Scraping Reddit in Go: JSON Pagination, Rate Limits and Production Limits

How the scraper works

Reddit’s public .json listings expose posts in a format a small Go program can parse without much machinery. The scraper requests a listing, decodes the response into structured data, and reads the after cursor when it needs to ask for the next page. That cursor is the key to ordinary pagination: it tells the client where to resume, so it can move through a listing without treating every request as a fresh search.

The mechanics are simple, but the request pattern still matters. A client should identify itself with a clear User-Agent, which gives the service basic information about the software making the request. It should also handle HTTP 429 responses by backing off instead of immediately retrying. That response signals that the server is limiting requests; repeated attempts can add load without making the data arrive sooner.

This is a modest engineering task. Go’s built-in support for HTTP requests and JSON decoding is enough for a compact implementation, and the response format gives the program recognizable fields to work with. The harder part isn't decoding a page. It's deciding how the scraper should behave when the service limits requests or stops returning useful results.

Useful for small projects, limited at scale

Teams may use Reddit posts to track brand mentions, find product requests, or assemble datasets for analysis. Those jobs can begin with a narrow set of queries and a small volume of public material, where a basic scraper may be adequate. A developer exploring an idea or collecting a limited sample can often learn a great deal before needing a larger data pipeline.

The boundary appears when the work depends on broad or repeated collection. Listings stop at about 1,000 items, so following the after cursor doesn't create an unlimited archive. Pagination moves through the results the listing makes available; it doesn't remove the listing's ceiling. For a project that needs older material or a much larger corpus, that distinction changes what the scraper can deliver.

Access can be another constraint. Cloud and datacenter IPs often receive a flat 403, according to the source. That means a script can be correctly formed, identify itself, and still fail to retrieve listings from the environment where a team runs its production systems. A small local experiment may therefore give an overly optimistic picture of how the same code will behave when deployed.

This is more significant than it looks. Teams often treat data collection as a programming problem, then discover that access conditions and available result depth define the actual product they can build. Better parsing won't solve a hard response limit, and adding retries won't turn a denied request into an authorized one.

Why the API price change matters

Since the 2023 API pricing changes, scraping has become more appealing to developers with small budgets. The economic logic is easy to understand: if a project is exploratory, has limited resources, or needs only a modest amount of public material, an unofficial collection method can look like a lower-cost starting point. That can make a simple scraper useful during evaluation, when a team is still testing whether the data answers a real question.

But a lower apparent cost doesn't mean the same thing as a dependable data source. A scraper puts the collection process under the team's control, while the team remains dependent on the service's listing behavior and access decisions. That trade-off can be reasonable for a small project. It becomes harder to accept when the data must arrive consistently, cover a defined range, or support work that other people rely on.

There's also a difference between getting data and being able to explain how it was gathered. A clear User-Agent and careful handling of HTTP 429 responses make a client more responsible and easier to diagnose, but they don't guarantee access. If you're working in this space, treat these details as operating requirements, not as a promise that the source will provide every result you want.

Choosing a route for production

For production workloads, the source points to Reddit’s official OAuth API or a managed endpoint as the more practical route. Those options offer a route intended for sustained access, though the source doesn't specify their pricing, limits, or exact coverage. A team should judge them against its actual needs: how much material it needs, how often it needs updates, and what level of continuity the work requires.

A managed endpoint can also shift some collection work outside the team's own code, while an official API gives developers a defined integration path. Neither label alone settles whether an option fits. The relevant comparison is whether it can provide the data volume and access pattern the project needs, at a cost the team can support.

And yet, a scraper can still have a place alongside those routes. It may help a team inspect a small sample, prototype an analysis, or understand the shape of public listings before investing in a more durable setup. The useful boundary is clear: use the small tool to answer a small question, and don't mistake a successful test run for evidence that it can support a larger service.

What the limits mean for future work

The practical ceiling isn't just a technical inconvenience. A listing cap affects how complete a dataset can be, while access blocks affect whether the same process can run from a chosen environment at all. Together, those constraints make it difficult to plan a production workflow around public listings alone, especially when the work assumes wide coverage or regular collection.

(And this is the part most people overlook.) A scraper's reliability depends on the behavior of the service it reads, not just on the quality of its code. A cursor can guide a client through available pages, but it can't promise that the pages will include everything a research or monitoring task needs. Even a well-structured dataset may reflect the limits of the collection method.

For now, the sensible outlook is conditional. Small experiments can benefit from the low overhead of a Go scraper, provided developers respect rate limits and accept the listing cap and possible access denials. Projects that need sustained, broader collection should assess the official OAuth API or a managed endpoint early, before they build downstream work around a source they may not be able to reach consistently.

Useful for exploration. A narrow tool with a narrow ceiling.