Stuck on a problem and need help troubleshooting a persistent issue with our Genesys Cloud to AWS EventBridge integration. We are using a custom Python Lambda function to process routing.queue.conversation events. Lately, we have been seeing significant duplicate events arriving within milliseconds of each other. This is causing our downstream database to create phantom records and messing up our reporting metrics.

I have verified the EventBridge rule configuration, and it looks standard. The issue seems to be on the consumer side or potentially a retry loop in the platform. I need a robust deduplication strategy that doesn’t add too much latency. My current implementation uses a simple in-memory set to track eventIds, but since Lambda containers are ephemeral and can be recycled, this fails when the container persists longer than the duplicate window or when scaling out.

Here is the current handler logic:

def lambda_handler(event, context):
 seen_ids = set()
 for record in event['Records']:
 event_id = record['messageId']
 if event_id in seen_ids:
 print(f"Skipping duplicate: {event_id}")
 continue
 seen_ids.add(event_id)
 process_event(record['body'])
 return {'statusCode': 200}

I am considering using DynamoDB with a TTL attribute for deduplication, but I want to ensure I am not missing a simpler pattern. Is there a standard way to handle this in the Genesys Cloud ecosystem? Should I be checking a specific field in the event payload for a unique transaction ID instead of the generic messageId?

Environment: Python 3.9, AWS Lambda, Genesys Cloud API v2. I am open to suggestions on using boto3 efficiently for the dedup check without making the Lambda cold starts too slow.

This is typically caused by the inherent at-least-once delivery guarantee of EventBridge combined with Lambda’s retry mechanism on transient errors. If your handler throws an exception or times out, Lambda retries, creating duplicates. You must implement idempotency using the unique event ID provided in the Genesys Cloud payload.

Genesys Cloud events include a id field in the root payload. Use this as the key in a DynamoDB table with a TTL or a Redis set to track processed events.

import boto3
import json

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('ProcessedEvents')

def lambda_handler(event, context):
 gen_event = event['detail']
 event_id = gen_event.get('id')
 
 # Check if already processed
 if not event_id:
 return {"statusCode": 400, "body": "Missing event ID"}
 
 try:
 # Attempt to add event ID. If exists, condition fails.
 table.put_item(
 Item={'event_id': event_id, 'processed_at': event['time']},
 ConditionExpression='attribute_not_exists(event_id)'
 )
 except dynamodb.exceptions.ConditionalCheckFailedException:
 # Duplicate detected, exit gracefully
 return {"statusCode": 200, "body": "Duplicate ignored"}

 # Process logic here
 process_conversation(gen_event)
 return {"statusCode": 200}

Do not rely on deduplication windows alone. They fail under high throughput.

Ah, yeah, this is a known issue… DynamoDB overhead kills latency for high-volume queues. Use Redis with SETNX for O(1) idempotency checks instead.

import redis
r = redis.Redis(host='redis-host')
event_id = event['detail']['id']
if r.setnx(f"gc:event:{event_id}", 1):
 r.expire(f"gc:event:{event_id}", 3600)
 # process event

processed = set()
def handler(event, context):
eid = event[‘detail’][‘id’]
if eid not in processed:
processed.add(eid)

process

In-memory sets work for single-concurrency lambdas, but you'll hit race conditions if multiple instances scale up simultaneously.

- Lambda concurrency limits
- DynamoDB conditional writes
- EventBridge deduplication windows

the redis approach is fine for low volume, but you are ignoring the cost of network latency in a high-throughput scenario. also, setnx without proper serialization can cause key collision issues if the event id contains special characters.

better pattern is to use dynamodb with conditional writes. it is atomic, scalable, and handles the “at-least-once” guarantee without external dependencies. you need to handle the ConditionalCheckFailedException which indicates a duplicate.

here is the minimal repro for the lambda handler using boto3. note the expression_attribute_values usage to avoid injection.

import json
import boto3
from botocore.exceptions import ClientError

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('GenesysEventDedup')

def lambda_handler(event, context):
 # gc events structure: event['detail']['id'] is the unique conversation/event id
 event_id = event.get('detail', {}).get('id')
 if not event_id:
 raise ValueError("Missing event id in payload")

 try:
 table.put_item(
 Item={
 'event_id': event_id,
 'processed_at': event['time'] # store timestamp for ttl/debugging
 },
 ExpressionAttributeNames={
 '#eid': 'event_id'
 },
 ExpressionAttributeValues={
 ':eid': event_id
 },
 ConditionExpression='attribute_not_exists(#eid)'
 )
 # only reached if write succeeded (unique)
 process_event(event)
 except ClientError as e:
 if e.response['Error']['Code'] == 'ConditionalCheckFailedException':
 print(f"Duplicate event detected: {event_id}")
 return {'statusCode': 200, 'body': json.dumps('duplicate ignored')}
 raise

def process_event(event):
 # actual logic here
 pass

configure the dynamodb table with a ttl on processed_at to auto-clean old records. do not use in-memory sets unless you are running a single-threaded test. the condition expression is key here; it prevents the write if the key exists, returning the error you catch. this is the standard pattern for cxone webhook consumers.