Managing and scaling multiple Apify actors for large scale data projects

If you’re wrangling big data projects—think scraping millions of pages or orchestrating complex workflows—you’ll hit the limits of one-off scripts fast. Maybe you’ve tried running a single Apify actor and watched it crawl along, or you’ve tangled up multiple actors and lost track of what’s running, what’s failing, and where your bottlenecks are. This guide is for you: honest advice on how to manage and scale multiple Apify actors for real-world, large-scale projects. No hand-waving, no magic “just scale it” answers.

Why Multiple Actors? (And When You Shouldn’t Bother)

Actors are Apify’s term for reusable, containerized scraping or automation tasks. They’re flexible and easy to share, but the real power comes when you use many at once—say, one actor to crawl listings, another to fetch details, and a third to clean or export data.

But: More actors means more moving parts. You’ll need a good reason to split things up. Some solid use cases:

  • Parallelization: Need to scrape or process thousands of URLs at once? Multiple actors can split the load.
  • Separation of Concerns: Keep crawling, parsing, and post-processing in separate actors so you can update or debug them independently.
  • Different Resource Needs: Some tasks are CPU-heavy, some need more memory. Separate actors let you give each the right resources.
  • Error Isolation: If one actor crashes, it won’t take down your whole workflow.

When not to bother:
If your project is small, or you’re still prototyping, a single actor is simpler. Only split up when you’re hitting real problems—complexity for its own sake is a waste of time.

Step 1: Plan Your Actor Architecture

Before you start cloning actors like rabbits, sketch out what actually needs to be separated. Here’s a rough process:

  1. List Your Tasks: Break your project into distinct steps. E.g., crawl seed URLs, scrape detail pages, process images, export JSON.
  2. Look for Natural Boundaries: Where do you need different dependencies? Where do tasks run at different frequencies? Where does error isolation matter?
  3. Define Data Flow: How will data pass between actors—Dataset, Key-Value Store, or external storage?

Pro Tip:
Don’t overthink it. Most projects need 2–4 actors, not 10. Complexity multiplies fast.

Step 2: Set Up Communication Between Actors

Actors don’t magically talk to each other. You’ll need to set up the data flow yourself. The main options:

1. Datasets

  • Good for passing lists of URLs or results between actors.
  • Each actor can read/write to a named dataset.
  • Easy to debug—just inspect the dataset in the Apify console.

2. Key-Value Stores

  • Use for config, single files, or status flags.
  • Useful if you need to pass a token or “job done” signal.

3. External Storage (S3, GCS, etc.)

  • Needed if your outputs are huge, or if you’re integrating with other systems.
  • Adds complexity—use only if Apify’s built-in storage isn’t enough.

What works:
For 90% of cases, Datasets + Key-Value Store are enough. Don’t jump to S3 unless you really need it.

Step 3: Orchestrate Your Actors

Now you need to run actors in the right order, with the right inputs. There are three main ways:

1. Actor Chaining

  • One actor finishes, then programmatically starts the next.
  • Use the Apify SDK’s Apify.call() to trigger an actor from within another.
  • Lets you pass parameters and control flow tightly.

Sample (Node.js): js const inputForNextActor = { urlList: ... }; await Apify.call('user/next-actor', inputForNextActor);

When it works:
Good for simple pipelines where each step depends on the last.

2. Task Orchestration (via Apify API or Scheduler)

  • Use the Apify platform’s scheduler to trigger actors on a schedule.
  • Or write a “master” actor that starts multiple jobs in parallel using the API.
  • Lets you parallelize easily, but you’ll need to manage dependencies yourself.

When it works:
If you have big input lists and want to split them into batches running in parallel.

3. External Orchestration (e.g., Airflow, Node.js scripts)

  • For very complex flows, you might want to control actors from outside Apify.
  • Use the Apify REST API to start/monitor actors from your own orchestration tool.
  • More work to set up, but maximum flexibility.

What doesn’t work:
Don’t try to chain actors using “wait for X seconds” or hope the right dataset is ready. It’s brittle. Always check status or use webhooks.

Step 4: Parallelization and Scaling

Here’s where the rubber meets the road. You want to run lots of actors at once without melting your wallet or hitting rate limits.

1. Break Up Your Inputs

  • Don’t feed 50,000 URLs to one actor. Split them into chunks (batches of 500–2000).
  • Start one actor per chunk. This keeps memory usage manageable and lets you retry just the failed chunks.

2. Use Actor Tasks & Input Templates

  • Set up “Tasks” in Apify—basically, saved configurations for your actors.
  • Makes it easier to automate launching many jobs with different inputs.

3. Rate Limiting & Respecting Targets

  • Don’t be a jerk—if you hammer a website with 500 actors, you’ll get blocked fast.
  • Throttle requests inside your actor code (using autoscaledPool or similar).
  • Use Apify’s built-in proxy rotation to avoid bans.

4. Monitor Resource Usage

  • Apify gives you CPU/memory stats per actor run. Watch them.
  • If you’re hitting limits, tune your actor’s memory or split tasks further.

What works:
Batching and parallelization work best when your workload is naturally chunkable. If you’re scraping a site with complex login flows or lots of blocking, consider fewer, longer-running actors.

Step 5: Error Handling and Retries

At scale, something always breaks—network errors, site changes, timeouts. Plan for it.

1. Use Built-in Retries

  • Apify actors can be set to retry on failure. Set a reasonable number—2–3, not 10.
  • Capture errors and log them clearly so you know what went wrong.

2. Isolate Failures

  • Design your workflow so one chunk failing doesn’t stop the whole project.
  • Track which batches failed, and only re-run those.

3. Logging & Alerting

  • Use the Apify console or API to stream logs and monitor status.
  • For large projects, set up webhooks or API polling to get alerts on failure.

What doesn’t work:
Ignoring errors and hoping they’re rare—at scale, the error rate always goes up. Build in monitoring from the start.

Step 6: Monitor, Debug, and Iterate

No workflow is perfect on the first try. The best teams keep things simple and tune as they go.

  • Start small: Test with a handful of actors/batches.
  • Monitor: Watch for slowdowns, memory spikes, or weird failures.
  • Iterate: Tweak batch sizes, error handling, and resource settings.
  • Automate what matters: Only add complexity when it saves you time or headaches.

Real talk:
Most “scaling” problems are actually design problems—biting off too much at once, tangling up dependencies, or skipping error handling. Keep your actor network small, understandable, and ruthlessly practical.

Wrapping Up: Keep It Boring, Keep It Working

Managing and scaling multiple Apify actors isn’t rocket science, but it’s easy to get lost in the weeds. Start with a simple plan, split work into logical actors, connect them with datasets or key-value stores, and only parallelize when you have to. Keep an eye on your resource usage and error logs. When in doubt, simplify. The best large-scale data projects are the ones you can actually run, debug, and explain to your future self. Happy scraping.