Api Rate Abuse in Phoenix with Mongodb

Rate abuse in a Phoenix API backed by MongoDB typically occurs when an endpoint that queries or writes to MongoDB lacks sufficient request throttling. Without rate limits, an attacker can send many rapid requests that cause excessive load on both the Phoenix application and the MongoDB instance. In Phoenix, each request is handled independently, and if shared resources such as connection pools or indexes are not protected, bursts of traffic can saturate database cursors, exhaust memory, or trigger long-running operations that degrade responsiveness for legitimate users.

This combination is notable because MongoDB operations such as find, aggregate, or insert can consume significant server-side resources when poorly designed queries (e.g., missing indexes, large scans) are subjected to high request rates. For example, an endpoint like /api/users/:id that performs a direct lookup by a non-indexed field can become a vector for abuse: repeated calls with varying IDs may cause collection scans that increase latency and tie up database connections. Because Phoenix does not enforce quotas by default, the API surface remains exposed unless explicit controls are added at the endpoint or through a gateway.

Another risk pattern involves write-heavy endpoints such as POST /api/events that insert documents into MongoDB. Without rate limiting, an attacker can flood the collection with high-volume writes, leading to rapid storage growth, index pressure, and potential denial of service for other operations. Even when application-level logic attempts to batch or throttle, inconsistent enforcement across controllers or pipelines can leave gaps. Because MongoDB records operations in its internal execution metrics, sustained high-rate traffic often shows up as elevated lock percentages or long-running operations in monitoring tools, indicating resource contention introduced by rate abuse.

These issues are amplified when endpoints expose filtering or search capabilities backed by MongoDB aggregations. An attacker can craft payloads that trigger complex pipelines or large $in clauses, causing MongoDB to perform extensive in-memory work. In a Phoenix controller that directly maps HTTP parameters to aggregation stages, missing input validation and absence of rate controls can turn legitimate query features into abuse vectors. Therefore, protecting the Phoenix-to-MongoDB path requires both server-side enforcement of request rates and thoughtful schema and query design to reduce the impact of each individual request.

To mitigate rate abuse against MongoDB-backed Phoenix endpoints, apply targeted fixes at the controller, database, and infrastructure layers. Begin by implementing rate limiting directly in Phoenix pipelines to restrict the number of requests per client over a defined window. This ensures abusive traffic is throttled before it reaches MongoDB, reducing the chance of long-running operations or connection exhaustion.

Rate limiting with Phoenix pipelines

Use PlugAttack or a similar plug to enforce per-IP or per-token limits before hitting your MongoDB queries. Define a pipeline in your router and attach the plug to relevant scopes or endpoints.

defmodule MyAppWeb.RateLimitPlug do
  use Plug.Router
  import Plug.Attack, only: [attack_opts: 0]

  plug Plug.Attack

  attack_opts()

  plug :match
  plug :dispatch

  # Example: limit to 30 requests per minute per IP
  get "/api/events", MyAppWeb.EventController, :create do
    rate_limit: [max: 30, within: 60_000]
  end
end

MongoDB query safeguards in controllers

Ensure your MongoDB queries use indexes and bounded result sets. In Phoenix controllers that interact with MongoDB via a driver such as mongodb or an ODM, enforce limits and projections to minimize resource usage per request.

defmodule MyAppWeb.UserController do
  use MyAppWeb, :controller
  alias MongoDB.Collection, as: Users

  def show(conn, %{"id" => id}) do
    filter = %{"user_id" => id}
    projection = %{email: 0, password_hash: 1}
    opts = [%{limit: 1}, {projection: projection}]

    case Users.find_one(filter, opts) do
      {:ok, nil} -> send_resp(conn, :not_found, "Not found")
      {:ok, doc} -> json(conn, Map.take(doc, ["username", "settings"]))
      {:error, reason} -> send_resp(conn, :internal_server_error, reason)
    end
  end
end

Schema design and aggregation controls

Design MongoDB collections to support efficient lookups and aggregations. Create targeted indexes for common query filters and avoid wide scans. When using aggregations in Phoenix endpoints, validate and restrict pipeline stages to prevent expensive in-memory operations.

defmodule MyAppWeb.StatsController do
  use MyAppWeb, :controller
  alias MongoDB.Collection, as: Events

  def count_by_status(conn, params) do
    # Validate and bound the $match stage to reduce load
    status = Map.get(params, "status")
    if status in ~w(active archived) do
      pipeline = [
        %{"$match" => %{"status" => status}},
        %{"$group" => %{"_id" => "$category", "count" => %{"$sum" => 1}}}
      ]

      case Events.aggregate(pipeline, %{}) do
        {:ok, results} -> json(conn, results)
        {:error, _} -> send_resp(conn, :internal_server_error, "Aggregation failed")
      end
    else
      send_resp(conn, :bad_request, "Invalid filter")
    end
  end
end

Combine these approaches—pipeline-level rate limiting, bounded MongoDB operations, and disciplined schema design—to reduce the likelihood and impact of rate abuse against Phoenix services backed by MongoDB.