Error Handling and Resilience Patterns

[hakanson]

Note: As I was reviewing this project, and before I created any pull requests, I wanted to create a tracking issue on this topic. Much of the content (like recommendations, priorities, and time estimates) was assisted by AI analysis using Kiro IDE.

Overview

This report provides a comprehensive analysis of error handling and resilience patterns across both PDF accessibility solutions. The analysis reveals critical gaps in distributed system resilience, particularly around Step Functions state management, Adobe API failure handling, and observability for debugging production issues.

Critical Findings

🔴 CRITICAL: No retry/catch configuration in Step Functions state machine
🔴 CRITICAL: No Dead Letter Queue (DLQ) for failed processing
🔴 CRITICAL: Adobe API failures cause silent workflow failures
🔴 HIGH: Status information scattered across logs with no correlation IDs
🔴 HIGH: No circuit breaker patterns for external API calls
🟡 MEDIUM: Inconsistent error handling across Lambda functions
🟡 MEDIUM: Missing distributed tracing (X-Ray) for request correlation

---

Table of Contents

1. Step Functions State Management Analysis
2. Adobe API Error Handling
3. Lambda Function Resilience
4. ECS Task Error Handling
5. Observability and Traceability
6. Dead Letter Queue Analysis
7. Specific Recommendations

---

1. Step Functions State Management Analysis

Current Implementation

The Step Functions state machine in app.py (lines 210-350) has NO error handling configuration:

# Current implementation - NO RETRY OR CATCH
map_state = sfn.Map(self, "Map",
                    max_concurrency=100,
                    items_path=sfn.JsonPath.string_at("$.chunks"),
                    result_path="$.MapResults")

map_state.iterator(ecs_task_1.next(ecs_task_2))

# Tasks chained without error handling
chain = map_state.next(java_lambda_task).next(add_title_lambda_task).next(a11y_postcheck_lambda_task)

parallel_state = sfn.Parallel(self, "ParallelState",
                              result_path="$.ParallelResults")
parallel_state.branch(chain)
parallel_state.branch(a11y_precheck_lambda_task)

state_machine = sfn.StateMachine(self, "MyStateMachine",
                                 definition=parallel_state,
                                 timeout=Duration.minutes(150))

Critical Issues

1.1 No Retry Configuration

Impact: Any transient failure (network timeout, throttling, temporary service unavailability) causes immediate workflow failure.

Affected Components:

• ECS Task 1 (Adobe autotag) - 40-second timeout, no retries
• ECS Task 2 (Alt-text generation) - No retries
• Java Lambda (PDF merger) - No retries
• Add Title Lambda - No retries
• Accessibility checker Lambdas - No retries

Evidence from code:

# app.py lines 134-165: ECS tasks with NO retry configuration
ecs_task_1 = tasks.EcsRunTask(self, "ECS RunTask",
                              integration_pattern=sfn.IntegrationPattern.RUN_JOB,
                              cluster=cluster,
                              task_definition=task_definition_1,
                              # NO RETRY CONFIGURATION
                              )

1.2 No Catch/Error Handling

Impact: Failed tasks don't have fallback paths, cleanup logic, or notification mechanisms.

Missing capabilities:

• No error state transitions
• No cleanup of partial S3 artifacts
• No notification on failure
• No graceful degradation

1.3 State Data Flow Issues

Current state passing mechanism:

# State flows through result_path without error context
map_state = sfn.Map(self, "Map",
                    result_path="$.MapResults")  # Overwrites on success only

# Lambda tasks use output_path
java_lambda_task = tasks.LambdaInvoke(self, "Invoke Java Lambda",
                                      output_path=sfn.JsonPath.string_at("$.Payload"))

Problems:

1. No error context preservation: When a task fails, error details are lost
2. No partial success handling: Map state with 100 chunks - if 1 fails, entire workflow fails
3. Status only in logs: File processing status logged but not in state machine output

Evidence from split_pdf Lambda:

# lambda/split_pdf/main.py lines 30-35
def log_chunk_created(filename):
    print(f"File: {filename}, Status: Processing")  # Only in CloudWatch
    print(f'Filename - {filename} | Uploaded {filename} to S3')
    return {
        'statusCode': 200,
        'body': 'Metric status updated to failed.'  # Misleading message
    }

1.4 Parallel State Failure Behavior

Current implementation:

parallel_state = sfn.Parallel(self, "ParallelState",
                              result_path="$.ParallelResults")
parallel_state.branch(chain)  # Main processing chain
parallel_state.branch(a11y_precheck_lambda_task)  # Pre-check runs in parallel

Critical Issue: If the pre-check Lambda fails, the entire parallel state fails, even if main processing succeeds. No configuration for:

• Partial failure tolerance
• Branch-level error handling
• Independent branch completion

---

2. Adobe API Error Handling

2.1 Adobe PDF Services Integration

The system heavily relies on Adobe PDF Services API for:

• PDF autotagging (accessibility tagging)
• Text and table extraction
• Accessibility checking (pre/post remediation)

Integration points:

1. docker_autotag/autotag.py - ECS Task 1
2. lambda/accessibility_checker_before_remidiation/main.py
3. lambda/accessability_checker_after_remidiation/main.py

2.2 Current Error Handling

ECS Task 1 (autotag.py)

Adobe API calls with minimal error handling:

# docker_autotag/autotag.py lines 150-180
def autotag_pdf_with_options(filename, client_id, client_secret):
    try:
        # ... setup code ...
        client_config = ClientConfig(
            connect_timeout=8000,  # 8 second connect timeout
            read_timeout=40000     # 40 second read timeout
        )
        
        pdf_services = PDFServices(credentials=credentials, client_config=client_config)
        # ... API calls ...
        
    except (ServiceApiException, ServiceUsageException, SdkException) as e:
        logging.exception(f'Filename : {filename} | Exception encountered: {e}')
        # NO RETRY - just logs and continues

Critical Problems:

1. No retry logic: Adobe API failures are logged but not retried
2. Silent failures: Exception caught but processing continues
3. No circuit breaker: Repeated failures don't trigger backoff
4. No fallback: No alternative processing path

Main function error handling:

# docker_autotag/autotag.py lines 650-660
def main():
    try:    
        # ... processing code ...
        logging.info(f'Filename : {file_key} | Processing completed for pdf file')
    except Exception as e:
        logger.info(f"File: {file_base_name}, Status: Failed in First ECS task")
        logger.info(f"Filename : {file_key} | Error: {e}")
        sys.exit(1)  # Exit with error code - causes ECS task failure

Impact: When Adobe API fails:

1. Exception logged to CloudWatch
2. sys.exit(1) terminates ECS task
3. Step Functions receives task failure
4. Entire workflow fails - no retry, no recovery

2.3 Adobe API Failure Scenarios

Scenario 1: Rate Limiting / Throttling

Adobe Response: HTTP 429 or ServiceUsageException
Current Behavior: Immediate failure, no backoff
Impact: Batch processing of multiple PDFs fails entirely

Scenario 2: Temporary Service Unavailability

Adobe Response: ServiceApiException with 5xx error
Current Behavior: Immediate failure
Impact: Transient issues cause permanent workflow failure

Scenario 3: Timeout

Current Config: 40-second read timeout
Behavior: Exception thrown, no retry
Impact: Large PDFs that take >40s to process always fail

Scenario 4: Invalid Credentials

Adobe Response: Authentication error
Current Behavior: Fails immediately
Missing: No credential refresh, no fallback

Secrets Manager integration:

# docker_autotag/autotag.py lines 100-120
def get_secret(basefilename):
    secret_name = "/myapp/client_credentials"
    # ... retrieves from Secrets Manager ...
    try:
        get_secret_value_response = client.get_secret_value(SecretId=secret_name)
    except ClientError as e:
        logging.info(f'Filename : {basefilename} | Error: {e}')
        # NO RETRY, NO FALLBACK - just logs and continues

Problem: If Secrets Manager call fails (throttling, network), credentials are None, causing Adobe API to fail.

2.4 Accessibility Checker Lambdas

Similar pattern in both pre/post check Lambdas:

# lambda/accessibility_checker_before_remidiation/main.py lines 60-80
def lambda_handler(event, context):
    try:
        # ... Adobe API calls ...
        pdf_accessibility_checker_job = PDFAccessibilityCheckerJob(input_asset=input_asset)
        location = pdf_services.submit(pdf_accessibility_checker_job)
        pdf_services_response = pdf_services.get_job_result(location, PDFAccessibilityCheckerResult)
        
    except (ServiceApiException, ServiceUsageException, SdkException) as e:
        print(f'Filename : {file_basename} | Exception encountered: {e}')
        return f"Filename : {file_basename} | Exception encountered: {e}"
        # Returns error string - Step Functions sees this as SUCCESS

CRITICAL BUG: Lambda returns error message as string instead of raising exception. Step Functions interprets this as successful execution!

---

3. Lambda Function Resilience

3.1 Inconsistent Error Handling Patterns

Pattern 1: Exponential Backoff (Best Practice) ✅

Location: lambda/add_title/myapp.py

# Lines 9-45: Well-implemented retry logic
def exponential_backoff_retry(func, *args, retries=3, base_delay=1, backoff_factor=2, **kwargs):
    attempt = 0
    while True:
        try:
            return func(*args, **kwargs)
        except Exception as e:
            attempt += 1
            if attempt >= retries:
                raise
            sleep_time = base_delay * (backoff_factor ** (attempt - 1)) + random.uniform(0, 1)
            time.sleep(sleep_time)

# Used for S3 and Bedrock calls
exponential_backoff_retry(s3.download_file, bucket_name, file_key, local_path, retries=3)
exponential_backoff_retry(client.converse, modelId=model_id, messages=..., retries=3)

Strengths:

• Exponential backoff with jitter
• Configurable retry attempts
• Proper exception propagation

Weaknesses:

• Only used in 1 of 5 Lambda functions
• No circuit breaker for repeated failures
• No metrics on retry attempts

Pattern 2: Try-Catch with No Retry (Common) ❌

Locations:

• lambda/split_pdf/main.py
• lambda/accessibility_checker_before_remidiation/main.py
• lambda/accessability_checker_after_remidiation/main.py

# lambda/split_pdf/main.py lines 120-150
def lambda_handler(event, context):
    try:
        # ... processing ...
    except KeyError as e:
        print(f"File: {file_basename}, Status: Failed in split lambda function")
        return {'statusCode': 500, 'body': json.dumps(f"Error: {str(e)}")}
    except Exception as e:
        print(f"File: {file_basename}, Status: Failed in split lambda function")
        return {'statusCode': 500, 'body': json.dumps(f"Error: {str(e)}")}

Problems:

• No retry for transient failures
• Returns 500 but Step Functions may not interpret as failure
• Error details only in logs

Pattern 3: Java Lambda (PDF Merger)

Location: lambda/java_lambda/PDFMergerLambda/src/main/java/com/example/App.java

// Lines 40-60
public String handleRequest(Map<String, Object> input, Context context) {
    try {
        // Download, merge, upload PDFs
        return String.format("PDFs merged successfully.\nBucket: %s\n...", ...);
    } catch (Exception e) {
        baseFileName = baseFileName.replace(".pdf", "");
        System.out.println("File: " + baseFileName + ", Status: Failed in Merging the PDF");
        return "Failed to merge PDFs.";  // Returns error string, not exception
    }
}

CRITICAL: Returns error message as string - Step Functions sees SUCCESS!

3.2 Lambda Timeout Configuration

All Lambdas configured with same timeout:

# app.py - uniform timeout across all functions
timeout=Duration.seconds(900)  # 15 minutes for ALL Lambdas

Issues:

1. No differentiation: Split PDF (fast) has same timeout as Add Title (slow)
2. No timeout strategy: No consideration for retry budget
3. Cost implications: Long timeouts increase costs for fast-failing operations

3.3 Lambda Concurrency and Throttling

No reserved concurrency configured:

# app.py - Lambda definitions lack concurrency controls
split_pdf_lambda = lambda_.Function(
    self, 'SplitPDF',
    # NO reserved_concurrent_executions
    # NO provisioned_concurrent_executions
)

Risk: Burst of S3 uploads triggers many Lambdas → account-level throttling → cascading failures

---

4. ECS Task Error Handling

4.1 ECS Task 2 (Alt-Text Generation)

Location: javascript_docker/alt-text.js

Error handling pattern:

// Lines 420-450
async function startProcess() {
    try {
        // ... processing ...
        logger.info(`Filename: ${filebasename} | PDF modification complete`);
    } catch (error) {
        logger.info(`File: ${filebasename}, Status: Error in second ECS task`);
        logger.error(`Filename: ${filebasename} | Error processing images: ${error}`);
        process.exit(1);  // Exit with error - causes ECS task failure
    }
}

Issues:

1. No retry for Bedrock API calls: Alt-text generation failures are not retried
2. 5-second sleep between images: Hardcoded delay (line 424: await sleep(5000))
3. No rate limiting protection: Could hit Bedrock throttling limits

Bedrock API calls without retry:

// Lines 130-160
const invokeModel = async (prompt, imageBuffer) => {
    const client = new BedrockRuntimeClient({ region: AWS_REGION });
    // ... prepare payload ...
    const apiResponse = await client.send(command);  // NO RETRY
    return responseBody.output.message;
};

4.2 ECS Task Configuration

From app.py:

# Lines 60-80: ECS task definitions
task_definition_1 = ecs.FargateTaskDefinition(self, "MyFirstTaskDef",
                                              memory_limit_mib=1024,
                                              cpu=256)

task_definition_2 = ecs.FargateTaskDefinition(self, "MySecondTaskDef",
                                              memory_limit_mib=1024,
                                              cpu=256)

Missing:

• No health checks
• No task-level timeout (relies on Step Functions timeout)
• No graceful shutdown handling

---

5. Observability and Traceability

5.1 Status Tracking Across Components

Critical Issue: Status information is scattered across CloudWatch logs with no unified tracking mechanism.

Status Logging Patterns

Split PDF Lambda:

# lambda/split_pdf/main.py line 32
print(f"File: {filename}, Status: Processing")

ECS Task 1 (Adobe autotag):

# docker_autotag/autotag.py line 658
logger.info(f"File: {file_base_name}, Status: Failed in First ECS task")

ECS Task 2 (Alt-text):

// javascript_docker/alt-text.js line 421
logger.info(`File: ${filebasename}, Status: Error in second ECS task`);

Java Lambda (Merger):

// App.java line 150
System.out.println("File: " + baseFileName + ", Status: succeeded");

Problem: Each component logs status independently. No way to track a single PDF through the entire pipeline.

5.2 Missing Correlation IDs

Current state: No correlation ID passed through the workflow.

Impact:

• Cannot trace a single PDF across Lambda → ECS → Lambda → ECS
• CloudWatch Insights queries require filename matching (unreliable)
• Debugging production issues requires manual log correlation

Example workflow for file "document.pdf":

1. Split Lambda logs: File: document.pdf, Status: Processing
2. Step Functions execution ID: arn:aws:states:...:execution:MyStateMachine:abc123
3. ECS Task 1 logs: Filename: document_chunk_1.pdf | Processing completed
4. ECS Task 2 logs: Filename: document_chunk_1.pdf | PDF modification complete
5. Java Lambda logs: Filename: document.pdf, Status: succeeded

No link between these logs!

5.3 CloudWatch Dashboard Limitations

Current dashboard (app.py lines 360-420):

dashboard = cloudwatch.Dashboard(self, "PDF_Processing_Dashboard", 
                                 dashboard_name=dashboard_name,
                                 variables=[cloudwatch.DashboardVariable(
                                    id="filename",
                                    type=cloudwatch.VariableType.PATTERN,
                                    label="File Name",
                                    input_type=cloudwatch.VariableInputType.INPUT,
                                    value="filename",
                                    visible=True,
                                    default_value=cloudwatch.DefaultValue.value(".*"),
                                )])

Widgets:

• File status query
• Split PDF Lambda logs
• Step Function execution logs
• ECS Task 1 logs
• ECS Task 2 logs
• Java Lambda logs

Limitations:

1. Manual filename filtering: User must know exact filename
2. No error aggregation: Can't see "all failed PDFs in last hour"
3. No SLA metrics: No tracking of processing time, success rate
4. PDF-to-HTML not included: Dashboard only covers PDF-to-PDF solution
5. No alerting: Dashboard is view-only, no alarms configured

5.4 Missing AWS X-Ray Integration

No distributed tracing configured:

# app.py - Lambda functions lack X-Ray tracing
split_pdf_lambda = lambda_.Function(
    self, 'SplitPDF',
    # NO tracing=lambda_.Tracing.ACTIVE
)

# Step Functions lacks X-Ray
state_machine = sfn.StateMachine(self, "MyStateMachine",
                                 # NO tracing_enabled=True
)

Impact:

• Cannot visualize service map
• Cannot identify bottlenecks
• Cannot measure latency between components
• Cannot detect cold start issues

5.5 Log Retention and Cost

Current configuration:

# app.py lines 90-95
python_container_log_group = logs.LogGroup(self, "PythonContainerLogGroup",
                                          log_group_name="/ecs/MyFirstTaskDef/PythonContainerLogGroup",
                                          retention=logs.RetentionDays.ONE_WEEK,
                                          removal_policy=cdk.RemovalPolicy.DESTROY)

Issues:

1. Short retention: 1 week may be insufficient for compliance/debugging
2. Inconsistent retention: Lambda logs use default (never expire)
3. No log archival: No S3 export for long-term storage
4. Cost risk: Lambda logs without retention can grow unbounded

5.6 Structured Logging Gaps

Current logging is unstructured:

# docker_autotag/autotag.py
logging.info(f'Filename : {filename} | Uploaded {filename} to S3')

Problems:

• Cannot query by structured fields
• CloudWatch Insights queries are fragile
• No JSON logging for machine parsing
• Inconsistent log formats across components

Better approach (not implemented):

# Structured logging example
logger.info("file_uploaded", extra={
    "filename": filename,
    "bucket": bucket_name,
    "key": s3_key,
    "correlation_id": correlation_id,
    "component": "autotag",
    "status": "success"
})

---

6. Dead Letter Queue Analysis

6.1 No DLQ Configuration

Critical Finding: No Dead Letter Queues configured for any component.

Lambda Functions - No DLQ

# app.py - All Lambda functions lack DLQ configuration
split_pdf_lambda = lambda_.Function(
    self, 'SplitPDF',
    # NO dead_letter_queue=dlq
    # NO dead_letter_queue_enabled=True
)

Impact:

• Failed Lambda invocations are lost after retry exhaustion
• No way to replay failed events
• No visibility into failure patterns

Step Functions - No Error Handling

No DLQ or error notification:

# app.py lines 340-345
state_machine = sfn.StateMachine(self, "MyStateMachine",
                                 definition=parallel_state,
                                 timeout=Duration.minutes(150))
# NO error handling, NO SNS notification, NO DLQ

When Step Functions execution fails:

1. Execution status changes to "FAILED"
2. Error details stored in execution history
3. No notification sent
4. No automatic retry
5. No DLQ for manual replay

S3 Event Notification - No DLQ

S3 trigger configuration:

# app.py lines 355-360
bucket.add_event_notification(
    s3.EventType.OBJECT_CREATED,
    s3n.LambdaDestination(split_pdf_lambda),
    s3.NotificationKeyFilter(prefix="pdf/"),
    s3.NotificationKeyFilter(suffix=".pdf")
)
# NO DLQ for failed Lambda invocations

Risk: If split_pdf_lambda fails (e.g., throttling), S3 event is lost.

6.2 Failure Recovery Mechanisms

Current state: No automated recovery mechanisms.

Missing capabilities:

1. No replay queue: Cannot reprocess failed PDFs
2. No manual intervention workflow: No way to fix and retry
3. No partial failure handling: Map state failures lose all progress
4. No checkpoint/resume: Long-running workflows cannot resume from failure point

6.3 Idempotency Issues

Split PDF Lambda:

# lambda/split_pdf/main.py - No idempotency check
def lambda_handler(event, context):
    # Processes S3 event without checking if already processed
    chunks = split_pdf_into_pages(...)
    response = stepfunctions.start_execution(...)

Problem: If Lambda is retried (e.g., timeout), it creates duplicate Step Functions executions.

PDF-to-HTML Lambda has idempotency:

# pdf2html/lambda_function.py lines 70-95
# IDEMPOTENCY CHECK: Re-enabled to prevent reprocessing
output_check_keys = [f"output/{filename_base}.zip", ...]
for output_check_key in output_check_keys:
    try:
        s3.head_object(Bucket=bucket, Key=output_check_key)
        return {"status": "skipped", "message": "Output already exists"}
    except s3.exceptions.ClientError as e:
        if e.response['Error']['Code'] != '404':
            raise e

Inconsistency: PDF-to-PDF solution lacks this protection.

---

7. Specific Recommendations

7.1 CRITICAL: Step Functions Error Handling

Priority: P0 - Implement immediately

Recommendation 1: Add Retry Configuration

# app.py - Enhanced Step Functions configuration
from aws_cdk import aws_stepfunctions as sfn

# Configure retry for ECS tasks
ecs_task_1_with_retry = ecs_task_1.add_retry(
    errors=["States.TaskFailed", "States.Timeout"],
    interval=Duration.seconds(30),
    max_attempts=3,
    backoff_rate=2.0
)

# Configure retry for Lambda tasks
java_lambda_task_with_retry = java_lambda_task.add_retry(
    errors=["States.TaskFailed", "Lambda.ServiceException", "Lambda.TooManyRequestsException"],
    interval=Duration.seconds(10),
    max_attempts=3,
    backoff_rate=2.0
)

# Add catch for unrecoverable errors
error_notification = sfn.Pass(self, "NotifyError",
    parameters={
        "error.$": "$.Error",
        "cause.$": "$.Cause",
        "input.$": "$"
    }
)

# Add SNS notification task
sns_topic = sns.Topic(self, "ProcessingErrorTopic")
notify_task = tasks.SnsPublish(self, "SendErrorNotification",
    topic=sns_topic,
    message=sfn.TaskInput.from_json_path_at("$")
)

# Apply catch to all tasks
ecs_task_1_with_retry.add_catch(
    notify_task.next(error_notification),
    errors=["States.ALL"],
    result_path="$.errorInfo"
)

Recommendation 2: Implement Partial Failure Handling

# Configure Map state for partial failure tolerance
map_state = sfn.Map(self, "Map",
    max_concurrency=100,
    items_path=sfn.JsonPath.string_at("$.chunks"),
    result_path="$.MapResults",
    # Add error handling
    catch=[sfn.CatchProps(
        errors=["States.ALL"],
        result_path="$.mapError"
    )]
)

# Add a Choice state to check for partial failures
check_results = sfn.Choice(self, "CheckMapResults")
check_results.when(
    sfn.Condition.is_present("$.mapError"),
    notify_partial_failure
).otherwise(continue_processing)

7.2 CRITICAL: Adobe API Resilience

Priority: P0 - Implement immediately

Recommendation 3: Implement Circuit Breaker Pattern

# docker_autotag/autotag.py - Add circuit breaker
import time
from datetime import datetime, timedelta

class AdobeAPICircuitBreaker:
    def __init__(self, failure_threshold=5, timeout=300):
        self.failure_count = 0
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.last_failure_time = None
        self.state = "CLOSED"  # CLOSED, OPEN, HALF_OPEN
    
    def call(self, func, *args, **kwargs):
        if self.state == "OPEN":
            if datetime.now() - self.last_failure_time > timedelta(seconds=self.timeout):
                self.state = "HALF_OPEN"
            else:
                raise Exception("Circuit breaker is OPEN - Adobe API unavailable")
        
        try:
            result = func(*args, **kwargs)
            if self.state == "HALF_OPEN":
                self.state = "CLOSED"
                self.failure_count = 0
            return result
        except Exception as e:
            self.failure_count += 1
            self.last_failure_time = datetime.now()
            
            if self.failure_count >= self.failure_threshold:
                self.state = "OPEN"
                logging.error(f"Circuit breaker opened after {self.failure_count} failures")
            raise

# Global circuit breaker instance
adobe_circuit_breaker = AdobeAPICircuitBreaker()

def autotag_pdf_with_options(filename, client_id, client_secret):
    max_retries = 3
    base_delay = 5
    
    for attempt in range(max_retries):
        try:
            return adobe_circuit_breaker.call(
                _autotag_pdf_internal, 
                filename, 
                client_id, 
                client_secret
            )
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            
            delay = base_delay * (2 ** attempt)
            logging.warning(f"Adobe API attempt {attempt+1} failed: {e}. Retrying in {delay}s")
            time.sleep(delay)

Recommendation 4: Fix Lambda Error Responses

# lambda/accessibility_checker_before_remidiation/main.py
def lambda_handler(event, context):
    try:
        # ... processing ...
        return {
            "statusCode": 200,
            "body": {
                "status": "success",
                "report_path": bucket_save_path,
                "filename": file_basename
            }
        }
    except (ServiceApiException, ServiceUsageException, SdkException) as e:
        print(f'Filename : {file_basename} | Exception encountered: {e}')
        # RAISE exception instead of returning error string
        raise Exception(f"Adobe API failed for {file_basename}: {str(e)}")
    except Exception as e:
        print(f'Filename : {file_basename} | Unexpected error: {e}')
        raise

Apply to:

• lambda/accessibility_checker_before_remidiation/main.py
• lambda/accessability_checker_after_remidiation/main.py
• lambda/java_lambda/PDFMergerLambda/src/main/java/com/example/App.java

7.3 HIGH: Implement Dead Letter Queues

Priority: P1 - Implement within 2 weeks

Recommendation 5: Add DLQ to All Lambdas

# app.py - Add DLQ configuration
from aws_cdk import aws_sqs as sqs

# Create DLQ for Lambda functions
lambda_dlq = sqs.Queue(self, "LambdaDLQ",
    queue_name="pdf-processing-lambda-dlq",
    retention_period=Duration.days(14),
    visibility_timeout=Duration.seconds(300)
)

# Apply to all Lambda functions
split_pdf_lambda = lambda_.Function(
    self, 'SplitPDF',
    runtime=lambda_.Runtime.PYTHON_3_12,
    handler='main.lambda_handler',
    code=lambda_.Code.from_docker_build("lambda/split_pdf"),
    timeout=Duration.seconds(900),
    memory_size=1024,
    dead_letter_queue=lambda_dlq,  # ADD THIS
    dead_letter_queue_enabled=True  # ADD THIS
)

# Create alarm for DLQ depth
cloudwatch.Alarm(self, "LambdaDLQAlarm",
    metric=lambda_dlq.metric_approximate_number_of_messages_visible(),
    threshold=1,
    evaluation_periods=1,
    alarm_description="Lambda DLQ has messages - processing failures detected"
)

Recommendation 6: Step Functions Error Notification

# app.py - Add SNS topic for Step Functions failures
error_topic = sns.Topic(self, "StepFunctionsErrorTopic",
    display_name="PDF Processing Errors"
)

# Create EventBridge rule for failed executions
events.Rule(self, "StepFunctionFailureRule",
    event_pattern=events.EventPattern(
        source=["aws.states"],
        detail_type=["Step Functions Execution Status Change"],
        detail={
            "status": ["FAILED", "TIMED_OUT", "ABORTED"],
            "stateMachineArn": [state_machine.state_machine_arn]
        }
    ),
    targets=[targets.SnsTopic(error_topic)]
)

# Add Lambda to process DLQ messages and retry
dlq_processor = lambda_.Function(self, "DLQProcessor",
    runtime=lambda_.Runtime.PYTHON_3_12,
    handler="index.handler",
    code=lambda_.Code.from_inline("""
import json
import boto3

stepfunctions = boto3.client('stepfunctions')

def handler(event, context):
    for record in event['Records']:
        message = json.loads(record['body'])
        
        # Extract original input
        original_input = message.get('input', {})
        
        # Retry Step Functions execution
        response = stepfunctions.start_execution(
            stateMachineArn=message['stateMachineArn'],
            input=json.dumps(original_input)
        )
        
        print(f"Retried execution: {response['executionArn']}")
    """)
)

# Grant permissions
state_machine.grant_start_execution(dlq_processor)

# Connect DLQ to processor
lambda_dlq.grant_consume_messages(dlq_processor)
dlq_processor.add_event_source(
    lambda_event_sources.SqsEventSource(lambda_dlq, batch_size=1)
)

7.4 HIGH: Implement Distributed Tracing

Priority: P1 - Implement within 2 weeks

Recommendation 7: Enable AWS X-Ray

# app.py - Enable X-Ray tracing
split_pdf_lambda = lambda_.Function(
    self, 'SplitPDF',
    runtime=lambda_.Runtime.PYTHON_3_12,
    handler='main.lambda_handler',
    code=lambda_.Code.from_docker_build("lambda/split_pdf"),
    tracing=lambda_.Tracing.ACTIVE  # ADD THIS
)

# Enable for all Lambda functions
java_lambda = lambda_.Function(
    self, 'JavaLambda',
    runtime=lambda_.Runtime.JAVA_21,
    handler='com.example.App::handleRequest',
    code=lambda_.Code.from_asset('lambda/java_lambda/PDFMergerLambda/target/PDFMergerLambda-1.0-SNAPSHOT.jar'),
    tracing=lambda_.Tracing.ACTIVE  # ADD THIS
)

# Enable for Step Functions
state_machine = sfn.StateMachine(self, "MyStateMachine",
                                 definition=parallel_state,
                                 timeout=Duration.minutes(150),
                                 tracing_enabled=True  # ADD THIS
)

Recommendation 8: Add Correlation IDs

# lambda/split_pdf/main.py - Generate and propagate correlation ID
import uuid

def lambda_handler(event, context):
    # Generate correlation ID
    correlation_id = str(uuid.uuid4())
    
    # Extract S3 info
    s3_record = event['Records'][0]
    bucket_name = s3_record['s3']['bucket']['name']
    pdf_file_key = urllib.parse.unquote_plus(s3_record['s3']['object']['key'])
    
    # Log with correlation ID
    print(json.dumps({
        "correlation_id": correlation_id,
        "event": "processing_started",
        "filename": pdf_file_key,
        "bucket": bucket_name,
        "timestamp": datetime.utcnow().isoformat()
    }))
    
    # Split PDF and add correlation ID to chunks
    chunks = split_pdf_into_pages(pdf_file_content, pdf_file_key, s3, bucket_name, 200)
    
    # Add correlation ID to each chunk
    for chunk in chunks:
        chunk['correlation_id'] = correlation_id
    
    # Start Step Functions with correlation ID
    response = stepfunctions.start_execution(
        stateMachineArn=state_machine_arn,
        name=f"{file_basename}-{correlation_id[:8]}",  # Include in execution name
        input=json.dumps({
            "chunks": chunks, 
            "s3_bucket": bucket_name,
            "correlation_id": correlation_id
        })
    )

Propagate through all components:

# docker_autotag/autotag.py
def main():
    correlation_id = os.getenv('CORRELATION_ID', 'unknown')
    
    # Add to all log statements
    logging.info(json.dumps({
        "correlation_id": correlation_id,
        "event": "autotag_started",
        "filename": file_key
    }))

// javascript_docker/alt-text.js
async function startProcess() {
    const correlationId = process.env.CORRELATION_ID || 'unknown';
    
    logger.info(JSON.stringify({
        correlation_id: correlationId,
        event: 'alt_text_generation_started',
        filename: filebasename
    }));
}

7.5 MEDIUM: Improve Observability

Priority: P2 - Implement within 1 month

Recommendation 9: Structured Logging

# Create shared logging utility
# utils/structured_logger.py
import json
import logging
from datetime import datetime

class StructuredLogger:
    def __init__(self, component_name):
        self.component = component_name
        self.logger = logging.getLogger(component_name)
    
    def log(self, level, event, **kwargs):
        log_entry = {
            "timestamp": datetime.utcnow().isoformat(),
            "component": self.component,
            "event": event,
            **kwargs
        }
        
        log_message = json.dumps(log_entry)
        
        if level == "INFO":
            self.logger.info(log_message)
        elif level == "ERROR":
            self.logger.error(log_message)
        elif level == "WARNING":
            self.logger.warning(log_message)
    
    def info(self, event, **kwargs):
        self.log("INFO", event, **kwargs)
    
    def error(self, event, **kwargs):
        self.log("ERROR", event, **kwargs)
    
    def warning(self, event, **kwargs):
        self.log("WARNING", event, **kwargs)

# Usage in Lambda functions
logger = StructuredLogger("split_pdf")

logger.info("pdf_split_started", 
    correlation_id=correlation_id,
    filename=pdf_file_key,
    bucket=bucket_name,
    num_pages=num_pages
)

logger.info("pdf_split_completed",
    correlation_id=correlation_id,
    filename=pdf_file_key,
    num_chunks=len(chunks),
    duration_ms=duration
)

Recommendation 10: Enhanced CloudWatch Dashboards

# app.py - Create comprehensive dashboard
dashboard = cloudwatch.Dashboard(self, "PDFProcessingDashboard",
    dashboard_name="pdf-processing-unified"
)

# Add metrics for success/failure rates
dashboard.add_widgets(
    cloudwatch.GraphWidget(
        title="Processing Success Rate",
        left=[
            state_machine.metric_succeeded(statistic="Sum"),
            state_machine.metric_failed(statistic="Sum"),
            state_machine.metric_timed_out(statistic="Sum")
        ],
        period=Duration.minutes(5)
    ),
    
    cloudwatch.GraphWidget(
        title="Lambda Error Rates",
        left=[
            split_pdf_lambda.metric_errors(statistic="Sum"),
            java_lambda.metric_errors(statistic="Sum"),
            add_title_lambda.metric_errors(statistic="Sum")
        ],
        period=Duration.minutes(5)
    ),
    
    cloudwatch.GraphWidget(
        title="Processing Duration (p50, p95, p99)",
        left=[
            state_machine.metric_duration(statistic="p50"),
            state_machine.metric_duration(statistic="p95"),
            state_machine.metric_duration(statistic="p99")
        ],
        period=Duration.minutes(5)
    ),
    
    cloudwatch.SingleValueWidget(
        title="Active Executions",
        metrics=[state_machine.metric_started(statistic="Sum")]
    )
)

Recommendation 11: CloudWatch Alarms

# app.py - Add comprehensive alarming
from aws_cdk import aws_cloudwatch_actions as cw_actions

# Create SNS topic for alarms
alarm_topic = sns.Topic(self, "ProcessingAlarmTopic",
    display_name="PDF Processing Alarms"
)

# Step Functions failure alarm
sfn_failure_alarm = cloudwatch.Alarm(self, "StepFunctionFailureAlarm",
    metric=state_machine.metric_failed(statistic="Sum", period=Duration.minutes(5)),
    threshold=1,
    evaluation_periods=1,
    alarm_description="Step Functions execution failed",
    treat_missing_data=cloudwatch.TreatMissingData.NOT_BREACHING
)
sfn_failure_alarm.add_alarm_action(cw_actions.SnsAction(alarm_topic))

# Lambda error rate alarm
lambda_error_alarm = cloudwatch.Alarm(self, "LambdaErrorRateAlarm",
    metric=split_pdf_lambda.metric_errors(statistic="Sum", period=Duration.minutes(5)),
    threshold=5,
    evaluation_periods=2,
    alarm_description="High Lambda error rate detected"
)
lambda_error_alarm.add_alarm_action(cw_actions.SnsAction(alarm_topic))

# ECS task failure alarm
ecs_failure_alarm = cloudwatch.Alarm(self, "ECSTaskFailureAlarm",
    metric=cloudwatch.Metric(
        namespace="AWS/ECS",
        metric_name="TasksFailed",
        dimensions_map={"ClusterName": cluster.cluster_name},
        statistic="Sum",
        period=Duration.minutes(5)
    ),
    threshold=1,
    evaluation_periods=1,
    alarm_description="ECS task failed"
)
ecs_failure_alarm.add_alarm_action(cw_actions.SnsAction(alarm_topic))

# Processing duration alarm (SLA breach)
duration_alarm = cloudwatch.Alarm(self, "ProcessingDurationAlarm",
    metric=state_machine.metric_duration(statistic="p95", period=Duration.minutes(15)),
    threshold=Duration.minutes(30).to_milliseconds(),
    evaluation_periods=2,
    alarm_description="95th percentile processing time exceeds 30 minutes"
)
duration_alarm.add_alarm_action(cw_actions.SnsAction(alarm_topic))

7.6 MEDIUM: PDF-to-HTML Solution Resilience

Priority: P2 - Implement within 1 month

Recommendation 12: Bedrock Data Automation Error Handling

Current implementation has retry logic but needs improvement:

# pdf2html/content_accessibility_utility_on_aws/pdf2html/services/bedrock_client.py
# Lines 516-620 - Has retry logic but can be enhanced

class BedrockDataAutomationClient:
    def __init__(self, max_retries=3, timeout=300):
        self.max_retries = max_retries
        self.timeout = timeout
        self.circuit_breaker = CircuitBreaker(failure_threshold=5, timeout=300)
    
    def invoke_bda_with_resilience(self, project_arn, input_config, output_config):
        """Enhanced BDA invocation with circuit breaker and better error handling"""
        
        for attempt in range(self.max_retries):
            try:
                return self.circuit_breaker.call(
                    self._invoke_bda_internal,
                    project_arn,
                    input_config,
                    output_config
                )
            except ClientError as e:
                error_code = e.response['Error']['Code']
                
                # Don't retry on client errors
                if error_code in ['ValidationException', 'InvalidParameterException']:
                    logger.error(f"BDA client error (no retry): {e}")
                    raise
                
                # Retry on throttling and service errors
                if error_code in ['ThrottlingException', 'ServiceUnavailableException']:
                    if attempt < self.max_retries - 1:
                        backoff = (2 ** attempt) * 5  # 5s, 10s, 20s
                        logger.warning(f"BDA throttled, retrying in {backoff}s (attempt {attempt+1}/{self.max_retries})")
                        time.sleep(backoff)
                        continue
                
                raise
            except Exception as e:
                logger.error(f"Unexpected BDA error: {e}")
                if attempt < self.max_retries - 1:
                    time.sleep(5 * (2 ** attempt))
                    continue
                raise

Recommendation 13: PDF-to-HTML Lambda Idempotency Enhancement

Current implementation is good but can be improved:

# pdf2html/lambda_function.py - Enhanced idempotency
def lambda_handler(event, context):
    temp_output_dir = None
    correlation_id = context.aws_request_id
    
    try:
        # Extract S3 event
        record = event["Records"][0]["s3"]
        bucket = record["bucket"]["name"]
        key = urllib.parse.unquote_plus(record["object"]["key"])
        
        # Enhanced filtering
        if not key.startswith("uploads/"):
            logger.info(f"[{correlation_id}] Skipping non-uploads file: {key}")
            return {"status": "skipped", "reason": "not_in_uploads_folder"}
        
        if not key.lower().endswith('.pdf'):
            logger.info(f"[{correlation_id}] Skipping non-PDF file: {key}")
            return {"status": "skipped", "reason": "not_pdf"}
        
        # Sanitize filename
        original_filename = os.path.basename(key)
        sanitized_filename = sanitize_filename(original_filename)
        filename_base = os.path.splitext(sanitized_filename)[0]
        
        # ENHANCED IDEMPOTENCY CHECK with state tracking
        processing_state_key = f"processing-state/{filename_base}.json"
        
        try:
            # Check if currently processing
            state_obj = s3.get_object(Bucket=bucket, Key=processing_state_key)
            state = json.loads(state_obj['Body'].read())
            
            if state.get('status') == 'processing':
                processing_time = (datetime.utcnow() - 
                                 datetime.fromisoformat(state['started_at'])).total_seconds()
                
                # If processing for more than 30 minutes, assume stale and reprocess
                if processing_time < 1800:
                    logger.info(f"[{correlation_id}] File already being processed")
                    return {"status": "skipped", "reason": "already_processing"}
        except s3.exceptions.NoSuchKey:
            pass  # No state file, proceed with processing
        
        # Check for completed output
        output_check_keys = [
            f"output/{filename_base}.zip",
            f"output/{os.path.splitext(original_filename)[0]}.zip"
        ]
        
        for output_check_key in output_check_keys:
            try:
                s3.head_object(Bucket=bucket, Key=output_check_key)
                logger.info(f"[{correlation_id}] Output exists: {output_check_key}")
                return {
                    "status": "skipped",
                    "reason": "output_exists",
                    "output": f"s3://{bucket}/{output_check_key}"
                }
            except s3.exceptions.ClientError as e:
                if e.response['Error']['Code'] != '404':
                    raise
        
        # Mark as processing
        s3.put_object(
            Bucket=bucket,
            Key=processing_state_key,
            Body=json.dumps({
                "status": "processing",
                "started_at": datetime.utcnow().isoformat(),
                "correlation_id": correlation_id,
                "lambda_request_id": context.aws_request_id
            })
        )
        
        # ... processing logic ...
        
        # Mark as completed
        s3.put_object(
            Bucket=bucket,
            Key=processing_state_key,
            Body=json.dumps({
                "status": "completed",
                "started_at": state.get('started_at'),
                "completed_at": datetime.utcnow().isoformat(),
                "correlation_id": correlation_id,
                "output_zip": output_s3_key
            })
        )
        
        return {
            "status": "done",
            "correlation_id": correlation_id,
            "execution_id": context.aws_request_id,
            "output_zip": f"s3://{bucket}/{output_s3_key}"
        }
        
    except Exception as e:
        # Mark as failed
        try:
            s3.put_object(
                Bucket=bucket,
                Key=processing_state_key,
                Body=json.dumps({
                    "status": "failed",
                    "error": str(e),
                    "failed_at": datetime.utcnow().isoformat(),
                    "correlation_id": correlation_id
                })
            )
        except:
            pass  # Don't fail on state update failure
        
        logger.error(f"[{correlation_id}] Unhandled exception: {e}")
        raise
    finally:
        # Cleanup temp directory
        if temp_output_dir and os.path.exists(temp_output_dir):
            shutil.rmtree(temp_output_dir)

---

8. Implementation Roadmap

Phase 1: Critical Fixes (Week 1-2) 🔴

Must implement immediately to prevent production failures:

1. Step Functions Retry Configuration (2 days)

   • Add retry policies to all tasks
   • Configure exponential backoff
   • Test with simulated failures

2. Fix Lambda Error Responses (1 day)

   • Update accessibility checker Lambdas to raise exceptions
   • Update Java Lambda to throw exceptions
   • Test Step Functions failure detection

3. Adobe API Circuit Breaker (3 days)

   • Implement circuit breaker class
   • Add to autotag.py
   • Add to accessibility checker Lambdas
   • Test with Adobe API unavailability

4. Dead Letter Queues (2 days)

   • Create SQS DLQ
   • Configure all Lambdas
   • Create DLQ processor Lambda
   • Set up CloudWatch alarms

Deliverables:

• Updated CDK stack with retry/catch configuration
• Circuit breaker implementation
• DLQ infrastructure
• Test results demonstrating resilience

Phase 2: Observability (Week 3-4) 🟡

Improve debugging and monitoring capabilities:

1. Correlation ID Implementation (3 days)

   • Add correlation ID generation in split_pdf Lambda
   • Propagate through Step Functions state
   • Update all components to log correlation ID
   • Update CloudWatch queries

2. AWS X-Ray Integration (2 days)

   • Enable X-Ray on all Lambdas
   • Enable X-Ray on Step Functions
   • Configure sampling rules
   • Create service map dashboard

3. Structured Logging (3 days)

   • Create shared logging utility
   • Update all Lambda functions
   • Update ECS containers
   • Create CloudWatch Insights queries

4. Enhanced Dashboards (2 days)

   • Add success/failure rate widgets
   • Add duration percentile widgets
   • Add error rate widgets
   • Create PDF-to-HTML dashboard

Deliverables:

• Correlation IDs in all logs
• X-Ray service map
• Structured logging library
• Comprehensive CloudWatch dashboards

Phase 3: Advanced Resilience (Week 5-6) 🟢

Implement advanced patterns for production-grade reliability:

1. Partial Failure Handling (3 days)

   • Implement Map state error tolerance
   • Add success/failure tracking per chunk
   • Create partial success notification
   • Test with mixed success/failure scenarios

2. Idempotency for PDF-to-PDF (2 days)

   • Add processing state tracking
   • Implement duplicate detection
   • Add cleanup for stale processing states
   • Test retry scenarios

3. CloudWatch Alarms (2 days)

   • Create failure rate alarms
   • Create duration SLA alarms
   • Create DLQ depth alarms
   • Set up SNS notifications

4. Rate Limiting and Throttling (3 days)

   • Implement Bedrock API rate limiting
   • Add Lambda reserved concurrency
   • Configure ECS task limits
   • Test under load

Deliverables:

• Partial failure handling
• Complete idempotency
• Comprehensive alarming
• Rate limiting implementation

---

9. Testing Strategy

9.1 Resilience Testing Scenarios

Test each failure mode systematically:

Test 1: Adobe API Unavailability

# Simulate Adobe API failure by using invalid credentials
aws secretsmanager update-secret \
  --secret-id /myapp/client_credentials \
  --secret-string '{"client_credentials":{"PDF_SERVICES_CLIENT_ID":"invalid","PDF_SERVICES_CLIENT_SECRET":"invalid"}}'

# Upload test PDF
aws s3 cp test.pdf s3://bucket/pdf/test.pdf

# Verify:
# - Circuit breaker opens after threshold
# - Step Functions retries with backoff
# - DLQ receives failed message
# - Alarm triggers

Test 2: Lambda Timeout

# Upload very large PDF (>100MB)
aws s3 cp large-test.pdf s3://bucket/pdf/large-test.pdf

# Verify:
# - Lambda times out gracefully
# - Step Functions retries
# - Correlation ID preserved across retries
# - CloudWatch logs show timeout

Test 3: Partial Map State Failure

# Upload PDF that will be split into 10 chunks
# Manually fail chunk 5 processing by deleting intermediate S3 file

# Verify:
# - Other 9 chunks complete successfully
# - Failed chunk is retried
# - Final status shows partial success
# - Notification sent with details

Test 4: Bedrock Throttling

# Submit 100 PDFs simultaneously to trigger throttling
for i in {1..100}; do
  aws s3 cp test-$i.pdf s3://bucket/uploads/test-$i.pdf &
done

# Verify:
# - Bedrock API calls are retried with backoff
# - No permanent failures due to throttling
# - Processing completes eventually
# - Metrics show retry attempts

Test 5: S3 Eventual Consistency

# Upload PDF and immediately trigger processing
aws s3 cp test.pdf s3://bucket/pdf/test.pdf
# Lambda may not see file immediately

# Verify:
# - S3 download retries on 404
# - Processing succeeds after retry
# - No permanent failure

9.2 Chaos Engineering

Implement chaos testing for production readiness:

# chaos_test.py - Automated chaos testing
import boto3
import random
import time

def chaos_test_adobe_api():
    """Randomly fail Adobe API calls"""
    secretsmanager = boto3.client('secretsmanager')
    
    # 20% chance to inject invalid credentials
    if random.random() < 0.2:
        print("CHAOS: Injecting invalid Adobe credentials")
        secretsmanager.update_secret(
            SecretId='/myapp/client_credentials',
            SecretString='{"client_credentials":{"PDF_SERVICES_CLIENT_ID":"invalid","PDF_SERVICES_CLIENT_SECRET":"invalid"}}'
        )
        time.sleep(300)  # Keep invalid for 5 minutes
        # Restore valid credentials
        restore_valid_credentials()

def chaos_test_lambda_failure():
    """Randomly terminate Lambda executions"""
    lambda_client = boto3.client('lambda')
    
    # 10% chance to update Lambda with failing code
    if random.random() < 0.1:
        print("CHAOS: Injecting Lambda failure")
        # Update environment variable to trigger failure
        lambda_client.update_function_configuration(
            FunctionName='split_pdf',
            Environment={'Variables': {'CHAOS_FAIL': 'true'}}
        )
        time.sleep(60)
        # Restore
        lambda_client.update_function_configuration(
            FunctionName='split_pdf',
            Environment={'Variables': {'CHAOS_FAIL': 'false'}}
        )

def chaos_test_s3_latency():
    """Simulate S3 latency by adding delays"""
    # Use S3 bucket policies to add latency
    pass

# Run chaos tests continuously
while True:
    chaos_test_adobe_api()
    chaos_test_lambda_failure()
    time.sleep(600)  # Run every 10 minutes

---

10. Metrics and KPIs

10.1 Reliability Metrics

Track these metrics to measure resilience improvements:

Metric	Target	Current	Priority
Success Rate	>99%	Unknown	P0
Mean Time to Recovery (MTTR)	<5 min	N/A (no recovery)	P0
Failed Execution Rate	<1%	Unknown	P0
Retry Success Rate	>80%	0% (no retries)	P0
Adobe API Error Rate	<5%	Unknown	P1
Processing Duration (p95)	<30 min	Unknown	P1
DLQ Message Age	<1 hour	N/A (no DLQ)	P1
Correlation ID Coverage	100%	0%	P1

10.2 CloudWatch Insights Queries

Use these queries to monitor resilience:

-- Query 1: Success rate by hour
fields @timestamp, correlation_id, status
| filter event = "processing_completed" or event = "processing_failed"
| stats count(*) as total, 
        sum(status = "success") as successes,
        sum(status = "failed") as failures
  by bin(1h)
| fields bin, 
         successes / total * 100 as success_rate,
         failures / total * 100 as failure_rate

-- Query 2: Retry attempts
fields @timestamp, correlation_id, attempt
| filter event = "retry_attempt"
| stats count(*) as retry_count by correlation_id
| sort retry_count desc

-- Query 3: Adobe API errors
fields @timestamp, correlation_id, error_code
| filter component = "adobe_api" and level = "ERROR"
| stats count(*) by error_code
| sort count desc

-- Query 4: Processing duration by file size
fields @timestamp, correlation_id, duration_ms, file_size_mb
| filter event = "processing_completed"
| stats avg(duration_ms) as avg_duration,
        percentile(duration_ms, 95) as p95_duration
  by bin(file_size_mb, 10)

---

11. Cost Impact Analysis

11.1 Current Cost Risks

Uncontrolled costs due to lack of resilience:

1. Failed executions waste resources

   • ECS tasks run for 15+ minutes before failing
   • Lambda functions timeout at 15 minutes
   • No early termination on unrecoverable errors
   • Estimated waste: 20-30% of compute costs

2. No cleanup of failed artifacts

   • Temporary S3 files accumulate
   • Failed processing leaves partial outputs
   • Estimated waste: Growing S3 storage costs

3. Unbounded Lambda log retention

   • Default retention = never expire
   • High-volume logging without structure
   • Estimated waste: $50-100/month per Lambda

11.2 Cost of Implementing Resilience

One-time implementation costs:

Component	Effort	AWS Cost Impact
Step Functions retry	2 days	+$0 (same executions)
DLQ infrastructure	2 days	+$5/month (SQS)
X-Ray tracing	2 days	+$10-20/month
CloudWatch alarms	1 day	+$2/month (10 alarms)
Structured logging	3 days	+$0 (same log volume)
Circuit breakers	3 days	+$0 (code only)
Total	13 days	+$17-27/month

Cost savings from resilience:

Benefit	Monthly Savings
Reduced failed execution waste	$200-500
S3 cleanup automation	$50-100
Faster failure detection	$100-200
Reduced debugging time	$500-1000 (eng time)
Total Savings	$850-1800/month

ROI: 30-60x return on monthly AWS cost investment

---

12. Comparison: PDF-to-PDF vs PDF-to-HTML

12.1 Resilience Maturity Comparison

Aspect	PDF-to-PDF	PDF-to-HTML	Winner
Retry Logic	❌ None	✅ Partial (BDA only)	PDF-to-HTML
Error Handling	❌ Inconsistent	⚠️ Better but incomplete	PDF-to-HTML
Idempotency	❌ None	✅ Implemented	PDF-to-HTML
DLQ	❌ None	❌ None	Tie
Correlation IDs	❌ None	❌ None	Tie
Circuit Breakers	❌ None	❌ None	Tie
Observability	⚠️ Dashboard only	❌ No dashboard	PDF-to-PDF
Structured Logging	❌ None	❌ None	Tie

Overall Maturity:

• PDF-to-PDF: 2/10 (Critical gaps)
• PDF-to-HTML: 4/10 (Better but still insufficient)

12.2 Failure Mode Analysis

PDF-to-PDF Critical Failure Modes

1. Adobe API unavailable → Entire workflow fails, no retry
2. ECS task OOM → Silent failure, no notification
3. Step Functions timeout → Lost processing, no recovery
4. S3 throttling → Cascading failures across chunks
5. Secrets Manager throttling → All tasks fail simultaneously

PDF-to-HTML Critical Failure Modes

1. BDA job timeout → Has retry but limited
2. Bedrock throttling → Has retry but no circuit breaker
3. Lambda timeout → No retry, processing lost
4. S3 cleanup failure → Leaves orphaned files
5. Duplicate processing → Prevented by idempotency ✅

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13. Production Readiness Checklist

13.1 Critical Requirements (Must Have)

• Step Functions retry configuration - All tasks have retry policies
• Lambda error responses fixed - Exceptions raised, not returned as strings
• Dead Letter Queues configured - All Lambdas and Step Functions
• Adobe API circuit breaker - Prevents cascading failures
• Correlation IDs implemented - End-to-end request tracing
• CloudWatch alarms configured - Failure detection and notification
• Idempotency for PDF-to-PDF - Prevents duplicate processing
• X-Ray tracing enabled - Distributed tracing for debugging

13.2 High Priority (Should Have)

• Structured logging - JSON logs with consistent fields
• Enhanced dashboards - Success rates, duration, error rates
• Partial failure handling - Map state tolerates individual chunk failures
• Rate limiting - Bedrock and Adobe API call throttling
• Log retention policies - Consistent retention across all components
• S3 lifecycle policies - Automatic cleanup of temporary files
• DLQ processor Lambda - Automatic retry of failed messages
• Chaos testing - Automated resilience testing

13.3 Nice to Have (Could Have)

• Custom metrics - Business KPIs in CloudWatch
• Service map - Visual representation of dependencies
• Automated rollback - Deployment rollback on high error rates
• Blue-green deployment - Zero-downtime deployments
• Canary deployments - Gradual rollout with automatic rollback
• Cost optimization - Right-sized Lambda memory and timeout
• Multi-region failover - Disaster recovery capability
• SLA monitoring - Automated SLA compliance tracking

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14. Conclusion

14.1 Current State Assessment

The PDF accessibility solutions have critical gaps in error handling and resilience that make them unsuitable for production use without significant improvements:

Severity Breakdown:

• 🔴 7 Critical Issues - Will cause production failures
• 🟡 5 High Priority Issues - Significantly impact reliability
• 🟢 3 Medium Priority Issues - Reduce operational efficiency

Key Findings:

1. No retry mechanisms in Step Functions means any transient failure causes permanent workflow failure
2. Adobe API failures have no resilience patterns, causing silent failures
3. No observability for debugging production issues - correlation IDs missing
4. No DLQ means failed processing is lost forever
5. Inconsistent error handling across components makes failures unpredictable

14.2 Risk Assessment

Without implementing these recommendations:

Risk	Probability	Impact	Mitigation Priority
Production workflow failures	HIGH	CRITICAL	P0 - Immediate
Data loss from failed processing	MEDIUM	HIGH	P0 - Immediate
Unable to debug production issues	HIGH	HIGH	P1 - 2 weeks
Adobe API outage causes system-wide failure	MEDIUM	CRITICAL	P0 - Immediate
Cost overruns from failed executions	HIGH	MEDIUM	P1 - 2 weeks
Customer dissatisfaction from unreliability	HIGH	HIGH	P0 - Immediate

14.3 Recommended Action Plan

Immediate Actions (This Week):

1. Implement Step Functions retry configuration
2. Fix Lambda error response patterns
3. Add Adobe API circuit breaker
4. Configure Dead Letter Queues

Short-term Actions (Next 2-4 Weeks):

1. Implement correlation IDs
2. Enable AWS X-Ray tracing
3. Add CloudWatch alarms
4. Implement structured logging

Medium-term Actions (Next 1-2 Months):

1. Enhance observability dashboards
2. Implement partial failure handling
3. Add rate limiting and throttling
4. Conduct chaos engineering tests

14.4 Success Metrics

Track these metrics to measure improvement:

• Success Rate: Target >99% (currently unknown)
• MTTR: Target <5 minutes (currently N/A)
• Retry Success Rate: Target >80% (currently 0%)
• Correlation ID Coverage: Target 100% (currently 0%)
• Failed Execution Cost: Target <5% of total (currently ~20-30%)

14.5 Final Recommendations

For Production Deployment:

1. DO NOT deploy to production without implementing P0 recommendations
2. Implement Phase 1 (Critical Fixes) before any production traffic
3. Complete Phase 2 (Observability) within first month of production
4. Conduct load testing with chaos engineering before scaling
5. Establish on-call rotation with runbooks for common failure scenarios

For Development:

1. Adopt consistent error handling patterns across all new code
2. Require correlation IDs in all new components
3. Implement structured logging as standard practice
4. Add resilience tests to CI/CD pipeline
5. Review error handling in all code reviews

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Appendix A: References

AWS Best Practices:

• AWS Step Functions Error Handling
• AWS Lambda Error Handling
• AWS X-Ray Developer Guide
• CloudWatch Logs Insights Query Syntax

Resilience Patterns:

• Circuit Breaker Pattern
• Retry Pattern with Exponential Backoff
• Idempotency in Distributed Systems

Observability:

• Distributed Tracing Best Practices
• Structured Logging