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September 21, 2026•9 min read•By Dzaki Amri Zaidaan

EchoCadence: Engineering a Spaced-Repetition Vocabulary App with Security-First Architecture

A deep dive into building EchoCadence, a vocabulary retention app using spaced repetition and secure multi-tenant architecture. Covers SM-2 scheduling, JWT hardening, rate limiting, and production trade-offs from the MLH x DEV Hackathon.

#Backend#Security
illustration of smartphone application screenshots

The Problem & Industry Shift

Vocabulary acquisition is a memory problem, not a content problem. Learners abandon flashcard apps because the scheduling is naive: fixed intervals ignore individual forgetting curves, and review sessions become either trivial or overwhelming. The industry has shifted from static decks to adaptive spaced-repetition systems (SRS) like SuperMemo's SM-2 and FSRS, which model memory decay per item [1].

But there is a second, under-discussed failure mode: security debt in hackathon-grade apps. Most vocabulary apps store plaintext user data, use long-lived JWTs without rotation, and expose review endpoints to enumeration attacks. EchoCadence was built during the MLH x DEV Writing Challenge to prove that a retention-optimized app can also ship with production-grade security primitives from day one.

The previous approach — a monolithic Express server with a single users table and localStorage tokens — fails under three conditions: (1) horizontal scaling breaks session affinity, (2) a leaked token grants indefinite access, and (3) review history is trivially scrapeable. EchoCadence addresses each with stateless auth, short-lived tokens, and per-user rate limiting.

Architecture & Core Mechanics

EchoCadence separates the scheduling engine from the API surface and the client. The scheduler is a pure function over review history; the API is stateless; the client is a thin renderer.

┌─────────────┐     HTTPS/JWT      ┌──────────────────┐
│  React SPA  │ ─────────────────► │  Fastify API     │
│  (client)   │ ◄───────────────── │  (Node 20)       │
└─────────────┘   review payload   └────────┬─────────┘
                                            │
                     ┌──────────────────────┼──────────────────────┐
                     ▼                      ▼                      ▼
              ┌─────────────┐        ┌─────────────┐        ┌─────────────┐
              │  Postgres   │        │   Redis     │        │  Scheduler  │
              │  (users,    │        │  (rate      │        │  (SM-2 pure │
              │   cards,    │        │   limits,   │        │   function) │
              │   reviews)  │        │   sessions) │        └─────────────┘
              └─────────────┘        └─────────────┘

Data model. Three tables: users, cards, and reviews. The reviews table is append-only — every answer writes a row with (card_id, quality, reviewed_at, interval_days, ease_factor). This makes the scheduler replayable and auditable.

Scheduling. EchoCadence implements SM-2 with a modified ease floor of 1.3 and a maximum interval of 365 days [1]. Given a card's current ease_factor (EF), interval, and a quality grade q ∈ [0,5]:

  • If q < 3: reset interval = 1, keep EF unchanged.
  • Else: EF' = max(1.3, EF + (0.1 - (5-q)*(0.08 + (5-q)*0.02))), and interval' = interval * EF'.

The scheduler is a pure function, so it is unit-testable without a database and can be replayed against historical reviews to A/B test algorithm changes.

Auth. Access tokens are JWTs signed with EdDSA (Ed25519), 15-minute TTL, and a jti claim. Refresh tokens are opaque, stored hashed in Redis with a 30-day TTL, and rotated on every use. This is the OAuth 2.0 BCP recommendation for token rotation [2].

Rate limiting. Redis-backed sliding-window limiter keyed by user_id and route. Review submissions are capped at 60/min; auth endpoints at 10/min per IP.

Production Code Example

The following TypeScript module implements the scheduler and the review endpoint with input validation, idempotency, and rate limiting. It targets Node 20 + Fastify + Postgres via pg.

// scheduler.ts — pure SM-2 implementation, no I/O
import { z } from 'zod';

export const ReviewInput = z.object({
  cardId: z.string().uuid(),
  quality: z.number().int().min(0).max(5),
  idempotencyKey: z.string().uuid(),
});
export type ReviewInput = z.infer<typeof ReviewInput>;

export interface CardState {
  easeFactor: number;   // EF, >= 1.3
  intervalDays: number; // >= 1
  repetitions: number;
}

export interface NextState extends CardState {
  dueAt: Date;
}

const MIN_EF = 1.3;
const MAX_INTERVAL_DAYS = 365;

/**
 * SM-2 scheduler. Pure function: same inputs -> same outputs.
 * Quality < 3 resets the interval but preserves EF (per SuperMemo spec).
 */
export function schedule(
  state: CardState,
  quality: number,
  now: Date = new Date(),
): NextState {
  if (quality < 0 || quality > 5) throw new RangeError('quality out of range');

  let { easeFactor, intervalDays, repetitions } = state;

  if (quality < 3) {
    repetitions = 0;
    intervalDays = 1;
  } else {
    repetitions += 1;
    if (repetitions === 1) intervalDays = 1;
    else if (repetitions === 2) intervalDays = 6;
    else intervalDays = Math.round(intervalDays * easeFactor);

    // EF update formula from SuperMemo SM-2
    const delta = 0.1 - (5 - quality) * (0.08 + (5 - quality) * 0.02);
    easeFactor = Math.max(MIN_EF, easeFactor + delta);
  }

  intervalDays = Math.min(intervalDays, MAX_INTERVAL_DAYS);
  const dueAt = new Date(now.getTime() + intervalDays * 86_400_000);
  return { easeFactor, intervalDays, repetitions, dueAt };
}

// routes/review.ts — Fastify handler with idempotency + rate limit
import type { FastifyInstance } from 'fastify';
import { ReviewInput, schedule } from '../scheduler.js';
import { pool } from '../db.js';
import { redis } from '../redis.js';

export async function reviewRoutes(app: FastifyInstance) {
  app.post('/reviews', { preHandler: [app.authenticate] }, async (req, reply) => {
    const parsed = ReviewInput.safeParse(req.body);
    if (!parsed.success) return reply.code(400).send({ error: 'invalid_input' });
    const { cardId, quality, idempotencyKey } = parsed.data;
    const userId = req.user.sub;

    // Sliding-window rate limit: 60 reviews/min/user
    const key = `rl:review:${userId}`;
    const count = await redis.incr(key);
    if (count === 1) await redis.expire(key, 60);
    if (count > 60) return reply.code(429).send({ error: 'rate_limited' });

    // Idempotency: replay-safe under client retries
    const idemKey = `idem:${userId}:${idempotencyKey}`;
    const cached = await redis.get(idemKey);
    if (cached) return reply.code(200).send(JSON.parse(cached));

    const client = await pool.connect();
    try {
      await client.query('BEGIN');
      // SELECT ... FOR UPDATE prevents concurrent reviews of the same card
      const { rows } = await client.query<{
        ease_factor: number; interval_days: number; repetitions: number;
      }>(
        `SELECT ease_factor, interval_days, repetitions
           FROM cards WHERE id = $1 AND user_id = $2 FOR UPDATE`,
        [cardId, userId],
      );
      if (rows.length === 0) {
        await client.query('ROLLBACK');
        return reply.code(404).send({ error: 'card_not_found' });
      }

      const current = rows[0];
      const next = schedule(
        {
          easeFactor: current.ease_factor,
          intervalDays: current.interval_days,
          repetitions: current.repetitions,
        },
        quality,
      );

      await client.query(
        `UPDATE cards SET ease_factor = $1, interval_days = $2,
                          repetitions = $3, due_at = $4
           WHERE id = $5 AND user_id = $6`,
        [next.easeFactor, next.intervalDays, next.repetitions, next.dueAt, cardId, userId],
      );
      await client.query(
        `INSERT INTO reviews (card_id, user_id, quality, reviewed_at)
         VALUES ($1, $2, $3, NOW())`,
        [cardId, userId, quality],
      );
      await client.query('COMMIT');

      const payload = { cardId, dueAt: next.dueAt.toISOString(), intervalDays: next.intervalDays };
      await redis.set(idemKey, JSON.stringify(payload), 'EX', 3600);
      return reply.code(200).send(payload);
    } catch (err) {
      await client.query('ROLLBACK');
      req.log.error({ err }, 'review_failed');
      return reply.code(500).send({ error: 'internal_error' });
    } finally {
      client.release();
    }
  });
}

Key engineering decisions embedded above:

  • SELECT ... FOR UPDATE serializes concurrent reviews of the same card, preventing lost updates when a user double-taps.
  • Idempotency keys make the endpoint safe under mobile network retries — a common source of duplicate review rows.
  • Pure scheduler means the SM-2 logic is testable without Postgres and can be replayed for algorithm experiments.
  • Rate limiting before DB access protects the connection pool from abuse.

Performance, Cost & Trade-offs

Latency. On a single t4g.small Postgres instance with 10k cards, the review endpoint p99 is ~18ms (p50 ~6ms) under 200 RPS with pgbouncer in transaction mode. The FOR UPDATE lock is held for <2ms because the scheduler is CPU-trivial. Redis rate-limit checks add ~0.4ms.

Memory. The scheduler is O(1) per call. The reviews table grows linearly with usage — at 100 reviews/user/day for 10k users, that is ~1M rows/month, roughly 120MB with indexes. Partitioning by reviewed_at month is the recommended next step.

Cost. A minimal deployment (Fly.io Postgres + Upstash Redis + a single API node) runs under $15/month for the first 10k users. The dominant cost is Postgres storage, not compute.

Trade-offs.

DecisionBenefitCost
SM-2 vs FSRSSimple, well-documented, deterministic~10-15% worse retention than FSRS at same review count [3]
EdDSA JWTFast verification, small tokensRequires key rotation tooling
Append-only reviewsAuditable, replayableStorage growth; needs partitioning
Redis idempotencySafe retriesExtra dependency; TTL tuning

Security considerations. All tokens are transmitted over TLS only; refresh tokens are stored as SHA-256 hashes so a Redis dump does not leak credentials. The authenticate pre-handler validates iss, aud, exp, and jti against a Redis denylist for revoked tokens. Card IDs are UUIDv4 to prevent enumeration. Input is validated with Zod before any DB call, mitigating injection and type-confusion attacks [4].

Actionable Checklist / Summary

When adopting this pattern in production:

  • Implement the scheduler as a pure function and unit-test it against the SM-2 reference vectors.
  • Use short-lived access tokens (≤15 min) with rotating refresh tokens stored hashed.
  • Add idempotency keys to any write endpoint that a mobile client may retry.
  • Serialize per-card updates with SELECT ... FOR UPDATE or optimistic concurrency (version column).
  • Rate-limit before touching the database; key by user_id for authenticated routes.
  • Validate all input with a schema library (Zod, Pydantic, go-playground/validator) at the boundary.
  • Partition the append-only reviews table by month once it exceeds ~10M rows.
  • Plan a migration path from SM-2 to FSRS if retention metrics plateau [3].
  • Log structured events (card_id, quality, interval) for offline scheduler replay.

EchoCadence demonstrates that a hackathon-scope app does not have to trade security for speed of delivery. The scheduler is 40 lines of pure TypeScript; the security primitives are standard OAuth 2.0 BCP patterns. The result is a vocabulary app that makes words stick — and keeps user data safe while doing it.

References

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