Memory Leak in Chi with Cockroachdb

A memory leak in a Go service using the Chi router with CockroachDB as the backend typically arises from how database rows, transactions, or prepared statements are handled across request lifetimes. Chi encourages idiomatic routing and middleware composition, but does not enforce resource cleanup. When handlers open database rows (e.g., via db.Query or sqlx) and fail to close them, or do not drain rows before returning, each request can leave unclosed rows or result sets on the CockroachDB side. CockroachDB, as a distributed SQL database, maintains per-connection and per-session state; unclosed rows can hold cursors and network buffers, gradually increasing memory usage on both the application and database nodes.

In distributed deployments, a slow leak can be exacerbated by connection pooling. If the pool is misconfigured or if handlers do not release database handles (e.g., by deferring rows.Close()), the application may hold many open connections, each with active result sets. Because CockroachDB’s storage layer is optimized for long-running, bounded workloads, unbounded client-side resource retention can lead to increased GC pressure, growing heap profiles, and eventual latency degradation. These patterns often surface in profiles captured during continuous monitoring, where endpoints repeatedly hitting CockroachDB without proper cleanup show rising memory over time.

middleBrick scans can surface these issues indirectly by revealing missing timeouts, missing context cancellation, or inconsistent error handling that leads to unreleased resources. While the scanner does not inspect Go runtime profiles, its checks on input validation, rate limiting, and unsafe consumption can highlight endpoints that are more likely to leak under malformed or high-volume traffic. Developers should complement these findings with runtime profiling and observability to pinpoint the exact lines where rows or transactions are not released.

To prevent memory leaks, ensure every database interaction in Chi handlers follows strict resource ownership patterns. Use context to bound request lifetimes and guarantee cleanup, even during timeouts or client disconnects. Always defer closure of rows and transactions, and avoid storing database handles in request-scoped variables.

Example 1: Safe Query with Rows Closure

func getUserHandler(db *sql.DB) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        ctx, cancel := context.WithTimeout(r.Context(), 8*time.Second)
        defer cancel()
        rows, err := db.QueryContext(ctx, "SELECT id, name FROM users WHERE tenant_id = $1", tenantID(r))
        if err != nil {
            http.Error(w, err.Error(), http.StatusInternalServerError)
            return
        }
        defer rows.Close() // critical: ensures cursor and network buffers are released
        var results []User
        for rows.Next() {
            var u User
            if err := rows.Scan(&u.ID, &u.Name); err != nil {
                http.Error(w, err.Error(), http.StatusInternalServerError)
                return
            }
            results = append(results, u)
        }
        if err := rows.Err(); err != nil {
            http.Error(w, err.Error(), http.StatusInternalServerError)
            return
        }
        json.NewEncoder(w).Encode(results)
    }
}

Example 2: Transaction Handling with Rollback on Error

func updateInventoryHandler(db *sql.DB) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        ctx, cancel := context.WithTimeout(r.Context(), 10*time.Second)
        defer cancel()
        tx, err := db.BeginTx(ctx, nil)
        if err != nil {
            http.Error(w, err.Error(), http.StatusInternalServerError)
            return
        }
        // Explicit rollback on error paths; commit only after successful operations
        defer func() {
            if err != nil {
                tx.Rollback()
                return
            }
            err = tx.Commit()
        }()
        _, err = tx.ExecContext(ctx, "UPDATE inventory SET quantity = quantity - $1 WHERE product_id = $2", qty, productID)
        if err != nil {
            http.Error(w, err.Error(), http.StatusInternalServerError)
            return
        }
        w.WriteHeader(http.StatusOK)
    }
}

Example 3: Using sqlx with Struct Scanning

type Product struct {
    ID    int    `db:"id"`
    Name  string `db:"name"`
    Stock int    `db:"stock"`
}

func listProductsHandler(db *sql.DB) http.HandlerFunc {
    return func(w http.ResponseWriter, r *http.Request) {
        ctx, cancel := context.WithTimeout(r.Context(), 6*time.Second)
        defer cancel()
        rows, err := db.QueryContext(ctx, "SELECT id, name, stock FROM products")
        if err != nil {
            http.Error(w, err.Error(), http.StatusInternalServerError)
            return
        }
        defer rows.Close()
        var products []Product
        for rows.Next() {
            var p Product
            if err := sqlx.StructScan(rows, &p); err != nil {
                http.Error(w, err.Error(), http.StatusInternalServerError)
                return
            }
            products = append(products, p)
        }
        if err := rows.Err(); err != nil {
            http.Error(w, err.Error(), http.StatusInternalServerError)
            return
        }
        json.NewEncoder(w).Encode(products)
    }
}

Operational Practices

• Set request-scoped timeouts and ensure contexts are passed to CockroachDB calls.
• Monitor heap profiles in production to detect growth patterns correlated with endpoint usage.
• Use connection pool limits appropriate to your workload to avoid saturating CockroachDB client-side resources.