Solved: How do you monitor redundant and duplicate zaps?

#devops #programming #tutorial #cloud

🚀 Executive Summary

TL;DR: Duplicate Zapier runs, often caused by webhook retries or ‘chatty’ triggers, can lead to significant data issues like duplicate invoices. This guide outlines three primary methods to monitor and prevent redundant Zaps: implementing a ‘Guard Step’ with a shared log, utilizing idempotency keys for API interactions, and employing centralized state management with Redis for high-volume, mission-critical automation.

🎯 Key Takeaways

Duplication in Zapier workflows frequently originates from external factors like webhook retries or ‘chatty’ triggers, not always Zapier’s fault, necessitating internal Zap deduplication.
The ‘Guard Step’ is a no-code deduplication pattern that uses a shared, durable log (e.g., Google Sheet or Airtable) to store unique IDs and a Filter step to prevent reprocessing events already recorded.
Idempotency keys, sent in API request headers, offer a robust, industry-standard solution by shifting deduplication responsibility to the destination system for critical workflows like payment processing.

Tired of duplicate Zapier runs causing chaos? Learn how to monitor and prevent redundant Zaps using simple guard steps, idempotency keys, and robust state management for foolproof automation.

Taming the Two-Headed Hydra: How We Monitor and Kill Redundant Zaps

I remember the day our finance team stormed my desk, figuratively speaking. An automated Zap, which was supposed to be a simple “New Stripe Charge to Quickbooks Invoice” workflow, decided to run twice for every single transaction over a three-hour window. We had hundreds of duplicate invoices, confused customers, and a very unhappy CFO. It was a stark reminder that “set it and forget it” automation is a myth. The real work begins when you have to make it resilient.

This problem, which I see pop up all the time, isn’t always Zapier’s fault. It’s a classic distributed systems challenge. A webhook from a service might get retried, an “Updated Row” trigger in a spreadsheet can fire multiple times if a user saves, then immediately edits again. The trigger event itself is duplicated before your workflow even starts. So, how do you build a defense against this hydra?

The “Why”: Understanding the Root of the Duplication

Before we jump into fixes, you have to understand the cause. Duplicates usually happen for a few key reasons:

Webhook Retries: The source application (e.g., Stripe, Shopify) sends a webhook, doesn’t get a 200 OK response from Zapier fast enough, and assumes it failed. So, it helpfully sends it again.
“Chatty” Triggers: Some triggers, like “New or Updated Row” in Google Sheets, are inherently prone to firing multiple times for what a human considers a single action.
User Behavior: A user double-clicks a submit button on a form that triggers your Zap. Boom, two identical submissions.

The goal isn’t to stop these from happening—you can’t. The goal is to make your Zap smart enough to know it’s already done the work.

The Fixes: From Duct Tape to Fort Knox

I’ve seen a lot of solutions in my time. Here are the three main patterns we use at TechResolve, ranging from a quick fix to a full-blown architectural solution.

1. The Quick Fix: The “Guard Step”

This is my go-to for 90% of low-to-medium-stakes Zaps. It’s a simple, no-code deduplication pattern. The idea is to use a durable, shared place—like a Google Sheet or an Airtable base—as a log of what’s already been processed.

Here’s the flow:

Trigger: Your Zap fires as usual (e.g., New Webflow Form Submission).
Lookup Step: Immediately after the trigger, add a “Lookup Spreadsheet Row” (Google Sheets) or “Find Record” (Airtable) action. Search for a column where you store a unique ID from the trigger. This could be a transaction\_id, an email\_address + timestamp, or a unique submission\_id.
Filter Step: Add a Filter by Zapier step. The condition should be: Only continue if… the result from your Lookup Step “Does not exist”.
Create Record Step: If the Zap passes the filter, its first real action should be to create a row in that same Google Sheet/Airtable base, logging the unique ID you used in the lookup. This “claims” the event.
Rest of Zap: All your other actions follow.

The next time a duplicate trigger comes in with the same unique ID, the Lookup step will find the record, the Filter step will catch it, and the Zap will stop dead in its tracks.

Pro Tip: Don’t make this complicated. A Google Sheet with two columns, unique\_id and processed\_timestamp, is often all you need. It’s hacky, but it’s a visible, auditable log that anyone on the team can check.

2. The Permanent Fix: The Idempotency Key

Now we’re getting serious. If your Zap is interacting with a proper API (like creating a customer in Stripe, a deal in HubSpot, or posting to your own internal service), the professional-grade solution is to use an idempotency key.

An idempotency key is a unique token you generate and send along with your API request. The server-side API sees this key. The first time it receives a request with a specific key, it processes it. If it ever sees another request with that exact same key, it simply returns the result of the original request without running the logic a second time.

How to implement this in Zapier:

In your Zap, find a truly unique piece of data from your trigger. The charge\_id from Stripe is a perfect example.
Use a “Code by Zapier” step (or sometimes a “Formatter” step) to prepare this key if needed. Often, you can just use the raw value.
In your API request action (usually a “Webhooks by Zapier” POST request), you need to include this key in the request headers. The header name is often Idempotency-Key or X-Request-Id. Check the API documentation for the service you’re calling.

// Example of headers in a Webhooks by Zapier action

{
  "Content-Type": "application/json",
  "Authorization": "Bearer sk_test_12345...",
  "Idempotency-Key": "{{1.trigger_data__id}}" // Mapping the unique ID from the trigger
}

This is, by far, the most robust method for critical workflows like payment processing or data creation in a CRM. It puts the responsibility for deduplication on the destination system, which is where it belongs.

3. The ‘Nuclear’ Option: Centralized State Management

What if you have dozens of Zaps, all needing this protection, and a Google Sheet feels too slow or flimsy? This is where you bring out the big guns. It’s overkill for most, but for high-volume, mission-critical automation, we use a centralized key-value store like Redis.

The logic is similar to the “Guard Step” but infinitely faster and more scalable.

The high-level architecture:

Set up a small Redis instance (e.g., on AWS ElastiCache or DigitalOcean).
Your Zap’s first step is a “Code by Zapier” action that runs Python or JavaScript.
This code takes the unique ID from the trigger.
It attempts to write this ID to Redis using the SETNX command (“SET if Not eXists”). This command is atomic—it’s a single, indivisible operation.
If SETNX returns 1, the key was successfully set (it was the first time), and the code outputs a value like proceed: true.
If SETNX returns 0, the key already existed (it’s a duplicate), and the code outputs proceed: false.
A Filter step right after this code block checks that proceed is true before continuing.

Warning: This is an advanced technique. It introduces another piece of infrastructure (prod-redis-01) that you have to maintain and monitor. Don’t go down this road unless the cost of a duplicate event is incredibly high. For us, it was the final answer for our core payment and provisioning workflows.

Putting It All Together: A Decision Table

Still not sure which to use? Here’s how I decide.


Method	Best For…	Pros	Cons
Guard Step	Simple Zaps, internal tools, non-critical notifications.	No-code, easy to audit, fast to set up.	Can be slow, potential for race conditions.
Idempotency Key	Critical Zaps interacting with modern APIs (payments, CRMs).	Extremely reliable, industry standard.	Destination API must support it.
Centralized State	High-volume, complex systems where duplicates are catastrophic.	Blazing fast, scalable, atomic.	Adds infrastructure overhead and complexity.

At the end of the day, building resilient automation is about thinking defensively. Assume things will fail, assume events will be duplicated, and build your guardrails before you have to explain a few hundred duplicate invoices to your boss. Trust me, it’s a much better conversation to have.