Scalability Optimization for Codat API Using Retry and Rate Limiting

Use Cases

• Managing ACC-Based Rate Limits – Codat enforces request quotas using 1,000 × (1 + Number of ACCs) daily requests with a concurrency cap of 10 simultaneous requests per ACC. A rate limiter ensures that API calls adhere to these dynamic constraints.
• Retry Mechanism for 429 Responses – Retry-After headers are provided but lack per-endpoint granularity, requiring automated exponential backoff with jitter for stability.
• Distributed Retry Strategies – Sharded request queues ensure that each ACC’s API interactions are isolated, preventing quota exhaustion from a single high-volume integration.
• Request Prioritization – Critical requests (e.g., payment processing) must bypass retry backlogs when system load is high, using weighted fair queuing (WFQ).

Step-by-Step Breakdown

1. Rate Limiting for ACC-Based Quotas – Enforces request limits based on ACC-specific quotas, ensuring compliance with dynamic daily allowances.
2. Exponential Backoff with Jitter – Implements a retry strategy that gradually increases delays while adding randomness to prevent synchronized retry storms.
3. Priority-Based Request Handling – Uses a weighted queue to prioritize high-impact requests while delaying non-critical API calls.
4. ACC-Aware Traffic Routing – Ensures that each ACC’s request flow is independently managed to avoid quota exhaustion from a single overloaded integration.

Concrete Example

The Problem:
A fintech platform integrates with Codat to pull real-time financial data from multiple ACCs. As the user base scales:

• API quotas must be constantly recalculated based on connected companies.
• Uncoordinated retries cause API throttling, as requests ignore Retry-After headers.
• Critical requests (e.g., payments) get stuck in overloaded retry queues, leading to delays.

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The Solution:
By implementing a retry and rate-limiting middleware, API interactions remain efficient and quota-compliant:

• Rate limits are enforced per ACC to ensure even distribution of API consumption.
• Exponential backoff with jitter stabilizes retry behavior, reducing congestion and failed requests.
• Prioritization mechanisms allow urgent requests to be processed first, avoiding unnecessary delays.

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Retry & Rate Limiting Flow YAML Configuration

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yaml
name: CodatRetryAndRateLimitFlow

filter:
  url: "https://api.codat.io/*"

processors:
  RateLimiter:
    processor: Limiter
    parameters:
      - key: quota_id
        value: CodatACCQuota
      - key: limit
        value: dynamic  # Dynamically calculates based on 1000 × (1 + ACC count)
      - key: window
        value: 24h
      - key: strategy
        value: per_acc  # Ensures requests are tracked per Active Connected Company
      - key: concurrency_limit
        value: 10  # Maximum of 10 simultaneous requests per ACC

  RetryProcessor:
    processor: Retry
    parameters:
      - key: max_retries
        value: 5
      - key: backoff_strategy
        value: exponential_with_jitter
      - key: jitter_factor
        value: 0.2  # Introduces ±20% randomness to prevent synchronized retry storms
      - key: retry_on_status_codes
        value: [429, 500, 502, 503]
      - key: retry_after_header
        value: "Retry-After"
      - key: max_delay
        value: 30000  # 30 seconds max retry delay

  PriorityQueue:
    processor: WeightedQueue
    parameters:
      - key: priority_mapping
        value:
          "payment_processing": 1.0
          "financial_reports": 0.8
          "background_sync": 0.5
      - key: strategy
        value: weighted_fair_queuing  # Ensures high-priority tasks are processed first

flow:
  request:
    - from:
        stream:
          name: globalStream
          at: start
      to:
        processor:
          name: RateLimiter

    - from:
        processor:
          name: RateLimiter
          condition: above_limit
      to:
        processor:
          name: RetryProcessor

    - from:
        processor:
          name: RateLimiter
          condition: below_limit
      to:
        processor:
          name: PriorityQueue

    - from:
        processor:
          name: PriorityQueue
      to:
        stream:
          name: globalStream
          at: end

  response:
    - from:
        processor:
          name: RetryProcessor
      to:
        stream:
          name: globalStream
          at: end
          size

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Final Thoughts

By integrating rate limiting per ACC, exponential backoff with jitter, and priority-based queuing, API consumption remains scalable, stable, and responsive. This approach ensures efficient request distribution, prevents synchronized retry storms, and optimizes critical API workflows within Codat’s dynamic rate limit constraints.

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