Help: My rank tracking project stalled at 50k keywords, now what?

You definitely hit a rate limit or a queue backlog. "Unlimited" never means unlimited concurrent processing.

Check two things with support:
1. Actual daily or hourly API request quota for your plan.
2. The backend job queue status for your project. It's probably dead.

At 50k keywords, you're likely better off with a batch processing setup, not a real-time cloud dashboard. Consider splitting into smaller projects or using a tool built for raw data dumps, not pretty graphs. The data you got is probably fine, but the system can't keep up.

"Unlimited keywords" always has an asterisk. It's unlimited in storage, not unlimited in concurrent processing capacity. Your 50k batch likely overwhelmed their scheduler or proxy pool.

The data from the first week is probably accurate. The system didn't break until it couldn't allocate more resources to fetch new data points. You need to move from a "monitoring" tool to a "data processing" pipeline for this scale. Ask support for their maximum job execution time and the concurrency limit for search engine queries. If they're cagey, that's your answer.

Look at tools that give you raw CSV dumps via scheduled batch jobs, not live dashboards. You'll have to build your own visualization layer, but you won't hit these artificial queue limits. It's a different architecture built for volume, not for a marketing manager's dashboard.

Yeah, the "unlimited" got me too on my last project. It was for storage like you said, not for fetching everything at once.

That's a great point about asking for the job queue status specifically. When I hit something similar with Asana's API, support could see my project was stuck in a "pending" state for hours. It wasn't just slow, it was frozen.

Is splitting it into, say, five 10k projects the usual workaround? Or does that just create five separate stalled queues?

The splitting strategy is a common workaround, but it doesn't address the core architectural problem. It can help, but only if the platform's queue manages partitions independently. Often, you're just creating multiple jobs that compete for the same constrained proxy or compute pool, leading to five slow queues instead of one stalled one.

A more reliable method is to control the flow yourself. Instead of relying on the tool's scheduler, structure your project as a series of smaller, sequential batches executed via their API on a cron schedule. This turns a monolithic 50k job into a predictable pipeline.

The underlying issue remains: you're using a monitoring tool for a data extraction workload. For true scale, you need a system designed for batch data processing, not dashboard updates.

Exactly. Splitting often just shifts the bottleneck. The cron approach is correct, but requires API access and a stable external scheduler, which not all plans include.

Some platforms throttle at the account level, not per project. So your five 10k cron jobs might still share a single thread. You need to verify the concurrency model before building a pipeline.

The real fix is moving to a batch-first service, but that's a bigger shift.

Splitting can work, but as others noted, it depends entirely on the vendor's internal queue design. On one platform I used, splitting a large project created multiple jobs, but they all fed into a single, shared processing lane at the account level. So it just created more contention.

The more telling question for support would be: "Is your job queue per-project, per-account, or per-API-key?" If it's per-account, splitting won't help.

A better short-term test is to pause the main project and create a single, new 100-keyword project. If that also stalls or takes hours to complete, you've hit a global account/API limit. If it runs quickly, then splitting *might* work, because the queue could be per-project.

I've seen the per-account throttle issue firsthand with a monitoring API last quarter. It created exactly the scenario you described: multiple cron jobs just backed up behind a single rate limit token bucket.

To test user56's diagnostic, I built a simple benchmark. I'd recommend the OP run a similar test:

- Create a new, empty account on the same plan.
- Launch two 100-keyword projects simultaneously, one in each account.
- Measure the time to first data completion for each.

If the new account project finishes quickly while the original remains stalled, you've confirmed a per-account queue or resource pool. If both are equally slow, the bottleneck is likely at the data center or proxy infrastructure level, which splitting won't mitigate at all.

Your data from the first week is likely valid. The stall is a resource allocation problem, not a data corruption one.

50k is a common failure point for tools architected for dashboards, not batch processing. "Unlimited" usually refers to storage, not compute throughput.

Skip the generic support request. Ask them these two questions directly:
1. What is the maximum concurrent search engine queries per account on my plan?
2. What is the average job execution time for a 50k keyword project in your system?

Their answers, or refusal to answer, will tell you if you need a different tool. At this volume, you should be evaluating systems that output CSV dumps via API, not maintain a live UI.

Absolutely, those are the exact questions that cut through the marketing speak. If they can't give you a clear number for concurrent queries or a typical runtime, they're not built for your workload.

Just be prepared, sometimes even getting those answers means you're hitting a limit that's lower than you'd hope. That's when the pivot to a batch-first tool becomes unavoidable, unfortunately.

Agreed. Getting those numbers is the first step, but they're often non-linear.

I've seen concurrency limits that scale with project size, not just account level. So 50k might get 10 threads, but 5x10k gets 50. The system assumes smaller jobs are lower priority. Asking for the exact queue algorithm is the next question after the limits.