Memory Leak in Chi with Dynamodb

A memory leak in a Chi application that uses Dynamodb typically arises when responses from the database are retained in memory longer than necessary, or when request-scoped resources are not released. Chi is a lightweight HTTP router for Clojure, and its idiomatic patterns emphasize composable middleware and request/response maps. When developers integrate Dynamodb via the AWS SDK for Java (embedded within Clojure via interop), improper handling of SDK responses can cause objects—such as query results, paginated iterators, or connection contexts—to accumulate in memory.

Specifically, the vulnerability surface appears in these scenarios:

• Holding references to raw DynamoDB ItemCollection or QueryOutcome objects in long-lived atoms or caches without size bounds.
• Failing to consume or close paginated results from DynamoDB operations, leaving internal buffers unflushed and unreachable but still referenced.
• Using middleware that logs or inspects entire request/response maps that inadvertently include large DynamoDB payloads, causing retention in logs or in-memory structures.

Because Chi does not manage database connections explicitly, the leak is not in Chi itself but in how the application retains data obtained from Dynamodb. Under sustained traffic, these retained objects increase heap usage, leading to elevated garbage collection pressure and, in severe cases, out-of-memory errors. middleBrick scans can detect indicators such as unusually large response payloads or missing pagination handling patterns, flagging potential data exposure and resource exhaustion risks in the Security report.

An example pattern that can lead to retention issues is storing a DynamoDB query result directly in a session or global atom:

(defonce cache (atom {}))

(defn handler [request]
  (let [result (dynamodb/query table-key-conditionExpression ...)]
    (swap! cache assoc (:id request) result)
    {:status 200 :body (str "cached")}))

Here, the result may contain nested data structures that are large; keeping them in cache without eviction or size limits constitutes a memory leak. middleBrick’s inventory and data exposure checks can highlight missing data-size controls and recommend remediation.

Remediation focuses on ensuring that DynamoDB responses are consumed, transformed into lightweight Clojure data, and released promptly. Use bounded caches, strict pagination consumption, and avoid retaining raw SDK objects.

1. Consume paginated results fully and retain only necessary data:

(defn fetch-all [table key-condition]
  (loop [exclusive-start-key nil
         acc []]
    (let [resp (dynamodb/query table
                               :key-condition-expression key-condition
                               :exclusive-start-key exclusive-start-key)
          items (:items resp)
          new-acc (into acc items)]
      (if-continuation (:last-evaluated-key resp)
        (recur (:last-evaluated-key resp) new-acc)
        new-acc))))

2. Use a size-bounded cache (e.g., using bounding-box or similar libraries) instead of defonce, and store only extracted maps:

(require '[bounding-box.core :as bb])

(def cache (bb/bounded-map 100)) ; max 100 entries

(defn handler [request]
  (let [id (:id request)
        item (or (get cache id)
                 (let [resp (dynamodb/get-item table :key {:id id})
                       data (:item resp)]
                   (bb/put! cache id data)
                   data))]
    {:status 200 :body (pr-str item)}))

3. Ensure that streams and iterators are fully realized or closed. With the AWS SDK for Java, failing to iterate through a paginated response can leave resources unmanaged; always realize the collection:

(defn safe-query [table expr]
  (let [out (.query table expr) ; returns com.amazonaws.services.dynamodbv2.model.QueryResult
        items (into [] (map #(-> % .getItem (clj->js %))) (.getItems out))]
    (.setItems out nil) ; release reference if SDK permits
    items))

4. Avoid including large DynamoDB items in logs or request maps that middleware might retain. Filter sensitive or large fields before logging:

(defn strip-large [m]
  (dissoc m :largeBinaryField :hugeText))

(defn logging-middleware [handler]
  (fn [request]
    (let [response (handler request)]
      (when-let [body (:body response)]
        ;; do not log large bodies
        (when (<= (count (pr-str body)) 1000)
          (println "response body:" body)))
      response)))

These patterns reduce the risk of memory retention and align with data exposure controls. middleBrick’s continuous monitoring (Pro plan) can track these fixes over time, and the GitHub Action can gate builds if irregular payload sizes are detected in scans.

When using the MCP Server, you can scan APIs directly from your IDE to validate that DynamoDB integration follows these patterns during development.