  name: "steve"
numbers: Object
  add_123_n: 43
strings: Object
  add_101_s: "cat"
*/

You get individual sections back for each of the pieces. This also doesn't pass through unnecessary fields like dont_care which is a bit of a benefit over using passthrough to attempt to acplish this.

I think this is probably the best option unless you want to try and e up with a massive optional mapped type for both of the things I'm currently calling records. That would potentially have better types but you'll end up with a massive file to fully enumerate the fields.