Querying your Documents

In FrogBot, “querying” means filtering or searching through Documents within a Collection. The querying language in FrogBot is designed to be simple and powerful, allowing you to filter Documents with extreme precision through an intuitive and standardized structure. FrogBot provides three common APIs for querying your data:

Local API - Extremely fast, direct-to-database access
REST API - Standard HTTP endpoints for querying and mutating data
GraphQL - A full GraphQL API with a GraphQL Playground

Each of these APIs share the same underlying querying language, and fully support all of the same features. This means that you can learn FrogBot’s querying language once, and use it across any of the APIs that you might use. To query your Documents, you can send any number of Operators through your request:

import type { Where } from 'frogbot'

const query: Where = {
  color: {
    equals: 'blue',
  },
}

The exact query syntax will depend on the API you are using, but the concepts are the same across all APIs. More details.

You can also use queries within Access Control functions.

Operators

The following operators are available for use in queries:

Operator	Description
`equals`	The value must be exactly equal.
`not_equals`	The query will return all documents where the value is not equal.
`greater_than`	For numeric or date-based fields.
`greater_than_equal`	For numeric or date-based fields.
`less_than`	For numeric or date-based fields.
`less_than_equal`	For numeric or date-based fields.
`like`	Case-insensitive string must be present. If string of words, all words must be present, in any order.
`contains`	Must contain the value entered, case-insensitive.
`in`	The value must be found within the provided comma-delimited list of values.
`not_in`	The value must NOT be within the provided comma-delimited list of values.
`all`	The value must contain all values provided in the comma-delimited list. Note: currently this operator is supported only with the MongoDB adapter.
`exists`	Only return documents where the value either exists (`true`) or does not exist (`false`).
`near`	For distance related to a Point Field, comma separated as `<longitude>, <latitude>, <maxDistance in meters (nullable)>, <minDistance in meters (nullable)>`.
`within`	For Point Fields, filters documents based on whether points are inside the given area defined in GeoJSON. Example.
`intersects`	For Point Fields, filters documents based on whether points intersect with the given area defined in GeoJSON. Example.

If you know your users will be querying on certain fields a lot, add index: true to the Field Config. This will speed up searches using that field immensely. More details.

And / Or Logic

In addition to defining simple queries, you can join multiple queries together using AND / OR logic. These can be nested as deeply as you need to create complex queries. To join queries, use the and or or keys in your query object:

import type { Where } from 'frogbot'

const query: Where = {
  or: [
    {
      color: {
        equals: 'mint',
      },
    },
    {
      and: [
        {
          color: {
            equals: 'white',
          },
        },
        {
          featured: {
            equals: false,
          },
        },
      ],
    },
  ],
}

Written in plain English, if the above query were passed to a find operation, it would translate to finding posts where either the color is mint OR the color is white AND featured is set to false.

Nested properties

When working with nested properties, which can happen when using relational fields, it is possible to use the dot notation to access the nested property. For example, when working with a Song collection that has an artists field which is related to an Artists collection using the name: 'artists'. You can access a property within the collection Artists like so:

import type { Where } from 'frogbot'

const query: Where = {
  'artists.featured': {
    // nested property name to filter on
    exists: true, // operator to use and boolean value that needs to be true
  },
}

Writing Queries

Writing queries in FrogBot is simple and consistent across all APIs, with only minor differences in syntax between them.

Local API

The Local API supports the find operation that accepts a raw query object:

import type { FrogBot } from 'frogbot'

const getPosts = async (frogbot: FrogBot) => {
  const posts = await frogbot.find({
    collection: 'posts',
    overrideAccess: true,
    where: {
      color: {
        equals: 'mint',
      },
    },
  })

  return posts
}

GraphQL API

All find queries in the GraphQL API support the where argument that accepts a raw query object:

query {
  Posts(where: { color: { equals: mint } }) {
    docs {
      color
    }
    totalDocs
  }
}

REST API

With the REST API, you can use the full power of FrogBot queries, but they are written as query strings instead: https://localhost:3000/api/posts?where[color][equals]=mint To understand the syntax, you need to understand that complex URL search strings are parsed into a JSON object. This one isn’t too bad, but more complex queries get unavoidably more difficult to write. For this reason, we recommend to use the extremely helpful and ubiquitous qs-esm package to parse your JSON / object-formatted queries into query strings:

import { stringify } from 'qs-esm'
import type { Where } from 'frogbot'

const query: Where = {
  color: {
    equals: 'mint',
  },
  // This query could be much more complex
  // and qs-esm would handle it beautifully
}

const getPosts = async () => {
  const stringifiedQuery = stringify(
    {
      where: query, // ensure that `qs-esm` adds the `where` property, too!
    },
    { addQueryPrefix: true },
  )

  const response = await fetch(
    `http://localhost:3000/api/posts${stringifiedQuery}`,
  )
  // Continue to handle the response below...
}

Performance

There are several ways to optimize your queries. Many of these options directly impact overall database overhead, response sizes, and/or computational load and can significantly improve performance. When building queries, combine as many of these strategies together as possible to ensure your queries are as performant as they can be.

Indexes

Build Indexes for fields that are often queried or sorted by. When your query runs, the database will not search the entire document to find that one field, but will instead use the index to quickly locate the data. This is done by adding index: true to the Field Config for that field:

// In your collection configuration
{
  name: 'posts',
  fields: [
    {
      name: 'title',
      type: 'text',
      index: true, // Add an index to the title field
    },
    // Other fields...
  ],
}

To learn more, see the Indexes documentation.

Depth

Set the Depth to only the level that you need to avoid populating unnecessary related documents. Relationships will only populate down to the specified depth, and any relationships beyond that depth will only return the ID of the related document.

const posts = await frogbot.find({
  collection: 'posts',
  overrideAccess: true,
  where: { ... },
  depth: 0, // Only return the IDs of related documents
})

To learn more, see the Depth documentation.

Limit

Set the Limit if you can reliably predict the number of matched documents, such as when querying on a unique field.

const posts = await frogbot.find({
  collection: 'posts',
  overrideAccess: true,
  where: {
    slug: {
      equals: 'unique-post-slug',
    },
  },
  limit: 1, // Only expect one document to be returned
})

Use in combination with pagination: false for best performance when querying by unique fields.

To learn more, see the Limit documentation.

Select

Use the Select API to only process and return the fields you need. This will reduce the amount of data returned from the request, and also skip processing of any fields that are not selected, such as running their field hooks.

const posts = await frogbot.find({
  collection: 'posts',
  overrideAccess: true,
  where: { ... },
  select: {
    title: true,
  },

This is a basic example, but there are many ways to use the Select API, including selecting specific fields, excluding fields, etc. To learn more, see the Select documentation.

Pagination

Disable Pagination if you can reliably predict the number of matched documents, such as when querying on a unique field.

const posts = await frogbot.find({
  collection: 'posts',
  overrideAccess: true,
  where: {
    slug: {
      equals: 'unique-post-slug',
    },
  },
  pagination: false, // Return all matched documents without pagination
})

Use in combination with limit: 1 for best performance when querying by unique fields.

To learn more, see the Pagination documentation.

Start here

Basics

Managing Data

Features

Ecosystem

Deployment

Querying your Documents

Operators

And / Or Logic

Nested properties

Writing Queries

Local API

GraphQL API

REST API

Performance

Indexes

Depth

Limit

Select

​Operators

​And / Or Logic

​Nested properties

​Writing Queries

​Local API

​GraphQL API

​REST API

​Performance

​Indexes

​Depth

​Limit

​Select

​Pagination

Operators

And / Or Logic

Nested properties

Writing Queries

Local API

GraphQL API

REST API

Performance

Indexes

Depth

Limit

Select

Pagination