Api Rate Abuse in Hanami with Dynamodb

Rate abuse in a Hanami application that uses Amazon DynamoDB typically arises from insufficient request governance at the API layer rather than an issue inside DynamoDB itself. Hanami routes requests through a Rack-based stack, and if endpoints that perform DynamoDB operations are not explicitly rate-limited, an attacker can issue a high volume of requests in a short time. Because DynamoDB charges and scales based on consumed read/write capacity units, a burst of poorly controlled requests can cause excessive provisioned capacity consumption and amplify the impact of abuse.

When endpoints perform operations like GetItem, Query, or PutItem without per-client or per-identity throttling, the service becomes susceptible to resource exhaustion and elevated costs. For example, an endpoint such as /api/orders/:id that queries a DynamoDB table to retrieve order details can be hammered to trigger repeated scans or queries, stressing both the application and the database. MiddleBrick scanning can detect missing rate controls as part of its Rate Limiting check, identifying whether the unauthenticated attack surface allows disproportionate request volumes.

Additionally, because Hanami often exposes JSON APIs directly to the internet, the lack of request identifiers or token-based throttling means abusive clients can reuse or rotate IPs to bypass simple network-level limits. DynamoDB streams or DynamoDB Accelerator (DAX) may further propagate load if downstream consumers are not guarded. The combination of a Ruby web framework with flexible routing and a NoSQL database that does not enforce application-level rate policies means developers must implement controls explicitly to cap requests per user, API key, or IP.

To remediate rate abuse in Hanami when using DynamoDB, implement server-side request throtting close to the endpoint and enforce limits before issuing database calls. Below is a concrete Hanami endpoint that includes token-bucket style rate limiting using Redis as a shared store, ensuring that DynamoDB operations are only executed when the client is within allowed limits.

require 'redis'

class OrderEndpoint < Hanami::Endpoint
  REDIS = Redis.new(url: ENV.fetch('REDIS_URL', 'redis://localhost:6379/0'))
  RATE_LIMIT = 60        # requests
  RATE_WINDOW = 60       # per second

  def call(env)
    client_id = env['rack.request.query_hash']['client_id'] || env['warden'].user&.id
    return [401, {}, ['Unauthorized']] if client_id.nil?

    key = "rate:#{client_id}"
    current = REDIS.incr(key)
    if current == 1
      REDIS.expire(key, RATE_WINDOW)
    end

    if current > RATE_LIMIT
      return [429, { 'Content-Type' => 'application/json' }, [{ error: 'rate_limit_exceeded' }.to_json]]
    end

    # Proceed to DynamoDB operation
    result = fetch_order(client_id, env['rack.request.query_hash']['order_id'])
    [200, { 'Content-Type' => 'application/json' }, [result.to_json]]
  end

  private

  def fetch_order(client_id, order_id)
    table = Aws::DynamoDB::Client.new(resource: true).resource.table('orders')
    resp = table.get_item(key: { user_id: client_id, order_id: order_id })
    raise 'Not found' unless resp.item
    resp.item.to_h
  end
end

This approach ensures that each client is limited to a defined number of requests per window before any DynamoDB GetItem call is made. For distributed deployments, use Redis or a similar shared store to synchronize counts across application instances.

Alternatively, if you prefer middleware-level enforcement, integrate a Rack throttling gem and configure it to apply before routing to Hanami controllers:

# config/initializers/rate_limit.rb
use Rack::Attack

Rack::Attack.throttle('api/ip', limit: 100, period: 60) do |req|
  req.ip if req.path.start_with?('/api')
end

Rack::Attack.throttle('api/client_id', limit: 60, period: 60) do |req|
  if req.path.start_with?('/api')
    req.params['client_id'] || req.env['warden'].user&.id
  end
end

Rack::Attack.throttled_response = ->(env) {
  [429, { 'Content-Type' => 'application/json' }, [{ error: 'rate_limit_exceeded' }.to_json]]
}

Complement these controls by applying DynamoDB best practices such as using provisioned capacity with auto scaling, enabling DynamoDB Streams with guarded consumers, and monitoring consumed read/write capacity metrics. MiddleBrick’s Pro plan supports continuous monitoring to detect when rate-related anomalies occur across your API surface.