I recently needed to generate a departmental performance report focused on publication impact. While our institutional repository has the raw data, aggregating and analyzing citation counts for hundreds of papers was a daunting prospect. I used SciSpace's data to automate this, and the backend process turned out to be an interesting exercise in efficient data handling.
The core challenge was fetching and correlating data for a list of DOIs from our faculty. A naive sequential API approach would have been unacceptably slow. My solution was a Go service that:
* Implemented a worker pool pattern to parallelize API requests to SciSpace, with careful attention to rate limits.
* Used a local PostgreSQL staging table to store our DOI list, then enriched it with the fetched citation counts and publication metadata.
* Employed a Redis cache with a 24-hour TTL to store API responses for repeated queries during development and to handle redundant DOIs in our list.
```go
// Simplified worker example for batch processing
func fetchCitationWorker(id int, doiChan <-chan string, resultChan chan<- CitationResult) {
for doi := range doiChan {
// Check cache first
if cites, ok := cache.Get(doi); ok {
resultChan <- CitationResult{DOI: doi, Count: cites}
continue
}
// Call API
resp := callSciSpaceAPI(doi)
cache.SetWithTTL(doi, resp.Count, 24*time.Hour)
resultChan <- CitationResult{DOI: doi, Count: resp.Count}
}
}
```
The final step aggregated the results by research group and generated a ranked CSV. The parallel fetch reduced total latency from an estimated 15+ minutes to under 90 seconds for 500 papers. The key takeaways for similar projects are:
* Always batch and parallelize external API calls where permitted.
* Introduce a caching layer, even for short durations, to respect the data provider and speed up repeated runs.
* Store raw results in a transactional database before aggregation; it allows for easy re-calculation without re-fetching.
I'm curious if others have tackled similar bibliometric reporting and what your strategies were for managing API latency and data consistency. Did you consider a persistent materialized view for the aggregated results, or is the data volatile enough to warrant on-demand generation?
-- latency
sub-100ms or bust
Nice use of the worker pool pattern. I've done something similar but with Python's asyncio and aiohttp for a citation aggregation tool - the concurrency model is a bit different but the same rate-limit headache shows up.
One thing I'd flag about that Redis cache with 24-hour TTL: citations can shift within a day, especially if you're running this near a weekend or right after a new paper gets indexed. I ended up using a shorter TTL (6 hours) and a background refresh job that keeps the cache warm for the most frequently queried DOIs. That way the report is never looking at data that's more than half a day stale.
How did you handle the case where SciSpace returned an error or empty response for a DOI? Did you retry or just skip and log it?
Good point about the 24-hour TTL being too long for a dynamic metric like citations. I also started with a long TTL but saw drift.
For error handling, I implemented a simple exponential backoff retry for transient network errors. For permanent errors or empty responses (which happened for some pre-print DOIs), the worker logs the DOI to a separate "failed" table. The main reporting query uses a LEFT JOIN, so those papers just show a null citation count, which we can flag for manual review. The cache is only populated on successful fetches, so failed requests don't pollute it.
You mentioned a background refresh job for the hot items. Did you find that added significant complexity versus just accepting a 6-hour staleness window?
sub-100ms or bust
> You mentioned a background refresh job for the hot items. Did you find that added significant complexity?
That's exactly what I was wondering. For a beginner like me, even 6-hour staleness seems okay if it simplifies the architecture a lot. Was the refresh job hard to set up and monitor?
The refresh job is a standard cron pattern, but monitoring its effectiveness is the real complexity. You need to track not just that the job ran, but that it actually refreshed the items you expected. That means logging cache hits/misses and the age of data before refresh.
For a beginner, I'd skip it. A 6-hour TTL with a simple cache population-on-request is far more tractable. The operational burden of monitoring another moving part often outweighs the marginal freshness gain. The one exception is if your report is used for real-time decision-making, which for publication metrics it almost certainly isn't.
If you do go down that route, implement it as a separate, idempotent Go binary or Python script your scheduler calls. Don't bake it into your main service. That way you can monitor its runs independently and turn it off without affecting the core report generation.
Oh, that separate "failed" table for logging is smart. It keeps your main data clean.
The LEFT JOIN to still show the paper with a null count is a really nice user touch. Makes it obvious what's missing without breaking the whole report.
For a beginner like me, your retry logic and the failed table sound like a great balance - it's resilient but the logic is still pretty straightforward to follow. Did you consider adding a simple dashboard to just view that failed table?
The worker pool pattern for parallelizing API calls is a smart move, especially with rate limits. I've been working on something similar for aggregating engagement data from our marketing platform and hit the same scaling wall.
You mentioned a Redis cache for redundant DOIs. I'm curious if you also considered deduplicating the DOI list itself before sending it to the workers? It seems like a simple preprocessing step could save some cycles, though maybe the cache handles that elegantly enough.
Good call on the LEFT JOIN for failed items. That keeps the report usable. Did you track how many DOIs typically ended up in that "failed" table over time? I wonder if there's a pattern to which ones fail, like specific publishers or older entries.
Just here to learn.