Memory Leak in Flask with Dynamodb

A memory leak in a Flask application that uses DynamoDB typically arises from how responses and resources are handled, not from DynamoDB itself. In a black-box scan, middleBrick tests unauthenticated endpoints and observes runtime behavior. When a Flask route performs repeated DynamoDB operations—such as scanning large tables or querying without efficient filtering—it can accumulate unmanaged Python objects, long-lived database cursors, or improperly closed responses. These objects remain referenced in the process memory across requests, causing the RSS (resident set size) to grow over time. Because DynamoDB responses can include large payloads, especially with unindexed queries or scans, failing to consume or release stream bodies promptly can retain references in Python’s garbage collector. This pattern is observable in runtime scans as steady memory growth under repeated calls, and it maps to the Data Exposure and Input Validation checks in middleBrick, which detect large or unexpected payload handling. The issue is compounded when response parsing is inconsistent, for example when using boto3’s get_item or query without streaming or pagination controls, leading to increasing memory pressure that may degrade performance or availability. Such findings appear in the per-category breakdowns provided by middleBrick, which prioritize severity and include remediation guidance.

Apply consistent pagination and explicit resource handling to prevent accumulation of large response objects. Use Limit, pagination tokens, and context patterns to ensure responses are fully consumed and released. The following example demonstrates a robust Flask route using the DynamoDB DocumentClient with pagination and safe response handling.

from flask import Flask, jsonify
import boto3
from botocore.exceptions import ClientError

app = Flask(__name__)
dynamodb = boto3.resource('dynamodb', region_name='us-east-1')
table_name = 'my-table'

@app.route('/items')
def list_items():
    table = dynamodb.Table(table_name)
    response_items = []
    try:
        paginator = table.meta.client.get_paginator('scan')
        page_iterator = paginator.paginate(TableName=table_name, PaginationConfig={'PageSize': 10})
        for page in page_iterator:
            response_items.extend(page.get('Items', []))
        return jsonify({'count': len(response_items), 'items': response_items})
    except ClientError as e:
        return jsonify({'error': e.response['Error']['Message']}), 500

Key practices include:

• Use paginators for operations that can return large result sets, avoiding full table scans when possible.
• Explicitly release references to response bodies after processing.
• Configure reasonable PageSize values to bound per-request memory footprint.
• Handle exceptions to avoid leaking connections or leaving dangling references.

When integrating with middleBrick’s Continuous Monitoring on the Pro plan, these fixes can be validated across scans on a configurable schedule. The GitHub Action can fail builds if a scan detects memory-related findings tied to Data Exposure or Input Validation, helping to prevent regressions before deployment.