Apple Podcasts Charts Scraper — New & Rising, 32 Storefronts

Get the top podcast charts for up to 32 countries in a single run, and get what a chart alone never tells you: which shows are new, which ones are climbing, which ones chart in several countries at once, and which ones have stopped publishing.

This is an unofficial scraper. It is not affiliated with, endorsed by, or sponsored by Apple Inc. All trademarks belong to their respective owners.

It reads Apple's public chart feed directly — no headless browser, no proxies, no API key, no login. A 3-storefront run finishes in a few seconds.

What you get

One row per podcast, per storefront:

Field	What it is
storefront, storefrontName	Country code and country name
rank	Position in the chart, 1 = top
name, publisher	Podcast title and the account that publishes it
genres, primaryGenre	Genres from the chart, plus Apple's primary genre
podcastId, applePodcastsUrl, feedUrl	Apple id, public page, and the show's RSS feed
artworkUrl	Cover art at 600×600 instead of the 100×100 the feed returns
isNew	The show was not in the chart on your previous run
rankChange	Positions gained since your previous run (12 → 4 = +8)
peakRank	Best rank the show has reached across all your runs
daysInChart	How long it has been charting without interruption
alsoChartingIn, storefrontCount	The other countries where it charts right now
episodeCount, lastEpisodeAt, daysSinceLastEpisode	How active the show actually is
explicit, chartUpdatedAt, scrapedAt, sourceUrl	Explicit flag, Apple's chart timestamp, your run timestamp, source

What makes it different

Most chart scrapers give you a snapshot: who is at number 12 today. This one gives you three things a snapshot cannot contain.

Movement. Each run is compared against your previous one, stored between runs, so you see what entered the chart and what is climbing. Shows are tracked by their Apple id, so a rebranded podcast is not mistaken for a new one.

Cross-country presence. Every storefront you ask for is fetched in the same run, so a show breaking out in four countries at once is visible as such. A scraper that does one country per run cannot compute this at all.

Publishing activity. A podcast sitting at rank 30 whose last episode is from 2023 is a very different thing from one publishing weekly. daysSinceLastEpisode tells you which is which — useful when you are looking for shows to pitch, sponsor or benchmark against.

On the first run there is no history to compare against, so isNew, rankChange and daysInChart are null — not true, not 0. Claiming everything is new when we simply have no history would be a lie you could not detect. Run it a second time and the movement fields fill in.

Input

{
  "storefronts": ["US", "GB", "FR", "DE"],
  "chartSize": 100,
  "includeDetails": true,
  "genre": "",
  "timeoutSecs": 30
}

• storefronts — two-letter country codes. Verified: AR AT AU BE BR CA CH CL CO DE DK ES FI FR GB IE IN IT JP KR MX NL NO NZ PL PT RU SE SG TR US ZA. Other codes are attempted with a warning rather than refused.
• chartSize — 10 to 200 podcasts per chart, from the top down.
• includeDetails — episode count, latest-episode date and RSS feed. Costs one extra request per 100 unique shows, not one per row: a podcast charting in twelve countries is looked up once.
• genre — keep only podcasts whose genre contains this text (News, Comedy, True Crime…).

Typical uses

• Spot a podcast breaking out before it is obvious — new entries climbing in several countries at once.
• Build a prospect list of active shows in one genre, with their RSS feeds and publishing cadence.
• Track your own show, or a competitor's, across countries on a schedule.
• Feed chart movement into a newsletter, dashboard or dataset.

Pricing

$0.05 per storefront processed. A 3-country run costs $0.15; a 32-country sweep costs $1.60. You are charged only for storefronts that actually returned a chart — a country that fails costs you nothing. Chart size, enrichment and the genre filter are all included.

Running it on a schedule

Movement fields need a previous run to compare against, so this Actor is at its best on a schedule — daily or every few hours. Use Apify's Schedule on the Actor, and keep the same storefronts between runs so the history stays comparable.

Limits, stated plainly

• Apple publishes up to 200 podcasts per storefront. Nobody, including this Actor, can return more than the chart contains.
• The chart is the top-podcasts chart. It is not episode-level, and it is not a search index.
• rankChange compares against your previous run, whatever its date. Two runs three weeks apart give you a three-week delta, not a daily one.
• A show that leaves the chart and comes back is counted as new on its return: daysInChart measures an uninterrupted stay.
• Apple's chart timestamp (chartUpdatedAt) tells you how fresh the underlying data is. It usually refreshes several times a day, so running more often than that adds cost without adding movement.
• Storefronts run in parallel and stay isolated: one country failing never stops the others, and RUN_SUMMARY records exactly which ones failed and why.

Output

Alongside the dataset, each run writes a RUN_SUMMARY record: storefronts requested, how many succeeded, how many failed with the reason, total rows and how many shows chart in more than one country. Point a monitor at it and you will know a storefront broke before your users tell you.

Support

Found a bug or a storefront that should be on the verified list? Open an issue on the Actor page and it will be looked at.