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

A small scraper, with a short reach

Reddit’s public .json listings make a small Go scraper relatively easy to build. A client can request a listing, decode the JSON response into Go structs, and use the listing’s after value as a cursor for the next request. The mechanics are familiar to anyone who’s worked with paginated APIs: fetch a page, inspect its continuation marker, then repeat until the marker disappears or the client reaches a limit of its own.

That simplicity can be misleading. A scraper that works in a local test may behave differently when it runs from a cloud or datacenter IP, and pagination doesn’t mean a listing can be traversed indefinitely. Reddit’s public listings stop at roughly 1,000 items. A clear User-Agent and a measured response to HTTP 429 are sensible parts of a client, but neither removes that ceiling or guarantees access.

This is more significant than it looks. The first engineering task is often not decoding JSON; it’s working out whether the data source can support the collection job at the size and frequency the team needs.

What the cursor does—and doesn’t—promise

The after cursor is a navigation aid. It tells a client where to continue within a listing, so a program can move through successive batches without asking for the same first page over and over. In Go, that can be represented with a small response type containing the listing data and its continuation field, then a loop that builds the next request when a cursor is present.

But a cursor isn’t a promise of complete historical access. A listing is a bounded view, and its contents can change as new activity arrives. Even when a scraper follows every available page, the result is still constrained by the listing’s own reach. For a quick sample or a small monitoring task, that may be enough. For a historical dataset or a broad archive, it’s a structural limitation, not a bug that can be fixed by writing a more persistent loop.

Teams should define what “enough data” means before treating a successful scrape as evidence that the approach will scale. How many items are required? How frequently must the collection run? Does the project need older material, or only a current snapshot? Those questions determine whether a public listing is a useful source or merely a convenient prototype.

Rate limits and access failures

An HTTP 429 response indicates that the client has hit a rate limit. Backing off—waiting before retrying, and increasing the wait when failures continue—helps avoid turning a temporary limit into a stream of repeated requests. A good scraper should also stop or slow down when it receives that response rather than treating every failure as a reason to retry immediately.

HTTP 403 is a different obstacle. The source says cloud or datacenter IPs often receive a flat 403, which means the request may be refused before pagination or rate-limit handling can help. A valid JSON parser can’t solve an access denial. Nor does a descriptive User-Agent ensure a request will be accepted; it makes the client identifiable, but it doesn’t grant permission or access.

Here’s the thing: these responses affect the operating model, not just the code. A script that succeeds from one environment may fail from another. Teams can spend time tuning retries and request timing only to discover that the more basic constraint is the network location from which they’re collecting.

Why teams consider scraping

The use cases in the source—product feedback, brand mentions, and machine learning datasets—share a need to gather user-generated material in a form that can be searched or analyzed. A small scraper can make that work feel accessible: the data arrives as JSON, the implementation can be compact, and a team can test an idea without first building a larger integration.

That convenience has limits. Data collection is only one part of a useful pipeline. Teams still need to decide which fields matter, how to handle missing or changing fields, how to avoid collecting duplicate records, and how to store results so later analysis can trace where they came from. A feed that’s easy to fetch can still produce a dataset that’s difficult to maintain.

For machine learning work in particular, volume alone doesn’t establish value. A bounded sample may be adequate for exploring a question, but it may not represent the range of discussion a team hoped to study. For brand monitoring, a listing’s reach and timing can shape which mentions appear in the collected material. The collection method becomes part of the evidence, and its limits should be visible to the people interpreting the results.

Scraping after the API pricing changes

The source connects renewed interest in scraping among smaller projects to Reddit’s 2023 API pricing changes. That context helps explain the appeal: when an official API becomes more costly for a particular use, teams may look for another way to retrieve public information. A public JSON listing can appear to offer a lower-friction starting point, especially for experiments with modest requirements.

Yet the apparent simplicity can shift costs rather than remove them. Engineers must account for access failures, bounded listings, changing responses, and the work of running and maintaining the collector. A prototype may be inexpensive to write; a dependable collection process has a wider set of requirements. The more a project depends on uninterrupted or repeatable access, the more those operational costs matter.

That said, scraping can still be a reasonable fit for a limited experiment. The important distinction is between learning whether an analysis is useful and building a production workflow that other teams rely on. A tool that answers the first question may be a poor foundation for the second.

Choosing a production path

For production workloads, the source points to Reddit’s OAuth API or a managed endpoint as the more practical route. Those options can provide a clearer integration path than relying on public listing behavior, although the right choice depends on the project’s access needs and operating constraints. The key is to assess the route against the actual workload rather than assuming that a small scraper will remain suitable because it worked in an initial test.

If you’re working in this space, make the expected collection volume, frequency, and time range explicit before choosing an implementation. Then test the access path from the environment where the system will run. A success from a developer’s machine doesn’t answer whether a cloud deployment will receive the same response, and a few pages of results don’t establish that a listing covers the dataset a team needs.

Managed access can also change the maintenance burden: a team may spend less effort handling request behavior itself, while depending on an external endpoint. That trade-off deserves scrutiny. Teams should understand what the endpoint returns and whether its coverage fits the task, rather than treating “managed” as a synonym for complete.

What the limits mean for future use

The practical lesson is straightforward: public .json listings can make a useful small-scale collector, but their cursor, status codes, and access conditions define a real boundary. A Go implementation can handle pagination cleanly and respond politely to 429 responses; it can’t make a listing exceed roughly 1,000 items or turn a 403 into granted access.

For exploratory work, that may be an acceptable compromise. For a sustained dataset or a production service, the limits deserve attention before the scraper becomes a dependency. The choice between a public listing, OAuth, and a managed endpoint is therefore less about which one is easiest to demonstrate and more about which one can support the project’s actual collection needs.

And the part many teams overlook: a collection method shapes the conclusions drawn from the data. If the source is bounded or inconsistently accessible, the resulting analysis inherits those constraints. A small scraper can be a good beginning. A reliable data strategy needs a more candid account of what that scraper can reach.