I have a setup where there is a node.js application that writes to a kinesis stream using putRecords for two items. The kinesis stream feeds a Lambda function that is configured with an eventSourceMapping with a batch size of 1. However, when I debug what the Records Kinesis feeds the lambda for an invocation, I see that the lambda is processing two records. Both of these records are on the same shard and have a sequence number which is pretty close.

The interesting thing is that if I switch the node.js published to use putRecord instead of putRecords, the kinesis records have a slightly farther apart sequence number and the lambda is fed the records individually.

The java code to configure the event source mapping
```
        AWSLambdaAsync lambdaClient = AWSResourceFactory.getLambdaClient();
        CreateEventSourceMappingRequest request = new CreateEventSourceMappingRequest();
        request.setEventSourceArn(kinesisArn);
        request.setFunctionName(functionName);
        request.setEnabled(true);
        request.setStartingPosition(EventSourcePosition.TRIM_HORIZON);
        request.setBatchSize(1);
        lambdaClient.createEventSourceMapping(request);
```

The localstack lambda output that shows multiple records being read at a time:

```
{
	"Records": [{
		"eventID": "shardId-000000000000:49593014281924840790139112603493819506073986345293840386",
		"eventSourceARN": "arn:aws:kinesis:us-east-1:000000000000:stream/MyStream",
		"kinesis": {
			"partitionKey": "ed0f756b97234f4fae9e3d1a886b8fbc",
			"data": "myData1",
			"sequenceNumber": "49593014281924840790139112603493819506073986345293840386"
		}
	}, {
		"eventID": "shardId-000000000000:49593014281924840790139112603495028431893600974468546562",
		"eventSourceARN": "arn:aws:kinesis:us-east-1:000000000000:stream/MyStream",
		"kinesis": {
			"partitionKey": "abee63a9299385699d65e89a290a9065",
			"data": "myData2",
			"sequenceNumber": "49593014281924840790139112603495028431893600974468546562"
		}
	}]
}
```

}
    arn_to_lambda.get(arn).aliases[alias] = new_alias
    return new_alias
@cloudwatched('lambda')
def run_lambda(event, context, func_arn, version=None, suppress_output=False, asynchronous=False):
    if suppress_output:
        stdout_ = sys.stdout
        stderr_ = sys.stderr
        stream = StringIO()
        sys.stdout = stream
        sys.stderr = stream
    try:
        func_arn = aws_stack.fix_arn(func_arn)
        func_details = arn_to_lambda.get(func_arn)
        if not func_details:
            return not_found_error(msg='The resource specified in the request does not exist.')
        if not context:
            context = LambdaContext(func_details, version)
        result, log_output = LAMBDA_EXECUTOR.execute(func_arn, func_details,
            event, context=context, version=version, asynchronous=asynchronous)
    except Exception as e:
        return error_response('Error executing Lambda function %s: %s %s' % (func_arn, e, traceback.format_exc()))
    finally:
        if suppress_output:
            sys.stdout = stdout_
            sys.stderr = stderr_
    return result
def exec_lambda_code(script, handler_function='handler', lambda_cwd=None, lambda_env=None):
    if lambda_cwd or lambda_env:
        exec_mutex.acquire()