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Rate Limiter Implementations in Go

Production-ready rate limiting algorithms for protecting APIs and managing resource consumption.

Algorithms Included

1. Token Bucket

• Best for: APIs needing smooth rate limiting with burst capability
• Pros: Allows bursts, smooth traffic shaping, memory efficient
• Cons: Tokens can accumulate leading to large bursts

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package ratelimiter

import (
	"fmt"
	"sync"
	"sync/atomic"
	"testing"
	"time"
)

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package main

import (
	"fmt"
	"log"
	"time"
)

func main() {
	// Example 1: Token Bucket (smooth rate limiting)

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// Package ratelimiter provides multiple rate limiting algorithms for API protection.
// Includes Token Bucket, Sliding Window Log, and Sliding Window Counter implementations.
package ratelimiter

import (
	"sync"
	"time"
)

// RateLimiter defines the interface for all rate limiters

hawaijar

Consistent Hashing Implementation in Go

A production-ready consistent hashing implementation with virtual nodes for building distributed systems.

Features

• Virtual nodes for better key distribution (configurable replica count)
• O(log n) lookups using binary search on sorted ring
• Thread-safe operations with RWMutex
• Minimal key redistribution when nodes join/leave

hawaijar

package main

import (
	"fmt"
	"log"
)

func main() {
	// Create a consistent hash ring with 150 virtual nodes per physical node
	ring := consistenthash.New(150)

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// Package consistenthash implements a thread-safe consistent hash ring with virtual nodes.
// It uses Google's B-tree for efficient O(log n) lookups and provides configurable replication.
package consistenthash

import (
	"fmt"
	"hash/crc32"
	"sort"
	"sync"
)

hawaijar

import { findNthFib, minChange, sumPossible } from "../FibAndCoinIssues";

describe("Testing Finding Nth Fibonacci", () => {
  test("findNthFib(0) == 0", () => {
    expect(findNthFib(0)).toBe(0);
  });
  test("findNthFib(5) == 5", () => {
    expect(findNthFib(5)).toBe(5);
  });
  test("findNthFib(35) == 9227465", () => {

hawaijar

const cache = [0, 1];
export function findNthFib(n: number, cache = [0, 1]) {
  // check if the number is already calculated
  if (n in cache) {
    return cache[n];
  }
  cache[n] = findNthFib(n - 1, cache) + findNthFib(n - 2, cache);

  return cache[n];
}

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export class MinHeap {
  private length: number = 0;
  private data: number[] = [];

  private getParentIndex(idx: number): number {
    return Math.floor((idx - 1) / 2);
  }
  private getLeftIndex(idx: number): number {
    return 2 * idx + 1;
  }