Explaining Hubspot's RemoteDisconnected error

This post was sparked by this HubSpot forum thread.

When working with Hubspot APIs, you might come across this error:

ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response'))

At Stacksync, where we specialize in real-time and bidirectional syncs between HubSpot and databases such as PostgreSQL, Snowflake, and BigQuery, this issue is something we see often. We’ll explore the root cause of this error and how Stacksync’s infrastructure handles it.

What causes RemoteDisconnected in Hubspot?`

This error occurs when an API server (like Hubspot) closes the connection abruptly without sending any response to the client. This leaves your application hanging, waiting indefinitely for a response that never comes.

Several factors can trigger this error:

• Server-side issues: HubSpot’s servers may be overloaded or experiencing timeouts due to high traffic.
• Network instability: Inconsistent connections between your infrastructure and HubSpot’s servers can cause premature disconnections.
• Concurrency problems: When multiple processes attempt to access the same API resources without proper coordination, the server can become overwhelmed and drop connections.

In the forum thread, @camera was working with 700k contacts and receiving RemoteDisconnected intermittently. This is a common cause for RemoteDisconnected, where Hubspot's API struggles to maintain a stable connection. Even after implementing retry logic, the error can reoccur sporadically, making it difficult to pinpoint a singular cause.

For real-time systems, this lack of consistency can significantly impact your architecture. Unpredictable drops in connection leads to partial data transfers, inconsistent data states between systems, and increased manual intervention to recover from the failure.

How we resolve the RemoteDisconnected error

At Stacksync, we’ve built our infrastructure to handle these issues gracefully, ensuring that large-scale data syncs between HubSpot and databases like PostgreSQL, BigQuery, and Snowflake remain resilient.

Retry mechanism with exponential backoff

Stacksync uses a retry module that applies exponential backoff to each request. Instead of bombarding HubSpot with frequent retries, we progressively increase the delay between retry attempts, allowing the API to recover before resuming the data sync.

Exponential backoff ensures that our system responds gracefully to temporary server or network issues, reducing the likelihood of overloading HubSpot’s servers or hitting rate limits. Here’s how you might implement this in Python using requests:

from urllib3.util import Retry
from requests.adapters import HTTPAdapter
import requests

retry_strategy = Retry(
    total=5,  
    backoff_factor=2, 
    status_forcelist=[429, 500, 502, 504],
    allowed_methods=["GET", "POST"],
    raise_on_status=False  
)

adapter = HTTPAdapter(max_retries=retry_strategy)
http = requests.Session()
http.mount("https://", adapter)

This retries the connection up to five times with increasing delays at a factor of 2.

Coordinated parallel processing

Handling large-scale data syncs requires parallel processing, but this introduces complexity. Multiple syncs operating in parallel can easily cause conflicts, especially if they’re not coordinated correctly. At Stacksync, we’ve implemented process coordination to manage how parallel jobs interact with HubSpot’s API.

By orchestrating read and write operations and coordinating API usage across processes, we prevent race conditions and minimize the chance of triggering RemoteDisconnected errors. This is critical for maintaining stability in high-volume syncs, where multiple processes could otherwise conflict and crash the connection.

Dynamic rate limit handling

HubSpot enforces strict API quotas, and exceeding these limits can result in throttled requests and dropped connections. Stacksync solves this by reading X-Hubspot-RateLimit-Remaining and X-Hubspot-RateLimit-Reset headers, then throttling requests accordingly.‍‍

import time
import requests

def fetch_contacts():
    url = "https://api.hubapi.com/crm/v3/objects/contacts"
    headers = {
        'Authorization': 'Bearer YOUR_ACCESS_TOKEN'
    }

    response = requests.get(url, headers=headers)
    
    # Check the rate limit headers
    remaining_requests = int(response.headers.get('X-HubSpot-RateLimit-Remaining', 0))
    reset_time = int(response.headers.get('X-HubSpot-RateLimit-Reset', 0))
    
    # If we are close to hitting the rate limit, wait until reset
    if remaining_requests < 10:
        wait_time = reset_time - int(time.time())
        print(f"Rate limit reached, sleeping for {wait_time} seconds.")
        time.sleep(wait_time)
    
    # Process the response (e.g., retrieve contacts)
    return response.json()

# Example usage
contacts = fetch_contacts()

In this example:

• We extract the remaining API requests from the X-HubSpot-RateLimit-Remaining header.
• If the remaining requests are below a certain threshold (10), we pause the script until the reset time specified in the X-HubSpot-RateLimit-Reset header.

This ensures we don’t exceed HubSpot’s API rate limits while keeping the sync process smooth.

Why Stacksync is the better solution

While error handling is critical, Stacksync’s approach to syncing HubSpot with your databases goes beyond solving RemoteDisconnected errors. Here are additional features that make Stacksync the go-to solution for Hubspot data syncs.

1. Real-time, two-way data sync

Unlike traditional batch-processing tools, Stacksync provides real-time, bidirectional syncs between HubSpot and your databases. This means you can make updates to your CRM from your database using SQL and vice versa, ensuring that data is always current across systems.

2. No-code integration setup

Stacksync’s GUI allows you to configure complex syncs between HubSpot and databases like PostgreSQL, Snowflake, and BigQuery without writing any code. Simply choose your systems, map your fields, and let Stacksync handle the heavy lifting. This not only speeds up setup but also reduces the likelihood of errors due to misconfigured APIs or rate limits.

3. Resilience and monitoring

Stacksync is designed with resilience at its core. If a system temporarily goes down, our architecture ensures that any missed data is queued and synced once the system is back online. Additionally, our built-in monitoring and logging give you full visibility into your syncs, allowing you to identify and resolve any issues quickly.

4. Optimized API usage

HubSpot's API limits can be costly if not managed carefully. Stacksync helps you reduce costs by optimizing API usage, avoiding unnecessary requests, and intelligently spacing retries. This ensures you get the most value out of your API credits while keeping your syncs efficient and reliable.