Building a Simple Cache System in N8N: Save Your API Calls

In this post, we'll explore how to implement a simple but powerful caching system in N8N using a Code Tool node. This solution is particularly useful when working with rate-limited APIs or when you need to optimize workflow performance.

The Problem

When building workflows in N8N, we often face scenarios where we:

• Make repeated API calls to get the same data


• Hit rate limits with external APIs


• Need to optimize workflow performance


• Want to save temporary data between workflow runs

The Solution

We'll build a versatile caching system that can:

• Store and retrieve any data with a key


• Handle JSON objects


• Automatically expire cache after 24 hours


• Provide cache management functions

2. Rate Limit Management

// Store rate limit status

{

  "query": {"rate_limit_github": "remaining_calls: 4999"}

}

// Check before making calls

{

  "query": "rate_limit_github"

}

3. Temporary Data Storage

// Store processing status

{

  "query": {"job_status_123": "processing"}

}

// Update when done

{

  "query": {"job_status_123": "completed"}

}

4. User Preferences

// Store user settings

{

  "query": {"user_123_preferences": {"theme": "dark", "language": "en"}}

}

Cache Management

View Cache Contents

{

  "query": "show"

}

Output:

api_response_12345: API_RESPONSE_DATA

rate_limit_github: remaining_calls: 4999

job_status_123: completed

Delete Cache Entry

{

  "query": "delete api_response_12345"

}

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Integration Examples

Example 1: API Rate Limit Protection

1. Check cache for rate limit status


2. If approaching limit, wait


3. Make API call


4. Update rate limit cache


5. Cache API response

Example 2: Smart Data Refresh

1. Check cache for data


2. If expired, fetch new data


3. Update cache


4. Continue workflow

Best Practices

1. 
   

   Key Naming: Use consistent key naming patterns:

   
   
   
   
   
   • api_response_[endpoint]
   
   
   • user_[id]_[data_type]
   
   
   • job_[id]_[status]
   
   
   
   



2. Cache Duration: Modify the TTL based on your needs:
   
   

CACHE_TTL = 24 * 60 * 60  # 24 hours in seconds

1. 
   Error Handling: Always have fallback logic for cache misses:
   

if (cached_response) {

    // Use cached data

} else {

    // Fetch fresh data

}

Conclusion

This simple caching system can significantly improve your N8N workflows by:

• Reducing API calls


• Improving response times


• Managing rate limits


• Providing temporary storage

Remember to adjust the cache duration and storage method based on your specific needs. For production environments, you might want to consider using Redis or a database for more robust caching.

Next Steps

• Add cache compression for large datasets


• Implement cache versioning


• Add batch operations


• Consider distributed caching for multi-node setups

Feel free to modify and expand upon this implementation for your specific use cases!