How do you handle 1M+ API requests daily while keeping response times under 100ms? Here's how we tackled this challenge in building an enterprise integration platform that processes the files for organizations with 50,000+ records.
Let's break down the scale:
A single enterprise client has 50,000+ employees
Each employee record requires ~20 API calls across internal services
Multiply this across multiple enterprise clients
Result: Over 1M+ API requests daily, each needing to be fast and reliable
In modern HR systems, managing employee benefits at enterprise scale isn't just about moving data - it's about orchestrating a complex dance of internal and external systems. For each benefits processing cycle, our platform needs to:
Gather employee information across multiple services
Transform massive datasets into vendor-specific file formats
Securely transfer large files (often 100MB+) to benefit providers via SFTP
Process incoming response files containing personalised benefit calculations
Update tens of thousands of employee records in real-time
Core Challenges in Enterprise Benefits Integration
When a single enterprise client with 50,000 employees initiates benefits processing:
Each employee record requires ~20 internal API calls
Results in 1M+ daily API requests
Generates and processes large files (100MB+ CSVs)
Must maintain sub-100ms response times for real-time operations
Data Consistency
With multiple services involved (HRIS, Role History, etc.), maintaining consistency becomes critical:
Concurrent employee data updates must be handled
Data must remain consistent across async operations
Partial data availability scenarios need graceful handling
Transaction boundaries must be clearly defined
File Processing
Large-scale file operations bring their own complexities:
Efficient generation of vendor-specific CSV formats
Memory-efficient processing of 100MB+ files
SFTP transfer reliability for large files
Handling partial file processing scenarios
Security
Processing sensitive employee data requires robust security measures:
Protection against SSRF (Server Side Request Forgery) attacks
Vendor files might contain malicious URLs
Need for strict URL validation
Prevention of internal network exposure
Secure handling of demographic data
Compliance with data protection regulations
Reliability
At enterprise scale, reliability becomes paramount:
Network failures during file transfers
Service timeouts and degradations
Vendor system downtimes
Need for robust retry mechanisms
Clear failure recovery paths
Operational Visibility
Managing millions of transactions requires:
Real-time monitoring capabilities
Quick failure detection
Comprehensive audit trails
SLA compliance tracking
Debugging capabilities across distributed calls
Engineering Solutions: Architecture Patterns & Decisions
Let's explore the key architectural decisions we made when building our benefits integration platform.
Our initial approach treated each employee record independently. This seemed logical - process one employee, move to the next. However, with 50,000+ employees, each requiring 20 API calls, this quickly became unsustainable.
Consider this scenario: Processing benefits for a large tech company with offices across five locations. Our initial implementation would make:
20 API calls × 50,000 employees = 1M API calls
Each call adding 100ms network latency
Sequential processing taking hours
The solution? Batch processing by organizational structure:
Group employees by location and department
Fetch data in bulk (e.g., all Seattle engineering)
Process related records together
This reduced our API calls by 95% and cut processing time from hours to minutes.
Caching Strategy: The Two-Layer Decision
We faced a critical decision: cache everything or cache selectively? Consider the data patterns:
Employee demographics change rarely
Role and salary information updates frequently
Benefits eligibility rules remain static
Deduction calculations change monthly
This led to our two-layer caching strategy:
L1 (In-memory):
L2 (Distributed):
Employee demographics
Historical calculations
Department structures
File Processing: Why Streaming Won Over Batching
Early in development, we tried loading entire files into memory. This worked in testing with 100 employees but failed spectacularly with 50,000. A single file with full employee data could exceed 100MB.
Consider the memory implications:
50,000 employees × 2KB per record = 100MB
Multiple files being processed simultaneously
Additional memory for processing
The streaming pattern emerged as the clear winner:
Process records as they arrive
Maintain constant memory footprint
Enable parallel processing of chunks
Data Flow and Processing
Monitoring & Observability: Tracking Millions of Operations
Alert Thresholds: Three-Tier Monitoring Strategy
File Transfer Failures:
Data Processing:
P1 (Warning) - Business Hours Response
Performance Degradation:
Service Health:
API latency >200ms (p95)
Memory usage >85%
Disk usage >90%
P2 (Investigation) - Weekly Review
Trend Analysis:
20% increase in processing time
10% drop in cache efficiency
Gradual error rate increase
Lessons Learned: Technical Insights From the Trenches
What Worked Well
- Batch Processing Strategy Our decision to process by organizational units rather than individual employees paid off tremendously:
Reduced API calls by 95%
Better resource utilization
Simpler error handling at batch level
Natural fit for enterprise structure
- Two-Layer Caching The split between in-memory and distributed caching proved crucial:
Hot data (eligibility rules) stayed ultra-fast
Distributed cache maintained consistency
Memory usage remained predictable
Clear cache invalidation patterns
Unexpected Challenges
- File Processing Complexity Real example from vendor file:
Issues encountered:
Commas in values breaking CSV parsing
Mixed date formats in same file
Hidden characters causing validation failures
File size variations (100KB → 100MB)
- State Management Challenges Example race condition we encountered:
func ProcessBenefits(employeeID string) error {
demo := getDemographic(employeeID)
calculateBenefits(demo)
}
type DemographicData struct {
Data Employee
Version int64
}
func ProcessBenefitsWithVersion(employeeID string) error {
demo := getDemographicWithVersion(employeeID)
return processWithOptimisticLock(demo)
}
Conclusion: Building for Enterprise Scale
Building an integration platform that processes benefits for enterprises with 50,000+ employees taught us valuable lessons about scale, reliability, and system design. Let's recap our journey:
Key Takeaways
- Scale isn't just about handling large numbers; it's about:
Smart batching over individual processing
Strategic caching decisions
Efficient resource utilization
Predictable performance patterns
- Reliability at enterprise scale means:
- The right architecture decisions early on matter:
Batch processing saved us from millions of API calls
Two-layer caching proved invaluable
Investment in monitoring paid dividends
File processing needed more attention than expected
Moving Forward
As we continue to evolve this platform, our focus remains on:
Repository Link
https://github.com/AkshayContributes/load-balancer
The journey from processing a single employee's benefits to handling enterprises with 50,000+ employees has been one of continuous learning and adaptation. The lessons learned here continue to influence our architectural decisions and system design approaches.
