Data connector API rate limits exceeded when syncing multiple cloud sources

BA/BI IBM Cognos Analytics
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1

Our Cognos data connectors are hitting API rate limits when pulling from multiple cloud data sources simultaneously. We’re integrating Salesforce, Google Analytics, and Azure SQL, each with their own rate limits (Salesforce: 100k API calls/day, Google Analytics: 50k/day).

We don’t have proper rate limit throttling implemented - connectors just retry failed requests immediately, which makes the problem worse. Request scheduling is non-existent, so all three connectors try to sync at the same time every hour.

API quota management is manual right now - we have to monitor usage and adjust sync frequencies when we approach limits. Adaptive backoff would help, but we’re not sure how to implement it across multiple connectors with different rate limit policies.

Errors we’re seeing:


HTTP 429 Too Many Requests
Retry-After: 3600
Quota exceeded: 102,456 of 100,000 calls

How do others handle rate limiting across multiple cloud API integrations?

2

I’ll provide a complete solution for managing API rate limits across multiple cloud data sources in Cognos Analytics.

Rate Limit Throttling: Implement a centralized rate limiter using token bucket algorithm. Create a quota manager service:

class QuotaManager {
  Map<String, TokenBucket> buckets = new HashMap<>();

  boolean acquireToken(String apiName) {
    TokenBucket bucket = buckets.get(apiName);
    if (bucket.tryConsume(1)) {
      return true;
    }
    // Calculate wait time until token available
    long waitMs = bucket.timeUntilRefill();
    Thread.sleep(waitMs);
    return bucket.tryConsume(1);
  }
}

Configure each API’s limits:


salesforce: 100000 tokens/day = 69 tokens/minute
google_analytics: 50000 tokens/day = 34 tokens/minute
azure_sql: unlimited (but still rate limit to 100 req/sec)

Request Scheduling: Implement intelligent scheduling that respects both rate limits and business priorities:

class ConnectorScheduler {
  // Stagger connector start times
  schedule("salesforce", "0 5 * * *");  // 00:05
  schedule("google_analytics", "0 20 * * *");  // 00:20
  schedule("azure_sql", "0 35 * * *");  // 00:35

  // Add jitter to prevent exact-hour clustering
  long jitter = ThreadLocalRandom.current().nextLong(0, 900000); // 0-15 min
  scheduledTime += jitter;
}

For high-priority data sources, schedule more frequent syncs but with smaller batch sizes. Low-priority sources sync less frequently with larger batches.

API Quota Management: Build a quota monitoring dashboard that tracks usage in real-time:


Salesforce: 87,234 / 100,000 (87%) - 4.2 hours until reset
Google Analytics: 45,678 / 50,000 (91%) - 2.1 hours until reset
Azure SQL: N/A (no limits)

Implement automatic throttling when approaching limits:

if (quotaUsage > 0.9) {
  // Approaching limit - reduce request rate by 50%
  adjustThrottle(0.5);
} else if (quotaUsage > 0.95) {
  // Very close to limit - pause until quota resets
  pauseUntilReset();
}

Store quota metadata in a shared cache (Redis) so all Cognos instances coordinate their API usage. This prevents different nodes from independently exceeding limits.

Adaptive Backoff: Implement sophisticated backoff that adapts to each API’s behavior:

class AdaptiveBackoff {
  int attempt = 0;

  long getBackoffMs(Response response) {
    if (response.hasHeader("Retry-After")) {
      // Honor server's retry directive
      return parseRetryAfter(response);
    }

    // Exponential backoff with jitter
    long baseDelay = Math.min(1000 * Math.pow(2, attempt), 300000); // Max 5 min
    long jitter = ThreadLocalRandom.current().nextLong(0, baseDelay / 4);
    return baseDelay + jitter;
  }
}

Different APIs need different backoff strategies:

  • Salesforce: Exponential backoff starting at 30 seconds
  • Google Analytics: Fixed 60-second backoff (they’re strict about rate limits)
  • Azure SQL: Immediate retry with circuit breaker (fails fast on sustained errors)

Implement circuit breaker pattern to prevent cascading failures:

if (consecutiveFailures > 5) {
  openCircuit(); // Stop trying for 5 minutes
  notifyAdministrators();
}

For your specific scenario with Salesforce exceeding 100k daily quota:

  1. Enable batch API requests (reduces calls by 80%)
  2. Implement delta sync - only fetch changed records since last sync
  3. Use Salesforce Bulk API for large data loads (separate quota)
  4. Cache frequently accessed reference data locally

For Google Analytics:

  1. Switch to BigQuery export for historical data (no API calls)
  2. Use API only for last 7 days of data
  3. Implement aggressive caching of dimension/metric metadata
  4. Consider Google Analytics 360 if you need higher quotas

Monitor these metrics daily:

  • API calls per connector per hour
  • Quota utilization trends
  • Backoff frequency and duration
  • Failed sync attempts

Set alerts when any connector exceeds 85% of its daily quota before 6 PM - this indicates you need to optimize batch sizes or sync frequency. With these implementations, you should stay well under rate limits while maintaining data freshness.

3

You need a centralized rate limiter that tracks quota across all connectors. Don’t let each connector manage its own rate limits independently. Implement a token bucket algorithm that allocates API calls fairly across connectors based on their priorities.

4

The Retry-After header tells you exactly when to retry. Honor it! Implement exponential backoff starting at the Retry-After value. Also check if your APIs support batch requests - fetching 100 records in one API call instead of 100 separate calls reduces quota consumption dramatically.