Utilize the double parsing of JSON information (or opt for an alternative technique for dividing the data

Looking for the most effective approach to breaking down a large data object retrieved from AJAX. When sending just one part (like paths), I typically use JSON.parse(data). However, my goal is to split the object into individual blocks first and then parse something like JSON.parse(data['paths']).

Below is a snippet of the JSON data:

{
    "paths": {
        "type": "FeatureCollection",
        "features": [{
            // Path feature details
        }]
    },
    "beds": {
        "type": "FeatureCollection",
        "features": [{
            // Beds feature details
        }]
    }
}

Sample JavaScript code:

   $.ajax
    dataType: 'text'
    url: 'map.json'
    success: (data) ->

Rails code generating the call:

data = { buildings: @geopaths, lawns: @geobeds }

respond_to do |format|
  format.json { render json: data }
  format.html
end

UPDATE: I initially refrained from explaining my requirements in detail as it might complicate matters. Simply put - I gather data from a database and send it to JavaScript for mapping various layers. Each layer, such as paths or beds, is encoded as GeoJSON in Rails before being transmitted via AJAX commands to JavaScript. While handling single layers poses no issue, I now need to incorporate multiple layers on the map. Given that AJAX typically handles a single object, I merge path and bed data into one object. My challenge lies in separating and utilizing these different portions of the object dynamically.

Here is my graphical representation:

paths = bunch of GeoJSON data
beds = bunch of GeoJSON data
Use AJAX to transmit paths and beds to JavaScript
Create pathMarkers using JSON.parse for the paths data
Create bedMarkers using JSON.parse for the beds data

If required, I can create a sample and share it on bitbucket for better understanding.

Answer №1

If I understand correctly, your concern is about integrating different data layers into a geo library like Leaflet.js. In this case, it should be sufficient to make a single AJAX request as long as the JSON payload isn't too large and doesn't cause browser crashes.

Since you haven't shared much of your code, here's a basic example of how you can achieve this:

Firstly, create the map object: Things need to start somewhere, right?

const map = L.map(id).setView([1.2345, -1.2345], 10);

Initiate the AJAX request to retrieve the geoJSON file.

$.ajax({
  dataType: "json",
  url: '/json/lives/here.json',
  data: {} /* Add any additional parameters for the server here */,
  success: success
});

The main question now is: "How do I access each feature collection?"
The success or

done</code function will handle that once the data is received and ready to be added to the map.</p>

<p>When using jQuery's AJAX method with <code>dataType: 'json'
, the response automatically gets parsed. You can then access it like a regular JS object in the success callback function:

function success (data) {
    // The parsed JSON data is now a JS object.
    // Iterate over each key in the object and pass its data to L.geoJSON() to add it to the map.
    for (var geojsonFeatureCollection in data) {
        if (data.hasOwnProperty(geojsonFeatureCollection)) {
            L.geoJSON(geojsonFeatureCollection, {/* options */}).addTo(map);
        }
    }
}

In terms of AJAX and a single object: AJAX functions similarly to other browser requests.
While you send one request at a time, you receive one response from the server. However, the content of the response can vary widely. In your case, the response consists of a JSON text file that gets parsed into a JS object, allowing you to work with its different keys (beds, paths) and pass them to Leaflet's geoJSON method for rendering on the map.

Answer №2

I've encountered a similar issue in the past when dealing with large payloads on mobile devices (specifically iOS with Phonegap). One potential solution is to explore OboeJS, a library that can be found at .

This library allows for streaming of JSON requests, enabling you to process data in smaller chunks. It could potentially help address your problem.

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