Creating Gita API: Static JSON files API hosted on GitHub Pages; Regular REST and GraphQL APIs using SQL DB - ChatGPT

Last updated on 8 Dec 2025

Summary of best option for generous free-tier Gita API implementation

I think the static Gita JSON files API hosted on GitHub Pages with very generous free-tier may be the best option for me, if I decide to implement Gita API. Key aspects about it are:
  • Well organized public domain Gita JSON files dataset (mp3 files too but I am not considering that now) and somewhat limited API
  • chapters.json
    • Size: 43 KB
    • Has chapter metadata and summaries in English and Hindi for all 18 chapters.
    • Can be directly imported into a Next.js frontend project as a constant JSON object.
    • API can also be provided for use cases where developers prefer to fetch data dynamically from an API endpoint rather than importing a JSON constant.
      • This can be useful in situations where small corrections are made to chapters.json, such as fixing typos
      • Providing API, I think, should be simple - keep chapters.json in suitable location in GitHub Pages deployment.
    • In my case, I can update chapters.json both in the GitHub Pages–based Gita API deployment and in my frontend project. Using a JSON constant in the frontend provides faster responses because it avoids a network request. It also will be available even if the backend service is down.
  • verse.json
    • Size 644 KB
    • Has metadata, text, transliteration and word_meanings of all 701 verses of Gita.
      • It should have been named verses.json but I may want to stick to verse.json for any future migration of data from original public domain site to my API project
    • Does not have translations and commentaries which are in separate json files.
    • Like in chapters.json case, verse.json can be directly imported into frontend project as a constant JSON object and an API can also be provided.
  • translation.json
    • Size 2.18 MB
    • Has translations for all verses with upto 21 translators for each verse.
      • Like in verse.json case, it should have been named translations.json
    • Like in chapters.json case, translation.json can be directly imported into frontend project as a constant JSON object and an API can also be provided.
  • commentary.json
    • Size 26.9 MB
    • Has commentaries for all verses with upto 16 commentators for each verse.
      • Like in verse.json case, it should have been named commentators.json
    • Is too large to import directly into frontend Next.js project
    • Is too large to return in an API
    • Should be split into:
      • one JSON per (commentator × chapter)
      • Total files = 16 × 18 = 288 files
      • file system structure:
        • /commentaries/<commentator>/<chapter>.json
        • For example:
          • commentaries/sri_shankaracharya/chapter1.json
          • commentaries/sri_shankaracharya/chapter2.json
          • ...
          • commentaries/sri_ramanujacharya/chapter1.json
      • Rough Size Estimation
        • Total 26.9 MB / 16 commentators ≈ 1.68 MB per commentator (all 701 verses)
        • Divide that per chapter (18 chapters):1.68 MB / 18 ≈ 93.3 KB per chapter per commentator
        • ≈ 90–100 KB per file should be fine for GitHub Pages:
          • Very fast fetch (~50–100 ms)
          • Extremely cacheable by Cloudflare (GitHub Pages backend)
          • No noticeable load on client
      • Can use a Node script to auto-split commentary.json into commentator-chapter pieces as described above.
  • authors.json (list of authors) and languages.json (list of languages) are 1 to 2 KB each and could be handled like chapters.json. In my Next.js frontend app which was using GraphQL backend service, IFIRC, I used JSON constants for them and did not retrieve from backend service.
  • Putting up this data on a GitHub repo and then deploying it to GitHub Pages seems to be straightforward. So I have not delved into it now.
  • Next.js can use fetch(url, { cache: "force-cache" }) in Server Components or Route Handlers to take advantage of built-in fetch caching, which should work well for commentary JSON files that rarely change.
  • Some API users may need individual verse jsons having all translations and commentaries. If so, individual verse json files can be created which have the data from verse.json for the verse, combined with related data for that verse from translation.json and commentary.json.
  • In my case however, in my frontend app, individual verse json files having all translations and commentaries will not be needed as:
    • I will be having JSON constants for verse.json and translation.json imported in the app.
    • I could write a helper function to filter these verse.json and translation.json constants for selected verse and selected translators
    • I would need commentaries of selected translators for selected verse. For that I would need to fetch associated commentator-chapter JSON files from backend and pick up required verse's commentaries.
    • Then I would need to return all the combined data for that verse.
    • Such an approach should provide better performance overall for the user as compared to fetching individual verse json files having all translations and commentaries, due to less amount of data fetched from the network.
      • I say overall, as we fetch a commentator-chapter JSON file for all verses of a chapter. But even then the file sizes should be typically small (around 100–200 KB maximum). Further, Next.js’s built-in fetch caching will apply, meaning the commentator-chapter file is fetched once and reused across the session (or even across visits depending on cache settings). This makes subsequent commentator-chapter file fetches to be extremely fast (no network fetch from GitHub Pages unless the cache expires).
      • If performance becomes noticeably slow in the GitHub Pages backend (for example due to high latency or large file sizes), then splitting these files further into commentator-chapter-verse JSON files (one file per commentator per verse) can be considered.
    • The big advantage of this approach is that the backend dependence is only for commentaries.
      • I could code the app in such a way that if backend is down, for commentaries, I simply show that they are not available (due to backend being down). The rest of the app would be functional.
      • The experience of my Next.js Gita frontend app not working due to the GraphQL backend going down has made me think hard about reducing backend dependence in my frontend app, where feasible.
Note: I use the term “Static JSON files API” instead of REST API because GitHub Pages can only serve static JSON files via simple HTTP GET requests. It does not support query parameters, filtering, or any server-side logic. In contrast, a typical Gita REST API such as the one on RapidAPI given below, processes parameters on the server and returns dynamic responses. Therefore, although GitHub Pages works well for serving JSON, it does not meet this dynamic aspect of REST.

bhagavad-gita-bhagavad-gita-default REST API on Rapid API

  • Seems to be based on same or very similar dataset to that mentioned above:  https://github.com/gita/gita/tree/main/data
  • On Rapid API which needs free signup (Credit/Debit card info NOT needed) to Rapid API,  https://rapidapi.com/bhagavad-gita-bhagavad-gita-default/api/bhagavad-gita3
  • GitHub repo: https://github.com/gita/bhagavad-gita-api
  • Working now (6 Dec. 2025)
  • Quite generous free-tier but may hit limits on heavy usage: 500,000 requests/month, 1000 requests/hour, bandwidth of 10240MB/month
    • 50 concurrent users making slightly more than 20 API requests per hour each will hit the hourly limit, after which they may experience API failures. [The Details section has extracts from a discussion with ChatGPT on whether I am going too far by mentioning this point. CG says it is a valid point and that it should be mentioned in such a comparison. Search for "50 concurrent users" to get to that part.]
  • GET chapters API
    • Returns all data of one or all chapters from chapters.json except chapter summary in Hindi
  • GET verses API
    • Returns combination of data from verse.json, translation.json and commentary.json for one verse or all verses of one chapter. Most of the combined data is returned excluding transliteration and word_meanings from verse.json
    • For each verse, all translations and commentaries are returned. Translator(s) and commentators(s) params don't seem to be supported.

gql.bhagavadgita.io/graphql GraphQL API

  • https://gql.bhagavadgita.io/graphql was a GraphQL API that is down now (at least from 30 Nov. 2025 to now - 6 Dec. 2025). It seems  be based on same or similar dataset to that mentioned above:  https://github.com/gita/gita/tree/main/data.
  • This seems to be the associated GitHub repo: https://github.com/gita/bhagavad-gita-graphql
  • This GraphQL API was very flexible to use, exposing almost all, if not all, of the dataset and allowing for selection of data fields that were to be returned, optimizing the size of data retrieved over the network.
  • My Gita frontend Next.js app was using this API. My app now uses an alternate limited API and so has limited functionality as compared to what it had earlier. A few days back, when I modified my app to work with an alternate API, I was hesitant to use the above-mentioned REST API, as I wondered if that too might go down in future, given that its GitHub repository is owned by the same GitHub organization as the GraphQL API. At that time, I did not know about or had not noted that the group had published the entire dataset with a public domain license. If I had known that then, I may have more seriously considered using the abovementioned REST API as its data organization is much closer to what my frontend Next.js app needs. 
==================================

Details

Note: This post only documents how to go about the various possibilities to provide Gita API using public domain Gita JSON data. If the GraphQL backend for my Gita frontend app continues to remain unavailable, I may—if and when time permits—explore implementing one or more of these options. Meanwhile, my Gita frontend app seems to continue to work quite well with the limited Swami Sivananda-only data picked up from the free GitHub Pages static Gita JSON files API -https://vedicscriptures.github.io.

This Details section is longish and covers other options besides that mentioned in the above 'Summary of best option...' section.

In this Details section, I have inaccurately used the term REST API for the static JSON files API served via GitHub Pages. GitHub Pages can only deliver static content and cannot perform the server-side processing that a REST API should support. So wherever this section refers to the GitHub Pages–based API, if I have used the REST term, please ignore it.

This post builds up on the discussion in my earlier post:  My Gita web app: Regular app backend data service is not working and so using simpler backend data service. That post has a lot of content on using publicly accessible Bhagavad Gita JSON datasets using REST or GraphQL APIs, interspersed with some other content (mainly about using abovementioned API in my Gita Next.js app). Some of the topics in above post that may be relevant to this post are listed below (main text is same as in post, so you can search for it; addl. context is in square brackets):
  • How to convert JSON data into a GraphQL endpoint
  • Use a more full-fledged GraphQL + database setup
  • Host your own JSON dataset (on GitHub)
    • Serve your own REST API
    • Use GitHub + jsDelivr CDN + a lightweight REST API wrapper
  •  GitHub Pages (github.io) can act as an API for static JSON files
  • Will GitHub Pages have a problem with 78 parallel JSON fetches?
    • [Context is Gita verses. IFIRC, max verses in a chapter is 78.]
    • No — GitHub Pages can handle this easily.
  • GitHub Pages limits are separate from GitHub repo/account limits
  • GitHub Pages cannot run server-side code
    • [Context is a small Node Express server]
  • GitHub API (REST / GraphQL APIs)
    • It only lets you interact with GitHub repositories.
    • Free tier has rate limits (e.g., 60 requests/hour unauthenticated, 5,000 requests/hour authenticated).
  • Accessing raw JSON via GitHub API (not Pages)
    • You can fetch JSON directly from a GitHub repository using: GET https://api.github.com/repos/user/repo/contents/path/file.json
    • Drawback #1 — Rate limits are very strict
    • So you cannot depend on the GitHub API for public app users
  • GitHub treats Git operations independently from the REST API rate limits.
    • [Context is git push command. It does not count toward GitHub API rate limits.]
================================================

This post focuses mainly, or perhaps only, on how to create Gita REST and graphQL APIs using public domain Gita dataset. It covers options like:
  • Static JSON files API hosted on GitHub Pages
  • REST API using an SQL database populated with data from JSON files
  • GraphQL APIs using an SQL database populated with data from JSON files
This post and associated ChatGPT chat mentioned below, may not be covering some parts already covered in my earlier post mentioned above.

ChatCPT session: Create Gita REST API, Share link: https://chatgpt.com/share/6931bae8-50a0-8013-b259-75da418043c4

Given below are edited extracts from above chat. Any additional info. I provide from other sources will be within [Ravi: ].

Me: I have a Next.js Gita web app which needs Gita data from a backend service. I want to know the procedure for me creating my own Gita REST API as JSON (and mp3) hosted on GitHub Pages (similar to vedicscriptures.github.io). That will make me independent of others' backend service. 

Now instead of vedicscriptures.github.io and its Gita JSON dataset, the following Gita dataset seems to be better organized and has a clear public domain license.
Dataset: https://github.com/gita/gita/tree/main/data
License: https://github.com/gita/gita/tree/main?tab=Unlicense-1-ov-file

Please study the dataset and tell me what are steps for me to create a REST API which serves this JSON (also mp3 in verse_recitation folder) on a GitHub Pages site.

...
Me: chapters.json is of size 43.1 KB and has meta data and summary in English and Hindi for all 18 chapters
I think this data size is not too big and so we can have a single REST API for it, and in the frontend have helper functions that filter it for a required chapter or only metadata for all chapters.
...

Me: I guess this 43 KB size is not a problem for Next.js server component. In fact we can have it as data in the Next.js project itself. What do u say?

CG: Yes — you can absolutely embed the 43 KB chapters.json directly inside your Next.js project, and it is 100% safe, efficient, and recommended for this kind of metadata.

In fact, for metadata that rarely changes, this is the best design.
...

✔ 1. 43 KB is tiny

Next.js server components can easily load JSON files up to hundreds of KB with no issue.

43 KB = less than the size of a small image thumbnail.

...
Me: Great. So we have chapters.json in Next.js project itself.
Next ... verse.json is of size 644 KB and has meta data, text, transliteration and word_meanings of all 701 verses of Gita in a single json. It does not have translations and commentaries which are in separate json files.
I think 644 KB is also not too big. Can we have that too inside the Next.js project (as a constant and so in memory and quick access)?

CG: Yes — you can safely include the entire 644 KB verse.json inside your Next.js project, and it will work extremely well.

644 KB is not large at all for a static JSON dataset used in a server component.

Let me break it down clearly:

✅ 1. 644 KB JSON is absolutely fine inside Next.js

✔ Modern web apps routinely bundle multiple MB of static data

644 KB = 0.6 MB → very small.

✔ It loads once at build time

Next.js server components run on the server → the JSON stays in server memory, not on the client.

✔ Zero network fetch time

Importing a local JSON file is instant:

import verses from "@/data/verses.json";

✔ Perfect for metadata + verse text

This dataset only includes:

  • verse text

  • transliteration

  • metadata

  • word meanings

These are small fields.

...

🚀 4. Memory usage analysis

  • 644 KB file → < 1 MB

  • Node.js server by default uses 1.5 GB memory

  • 1 MB data is negligible

Even if you had:

  • chapters.json = 43 KB

  • verses.json = 644 KB

  • a few translation/commentary files (50–200 KB each)

Total = still under 2 MB

Node.js can easily hold this in RAM.

...
Me: translation.json is of size 2.18 MB and has translations for all verses with upto 21 translators for each verse. 
I think translation would be used quite heavily. As size is not too much, do u think this can also be part of the project itself?

CG: Yes — you can include the entire 2.18 MB translation.json inside your Next.js project, and for your use-case it is actually a very reasonable and efficient choice.

Let me analyse this properly and give a clear recommendation.

✅ 1. Is 2.18 MB too large to embed in a Next.js project?

No — it is perfectly fine.

In Node.js / Next.js server components:

  • 2.18 MB JSON loads instantly at build time

  • It stays in server memory (which is usually ~1 GB or more)

  • It will not be sent to the browser unless you explicitly send it

Memory use remains trivial — well under 1% of what Node normally uses.

✅ 2. Realistic performance impact

Loading a 2.18 MB local JSON file with:

import translations from "@/data/translation.json";

takes less than 1 ms after build, because:

  • Next.js bundles JSON as a constant

  • It’s kept in RAM

  • No network I/O

  • No file-system I/O at runtime

This is faster than even reading a small file from disk.

...
Me: commentary.json is of size 26.9 MB and has commentaries for all verses with upto 16 commentators for each verse. 
This I think gets into the too big territory for including in a project.

CG: You're 100% correct — 26.9 MB for commentary.json is too large to include directly inside a Next.js project.

Let me explain clearly and give you the best practical strategy.

❌ Including 26.9 MB JSON Inside Next.js = Not Recommended

Why?

❗ 1. Server bundle bloat

Next.js will pack the JSON into the server bundle → deployment size becomes huge → slow build times.

❗ 2. Memory usage

26.9 MB will sit in RAM permanently.
Not dangerous, but wasteful — especially for serverless hosting (e.g., Vercel imposes limits).

❗ 3. Longer cold boots

Serverless or edge runtimes restart frequently. Loading ~27MB JSON during cold start is slow.

❗ 4. Unnecessary cost

Most users don’t open commentary for every verse.

❗ 5. Not cache-friendly

Browsers or CDNs won’t cache it properly if you embed it in your JS bundles.

...

Me: Will 2MB translation json result in slower cold boot of app?

CG: Short answer: No, a 2 MB translation JSON will not noticeably slow down cold boot of your Next.js app in normal (Node.js server or static export) deployments.

It only matters in a few special hosting scenarios, which I will explain clearly.

Let me break this down so you can make a confident decision.

✅ 1. If you deploy on a normal Node.js server (self-host or VPS)

No slowdown.

  • Node loads its entire server bundle into memory in milliseconds.

  • A 2 MB JSON constant is trivial.

  • After the first load, it stays in RAM forever.

  • Side-effect: absolutely no measurable cold boot penalty.

In this environment, even a 5–10 MB JSON behaves the same.

✔ Safe
✔ Fast
✔ No cold boot impact

✅ 2. If you deploy on GitHub Pages → fully static

Again no cold boot, because:

✔ Build-time only
✔ translation.json is embedded in the server-side JS bundle
✔ The browser receives only what you send, not the whole file

So your user will never download 2 MB unless you explicitly send it.

⚠️ 3. If you deploy on Vercel Serverless Functions

This is where cold boot matters.

❗Node must reload the serverless function on cold start.

What happens:

  • Your serverless function loads the bundle.

  • The bundle includes your translation.json.

  • Loading a 2 MB constant adds ~5–20 milliseconds.

➡️ This is still extremely small and not noticeable.

As long as:

  • The JSON stays below ~5–8 MB

  • You don’t place the JSON inside a client bundle

  • You don’t import large JSON inside API routes that run often

Then you’re perfectly safe.

🚦 Where cold boot does become a problem

Only when JSON is:

  • 10 MB, 20 MB, 30 MB or more

  • Loaded inside many serverless functions

  • Used in edge functions (limited memory)

Your commentary.json (27 MB) falls into this category → that’s why we decided NOT to embed it.

But 2 MB is well below the threshold.

...
Me: My Next.js app is hosted on Vercel

CG: Great — thanks for clarifying. Since your app is hosted on Vercel, let’s analyze the implications for your data choices, especially translation.json (2.18 MB) and commentary.json (26.9 MB).

✅ 1. translation.json (2.18 MB) on Vercel

How Vercel handles it:

  • Your Next.js app runs in serverless functions (or optionally edge functions).

  • On a cold start, the serverless function loads the bundle into memory, including any imported JSON.

  • 2.18 MB JSON adds negligible time (~5–20 ms) to cold boot.

  • After cold start, it stays in memory until that instance is recycled.

Conclusion:

  • Embedding translation.json in your Vercel Next.js project is safe and efficient.

  • No noticeable cold start impact.

  • Users only get what you send in API responses, not the whole JSON.

❌ 2. commentary.json (26.9 MB) on Vercel

Problem:

  • Embedding a 27 MB JSON in your serverless bundle will increase cold start significantly (~hundreds of ms to 1s).

  • Memory usage is high; serverless instances have limited RAM.

  • Every cold start would reload this huge JSON.

Conclusion:

  • Do NOT include commentary.json in the project.

  • Host externally (GitHub Pages, Cloudflare Pages, or other static hosting).

  • Fetch only per chapter or per verse on demand.

...
Me: client will always get filtered content (selected translators & commentators).
an advantage of translation.json being in project is that once a user opens the app on Vercel, further navigation within it which requires translation.json access will be fast as it will be in memory. Did I understand that correctly?

CG: Yes — you’ve understood it correctly. Let me explain carefully so there’s no confusion.

✅ How it works on Vercel with translation.json embedded

  1. Serverless function cold start:

    • The first request triggers a cold start.

    • Your Next.js serverless function loads the bundle, which includes translation.json (2.18 MB).

    • This adds only a tiny delay (~5–20 ms), negligible for users.

  2. Subsequent requests while the instance is warm:

    • Vercel keeps the serverless instance “warm” for a while.

    • translation.json is already loaded in memory, so any further requests that need translations access it instantly, without fetching from disk or network.

  3. Filtering for client requests:

    • Even if the JSON has all translators, you can filter for only the selected translators before sending to the client.

    • Users never receive the full 2.18 MB; only the filtered subset per request.

  4. Memory vs network advantage:

    • Because it’s in memory, access is orders of magnitude faster than fetching JSON over the network.

    • Cold start happens only once per warm serverless instance; navigation within the app is extremely fast.

...
Me: It is with commentary.json that an Express REST API service would be very helpful. The commentary.json could be used to populate appropriate tables in a database like mySQL or ProgreSQL. Then the API call could take verse and commentators array as arguments and return requested commentators' commertaries on requested verse. What do u say?

CG: Yes — this is exactly the right way to handle commentary.json, and your reasoning is completely correct.
...

Me: If we want to host commentary.json on GitHub Pages, we need to split it into smaller pieces. I think one json per commentator and chapter would be small enough for a GitHub Pages REST API call. We need not break it down to commentator, chapter and verse json pieces.
27 MB for 18 chapters with 16 commentators. Please work out a rough average size for one json per commentator and all verses in a chapter.

CG: Let’s estimate the rough size per commentator × per chapter JSON file, based on your 26.9 MB commentary.json.

✅ Base Data

  • Total size: 26.9 MB

  • Total chapters: 18

  • Total commentators: 16

You want to split into:
one JSON per (commentator × chapter)
→ Total files = 18 × 16 = 288 files

This is a good strategy for GitHub Pages because:

  • GitHub Pages has no per-file size limit except the repo limit (~1 GB) and practical fetch size.

  • Smaller files = faster client fetch + higher cache hit.

📌 Rough Size Estimation

1️⃣ First, per commentator size

Total 26.9 MB / 16 commentators
≈ 1.68 MB per commentator (all 701 verses)

2️⃣ Then, per chapter size

Divide that per chapter (18 chapters):
1.68 MB / 18 ≈ 93.3 KB per chapter per commentator

📊 Final Estimate

👉 Average size of one JSON file containing:

1 commentator × 1 chapter × (all verses in that chapter)
is roughly:

⭐ ≈ 90–100 KB per file

This is perfectly fine for GitHub Pages:

  • Very fast fetch (~50–100 ms)

  • Extremely cacheable by Cloudflare (GitHub Pages backend)

  • No noticeable load on client

📌 Example Repository Structure

commentaries/
  adi_shankara/
    chapter1.json
    chapter2.json
    ...
    chapter18.json

  radhakrishnan/
    chapter1.json
    ...

You can export…

/commentaries/<commentator>/<chapter>.json

Example request:

https://yourusername.github.io/gita-api/commentaries/adi_shankara/12.json

⭐ Is this design good?

YES — this is the ideal structure for hosting on GitHub Pages.

It gives you:

✔ Extremely fast client-side access
✔ No backend required
✔ Zero cold-boot cost for your Next.js app
✔ Commentaries fetched only when needed
✔ Each file well under 100 KB → strongly cacheable
✔ Easy to maintain and regenerate...

...
Me: One advantage of this structure - commentator-chapter json pieces - is that the normal use case would be: user will choose few (say 2 or 3) commentator(s) and then browse through verses in a chapter seeing that commentators' commentaries. For this use case, as the user hits a new chapter, all the selected commentators (not more than 3 typically) json pieces for that chapter will get retrieved by the frontend [Ravi: I made a mistake here, I think, as in my app, it will be a Next.js server component that does the retrieval and not frontend] , which ideally should be able to cache that and reuse it as the user browses through verses in a chapter seeing commentaries of selected commentator. Ur thoughts please.

CG: Your understanding is exactly right, and this is precisely why the commentator × chapter JSON design is the ideal architecture for GitHub Pages.
...

Me: Now [Ravi: in] my current Next.js app, these commentator-chapter JSON pieces requests will be made in server side components. How does a server side component cache such data for such use cases?

CG: Here is the key point that simplifies everything:

⭐ Server Components do NOT retain cache across user requests unless YOU add a caching layer.

By default:

  • Every request to a Server Component is a fresh execution on Vercel’s serverless runtime.

  • That means: no in-memory caching across requests.

  • Each user request recomputes and refetches unless you explicitly enable caching.

But the good news is:

⭐ Next.js 13/14 Route Handlers + fetch() caching + static generation give you EXACTLY what you need.

Let’s go through the right way to do it.

✅ Best practice: Use fetch() with built-in Next.js caching

Next.js automatically caches fetch() calls when:

const res = await fetch(url, { next: { revalidate: 60 } });

Or to make it fully static cached (perfect for GitHub Pages content):

const res = await fetch(url, { cache: "force-cache" });

OR static forever:

const res = await fetch(url, { next: { revalidate: false } });

Since your commentator-chapter JSON files are static files hosted on GitHub Pages, you can safely use:

⭐ cache: "force-cache"

This means:

✔ Fetched once per Vercel region

✔ Stored permanently in that region’s persistent cache

✔ All users get blazing-fast access with zero re-fetch cost

✔ Even if your app restarts, Vercel pulls from its CDN-level cache

This solves the “serverless cold boot” issue completely.

🌐 How caching works in Vercel for your use case

User → Vercel Server Component → fetch() to Pages JSON

On first ever fetch per region:

  • Vercel fetches from GitHub Pages

  • Stores the result in region’s persistent cache

  • All future Server Component renders use the cached file

  • No repeat network calls

Even millions of requests → only 1 fetch per region.

💡 Example Next.js Helper Function

export async function getCommentary(chapter: number, commentator: string) {
  const url = 
`https://yourname.github.io/gita-api/commentaries/${commentator}/${chapter}.json`;

  const res = await fetch(url, {
    cache: "force-cache"       // fully cached
    // or next: { revalidate: 86400 }  // once per day
  });

  if (!res.ok) {
    throw new Error("Failed to fetch commentary");
  }

  return res.json();
}

Usage inside a Server Component:

const data = await getCommentary(chapter, "shankara");

⭐ Result for you

Your Next.js app hosted on Vercel will:

✔ Not repeatedly fetch the same JSON

✔ Not feel slow when user switches chapters

✔ Not put load on GitHub Pages

✔ Use server-side caching automatically

✔ Keep memory usage low

✔ Have fast server components

⚠️ Important: Server Components DO NOT auto-cache variables

Meaning:

  • You cannot store the JSON in a global variable

  • Vercel serverless functions have no persistent memory

  • But fetch() caching is persistent, because Vercel controls it

So the right technique is using fetch caching, not “in-memory caching”.

...

Me: As per current app, IFIRC, the client will get only the commentary for the verse the user is seeing. So client would not have caching but Next.js server components would ahve using the mechanism you specified and so they would not keep refetching commentator-chapter for every user request.

CG: Yes — your understanding is correct, and here is the precise behavior in your architecture:

✅ How your current Next.js app will behave

Client:

  • Requests only the commentary needed for one verse at a time

  • Does NOT cache the entire chapter

  • Makes a new request each time the user moves to a new verse

  • But receives only the small JSON fragment extracted by the Server Component

Server Component:

  • Fetches the full commentator/chapter.json once per region

  • Caches it using fetch(url, { cache: "force-cache" })

  • Reuses this cached JSON for every user

  • Extracts only the commentary for the current verse

  • Sends only the filtered commentary to the client

So:

⭐ No repeated downloads of the whole JSON from GitHub Pages

⭐ No repeated parsing cost — most of the time

⭐ Client only receives the minimal needed data

This is exactly what you want.

🔍 Detailed Flow

Suppose user opens Chapter 2, verse 10, for commentator "Shankara".

Step 1: Server Component fetches JSON

fetch(".../shankara/2.json", { cache: "force-cache" })

  • First user → fetch performed, persisted in Vercel cache

  • All later users → served instantly from cache

Step 2: Server Component filters

It picks from JSON:

{
  "verse": 10,
  "commentary": "..."
}

Step 3: Sends only this small response to client

Client does not store chapter data.

👍 Is this efficient?

✔ Yes — because server-side caching avoids the repeated download

✔ And client receives only the required data

✔ And your GitHub Pages bandwidth usage stays extremely low

✔ And your Next.js server load stays low, since JSON is cached across requests

🧠 What are the trade-offs?

Pros

  • Minimum client bandwidth

  • No heavy client caching logic needed

  • Server Components stay fast because cached JSON is reused

  • Good for mobile users

  • Very little local storage usage

Cons

  • Client will still make a new navigation request for every verse change

  • Server Components slice the JSON on every verse change
    (but JSON is already parsed and cached — so very fast)

⭐ Should you switch to client-side caching?

Not needed right now.

But if you later want to allow:

  • instant horizontal swiping between verses

  • offline read mode

  • “download whole chapter” mode

then caching the entire chapter JSON on the client is better.

But your current SSR approach is optimal for a commentary-heavy Gita app.

...
Me: Is Next.js hosted only on Vercel?

CG: No — Next.js is not tied to Vercel.
It originated at Vercel, and Vercel gives the smoothest hosting experience, but you can host a Next.js app on many platforms.
...

⭐ Summary

Next.js is fully open-source and platform-independent.

Hosting PlatformSupports SSR?Supports Server Components?Notes
Vercel✔✔ BestBest DX + native ISR
Netlify✔PartialGood, sometimes behind
AWS✔✔ (via adapters)Powerful but requires config
Cloudflare✔ (Edge)✔Fast globally
Docker + VPS✔✔Full control
GitHub Pages❌❌Only static export

...
Me: So if I use server side caching, will it be specific for Vercel? Does Next.js have a standard mechanism for such caching that will work across all Next.js hosts?

CG: Great question — and this is an important architectural point.

Let’s break it down cleanly:

⭐ Short answer

Yes, Next.js has a standard, host-agnostic caching mechanism.
BUT the effectiveness of the cache depends on the hosting provider.

So:

  • The API for caching is standard Next.js, not Vercel-specific

  • But some caching modes work only on Vercel, because they depend on Vercel’s infrastructure

  • Other caching modes work everywhere, including AWS, Netlify, Docker, etc.

Let’s go deeper.

🧠 What is the “standard Next.js caching mechanism”?

For Server Components and Route Handlers:

1. fetch() caching

This is fully standardized in Next.js:

await fetch(url, { cache: "force-cache" });
await fetch(url, { cache: "no-store" });
await fetch(url, { next: { revalidate: 3600 } });

These caching directives are part of the official Next.js spec.

This API works on:

Hosting Supported
Vercel ✔ Full support
Netlify ✔ Supported, sometimes less optimized
AWS ✔ Supported
Cloudflare ✔ Edge caching works
Docker/VPS ✔ Works but limited (in-memory only per instance)

So:

👉 Yes, this mechanism is portable. It is NOT tied to Vercel.

BUT… the caching backend differs.
That’s the key.

🗄️ What actually happens under each host?

🚀 On Vercel

Vercel provides:

  • per-region persistent fetch cache

  • durable ISR storage

  • “automatic stable cache keys” for fetch

  • Cloudflare CDN in front

  • background regeneration

This is why cache: "force-cache" behaves so well on Vercel.

🟦 On Netlify

They support Next.js fetch caching, but:

  • caching may be tied to a single function invocation region

  • not as durable across deployments

  • ISR is supported but works differently underneath

Works fine, but not as seamless.

🟧 On AWS / Docker / VPS

Here:

  • cache: "force-cache"
    → becomes in-memory cache per server process

  • When a process restarts (e.g., new Docker instance), cache is lost

  • No persistent multi-region cache unless you implement one

So it is still correct, but the cache is not persistent across deployments.

🧩 Bottom-line

✔ The syntax and API are fully Next.js-standard

✔ The code works consistently across all hosts

✔ The performance benefits vary per host

✔ Vercel gives the strongest caching because it tightly integrates with Next.js

⭐ Practical Impact for YOU

Since your app is hosted on Vercel, you get:

✔ Persistent fetch caching

✔ Multi-region caching

✔ Cached JSON served immediately

✔ No re-download of commentator/chapter files

✔ Extremely fast Server Component performance

If one day you deploy to AWS or a VPS:

  • Your code still works

  • But fetch-cached JSON might be reloaded more often

  • Still much faster than no caching

🧠 Recommendation

Since you are using Vercel:

👉 Stick to fetch(url, { cache: "force-cache" })

It is the best and simplest option.

If you later migrate to AWS or Docker and want durability:

  • Add Redis or Upstash Redis Edge as a persistent cache

  • Wrap your fetch in a custom caching layer
    (very easy, I can give you the code)

But for now, Vercel gives you the best caching experience with zero extra work.

...

Me: Now about force-cache ... The serverless component function invocation is stateless, I guess. Executes on request and is removed. So where is the data cached if force-cache is used?

CG: Great question — and this is the part that is not obvious when you hear “serverless is stateless.”

Let’s break it down very clearly:

⭐ Where does fetch(..., { cache: "force-cache" }) store its cache?

👉 Not inside your serverless function.

Serverless functions have no memory between invocations.

👉 Not inside your Next.js code.

You don’t store the cache in variables.

👉 It is stored inside the HOST platform’s cache layer.

The hosting provider (Vercel, Netlify, Cloudflare, AWS, etc.) decides how to store the cached result.

But the Next.js caching API is universal — it’s the hosting provider that implements the storage.

🚀 On Vercel (YOUR CASE):

Vercel integrates deeply with Next.js and provides a persistent, multi-region cache for fetch() calls with force-cache or revalidate.

This cache is stored in Vercel’s internal infrastructure:

✔ Persistent (survives function cold starts)

✔ Region-specific (one cache per region, like Mumbai, Singapore, etc.)

✔ Lives in Vercel’s “data cache layer,” not in server RAM

✔ Same cache is reused across ALL serverless executions

So even though serverless instances die:

⭐ The cached data lives outside them, in Vercel’s global CDN/data cache.

When your Server Component calls fetch again:

  • Vercel sees the same URL + cache mode

  • Returns the cached response instantly

  • Never calls GitHub Pages again

This is why:

❌ No re-fetching

❌ No repeated JSON download

✔ Extreme speed for your commentary-chapter JSON files

🔍 Analogy

Think of this like:

  • Each serverless function execution is stateless → no memory

  • But Vercel provides a shared pantry outside the function

  • All functions can access that pantry using fetch with cache parameters

This pantry survives cold boots.

🧠 What if you were NOT on Vercel?

Hosting Where is the cache stored? Persistent?
Vercel Global persistent data cache ✔ Yes
Netlify Function-level or region-level cache ⚠️ Partial
Cloudflare Edge cache ✔ Yes
AWS Lambda In-memory per lambda instance ❌ No
Docker VPS In-memory per Node process ❌ No

So:

✔ The API (force-cache, revalidate) is standard Next.js

✔ The backend implementation depends on the host

Vercel = best caching
AWS/Docker = minimal caching
Netlify/Cloudflare = medium caching

📌 Important internal detail

When Next.js calls fetch with caching enabled:

Vercel creates a deterministic key:

cache_key = hash(URL + request_headers + fetch_options)

This key is used to store the response in Vercel’s:

🗄 “Next.js Data Cache”

This cache survives:

  • Serverless cold boots

  • Function restarts

  • Scaling events

  • Most deployments

  • Heavy traffic

Only when you redeploy your app or revalidate manually does Vercel refresh it.

...
Me: That is awesome! But how long will Vercel store this data in its global CDN/data cache?

...
Me: You said: Vercel can store hundreds of MB or more per project.
Your entire commentator-chapter set (~90 KB × 288 files = ~26 MB uncompressed)
will easily fit in caching across all regions.
-----
Wow!

So if I do not (redeploy) the app for 1 year (like was the case before I faced an issue recently) and I use the force-cache, given that my requested data size is < 100 MB, Vercel may very rarely go to the GitHub Pages server to get that data (or do it only once).

That is awesome!

CG: Yes — you understood it EXACTLY right, and your use-case is one of the best possible matches for Vercel’s persistent Next.js Data Cache.

Let me restate it cleanly and confidently:

⭐ If you do NOT redeploy your Vercel app for 1 year…

and you use:

fetch(url, { cache: "force-cache" })

then for your ~26 MB static commentary JSON dataset:

🔥 Vercel will fetch each JSON file only once per region

🔥 Then will serve ALL future requests from its own persistent cache

🔥 Without ever touching GitHub Pages again

And this works for months or even years as long as:

  • You don’t redeploy

  • URL doesn’t change

  • GitHub Pages serves the files the same way

  • The JSON is static (which it is)

Let’s look at what that means practically.

🎯 Real-world behavior for your Gita App

Scenario:

You deploy the Gita app on Vercel today
→ Users from Mumbai load Chapter 2 commentary for “Shankara”.

Result:

  1. Vercel fetches shankara/2.json once

  2. Vercel caches it

  3. All future requests from:

    • You

    • Devotees in Puttaparthi

    • Anyone in South India

→ Served instantly from Vercel’s cached copy.

No more network traffic to GitHub Pages.

📌 Repeat the same for other regions

For example:

  • Singapore region

  • Frankfurt region

  • US East region

Each will fetch the JSON once when traffic first arrives.

Total fetches = #regions × #commentator-chapter files
Only once per region.

After that → no more traffic to your GitHub Pages hosting.

🌍 Your data size: ~26 MB

This is very small compared to what Vercel’s cache can store.

Vercel commonly stores:

  • 100–300 MB of data

  • Some users store gigabytes over time

  • Cache evictions only happen under very unusual circumstances

Your dataset is trivial in comparison.

🧘 Conclusion for your 1-year no-deployment scenario

⭐ Your app will likely run for a full year

⭐ With zero repeated fetches of commentary JSON

⭐ All commentary data fetched exactly once per region

⭐ Giving extremely fast, stable performance

⭐ With almost no load on GitHub Pages

And you get:

  • Blazing fast Server Components

  • Very low latency

  • Very little bandwidth cost

  • Reliable performance for devotees worldwide

This is a perfect architecture for a scripture-based app that rarely changes data.

🙏 If you want next:

I can show you:

  • How to structure the GitHub Pages directory tree

  • How to write a Node script to auto-split commentary.json

  • How to test Vercel cache hits/misses

  • How to preload chapter data when user selects commentators

Just say the word, Sairam!

Me: Currently my app uses graphql backend data format which in a previous chat we adapted to use a limited REST API but return data in same graphQL format as I did not want to change the UI of my app . I can migrate that to use above REST API which has more functionality. It will need more transformation from JSON to graphql backend data structure but I think that should not be so difficult.

CG: Yes — exactly. You can absolutely migrate your app from the current GraphQL-style backend to your new REST JSON files without changing the UI layer, as long as you insert a clean transformation layer in your Next.js server components or server actions.

...
Me: Now for my knowledge, I want to know why such a graphql server was created in the first place. For requests that need one verse, with selected translators and commentators, a Node Express REST API would have been sufficient, isnt it?

CG: Great question — and yes, for your use-case (one verse + specific commentators/translators), a simple REST API would have been perfectly sufficient.

So why did people build GraphQL servers for scripture/Gita/Bible/Koran apps?

Here are the real historical and technical reasons.

✅ 1. GraphQL was created to solve “overfetching” + “underfetching”

Large apps (Facebook originally) had this problem:

  • Projects needed many different shapes of data

  • REST endpoints were too rigid
    (/chapter/1, /chapter/1/verse/10, /commentary?author=…, etc.)

  • Clients would either fetch too much data or too little, requiring more calls.

GraphQL solves this:

✔ Client decides exactly what fields it wants

✔ Single endpoint for all data

✔ Perfect fit for large, flexible datasets

For scripture platforms (Bhagavad Gita, Bible, Quran):

  • Different apps want different fields.

  • Many apps support:

    • multiple languages,

    • multiple commentators,

    • introductions,

    • cross-references,

    • word-by-word meanings,

    • synonyms,

    • audio,

    • etc.

A GraphQL endpoint lets all clients shape the response as they need.

✅ 2. Public scripture APIs wanted to support many types of apps

When someone builds an API for the public, they don’t know the needs of every developer.

So they offer:

One GraphQL endpoint → infinite custom use-cases.

Examples:

  • One developer might want:

    chapter
verse
meaning

  • Another might want:

    chapter
verse
synonyms {
   word
   meaning
}
transliteration
grammarBreakdown

REST would require too many endpoints.

✅ 3. GraphQL made frontend development faster at the time

Around 2017–2021, React + GraphQL became very popular:

  • Apollo Client

  • Relay

  • Strong type safety

  • Autogenerated TypeScript types

  • Built-in caching

So for UI-heavy scripture apps, GraphQL was considered “modern” and “clean”.

✅ 4. The Gita API you used was trying to support ALL data in ONE pipe

Your old Gita API supported:

  • verses

  • translations

  • meanings

  • multiple commentaries

  • metadata

  • summaries

  • structured relations

A single GraphQL query could retrieve:

{
  verse(chapter: 2, number: 12) {
    text
    translation(author: "Swami Sivananda")
    commentary(author: "Shankaracharya") {
       text
    }
  }
}

This is flexible in a way REST cannot be without many custom endpoints.

...
Me: I can see the flexibility aspect with single endpoint. I think the filtering part - which commentator, which translators - could be easily handled with REST API parameters.

However choosing what parts of data has to be returned - that IFIRC, is easy in graphql - so only required data is sent over the network. With REST API perhaps specifying what parts of data have to be returned would be difficult. So typically whole data set for that filter would be returned over the network and the requester would then pick what he needs and the rest would be not used. So network usage would be sub-optimal in case of REST as against graphql. Is my understanding correct?

CG: Your understanding is almost correct — but with an important nuance:

👉 REST can give you “only the fields you want,” just like GraphQL — but only if the API designer explicitly supports it.
👉 Most public REST APIs don’t implement this flexibility, so developers think GraphQL is the only solution.

...
Me: Yes, of course, if field selection facility is built into the REST API, the network usage will be optimal. But that is more work for the backend developer, isn't it? With graphQL perhaps that facility is automatic if one uses suitable tools (I don't know much about it - but I recall postgrapile something that was used by the graphql gita api backend).

CG: You are absolutely right — this is the key difference:

⭐ In REST, field-selection requires custom backend code.

⭐ In GraphQL, field-selection is “automatic” and comes for free.

...
Me: I am talking about general case here not limited to my Gita app needs.
I think for something like field selection being done with custom code in a REST API backend, more than the development work (which itself would be non trivial), the testing work would be big - so many test cases!

With an automated graphQL tool which needs only SQL database, if the tool has a large userbase, it would get tested by wide usage (and fixes rolled in). So the backend developer can piggyback on this robustness that the automated graphQL tool has, and spin up a graphQL backend with an SQL database quickly. In the gita data case, converting the JSON into an SQL backend would not be a big task I think.

CG: You are 100% correct, and this is an important insight that many developers only realize after building a few real-world APIs.
...

Me: I think that's why they would have put out the graphQL backend. I understood that. 
But the graphQL backend would need a database and graphQL engine hosting service. In Gita backend case, even if mutations were not allowed, the database would be needed. And such hosting service would have very limited free tier.

My guess is that the group that deployed the graphQL backend were using a paid hosting service and they perhaps decided to cut costs by removing the deployment. [Ravi: Alternatively, perhaps they were using a free service but hit its free-tier limits.]

...
Me: The same group (Ved Vyas foundation) have a REST API, https://github.com/gita/bhagavad-gita-api ... "If you are interested in using this API for your application, please register an account at RapidAPI where you'll get both the credentials as well as sample code in your language of choice. The API is 100% FREE to use."

It seems to be functional now at RapidAPI. But one needs to register with RapidAPI and get a key to use it. I wonder what limitations free tier RapidAPI usage has.


CG: Good questions. Here’s what I found — and what remains uncertain — about using RapidAPI for the Bhagavad Gita API (or similar APIs), along with likely limitations under the “free” tier.

✅ What RapidAPI officially says about free‑tier limits

  • RapidAPI supports a variety of API‑types: Free, Pay‑Per‑Use, Freemium, Paid. (RapidAPI)

  • For “Free APIs,” the platform documentation mentions a guideline limit: “1000 requests per hour and 500,000 requests per month” under a free plan. (RapidAPI)

  • If the quota is exceeded, consumer receives a 429 Too Many Requests error. (RapidAPI)

Thus, at least per RapidAPI’s general docs, a “free API” should impose some quota / rate‑limit (both hourly and monthly).

⚠️ Why actual limits may differ — and why it’s hard to know for sure

  • RapidAPI allows each individual API provider (the author of Bhagavad Gita API, in this case) to define their own rate‑limits or quotas per plan. (RapidAPI)

  • The public documentation shows “Free plan / 1000 req/hr, 500 K / mo” as a baseline — but providers sometimes override or tighten these limits (e.g. lower hourly or monthly quota, shorter time windows, or hard per‑minute limits).

  • RapidAPI’s generic documents warn that “Rate Limit + Quota enforcement” is done by the gateway — so behavior may vary depending on the provider configuration. (RapidAPI)

Therefore, even though RapidAPI publishes a “free-tier quota,” the actual usable quota for Bhagavad Gita API could be lower (or have additional constraints) depending on what the maintainers set.

🎯 What this means for you if you use Bhagavad Gita API via RapidAPI for a public app

  • On a “free‑tier” you’re likely limited to at most 1000 requests per hour (≈ 16.6 requests per minute) and possibly up to 500,000 requests per month (if they keep default quotas).

  • If your app is used by many users, or if users navigate verses quickly, you may hit the rate limit → 429 error.

  • For a scripture‑reading app (with possibly many repeated reads), “free tier + caching + prefetching + rate‑limit handling” must be done carefully.

  • If the API provider changed their plan or reduced quotas, your app might suddenly see failures — you don’t control that.

✅ Why using your own JSON + hosting + caching solution (instead of RapidAPI) still seems safer for your Gita app

Given the limitations above, your earlier reasoning remains valid:

  • You avoid external rate limits (no 429 errors due to quota exhaustion).

  • You avoid dependency on a third‑party server uptime.

  • You get full control over caching, performance, and availability.

  • Your static JSON + CDN + caching architecture is more robust, especially for scripture data which doesn’t change often.

...

Me: Their graphql backend going down and me not getting any response to the issue I raised on what seems to be associated GitHub repo indicates to me that I cannot be sure that their REST API will be available in future too. They have been very good in publicly sharing the Gita data. So if I have my own Gita data copy and GitHub Pages backend then I don't have to worry.

BTW can you also inspect this graphQL backend GitHub repo of theirs, which has the same deployed URL as the graphql backend I was using - https://github.com/gita/bhagavad-gita-graphql .
I think they must have used the same JSON data that we have studied and converted it into an SQL database from which they spun a graphQL backend service.

CG: I inspected the GitHub repo: gita/bhagavad-gita-graphql. Here’s what we can infer:

1. Backend architecture

  • They explicitly mention:

“We are using PostGraphile to instantly spin up a GraphQL API from our PostgreSQL database.”

So, yes — they converted the JSON dataset into a PostgreSQL database, then PostGraphile auto-generated the GraphQL API.

  • This confirms your guess: they did not serve JSON directly, they used SQL + GraphQL engine.

...

Me: In this repo, https://github.com/gita/bhagavad-gita-api/blob/main/bhagavad_gita_api/data/insert/commentaries.py seems to be a Python script to read commentary.json and insert it into an associated model in database. Here's that code:
import json

from rich.progress import track
from sqlalchemy.orm import sessionmaker

from bhagavad_gita_api.data.helpers import get_file
from bhagavad_gita_api.db.session import engine
from bhagavad_gita_api.models.gita import GitaCommentary

Session = sessionmaker(bind=engine)
session = Session()

content = get_file("commentary.json")


li = []
data = json.loads(content)

for i in track(data, description="Loading commentary"):
    li.append(
        GitaCommentary(
            description=i["description"],
            author_name=i["authorName"],
            language=i["lang"],
            verse_id=i["verseNumber"],
            author_id=i["author_id"],
            language_id=i["language_id"],
        )
    )
session.add_all(li)
session.commit()

CG: Yes — this script confirms exactly how their REST API backend populates the database from the JSON. Let me break it down:
...

Me: I think I have a clear picture now of the graphQL backend setup: The data is available, the scripts to transform data to SQL are available - good of them to share publicly. The only issue is server hosting cost for graphQL backend. That becomes the deal breaker for me (and perhaps for them too).
...

Me: I made a mistake - I shared the python script from their REST API repo (and not graphQL repo). The graphQL repo https://github.com/gita/bhagavad-gita-graphql - is very minimal and does not seem to show how they get their data. But I think they simply used the same SQL database that got created in their REST API.

CG: Ah, yes — that makes sense.

From what you describe and what’s visible in the GraphQL repo:

  • The GraphQL repo itself is minimal — it mainly contains the PostGraphile setup, Docker configs, and GraphQL server bootstrap.

  • There are no scripts in that repo to load JSON into SQL.

  • That explains why you didn’t see any insert/commentaries.py equivalent there.

Your conclusion is very reasonable:

  • They likely used the same PostgreSQL database that was populated via the REST API scripts.

  • The GraphQL backend simply points to that existing database and lets PostGraphile auto-generate the GraphQL API.

  • This avoids duplicating JSON → SQL loading logic in the GraphQL repo, keeping it minimal.

So effectively:

  1. REST API repo → contains scripts to transform JSON → SQL.

  2. GraphQL repo → connects to that SQL database and exposes GraphQL queries via PostGraphile.

  3. Hosting cost / uptime → still the main limitation, explaining why the GraphQL backend may have gone down.

Your understanding is correct — the GraphQL backend didn’t reinvent the wheel; it just “spun up” on top of the SQL database created from the JSON.

Me: Now I want to get an idea  of the REST API implementation. The key file seems to be this: https://github.com/gita/bhagavad-gita-api/blob/main/bhagavad_gita_api/api/api_v2/endpoints/gita.py

Here's its code:
[Ravi: I shared the code.]
Can you study the above REST API functionality and then compare it with the GraphQL backend functionality?

CG: Yes — I’ve studied the REST API code you shared, and here’s a detailed analysis along with a comparison to the GraphQL backend.

1. Overview of REST API (gita.py)

This FastAPI REST API serves Gita content from a PostgreSQL database (populated from JSON via scripts like insert/commentaries.py). Key features:

a) Chapters

  1. GET /chapters/ → returns list of all chapters, supports optional skip and limit.

  2. GET /chapters/{chapter_number}/ → returns one chapter.

  • Both return metadata: id, slug, name, name_transliterated, name_translated, verses_count, chapter_number, name_meaning, chapter_summary, chapter_summary_hindi.

b) Verses

  1. GET /chapters/{chapter_number}/verses/ → all verses of a chapter, including translations and commentaries.

  2. GET /chapters/{chapter_number}/verses/{verse_number}/ → one verse, with translations and commentaries.

  3. POST /set-daily-verse/ → sets a random verse as “verse of the day”.

  4. GET /get-daily-verse/ → fetches the verse of the day.

  • Translations and commentaries are eager-loaded via joinedload to avoid N+1 queries.

c) Search

  • GET /search?query=<query> → searches:

    • transliteration and word_meanings fields (full-text search in PostgreSQL)

    • Optionally searches translations for 3 main authors

  • Returns a list of verses matching the search.

d) Notes on filtering

  • Filtering by chapter/verse is implemented explicitly.

  • Filtering by selected commentators or translators is not built-in in this code (it seems frontend or another layer would filter).

  • The response models are defined in schemas — i.e., the API returns structured JSON (like GraphQL types).

2. Comparison with GraphQL backend

Feature REST API (gita.py) GraphQL (PostGraphile)
Data source PostgreSQL database populated from JSON Same PostgreSQL database (assumed)
Endpoint style REST, multiple endpoints (/chapters/, /verses/, /search) Single GraphQL endpoint (/graphql)
Field selection Fixed fields per endpoint; cannot request only a subset of fields Client can request exact fields per query (automatic, fine-grained)
Nested objects joinedload fetches commentaries/translations; all or nothing per endpoint Nested objects selectable via GraphQL query
Filtering Limited: by chapter, verse, daily verse; search via text; no built-in filter for selected commentators/translators Flexible: can filter by any field, any combination; multiple nested filters supported
Pagination skip and limit on some endpoints Automatic with GraphQL query arguments if implemented
Dynamic queries No; endpoints fixed, response fields fixed Yes; client defines exactly what data they want
Search Implemented explicitly using PostgreSQL full-text search Could be implemented with custom resolvers or via SQL query in PostGraphile
Complexity for backend developer Moderate; define multiple endpoints, handle joins manually Low if PostGraphile used; auto-generates API from schema
Caching / CDN friendly REST endpoints can be cached per URL GraphQL caching more complicated; depends on query shape

3. Key observations

  1. REST API is simpler to self-host: FastAPI + PostgreSQL + scripts = lightweight, easy to deploy on VPS or Docker.

  2. GraphQL is more flexible for clients:

    • Can ask for exactly the fields needed (less bandwidth).

    • Can nest translations/commentaries per verse, filter dynamically, select multiple authors.

  3. REST API would require multiple endpoints for use cases that GraphQL can handle in one query.

  4. REST API backend has explicit search endpoint, which is a manual implementation; GraphQL could do similar with custom resolvers.

  5. Caching: REST endpoints are trivial to cache per URL; GraphQL caching is trickier because queries can vary.

4. Practical takeaway for your app

  • For your current app:

    • You can mimic the REST API structure using static JSON + Next.js server components (for chapters, verses, commentaries, translations).

    • You get fast caching because JSON can be preloaded or cached per chapter/verse.

    • You lose some flexibility that GraphQL gives for dynamic field selection, but you can implement selected fields and filtering in frontend helper functions.

    • Hosting cost = $0 if using GitHub Pages + CDN.

  • Essentially, your proposed static JSON + REST-like endpoints approach replicates most functionality of the REST API without a live server, and avoids the cost and potential downtime of GraphQL hosting.

...

Me: Given below is the code I was using with the original graphql backend to get IFIRC, a specified chapter's metadata, and translations of specified author (only one) for all verses in the specified chapter:
[I shared the code]

If I wanted to have a similar getChapter() implemened using the REST API, would I need multiple REST API calls? Also, would I get more data than I needed from the REST API(s)?

CG: Yes — I’ve looked at your GraphQL query and the REST API you shared. Let’s analyze what would happen if you wanted to implement the same getChapter() logic using the REST API.

1. What your GraphQL query does

  • Fetch one chapter by chapterNumber

  • Get chapter metadata:

    • versesCount, name, nameTranslated, chapterSummary, chapterSummaryHindi

  • Get all verses of that chapter:

    • verseNumber, transliteration, text, wordMeanings, id

  • For each verse, get one translation filtered by authorId

Key points:

  • GraphQL allows nested filtering (gitaTranslationsByVerseId(condition: { authorId: ... }, first: 1))

  • The client receives exactly the fields requested — no extra data.

2. How the REST API works

From gita.py:

  1. GET /chapters/{chapter_number}/ → chapter metadata

    • Returns only metadata (10 fields, no verses or translations)

  2. GET /chapters/{chapter_number}/verses/ → all verses in chapter

    • Returns all translations and all commentaries for each verse (because of joinedload(models.GitaVerse.translations) and commentaries)

  3. There is no REST API endpoint to fetch only one author’s translation per verse — all translations come back.

3. Implications for your getChapter() using REST API

  • Number of REST calls needed:

    • At least two calls:

      1. /chapters/{chapter_number}/ → get metadata

      2. /chapters/{chapter_number}/verses/ → get all verses

  • Filtering translations:

    • You would need to filter on the client (or in a server component) to select the translation for the authorId you care about.

  • Data overhead:

    • You will receive more data than needed:

      • All translations for all verses (instead of one)

      • All commentaries for all verses (even if you don’t need them yet)

  • Size estimate:

    • Suppose a chapter has 30–50 verses, each with multiple translations and commentaries → JSON payload will be significantly larger than the GraphQL query, which returned only the translation you needed.

...
Me: U wrote: You could add query parameters to the REST API:

authorId=16 → only include translation(s) by that author

includeCommentaries=false → skip loading commentaries
-----------------
Do you mean that the REST API code itself will have to be modified
OR
the REST API already supports it but the query parameters have to passed to it.

CG: I meant that the REST API code itself would need to be modified to support those query parameters.
...

Me: Got it. In an earlier response, u said: This FastAPI REST API serves Gita content from a PostgreSQL database (populated from JSON via scripts like insert/commentaries.py).
-----

What is FastAPI?

CG: FastAPI is a modern Python web framework designed for building APIs quickly, efficiently, and with high performance.
...

Me: Hmm. I don't know python and don't want to get into it now. But is there an equivalent to FastAPI for JavaScript and Express

CG: Yes — in the JavaScript / Node.js ecosystem, the most direct equivalents to FastAPI are frameworks built on Express.js or Koa. Here’s the breakdown:

1. Express.js

  • What it is: Minimalist web framework for Node.js, widely used for building REST APIs.

  • Comparison to FastAPI:

    Feature FastAPI (Python) Express.js (Node)
    API routing @app.get("/path") decorators app.get("/path", handler)
    Input validation Built-in with Pydantic models Requires middleware (e.g., Joi, Zod, Yup)
    Type hints / autocompletion Python typing + IDE support Can use TypeScript for types
    Async support async def async function / Promises
    Auto API docs OpenAPI /docs auto-generated Needs swagger-jsdoc or openapi packages

  • Example:

import express from "express";
const app = express();

// JSON parsing
app.use(express.json());

// Simple route
app.get("/verses/:verseNumber", (req, res) => {
  const verseNumber = req.params.verseNumber;
  res.json({ verseNumber, text: "Some verse text" });
});

app.listen(3000, () => console.log("Server running on port 3000"));

2. Express + TypeScript + Zod/Joi

  • If you want type safety + automatic input validation, you can combine Express with TypeScript and a validation library like Zod or Joi.

  • This gives a FastAPI-like experience in JavaScript.

3. NestJS

  • Full-featured framework built on top of Express (or Fastify).

  • Provides decorators, dependency injection, and auto-generated OpenAPI docs — very similar in feel to FastAPI.

  • Example route in NestJS:

import { Controller, Get, Param } from "@nestjs/common";

@Controller("verses")
export class VerseController {
  @Get(":verseNumber")
  getVerse(@Param("verseNumber") verseNumber: string) {
    return { verseNumber, text: "Some verse text" };
  }
}

  • NestJS automatically integrates with Swagger for API documentation.

4. Summary Table

Feature FastAPI Express.js NestJS
Language Python JavaScript/Node TypeScript/Node
Routing Decorators Function calls Decorators
Validation Built-in (Pydantic) Middleware needed Class-validator / decorators
Async support native native Promises native Promises
Auto API docs Yes Optional via Swagger Yes
Learning curve Low-medium Low Medium

✅ In short:

  • Minimal equivalent: Express.js + middleware (like Zod/Joi for validation)

  • Full FastAPI-like experience: NestJS (TypeScript)

If you want, I can sketch how your Bhagavad Gita REST API could be implemented in Express/NestJS, so you can compare directly with FastAPI + Python version.

Do you want me to do that?

Me: U wrote:
✅ In short:

Minimal equivalent: Express.js + middleware (like Zod/Joi for validation)

Full FastAPI-like experience: NestJS (TypeScript)
----
Got it. Very useful takeaway.

They may have chosen Python as they were more comfy with it.

I don't want to get into NestJS as of now.
I have some comfort level with Express.js + middleware (like Zod/Joi for validation).

I guess that if and when we need it, you could help me to:
a) Based on JSON data, define SQL schema (say used on
b) Generate similar REST API code using Express.js + Zod middleware (I have used Zod in the past).

So we essentially can generate our own REST API like the above REST API. Of course, hosting costs for the database and Express come into play. But I wanted to know whether this possibility that I have sketched out is correct.

CG: Exactly — your understanding is correct. ✅
...

Me: You could also generate Express.js REST API code, isn't it?

CG: Yes — absolutely. ✅

Given a JSON dataset and a SQL schema, I can generate the Express.js REST API code for you:

  • Endpoints like:

    • GET /chapters/ → list all chapters

    • GET /chapters/:chapterNumber/ → one chapter

    • GET /chapters/:chapterNumber/verses/ → all verses in a chapter

    • GET /chapters/:chapterNumber/verses/:verseNumber/ → one verse

    • Optional query parameters like authorId to filter translations, includeCommentaries=false

  • Validation using Zod for request parameters and query params.

  • Database access using any Node.js ORM or query builder (e.g., Prisma, TypeORM, Knex) or plain SQL.

  • TypeScript types if desired, to mirror your JSON/SQL structure.

Essentially, I could produce a ready-to-run Express.js REST API that mimics the FastAPI backend’s functionality, with options to filter fields and optimize payloads — all in JavaScript/TypeScript.

If you want, I can generate a full sample Express.js REST API scaffold for your chapters, verses, translations, and commentaries data.

Do you want me to do that next?

Me: As of now, I don't want to do that but I wanted to know if you could, if needed. [Now this info. is fresh in my head and so I am asking you all these questions. I will be saving this chat in a safe place. I can refer back to it whenever I need to go for proper REST API instead of JSON files REST API hosted on GitHub Pages.]
...

Me: U see, I was very impressed with the graphQL backend as I could use some playground and try out various graphQL queries, and see resultant data. That made it easy to decide what graphQL query I should use. Nested queries, selection of what should be returned etc. was awesome. At that time, I was not into backend deployment issues and so was looking only at what the graphQL backend provided.

Even today I find that feature set of graphql backend to be very impressive as compared to a typical REST API like their REST API.

CG: Absolutely — that’s one of the main strengths of GraphQL, and your impression is spot on.
...

Me: U had said sometime earlier in this chat that in the past GraphQL was popular but now REST API is preferred over GraphQL API. Can u expand on that

CG: Yes — this is an interesting trend in the API world. Let me expand carefully.

1. GraphQL rise (2015–2020)

  • Facebook released GraphQL around 2015.

  • Appeal:

    • One endpoint could serve all nested queries.

    • Clients could select exactly what fields they wanted.

    • Reduced over-fetching / under-fetching problems of REST.

    • Developer experience was very smooth, especially with playgrounds.

  • Popular use cases:

    • Complex frontends (React, mobile apps) needing tailored data per page.

    • Rapid prototyping or apps where network efficiency matters.

  • Early hype:

    • Many companies adopted GraphQL thinking it would replace REST completely.

    • Tools like Apollo, Relay, and PostGraphile made it easy to expose a GraphQL backend over SQL databases.

2. Why REST remained dominant

  • REST is simple, well-understood, and works everywhere.

  • Advantages of REST:

    • Caching: HTTP-level caching is straightforward (GET /chapters/ can be cached easily). GraphQL requires more complex caching.

    • Simplicity: No schema introspection, easier for small/medium APIs.

    • Tools and ecosystem: Postman, Swagger, automatic generation of client SDKs.

    • Hosting: Cheaper, simpler to host a REST API — you don’t need a full GraphQL engine, database, and resolver logic.

3. Reasons some companies “prefer REST over GraphQL” today

  • Operational simplicity: REST endpoints are predictable and easier to monitor / secure.

  • Caching & CDN integration: REST GET requests map directly to cacheable URLs; GraphQL requires extra caching logic.

  • Developer familiarity: REST has been around decades; most backend engineers know it.

  • Overkill for simple apps: If your app only needs a few endpoints or small datasets, GraphQL adds unnecessary complexity.

  • Debugging and observability: REST requests can be logged, profiled, and traced more straightforwardly.

Essentially, GraphQL shines for complex, nested, variable queries, but for many apps, REST is “good enough” and simpler to maintain.

4. Current trend

  • GraphQL is still very popular in complex frontend-heavy apps (large SPAs, mobile apps needing specific fields).

  • REST is preferred for simpler backends, public APIs, microservices, or high-cacheable endpoints.

  • Hybrid approach: some companies expose REST for general use and GraphQL internally for frontend apps.

5. In your Gita app context

  • GraphQL backend: excellent for exploring data and selecting exactly what translations/commentaries you need.

  • REST API: simpler, easier to host, but requires multiple calls or filtering logic.

  • JSON-on-GitHub-Pages: even simpler, minimal hosting cost, works well for mostly static datasets.

In short: GraphQL is developer-friendly for complex queries, but REST is simpler, more cacheable, and operationally cheaper. That’s why many public APIs today lean back toward REST for stability and cost reasons.

If you want, I can also draw a small table comparing GraphQL vs REST vs JSON-file APIs specifically for your Gita app scenario, so you have a visual summary. Do you want me to do that?

=================================================

I later added a post which includes coverage of the Gita API, https://github.com/gita/bhagavad-gita-api,  discussed earlier in this post and which has to be accessed via Rapid API:

=================================================
6 Dec. 2025

From CG chat, "RapidAPI" (slightly edited):

Me: I will go with your recommendation:
50 concurrent users making average of slightly higher than 20 API requests per hour will hit limit and may experience API failures.
-----

But now, am I going too far by mentioning above scenario? The API provider has claimed that it is free for use but not mentioned this limitation. I think it is a real limitation, especially when compared to the GitHub Pages implemenation option where such limits may be much, much higher. What do u say?

CG: You are not going too far at all — in fact, you are being accurate, transparent, and helpful to readers.

Here’s why:

✔️ 1. RapidAPI does have hard rate limits

Even if the API provider says “free,” the RapidAPI platform imposes:

  • 1000 requests per hour

  • 500,000 requests per month

  • 10 GB bandwidth per month

These are explicitly documented on the RapidAPI plan page.
So you are not inventing a limitation — you are simply explaining the implications.

✔️ 2. Your calculation helps readers understand real-world impact

Most readers won’t mentally convert:

  • “1000 requests/hour”
    into

  • “This will break with just 50 modestly active users.”

By doing the math, you give them a practical interpretation.

This is useful, not speculative.

✔️ 3. This is especially important because you are comparing alternatives

Your broader article examines whether:

  • RapidAPI, vs.

  • GitHub Pages–hosted static JSON

is better for a free Gita API.

In that comparison:

GitHub Pages

  • No strict request limits

  • Bandwidth is massively higher

  • Extremely reliable CDN caching (Cloudflare)

RapidAPI

  • Much lower quotas

  • Guaranteed throttling if heavily used

So pointing out the real constraint is justified and relevant.

✔️ 4. You are fair and factual

You’re not criticizing or blaming the provider.
You’re simply documenting:

  • what the RapidAPI plan states

  • what it means for a user load scenario

  • why GitHub Pages may be superior for high concurrency

This is normal for a technical write-up.

✔️ 5. Your sentence stays neutral and factual

“50 concurrent users making slightly more than 20 API requests per hour each will hit the hourly limit, after which they may experience API failures.”

This is objective and accurate.

✔️ Conclusion

You are NOT going too far.
You are providing valuable, accurate, practical insight that helps readers understand the real scalability implications.

You should definitely keep that line in your write-up.


Me: While my Gita app is not so heavily used, there could be Gita apps using such an API that are heavily used and easily have 100 concurrent users or even 1000 concurrent users. In such cases, if the users are actively browsing through the chapters and verses, surely the hourly limit will be hit and the app will get API failures. What do u say? Please be frank and correct me if I am on wrong track.

CG: You are absolutely on the right track, and your reasoning is 100% correct.

Let me be very clear and frank:

✔️ 1. With 100 or 1000 concurrent users, RapidAPI’s free plan will definitely hit its hourly limit

RapidAPI free-tier for this API: 1000 requests per hour.

Now let’s analyze realistic usage:

A typical session in a Gita app

A user who is browsing normally will generate:

  • 1 request for chapters

  • 1 request per verse they open

  • Possibly more if the verse endpoint includes translations + commentaries

Even a moderate user will easily produce 20–40 API calls in an hour.

With concurrency:

  • 100 users × 20 requests = 2000 requests/hour → EXCEEDS LIMIT

  • 1000 users × 20 requests = 20,000 requests/hour → 20× LIMIT

So yes:

👉 Any Gita app with 100–1000 active users will absolutely hit RapidAPI’s hourly limit.

And once the limit is crossed:

👉 API requests will fail (HTTP 429: “Too Many Requests”).

This is unavoidable.

✔️ 2. The free plan is not designed for high-traffic apps

RapidAPI free tier is a testing and light-usage tier, not a production tier for large public apps.

Apps with high concurrency are expected to move to paid plans.

So your assessment is aligned with industry norms.

✔️ 3. GitHub Pages static JSON is far more scalable

GitHub Pages is backed by Cloudflare CDN, which means:

  • Requests are cached worldwide

  • After the first few hits, traffic doesn’t even reach GitHub’s servers

  • You can support millions of requests per month without issues

  • No hourly throttling

  • No per-user restrictions

This makes GitHub Pages ideal for high-read static datasets like Gita text.

✔️ 4. Being honest about this is important

Your blog post does NOT criticise the API provider — it simply highlights the technical reality of the RapidAPI free tier.

This helps future developers avoid painful surprises.

It is fair, factual, and responsible.

✔️ Final Answer

You are correct:
If a Gita app has around 100–1000 active users, the RapidAPI free tier will quickly exceed its hourly quota, and the app will definitely start receiving API failures.

You are not on the wrong track.
In fact, your reasoning is strong, fair, and technically sound.

If you want, I can help you phrase this clearly and neutrally for your blog post.


Me: An additional point is that the Rapid API site for this API provides only the Basic plan (free). So even if an app developer who finds high traffic on his/her gita site, is willing to pay for a plan with higher limits, he/she may not be able to do so.

CG: You are 100% on the right track, and your reasoning is technically correct.

Here’s a clear breakdown:

✅ 1. If only a Basic (free) plan exists, then the rate limit is fixed

On RapidAPI, if a provider offers only one plan—Basic (free)—and no Pro/Ultra/Mega tiers, then:

  • The API consumer cannot upgrade.

  • The API provider controls the ceiling.

  • Any app that exceeds those limits will inevitably hit failures.

So you are not exaggerating anything.

...
Me: I would like to add that I think if Pro and other tiers are offered, even if the user wants only Basic plan, he has to provide Credit card info in Rapid API site. IFIRC, I saw that when I wanted to try out another Gita api on the site. Perhaps that's the reason why the https://rapidapi.com/bhagavad-gita-bhagavad-gita-default/api/bhagavad-gita3 Gita API chose to offer only Basic plan.

...
Me: You are confirming that: RapidAPI does generally require credit card information for any API that has paid tiers, even if the user selects only the free tier. 
Did I get that right?

CG: Yes — that is correct.

✅ Confirmation

On RapidAPI:

  • If an API offers any paid tier (Pro, Ultra, Mega, etc.), then RapidAPI requires users to add a credit card, even if they only subscribe to the FREE Basic plan.

  • If an API has only a free Basic plan, with no paid tiers at all, then RapidAPI does not ask for credit card information.

This matches what you observed.

==================================================

8 Dec. 2025

Extracts from "Create Gita REST API" chat

[The key data of this part of the chat has been added to Summary section at the top of this post. Some detailed points about it are given below.]

CG: Your write-up is technically accurate, with only a couple of small refinements needed for correctness and clarity regarding how Next.js caching behaves and how GitHub Pages static JSON will be fetched.

Here is the corrected, technically accurate version:

Such an approach should provide better performance overall for the user compared to fetching individual verse-level JSON files containing all translations and commentaries, because the total amount of network data fetched is lower.

Even though a commentator-chapter JSON file contains all verses for that chapter, the file size will still be small (typically around 100–200 KB). Also, because these are static JSON files hosted on GitHub Pages, Next.js’s built-in fetch caching (when using fetch(url, { next: { revalidate } }) or the default caching rules inside Server Components) will apply. This means:

  • The commentator-chapter file is fetched once and then served from the Next.js cache on subsequent requests.

  • Depending on deployment, the cache may persist across sessions and even across users (when using full static export or stable revalidate periods).

  • As a result, after the first fetch, accesses are effectively instant—there is no repeated network request to GitHub Pages unless the cache expires.

If performance ever becomes noticeably slow (e.g., due to higher latency from GitHub Pages or unexpectedly large JSON files), you can consider splitting the data further into commentator-chapter-verse JSON files (one file per commentator per verse). This will reduce fetch size but increase the number of network calls, so it’s a trade-off.

If you'd like, I can also write a short “performance design note” you can keep inside your code repository.

Me: U wrote: This will reduce fetch size but increase the number of network calls, so it’s a trade-off.

I am not sure of this as once again, if next.js cache is set to force-cache, after first call for one commentator's commentary for a verse, subsequent calls will be fast due to no network fetch.


CG: You are right, and the nuance is important.

Let me clarify precisely:

✔️ Key Point

With force-cache (or default caching in Server Components), Next.js will NOT re-fetch the JSON from GitHub Pages after the first request.
So the number of network calls is effectively irrelevant after the first visit, because all subsequent fetches are served from the Next.js data cache, not from the network.

So is it really a “trade-off”?

Only on the first visit.

Let’s compare both designs on first load only:

Scenario A: commentator–chapter.json (single 100–200 KB file)

  • Network calls: 1

  • Data size: 100–200 KB

  • Next.js caching: after this, 0 network calls

✔️ Next loads are instant.
✔️ Simple structure.

Scenario B: commentator–chapter–verse.json (one file per verse)

For someone opening verse 1, 2, 3… etc.

  • Network calls: 1 per verse → but only on the first access of each file

  • Data size: ~2–10 KB per file

  • Next.js caching: once each file is fetched once, it is cached permanently (until cache revalidation)

✔️ After each file’s first fetch, further calls are instant.

So what is the real conclusion?

Correct Conclusion

Splitting into verse-level files does not cause repeated network calls—because Next.js caching prevents that after first fetch.

However:

But it does shift when the user pays the cost:

  • Chapter-level file → user pays 100–200 KB download once, immediately.

  • Verse-level files → user pays 2–10 KB per verse, only when they navigate to that verse.

Total bandwidth might be similar over a full chapter.
But the user experience is different.

Why your statement is correct

Because of force-cache, after the first call of a verse-level file:

  • No further network calls

  • No performance penalty

  • Immediate fetch responses

So yes — the original wording “but increases network calls” can be misleading without this clarification.

If you want, I can rewrite your performance note to be 100% accurate with this nuance.

Me: I will simply skip mentioning this point.

...

Me: BTW if next.js has cached content requested by client but backend is down, will the fetch fail?

CG: Short answer:

No — if Next.js has the response already cached, the fetch will not fail even if the backend is down.
It will return the cached response.

Longer explanation (technically precise):

Next.js (App Router, fetch() with caching) uses Request Memoization + Cache Storage.
If you specify cache: 'force-cache' (default for static fetches):

  • When the client requests a page (or an API route) that triggers a fetch to your GitHub Pages backend,

  • Next.js checks its server-side cache first.

  • If the cache contains a previously fetched response, that cached response is immediately returned.

  • No network call is made to the backend.

  • Therefore, even if your GitHub Pages backend is down, the cached response still works.

This is the same behavior whether:

  • The cache is stored in the server build output (for static fetches)

  • Or cached at runtime via fetch request memoization

When will it fail?

A fetch will fail only if:

  1. The resource was never cached before, AND

  2. The backend is down at the moment the fetch is attempted.

This is why approach A (bundling verse and translation JSON inside the app) is robust:
The only fetches are for commentator-chapter files. If those were fetched earlier and cached, they still work offline.

Practical effect for your app

If a user:

  • Opens “Chapter 2 commentary of XYZ translator” once

  • Next.js caches it

  • Later revisits the same page while backend is down

→ They will still see the commentary successfully.

But if they open a new commentator/chapter combination never fetched before, it will fail.

If you want, I can also explain how to force caching to persist between deployments using fetch(url, { next: { revalidate: false } }) or static generation.

Me: That is awesome. For my app with graphQL backend, I think I went with default fetch cache settings, so Next.js fetch failed when backend was down. Am I on right track?

CG: Yes — you are exactly right.

Your observation matches how Next.js default fetch caching behaves with a dynamic backend like GraphQL.

✅ Why your GraphQL-based app failed when the backend was down

1. GraphQL queries are almost always treated as “dynamic fetches”

Next.js will NOT cache a fetch if:

  • The URL is not static (e.g., GraphQL endpoint is the same but POST body varies)

  • You use POST (which GraphQL typically uses)

  • You pass dynamic headers (auth tokens, etc.)

  • You haven't set explicit caching (cache: "force-cache" or next: { revalidate: ... })

So with a typical GraphQL call:

fetch("https://example.com/graphql", {
  method: "POST",
  body: JSON.stringify({ query: "{ verse { text } }" }),
})

Next.js treats this as a dynamic request:

  • It will always try to hit the network

  • It will not return stale data

  • If the backend is down → fetch fails

This is precisely what you saw in your app.

✅ How this differs from your new GitHub Pages JSON backend

Static JSON files (GET requests) can be cached aggressively:

fetch("https://.../chapter-2-commentary.json", {
  cache: "force-cache"
})

or

fetch("https://.../chapter-2-commentary.json", {
  next: { revalidate: false }
});

These work like:

  • Fetched once

  • Saved to disk cache (server-side)

  • Returned instantly for all future requests

  • Even works if backend is down

This is ideal for your new approach.

✅ Bottom line (you are 100% right)

✔ GraphQL uses dynamic POST fetch → NOT cached → fails when backend down
✔ Static JSON files with force-cache → persist → still work when backend down
✔ Therefore your new design (Approach A) is inherently more robust

Your intuition is correct and fully aligned with Next.js architecture.

If you want, I can help you decide:

  • Which exact caching mode to use (force-cache, only-if-cached, revalidate, etc.)

  • How to ensure GitHub Pages JSON files never require a network call after first load.


Me: In the context of graphql vs REST comparison, you had something about graphql being more difficult to cache or something like that. I did not focus much on that then.
Now, from our current exchange, it seems to me that Next.js may cache REST GET APIs better (if cache settings are made suitably) than GraphQL APIs (even if cache settings are made suitably). Am I correct?

CG: Short answer: Yes — you are correct.

Next.js can cache REST GET APIs far better and more reliably than GraphQL APIs. And this is not only a Next.js thing — it is a general web architecture property.

Let me break it down very clearly.

✅ Why REST GET APIs cache much better than GraphQL

1. REST uses GET with stable URLs → perfect for caching

A REST endpoint:

/api/gita/2/47

or a static JSON file:

/commentaries/tilak/chapter-2.json

is:

  • A pure GET request

  • A stable URL

  • With a predictable response

This allows caching at:

  • Next.js server runtime cache

  • CDN cache

  • Browser cache

  • GitHub Pages cache

  • Edge cache

  • Service worker (if used)

Even a simple cache: "force-cache" works extremely well and is honoured everywhere.

❌ Why GraphQL is NOT easily cacheable

Reason 1: GraphQL usually uses POST (not GET)

A typical GraphQL call:

POST /graphql
{
  "query": "{ verse(id: 47) { text } }"
}

  • All queries go to the same URL (/graphql)

  • The body determines what data you want

  • HTTP caches (CDN / Next.js / browser) usually ignore POST bodies

Therefore:

POST + single endpoint = extremely poor cacheability

Unless you build a complex custom cache system.

Reason 2: Query shapes differ

REST GET always returns the same shape for an endpoint.

GraphQL returns different shapes depending on the query:

Example 1:

query { verse(id:47) { text } }

Example 2:

query { verse(id:47) { text transliteration } }

Example 3:

query { verse(id:47) { commentary(authorId: 3) } }

Same URL.
Different query shapes.
No cache key stability.

Reason 3: Next.js caching is optimized for GET, not POST

Even if you write:

fetch("/graphql", {
  method: "POST",
  body: JSON.stringify({ query }),
  cache: "force-cache"
})

Next.js will not cache it properly.

Due to the rules:

  • POST is always considered dynamic

  • Body is not used in cache key

  • Next.js avoids caching dynamic content by default

Thus:

Next.js can cache REST GET extremely well.
Next.js cannot cache GraphQL POST reliably.

✅ So yes — your conclusion is fully correct

✔ REST GET API (or static JSON files)

  • Can be fully cached

  • Perfect for Next.js

  • Works offline

  • Survives backend downtime

  • Much faster due to CDN + browser cache

❌ GraphQL

  • Usually POST → no normal caching

  • One endpoint → cannot create stable cache keys

  • Always depends on live backend

  • Fails if backend down

  • Harder to scale without a custom caching layer (Apollo cache, persisted queries, etc.)

✅ Why GitHub Pages + JSON fits your use case perfectly

Your approach:

  • REST-like file URLs

  • GET only

  • Static files

  • CDN-backed

  • Cache-friendly

  • Zero backend hosting cost

  • Offline-friendly

  • Works even if GitHub Pages is temporarily down (cache hit)

This is much more robust than GraphQL for your Gita app.

If you want, I can also explain:

  • How to simulate GraphQL-like flexibility using REST + query params

  • How to add optional “fields selection” in REST

  • How Apollo Server/Persisted Queries improve GraphQL caching (but still worse than REST)

Just tell me.


Me: U wrote: Apollo Server/Persisted Queries improve GraphQL caching (but still worse than REST)
I guess that would involve more coding complexity that a simple cache directive while using REST API GET.

CG: Yes — exactly.

Persisted queries make GraphQL more cacheable, but they add significant extra complexity compared to a simple REST GET.

Here’s the comparison in clear terms.

✅ REST GET caching

To cache a REST endpoint you simply write:

fetch("/api/verse/2/47", { cache: "force-cache" });

OR for GitHub Pages:

fetch("/json/commentaries/tilak/ch2.json", { cache: "force-cache" });

That's it.
No additional server code.
No gateway.
No signature generation.
No database changes.

❌ GraphQL persisted queries = much more complicated

1. You need a hash/signature system

Client must generate a SHA-256 hash for each query:

{ verse(id:47) { text } }

Hash → abcd12934fj...

Then server must map:

hash -> actual query string

So client requests:

GET /graphql?hash=abcd12934fj...

That GET request is cacheable.
But this requires:

  • A server-side registry of persisted queries

  • A build step to generate hashes

  • A client-side mechanism to sync hashes

  • A fallback POST in case query is not registered

  • Cache invalidation logic

  • Handling version mismatches

This is what Apollo Persisted Queries (APQ) does, but it's heavy, and overkill for small / medium APIs.

❌ Complexity summary for persisted GraphQL queries

Task Needed in REST? Needed in GraphQL Persisted Queries?
Build hash map of all queries ❌ No ✔ Yes
Custom server logic for hashing ❌ No ✔ Yes
Maintain registry file or DB ❌ No ✔ Yes
Sync hash map to client ❌ No ✔ Yes
Fallback from GET → POST ❌ No ✔ Yes
Manage version mismatches ❌ No ✔ Yes
Handle dynamic queries ❌ No ✔ Hard

REST GET wins by massive simplicity.

🧘 Final Conclusion (simple and clear)

REST GET + cache directive = trivial + reliable caching

fetch(url, { cache: "force-cache" });

Works everywhere automatically.

GraphQL persisted queries = powerful but complicated

  • Requires hashing

  • Requires registry

  • Requires special server/Gateway

  • Requires custom Next.js handling

  • Still not as straightforward as REST

  • Still breaks if backend is down (unlike cached REST files)

So yes — you are fully right:

GraphQL persisted queries improve caching but require much more coding complexity compared to a simple REST GET with caching.

If you want, I can also compare:

  • GraphQL “persisted queries” vs “automatic persisted queries (APQ)”

  • REST + ETag vs REST + immutable caching

  • Static JSON REST vs dynamic REST with caching

Just tell me.


Me: If I use REST API or even static JSON files GitHub Pages API without specific cache directive in my fetch statements, will some next.js fetch caching come into play?

CG: Short answer: Yes — even if you do NOT specify any cache directive, Next.js will apply its default fetch caching rules.

But the default behaves very differently depending on:

✔ whether the fetch runs in server component / server function

✔ whether you pass dynamic inputs (params, search params)

✔ whether the URL is static or external

Below is the exact behavior as of Next.js 14+ (used in 2025).

✅ 1. Default fetch caching in Next.js (no cache option specified)

Case A — Fetch done in a Server Component or Route Handler with a static URL

Example:

fetch("https://raw.githubusercontent.com/.../chapter1.json");

  • URL is static (fully known at build time)

  • No request body

  • GET request

  • No dynamic params

👉 Next.js automatically treats it as cache: 'force-cache'
👉 It becomes statically cached during build or first load
👉 GitHub Pages outage does NOT break this, because cache is used
👉 Result is reused across environments

This applies both to REST APIs and static JSON files.

❗ Case B — Fetch has dynamic params or dynamic URL

Example:

fetch(`https://example.com/api/chapter/${chapterId}`);

Here chapterId is dynamic.

👉 Next.js automatically sets cache: 'no-store'
👉 No caching
👉 Every request goes to backend
👉 If backend is down → fetch fails
👉 Even if previous response was cacheable, Next.js won’t use it

---snipped---

❗ Case C — Any POST request (method: "POST")

GraphQL is normally:

fetch(GRAPHQL_URL, { method: "POST", body: JSON.stringify(...) })

Default behavior:

  • Next.js forces dynamic fetches

  • Cache = no-store

  • No caching at all

  • If backend down → fetch fails

This is why your GraphQL API went down → app could not load anything.

✔ Case D — REST GET with dynamic params but using implicit caching directive from server

GitHub Pages sets strong caching headers:

Cache-Control: public, max-age=31536000, immutable

If you request:

fetch("/commentaries/tilak/ch2.json");

Even if the URL contains dynamic parts (e.g., tilak, chapter), the URL is still fully static, so Next.js caches it as static content.

But:

If your fetch URL depends on runtime inputs, like:

fetch(`${BASE_URL}/commentaries/${userSelectedAuthor}/ch${chapter}.json`)

Then:

  • Next.js cannot determine the URL at build time

  • So it treats it as dynamic

  • Default = no-store

  • Cache only applies at browser level, not at Next.js server level

Thus if GitHub Pages goes down → browser cache may still work
But Next.js server cache will not.

🎯 So in your app today…

When fetching static JSON from GitHub Pages:

fetch("https://raw.githubusercontent.com/.../chapter1.json")
// or
fetch("/data/commentaries/tilak/ch1.json")

✔ If URL does not depend on runtime variables
→ Next.js caches automatically (force-cache).
→ Backend outage does NOT affect already-cached files.

❌ But because you fetch based on chapter/author chosen by user:

fetch(`${backendUrl}/${author}/ch${chapter}.json`)

Next.js treats this as:

cache: "no-store"   (DEFAULT)

→ So your fetch will fail when backend is down (just like GraphQL).

📌 Conclusion

Yes, Next.js does apply default caching even if you don’t set a directive — BUT ONLY for static URLs.

In your case:

  • Your GraphQL API used POST → never cached → failures if backend down.

  • Your GitHub Pages REST/static JSON URLs depend on user selection → treated as dynamic → Next.js defaults to no-store.

  • Therefore, cache will not save you unless you explicitly set:

fetch(url, { cache: "force-cache" })

👉 Recommendation for your app

Always explicitly set:

fetch(url, { cache: "force-cache" });

Why?

  • Makes behavior predictable

  • Ensures content stays available even when backend is temporarily down

  • Leverages GitHub Pages’ strong immutable caching

  • Speeds up app significantly

If you want, I can help you design the best caching strategy for your verse/translation/commentary files with a small diagram.


Me: If I use fetch(url, { cache: "force-cache" });
but the url has dynamic part of commentator and chapter,
will next.js cache it OR as url is dynamic, not cache it?

CG: Yes — Next.js will cache it, even if the URL has dynamic parts, as long as you explicitly set cache: "force-cache" in the fetch call.

Let me explain clearly.

✅ Direct Answer

If you do:

fetch(`https://example.com/data/${commentator}/ch${chapter}.json`, {
  cache: "force-cache",
});

✔ Next.js WILL CACHE this request

✔ The fact that the URL is dynamic does NOT prevent caching

✔ Each unique full URL is cached separately

✔ If backend goes down later, cached content is still used (no network request)

❗ What changes when URL is dynamic?

Only the default behavior changes.

Default behavior (no cache option):

  • Static URL → cached

  • Dynamic URL → NOT cached (no-store)

But if you override with:

cache: "force-cache"

Next.js says:

“Okay, the developer intentionally wants this cached. I will cache it.”

This applies even for dynamic URLs, whether they include:

  • ${commentator}

  • ${chapter}

  • ${verse}

  • query params (?a=1&b=2)

  • or even URLs returned from a function

💡 Why this works

Next.js caches based on the final URL string, not based on whether the URL was static or dynamic in code.

Example:

const url = `https://example.com/tilak/ch2.json`;

await fetch(url, { cache: "force-cache" });

Next.js builds a cache key like:

https://example.com/tilak/ch2.json

If later you fetch:

https://example.com/tilak/ch3.json

That becomes a different cache entry.

🧠 Important Note: Cache is per unique URL

If a user asks for:

  • Tilak Ch2 → cached entry #1

  • Tilak Ch3 → cached entry #2

  • Sankara Ch2 → cached entry #3

All are cached independently.

🎉 Final Summary

Scenario Cached?
Dynamic URL with cache: "force-cache" ✅ YES (cached normally)
Static URL with cache: "force-cache" ✅ YES
Dynamic URL with default settings ❌ NO (treated as dynamic)
GraphQL POST request ❌ NO (always dynamic)

If you want, I can show how to structure your fetch functions so the cache is reused efficiently with Next.js’s internal caching + GitHub Pages max-age caching.

============================================================

Comments

Post a Comment