Syncing seems easy, right? Just grab data from System A, push it into System B, and you’re done. Bahahahaha! If only it were that simple.
This document serves as both a practical guide to syncing strategies in n8n and a technical deep-dive into the specific challenges of synchronizing Microsoft Teams messages. It’s meant to be comprehensive, detailed, and reflect real-world implementation challenges beyond what you’ll find in typical integration guides.
Syncing is inherently difficult, and the more complex a system is, the more points of failure exist. This increases the chances of syncs “not working” — whether that means completely breaking, half-working, or appearing to work while actually being wrong — and debugging these issues can be extremely difficult.
To explain the principles involved I’ll use the example of syncing tasks from Coda to n8n but the principles apply to almost any syncing scenario.
For Coda tasks —> n8n, I implemented the following logic:
Why use a holding table? This is the quickest, least complex way to reliably store the fact that there’s a new/modified task. The initial webhook only needs to receive the task_id (not all the task data), immediately acknowledge receipt, and quickly add it to the database. This approach:
If you were to make the webhook call directly to a complex sync workflow, you introduce significant risks:
And perhaps the most critical issue – race conditions. Even a small team working on tasks may end up with two people modifying a task within milliseconds of each other, or perhaps more realistically, bulk updates to tasks via scripts / other automations can happen within Coda. This can flood the n8n webhook with requests creating both a server load issue but more critically, creating a scenario where the same task is being updated at the same time.
By first putting everything into a holding table, we can decide how to sync this new/modified task into the system in a controlled manner.
For my implementation, I’ve set up a database trigger that fires whenever a new entry is added to the holding table. This trigger calls another n8n workflow (“Immediate sync”) with just the task_id.
You might wonder: If we want a controlled approach, why trigger an immediate sync? Because I like implementing more than one option. I generally start with allowing the sync workflow to trigger immediately but I can easily switch off the Supabase trigger if required. If the immediate syncing causes no issues then I keep it so an immediate sync does happen when Coda sends the task_id — it’s just done via the holding table and a database trigger instead of directly from Coda to the sync workflow to allow for easy re-configuration.
On the “Immediate sync” workflow, there’s also a scheduled trigger running every minute, pulling records from the “holding” table.
This apparent double-up is for good reason: the Supabase trigger may fail, so we check every minute for new holding records anyway. Also, failed syncs result in items staying in the holding table. The scheduled trigger allows an admin to simply clear the “processing_timestamp” field of any failed records, wait a minute, and those records will automatically be synced again.
When an item is pulled from “holding,” a “processing_timestamp” is set. The workflow ensures it only pulls records from holding that don’t have a “processing_timestamp.” So, any items in holding with a “processing_timestamp” indicate that the sync for that item failed. Clearing the timestamp allows that record to be picked up in the next sync cycle.
Another benefit of using Supabase triggers instead of direct webhook calls is that developers can modify or add a row directly in Supabase to trigger the n8n workflow. This is particularly useful for testing when developers don’t have admin access to Coda.
The “Immediate sync” workflow is configured to pull just one task from holding each time it executes (using LIMIT 1 in the SQL query). This is a deliberate design choice that differs from what many n8n users do. Importantly, the workflow is built so you can easily switch to processing multiple items by changing the SQL query to LIMIT x and adding loop nodes to control the flow, effectively disabling n8n’s internal looping behavior.
Why process one item at a time? Many n8n workflows pull multiple items at once and let n8n nodes handle the “looping” internally. While this seems efficient, it creates several significant problems:
For example, when an n8n node returns 500 tasks, that’s 500 output items. You can manually loop through those items with a loop node, but n8n also handles looping “internally” within nodes. If you send those 500 items to a switch node, it will run 500 times (essentially looping through each incoming item) and output 500 items… and so on. While n8n does allow you to select an item on the left side (incoming items) and automatically shows the corresponding output item on the right, once you have hundreds of items with complex logic, isolating issues becomes extremely challenging.
This “.item vs first()” problem becomes particularly acute when processing many items with complex relationships. For example, when a single task results in multiple comments and you try to process an individual comment, that comment (item) comes from a SINGLE task. If you’re processing ONE task at a time, the relationship between that task and its multiple comments is easy for n8n to maintain. But if you’re processing 500 tasks with varying numbers of comments, and then using switch nodes to route replies to different paths, n8n gets confused about which items relate to which, and it becomes extremely difficult for the developer to debug.
By processing one item at a time in separate workflow executions:
This approach essentially breaks everything into distinct workflows, making it much easier to track, debug, and manage the sync process.
While this method is undeniably slower, in most business scenarios, accuracy is far more important than speed.
If you do need to process multiple items within a single workflow execution, I recommend using explicit loop nodes instead of relying on n8n’s internal node iteration. This makes the looping behavior visible in the workflow diagram and easier to debug.
Deleted tasks and inconsistencies still need to be managed, typically through a “Bulk Sync” workflow. This workflow:
When using a merge node to compare items, you can set it to “ignore matching items” when matching by ID. This creates a resulting list that contains both items missing from the database (missed syncs) and items missing from the API source (deleted tasks). n8n helpfully labels each item with which input (1 or 2) it came from, so you can determine whether an item means “deleted from source platform” or “missing from database” and take appropriate action.
This approach does NOT automatically detect modified tasks, which means if a task update is missed due to a trigger failure, it will remain out-of-date until another update occurs or until a manual correction is made.
As you can see, with any syncing system, there will be items that get missed or not updated. It’s possible to cater to every scenario and implement logging at every step, but that can take considerable development time.
Now that we’ve established general syncing principles, let’s dive into the specific challenges of synchronizing Microsoft Teams messages.
Microsoft Teams presents several unique challenges for synchronization:
Unlike chat messages (where you can filter by date range), you cannot filter channel messages by date range. According to the Microsoft documentation (https://learn.microsoft.com/en-us/graph/api/channel-list-messages?view=graph-rest-1.0&tabs=http), you must retrieve ALL messages from a channel and then filter them client-side.
To work around this, I track the last successful sync timestamp directly on each channel record in the database. After retrieving all messages, the workflow only processes those with a modification timestamp after the last sync. I’ve also set an initial cutoff date of 2024-01-01, so even though the MS Teams node retrieves all messages (we can’t avoid that), the workflow ignores any messages from before 2024.
The Teams API doesn’t include replies with messages, and n8n doesn’t have a built-in node to retrieve replies. To address this, I use an HTTP request node to fetch replies for each message using the endpoint:
These replies are then added to the same “messages” table but with a “replyToId” field populated to link them to their parent message.
The Microsoft Teams API sometimes returns a 429 (rate limit exceeded) error when retrieving channel messages. This doesn’t happen consistently, which makes handling it tricky.
I’ve discovered that the MS Teams API returns only 20 messages per request, yet the n8n Teams node sometimes outputs hundreds of messages before failing. This indicates that n8n is handling pagination internally by making multiple rapid API calls in quick succession, which eventually triggers rate limiting.
Here’s a concrete example of what happens: the Teams node might successfully output 100 messages, but then fail with a throttling issue. This behavior is puzzling at first—how is n8n getting 100 messages when the API documentation says you only get 20 per request? The answer is that n8n is making multiple API calls internally to handle pagination, but doesn’t expose any controls to manage the rate of these requests.
To mitigate this, I’ve implemented error detection for 429 responses and added a loop that retries after a failure. However, this creates another issue: if 250 items get output, then the node errors, and I loop around to try again, it will output all 500 items on the second attempt—resulting in 250 duplicate items being processed.
This illustrates why using HTTP nodes directly can sometimes provide better control than using n8n’s specialized nodes. With HTTP nodes, you can:
The last point about headers is particularly important: rate limit information, pagination details, and token usage data (for LLMs) are typically found in HTTP response headers, which n8n nodes don’t expose to the workflow. This is a significant limitation when working with complex APIs.
Setting and using the “last sync timestamp” requires careful consideration:
Ideally, the last sync timestamp should be set at the very beginning of the workflow to capture the exact moment synchronization began. However, because of the potential for rate limiting errors mid-workflow, I’ve had to move this to the end of the workflow, only updating it after all messages have been successfully processed.
This creates a problem: the timestamp is now several minutes “too late” compared to when the workflow actually started, potentially missing messages that arrived during processing.
The n8n server sets timestamps in its local timezone (using $now), while Microsoft Teams likely uses UTC for message timestamps. Converting between these could introduce errors.
To address both issues, I implement a 1-day overlap when filtering messages. When reading the last sync timestamp, the workflow subtracts 24 hours to ensure that messages received during the previous sync are included.
This may seem inefficient, but it’s actually an elegant solution because:
Using an overlap is essentially a “cheat” to avoid dealing with timezone conversions, which are notoriously difficult to get right and often break when server configurations change.
Microsoft Teams does not truly delete messages—they’re merely flagged with a “deletedDateTime” property. This significantly complicates synchronization logic:
With systems that truly delete messages (like Zoho, WooCommerce, WordPress, Slack), you can simply compare all database records against all API records and identify deletions by what’s missing. Teams requires checking each message’s deletion status.
If a sync happens at 1pm, and someone deletes a message later that day, the next sync won’t automatically check that message because it was CREATED before the last sync. However, its “lastModifiedDateTime” will have been updated when it was deleted, so we need to check this timestamp rather than just creation time.
The workflow needs to handle scenarios like messages that are created and then deleted between syncs. This requires specific logic paths for “new and deleted” messages, as opposed to simply “new” or simply “deleted.”
For example, someone may create a message and then delete it before the message gets synced. The workflow needs specific logic to handle this case, where a message appears as both new (not in our database) and deleted (has a deletedDateTime value) simultaneously.
To make the sync process manageable:
The workflow is configured to not check for replies on messages older than two weeks. This dramatically reduces API calls while still ensuring recent activity is captured.
This optimization is necessary because there’s nothing in the main message data that indicates whether there are replies or how many replies a message has. Additionally, deleted replies don’t update the main message’s lastModifiedDateTime. Without this optimization, we would need to check every single message for replies by calling the specific endpoint for each message.
However, this optimization only works if:
If either condition is not met, the workflow reverts to checking all messages again.
Instead of trying to sync all channels at once, the workflow processes one channel at a time. This reduces the likelihood of rate limiting errors and makes debugging easier by isolating each channel’s sync process.
To prevent concurrent syncs of the same channel (which could happen if a workflow execution takes longer than the schedule interval), I’ve added a “processing” timestamp field. This is set at the beginning of the workflow to indicate a channel is being processed, and cleared upon completion.
Syncing between systems is never as straightforward as it initially appears. It requires careful consideration of:
The approach outlined in this document prioritizes reliability and accuracy over speed, recognizing that in most business contexts, it’s better for synchronization to be slow but correct than fast but unreliable.
While the specific implementation details focus on Microsoft Teams synchronization, the general principles—holding tables, controlled processing, explicit looping, and careful timestamp management—apply to virtually any synchronization scenario in n8n.
By anticipating the complex scenarios and edge cases described here, you can build sync workflows that gracefully handle the messy realities of real-world data integration.
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