rate-limiter

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I built and implemented 4 different Rate Limiting strategies with tests.

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# Rate Limiter from scratch 🔥

## How to try it

Clone it.

Run `npm install`.

We got 4 different mini "projects".

To start different servers:

1. Fixed Window Counter -> `npm run start-fixed-window-counter`
2. Token Bucket -> `npm run start-token-bucket`
3. Sliding Window Log -> `npm run start-sliding-window-log`
4. Sliding Window Counter -> `npm run start-sliding-window-counter`

## Tests

Tests have been written for every strategy.

The cool part is how amazing it was.

With `supertest` and `vitest`, you can write blazing fast tests for APIs.

TDD works well here! 🤩

Just run `npm run test`.

## Performance Testing with Postman

If you want to test with virtual concurrent users, you can use Postman. [Read here.](https://blog.postman.com/postman-api-performance-testing/)

![Screenshot 2024-01-09 at 07 46 48](https://github.com/narutosstudent/rate-limiter/assets/49603590/d00c6c34-6106-4d63-b721-26ccccd34cda)

## Code 💻

<details>
  <summary>🍿 Fixed Window Counter</summary>

---

How it works:
  1. Get IP of the one who requested.
  2. If IP doesn't exist, set a new value in `counters`.
  3. If IP exists. Check time difference.
  4. If difference between current and start time of IP's counter is greater than the rate limit window -> We reset the counter.
  5. OR If counter has not reached the request limit, increment it.
  6. ELSE Tell client 429, too many requests.

**Analogy:** Imagine a movie theater that sells tickets for each show. They have a policy: only 100 tickets can be sold per hour. This is to manage the crowd and ensure a comfortable experience for everyone. Each hour is a 'window' of time. At the start of each hour (say, 2 PM), the ticket count resets, regardless of how many were sold in the previous hour. If they reach 100 tickets at 2:45 PM, no more tickets are sold until 3 PM, when the next window starts.

```ts
export const rateLimitMiddleware = (
  req: express.Request,
  res: express.Response,
  next: express.NextFunction
) => {
  const ip = req.ip
  if (!ip) {
    res.status(500).send('No IP address found on request')
    return
  }

const currentTime = Date.now()

if (!counters.has(ip)) {
    counters.set(ip, { count: 1, startTime: currentTime })
    next()
    return
  }

const windowCounter = counters.get(ip)

if (windowCounter) {
    const difference = currentTime - windowCounter.startTime
    const isGreaterThanWindow = difference > rateLimitWindowInMs

if (isGreaterThanWindow) {
      // Reset the counter for the new window
      windowCounter.count = 1
      windowCounter.startTime = currentTime
      next()
    } else if (windowCounter.count < requestLimitPerWindow) {
      // Increment the counter and allow the request
      windowCounter.count++
      next()
    } else {
      // Rate limit exceeded
      res.status(429).send('Too Many Requests')
    }
  }
}
  ```
</details>

<details>
  <summary>🍿 Token Bucket</summary>

---

How it works:
  1. Each user has a bucket.
  2. When they make requests, we decrement some of their tokens.
  3. Every time they make requests, we try to refill the tokens.
  4. The refilling logic however is tied to the last time they refilled the bucket.
  5. An example would be if a user spams the requests, at some point `timeSinceLastRefillInSeconds` will be less than 1 if not 0.
  6. This would result in no new tokens being added.

**Analogy:** Token Bucket is a bit more difficult to understand. However, we can clarify it with an analogy. Imagine you have a bucket that is being filled with water at a constant rate through a tap. Each time you need water, you take a cup and scoop out some water from the bucket. The bucket represents your token bucket, and the water is the tokens. You can only scoop as much water as is available in the bucket. If the bucket is empty, you must wait until it fills up again to scoop more water. The rate at which the bucket fills up with water is the rate at which tokens are added to your bucket.

```ts
// Class
export class TokenBucket {
  capacity: number
  tokens: number
  refillRatePerSeconds: number
  lastRefill: number

constructor(capacity: number, refillRate: number) {
    this.capacity = capacity
    this.tokens = capacity
    this.refillRatePerSeconds = refillRate
    this.lastRefill = Date.now()
  }

refill() {
    const now = Date.now()
    const timeSinceLastRefillInSeconds =
      (now - this.lastRefill) / SECONDS_CONVERSION

// Add new tokens to the bucket since the last refill
    // Example: 10 tokens per second, 5 seconds since last refill = 50 new tokens
    // But don't exceed the capacity of the bucket
    // This way, if the bucket is not used for a long time, it will not be overflowing with tokens
    const newTokens = timeSinceLastRefillInSeconds * this.refillRatePerSeconds
    this.tokens = Math.min(this.capacity, this.tokens + newTokens)
    this.lastRefill = now
  }

allowRequest(): boolean {
    this.refill()
    if (this.tokens >= 1) {
      this.tokens -= 1
      return true
    }
    return false
  }
}

// Usage
const buckets = new Map<string, TokenBucket>()

export const rateLimitMiddleware = (
  req: express.Request,
  res: express.Response,
  next: express.NextFunction
) => {
  const ip = req.ip

if (!ip) {
    res.status(500).send('No IP address found on request')
    return
  }

const hasIpNoBucket = !buckets.has(ip)
  if (hasIpNoBucket) {
    buckets.set(ip, new TokenBucket(10, 1)) // Example: 10 tokens, refill 1 token/sec
  }

const bucket = buckets.get(ip)
  if (bucket && bucket.allowRequest()) {
    next()
  } else {
    res.status(429).send('Too Many Requests')
  }
}
  ```
</details>

<details>
  <summary>🍿 Sliding Window Log</summary>

---

How it works:
  1. For each IP, we keep track of requests' timestamps.
  2. We check if log exists. If not, we set initial value.
  3. If it does exist, we check all timestamps in the log.
  4. `slidingWindowInMs` -> Window of time we allow requests.
  5. `requestThreshold` -> The maximum number of requests we accept within the window.
  6. We filter out all timestamps outside the window.
  7. Check length is less than threshold.
  8. If not, tell user `429`, too many requests.

**Analogy:** Imagine a concert where a security guard logs the time each guest enters. The venue allows only 500 people per hour for safety. Throughout the event, the guard constantly checks the log to ensure no more than 500 people have entered in any rolling hour. If there are too many entries in the last hour, new guests must wait until the count falls below 500.

```ts
export const rateLimitMiddleware = (
  req: express.Request,
  res: express.Response,
  next: express.NextFunction
) => {
  const ip = req.ip
  if (!ip) {
    res.status(500).send('No IP address found on request')
    return
  }

if (!requestLogs.has(ip)) {
    requestLogs.set(ip, { timestamps: [Date.now()] })
    next()
    return
  }

const currentTime = Date.now()
  const log = requestLogs.get(ip)

if (log) {
    log.timestamps = log.timestamps.filter((timestamp) => {
      const difference = currentTime - timestamp
      const isWithinWindow = difference <= slidingWindowInMs

return isWithinWindow
    })

if (log.timestamps.length < requestThreshold) {
      // Allow request
      log.timestamps.push(currentTime)

next()
    } else {
      // Rate limit exceeded
      res.status(429).send('Too Many Requests')
    }
  }
}
  ```
</details>

<details>
  <summary>🍿 Sliding Window Counter</summary>

---

How it works:
  This is a bit different from the Sliding Window Log. It's less memory intensive as we only keep track of a counter and the last request timestamp. It's a mixture of Sliding Window Log and Fixed Window Counter. 🍹

1. For every IP, we keep track of the counter and the last request's timestamp.
  2. If a log already exists, we do a couple of checks.
  3. If the difference between last request's time and current time is greater than `slidingWindowInMs`, then we've to reset the counter. This would mean `slidingWindowInMs` e.g. 60 seconds, has passed since last request. Within `slidingWindowInMs`, we let maximum `requestThreshold` number of requests.
  4. **If that is not the case:** We want to figure out the amount to decrement from the counter.
  5. Check if updated `counter` is greater than the `requestThreshold`.
  6. If it is -> `429` response.
  7. If not, we move forward!

**Analogy:** A coffee shop serves a maximum of 100 cups per hour. Instead of tracking each order's time, they keep a running tally of the last hour's orders. After each new order, they adjust this tally, considering only the past hour from the current time. If the tally reaches 100, they pause new orders until the count drops as the window moves forward.

```ts
export const rateLimitMiddleware = (
  req: express.Request,
  res: express.Response,
  next: express.NextFunction
) => {
  const ip = req.ip
  if (!ip) {
    res.status(500).send('No IP address found on request')
    return
  }

if (!requestLogs.has(ip)) {
    requestLogs.set(ip, { counter: 1, lastRequestTimestamp: Date.now() })
    next()
    return
  }

const currentTime = Date.now()
  const log = requestLogs.get(ip)

if (log) {
    const timeElapsed = currentTime - log.lastRequestTimestamp
    const shouldResetCounter = timeElapsed > slidingWindowInMs

if (shouldResetCounter) {
      log.counter = 1
    } else {
      // Calculate the decrement amount based on the time elapsed
      // Example: Sliding window is 60 seconds, in milliseconds that is 60000
      // reqThreshold is 10
      // This equals to 60000 / 10 = 6000
      // Let's say timeElapsed is 30000, that means 30 seconds have passed
      // 30000 / 6000 = 5
      // So we decrement the counter by 5
      // This effectively calculates the number of requests that are outside of the sliding window
      // Those requests are no longer counted towards the rate limit (expired)
      const decrementAmount = Math.floor(
        timeElapsed / (slidingWindowInMs / requestThreshold)
      )

// `Math.max` is used to ensure the counter never goes below 0
      log.counter = Math.max(0, log.counter - decrementAmount)
    }

log.lastRequestTimestamp = currentTime

const shouldBlockRequest = log.counter >= requestThreshold
    if (shouldBlockRequest) {
      res.status(429).send('Too many requests')
      return
    }

log.counter++
    next()
  }
}
  ```
</details>

# What is a Rate Limiter?

A rate limiter is a tool that monitors the number of requests per unit of time that a client IP can send to an API endpoint. If the number of requests exceeds a certain threshold, the rate limiter will block the client IP from sending further requests for a certain period of time.

# Key Concepts

- **Limit:** The maximum number of requests that a client IP can send to an API endpoint per unit of time.
- **Window:** The unit of time that the rate limiter uses to track the number of requests sent by a client IP. It can be anything from seconds to days.
- **Identifier:** A unique attribute of a request that the rate limiter uses to identify the client IP.

# Designing a Rate Limiter

- Define the purpose of your rate limiter.
- Decide on the type of rate limiting e.g. fixed window, sliding window, etc.
- Type of Storage: In-memory or persistent storage.
- Request identifier: IP address, user IDs API key, etc.
- Counting and logging
- Code checks if request is within the limit or not.
- Send response to client.

# Silently dropping request

This can be better than returning 429 error to client. It's a trick to fool attackers thinking that the request has been accepted even when the rate limiter has actually dropped the request.

# Rate Limiting Strategies

## Fixed Window Counter

- **Mechanism**: The time frame (window) is fixed, like an hour or a day. The counter resets at the end of each window.
- **Use Case**: Simple scenarios where average rate limiting over time is sufficient.
- **Pros**: Easy to implement and understand.
- **Cons**: Can allow bursts of traffic at the window boundaries.

## Sliding Window Log

- **Mechanism**: Records the timestamp of each request in a log. The window slides with each request, checking if the number of requests in the preceding window exceeds the limit.
- **Use Case**: When a smoother rate limit is needed without allowing bursts at fixed intervals.
- **Pros**: Fairer and more consistent than fixed windows.
- **Cons**: More memory-intensive due to the need to store individual timestamps.

## Token Bucket

- **Mechanism**: A bucket is filled with tokens at a steady rate. Each request costs a token. If the bucket is empty, the request is either rejected or queued.
- **Use Case**: Useful for APIs or services where occasional bursts are acceptable.
- **Pros**: Allows for short bursts of traffic over the limit.
- **Cons**: Slightly complex to implement. Can be unfair over short time scales.

## Leaky Bucket

- **Mechanism**: Requests are added to a queue and processed at a steady rate. If the queue is full, new requests are discarded.
- **Use Case**: Ideal for smoothing out traffic and ensuring a consistent output rate.
- **Pros**: Ensures a very even rate of requests.
- **Cons**: Does not handle bursts well. Excess requests are simply dropped.

## Sliding Window Counter

- **Mechanism**: Similar to fixed window but smoother. The window slides with each request, combining the simplicity of fixed windows with some advantages of sliding log.
- **Use Case**: When you need a balance between fairness and memory efficiency.
- **Pros**: More consistent than fixed window, less memory usage than sliding log.
- **Cons**: Can be more complex to implement than fixed window.

## Rate Limiting with Queues

- **Mechanism**: Instead of rejecting excess requests, they are queued and processed when possible.
- **Use Case**: When losing requests is not acceptable, and delays are tolerable.
- **Pros**: No request is outright rejected.
- **Cons**: Can lead to high latency and resource consumption if the queue becomes too long.

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