Failure Modes Analysis for Batch Processing¶

Overview¶

This document analyzes all potential failure modes in the batch processing system and provides solutions to ensure exactly-once processing for each transaction.

🚨 Critical Failure Modes¶

1. Lambda Execution Failures¶

A. Lambda Timeout¶

Scenario: Batch processing takes longer than Lambda timeout (15 minutes max)
Impact: All events in batch are lost
Detection: CloudWatch timeout metrics
Solution:
Implement checkpointing every 100 events
Use Step Functions for long-running batches
Split large batches into smaller chunks

B. Lambda Memory Exhaustion¶

Scenario: Batch size too large for Lambda memory
Impact: Lambda crashes, events lost
Detection: CloudWatch memory metrics
Solution:
Limit batch size based on memory usage
Implement memory monitoring
Use ECS/Fargate for large batches

C. Lambda Cold Start¶

Scenario: New Lambda instance, slow startup
Impact: Increased latency, potential timeout
Detection: CloudWatch duration metrics
Solution:
Keep Lambda warm with scheduled invocations
Use Provisioned Concurrency

2. Database Operation Failures¶

A. DynamoDB Throttling¶

Scenario: Exceeding DynamoDB read/write capacity
Impact: Batch operations fail, partial data loss
Detection: DynamoDB throttled requests metric
Solution:
Implement exponential backoff
Use DynamoDB on-demand capacity
Implement circuit breaker pattern

B. DynamoDB Partial Batch Failures¶

Scenario: Some items in batch write fail, others succeed
Impact: Inconsistent state, duplicate processing risk
Detection: Batch write response with unprocessed items
Solution:
Implement idempotency keys
Use conditional writes
Retry failed items individually

C. DynamoDB Connection Failures¶

Scenario: Network issues, DynamoDB service unavailable
Impact: Complete batch failure
Detection: Connection timeout errors
Solution:
Implement retry logic with exponential backoff
Use multiple AWS regions
Implement circuit breaker

3. Data Processing Failures¶

A. Individual Event Processing Failures¶

Scenario: Rule evaluation fails for specific events
Impact: Some events fail, others succeed
Detection: Exception handling in rule engine
Solution:
Isolate failures to individual events
Implement dead letter queue for failed events
Provide detailed error logging

B. Metadata Loading Failures¶

Scenario: Cannot load user/merchant metadata
Impact: Rule evaluation may fail or use default values
Detection: Metadata loader exceptions
Solution:
Implement fallback values
Cache metadata with TTL
Retry metadata loading

C. Rule Configuration Errors¶

Scenario: Invalid rule definitions
Impact: Rule evaluation fails
Detection: Rule validation errors
Solution:
Validate rules before deployment
Implement rule versioning
Use default rules as fallback

4. Message Queue Failures¶

A. SQS Message Processing Failures¶

Scenario: Cannot parse SQS messages
Impact: Events lost or duplicated
Detection: JSON parsing errors
Solution:
Implement message validation
Use SQS dead letter queue
Implement message deduplication

B. SQS Visibility Timeout¶

Scenario: Processing takes longer than visibility timeout
Impact: Messages reprocessed, duplicates
Detection: SQS visibility timeout metrics
Solution:
Extend visibility timeout for large batches
Implement heartbeat mechanism
Use SQS long polling

5. External Service Failures¶

A. Third-party API Failures¶

Scenario: External services (fraud detection, etc.) unavailable
Impact: Processing blocked or incomplete
Detection: HTTP timeout/error responses
Solution:
Implement circuit breaker pattern
Use fallback logic
Async processing for non-critical services

🔄 Exactly-Once Processing Requirements¶

Idempotency Keys¶

Each event must have a unique idempotency key:

idempotency_key = f"{event_id}_{timestamp}_{hash(event_data)}"

Processing States¶

Events must track processing state: - PENDING: Initial state - PROCESSING: Currently being processed - PROCESSED: Successfully completed - FAILED: Processing failed - RETRY: Scheduled for retry

Checkpointing¶

Implement checkpointing every N events:

checkpoint_interval = 100  # Checkpoint every 100 events

Dead Letter Queue (DLQ)¶

Failed events must be sent to DLQ for manual review:

dlq_url = "https://sqs.region.amazonaws.com/account/dlq-name"

🛡️ Failure Handling Strategies¶

1. Circuit Breaker Pattern¶

class CircuitBreaker:
    def __init__(self, failure_threshold=5, timeout=60):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.last_failure_time = None
        self.state = "CLOSED"  # CLOSED, OPEN, HALF_OPEN

2. Exponential Backoff¶

def exponential_backoff(attempt, base_delay=1, max_delay=60):
    delay = min(base_delay * (2 ** attempt), max_delay)
    return delay + random.uniform(0, 1)

3. Batch Retry Logic¶

def retry_batch_operation(operation, items, max_retries=3):
    for attempt in range(max_retries):
        try:
            result = operation(items)
            return result
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            delay = exponential_backoff(attempt)
            time.sleep(delay)

4. Idempotency Check¶

def check_idempotency(event_id, idempotency_key):
    existing = dynamodb_client.get_item(
        table_name=settings.events_table_name,
        key={"event_id": event_id}
    )
    if existing and existing.get("idempotency_key") == idempotency_key:
        return True  # Already processed
    return False

📊 Monitoring and Alerting¶

Key Metrics to Monitor¶

Lambda Metrics
Duration
Memory usage
Error rate
Throttles
DynamoDB Metrics
Read/Write capacity units
Throttled requests
Error rate
SQS Metrics
Message age
Visibility timeout
DLQ message count
Business Metrics
Events processed per second
Processing success rate
Average processing time

Alerting Thresholds¶

Lambda error rate > 1%
DynamoDB throttling > 0.1%
SQS DLQ messages > 10
Processing latency > 30 seconds

🔧 Implementation Recommendations¶

1. Enhanced Batch Processor¶

Implement checkpointing
Add circuit breakers
Use idempotency keys
Implement retry logic

2. Dead Letter Queue¶

Create separate DLQ for each failure type
Implement DLQ processing Lambda
Add manual review interface

3. Monitoring Dashboard¶

Create CloudWatch dashboard
Set up alarms
Implement log aggregation

4. Testing Strategy¶

Chaos engineering tests
Load testing
Failure injection tests
Idempotency tests