https://github.com/webriots/rate
A high-performance rate limiter library for Go applications
https://github.com/webriots/rate
concurrency go golang high-performance lock-free rate-limiting thread-safe token-bucket zero-allocation
Last synced: 3 months ago
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A high-performance rate limiter library for Go applications
- Host: GitHub
- URL: https://github.com/webriots/rate
- Owner: webriots
- License: mit
- Created: 2025-05-13T20:40:24.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-21T05:41:05.000Z (5 months ago)
- Last Synced: 2025-05-21T06:31:11.642Z (5 months ago)
- Topics: concurrency, go, golang, high-performance, lock-free, rate-limiting, thread-safe, token-bucket, zero-allocation
- Language: Go
- Homepage:
- Size: 66.4 KB
- Stars: 131
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-go - webriots/rate - performance rate limiter library for Go applications ☆`156` (Utilities / Utility/Miscellaneous)
- awesome-go - webriots/rate - performance rate limiter library for Go applications ☆`149` (Utilities / Utility/Miscellaneous)
README
# Rate
[](https://pkg.go.dev/github.com/webriots/rate)
[](https://goreportcard.com/report/github.com/webriots/rate)
[](https://coveralls.io/github/webriots/rate?branch=main)
A high-performance rate limiter library for Go applications with multiple rate limiting strategies.
## Features
- **Ultra-Fast**: Core operations execute in single digit *nanoseconds* with zero allocations, enabling hundreds of millions of operations per second
- **Thread-Safe**: All operations are designed to be thread-safe using optimized atomic operations
- **Zero External Dependencies**: Relies solely on the Go standard library
- **Memory Efficient**: Uses compact data structures and optimized storage
- Packed representations for token buckets
- Custom 56-bit timestamp implementation
- Efficient atomic slice implementations
- **Multiple Rate Limiting Strategies**:
- TokenBucketLimiter: Classic token bucket algorithm with multiple buckets
- AIMDTokenBucketLimiter: Additive-Increase/Multiplicative-Decrease algorithm inspired by TCP congestion control
- **Highly Scalable**: Designed for high-throughput concurrent systems
- Multiple buckets distribute load across different request IDs
- Low contention design for concurrent access patterns
## Installation
```shell
go get github.com/webriots/rate
```
## Quick Start
### Token Bucket Rate Limiter
```go
package main
import (
"fmt"
"time"
"github.com/webriots/rate"
)
func main() {
// Create a new token bucket limiter:
// - 1024 buckets (automatically rounded to nearest power of 2 if not already a power of 2)
// - 10 tokens burst capacity
// - 100 tokens per second refill rate
limiter, err := rate.NewTokenBucketLimiter(1024, 10, 100, time.Second)
if err != nil {
panic(err)
}
// Try to take a token for a specific ID
id := []byte("user-123")
if limiter.TakeToken(id) {
fmt.Println("Token acquired, proceeding with request")
// ... process the request
} else {
fmt.Println("Rate limited, try again later")
// ... return rate limit error
}
// Check without consuming
if limiter.Check(id) {
fmt.Println("Token would be available")
}
}
```
[Go Playground](https://go.dev/play/p/UMxQDHfqKqD)
### AIMD Rate Limiter
```go
package main
import (
"fmt"
"time"
"github.com/webriots/rate"
)
func main() {
// Create an AIMD token bucket limiter:
// - 1024 buckets (automatically rounded to nearest power of 2 if not already a power of 2)
// - 10 tokens burst capacity
// - Min rate: 1 token/s, Max rate: 100 tokens/s, Initial rate: 10 tokens/s
// - Increase by 1 token/s on success, decrease by factor of 2 on failure
limiter, err := rate.NewAIMDTokenBucketLimiter(
1024, // numBuckets
10, // burstCapacity
1.0, // rateMin
100.0, // rateMax
10.0, // rateInit
1.0, // rateAdditiveIncrease
2.0, // rateMultiplicativeDecrease
time.Second,
)
if err != nil {
panic(err)
}
id := []byte("api-client-123")
// Try to take a token
if limiter.TakeToken(id) {
// Process the request
success := processRequest()
if success {
// If successful, increase the rate
limiter.IncreaseRate(id)
} else {
// If failed (e.g., downstream service is overloaded),
// decrease the rate to back off
limiter.DecreaseRate(id)
}
} else {
// We're being rate limited
fmt.Println("Rate limited, try again later")
}
}
func processRequest() bool {
// Your request processing logic
fmt.Println("Processing request")
return true
}
```
[Go Playground](https://go.dev/play/p/2AEPxptA2xd)
## Detailed Usage
### TokenBucketLimiter
The token bucket algorithm is a common rate limiting strategy that allows for controlled bursting. It maintains a "bucket" of tokens that refills at a constant rate, and each request consumes a token.
```go
limiter, err := rate.NewTokenBucketLimiter(
numBuckets, // Number of buckets (automatically rounded to nearest power of 2 if not already a power of 2)
burstCapacity, // Maximum number of tokens in each bucket
refillRate, // Rate at which tokens are refilled
refillRateUnit, // Time unit for refill rate
)
```
#### Parameters:
- `numBuckets`: Number of token buckets (automatically rounded up to the nearest power of two if not already a power of two)
- `burstCapacity`: Maximum number of tokens that can be consumed at once
- `refillRate`: Rate at which tokens are refilled
- `refillRateUnit`: Time unit for refill rate calculations (e.g., time.Second)
#### Input Validation:
The constructor performs validation on all parameters and returns descriptive errors:
- `refillRate` must be a positive, finite number (not NaN, infinity, zero, or negative)
- `refillRateUnit` must represent a positive duration
- The product of `refillRate` and `refillRateUnit` must not overflow when converted to nanoseconds
#### Token Bucket Algorithm Explained
The token bucket algorithm uses a simple metaphor of a bucket that holds tokens:
```
┌─────────────────────────────────────┐
│ │
│ ┌───┐ ┌───┐ ┌───┐ ┌ ┐ ┌ ┐ │
│ │ T │ │ T │ │ T │ │ │ │ │ │
│ └───┘ └───┘ └───┘ └ ┘ └ ┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ avail avail avail │
│ │
│ Available: 3 tokens │ Capacity: 5 │
└─────────────────────────────────────┘
▲
│ Refill rate: 100 tokens/second
┌───┐ ┌───┐
│ T │ │ T │ ... ← New tokens added at constant rate
└───┘ └───┘
```
**How it works:**
1. **Bucket Initialization**:
- Each bucket starts with `burstCapacity` tokens (full)
- A timestamp is recorded when the bucket is created
2. **Token Consumption**:
- When a request arrives, it attempts to take a token
- If a token is available, it's removed from the bucket and the request proceeds
- If no tokens are available, the request is rate-limited
3. **Token Refill**:
- Tokens are conceptually added to the bucket at a constant rate (`refillRate` per `refillRateUnit`)
- In practice, the refill is calculated lazily only when tokens are requested
- The formula is: `refill = (currentTime - lastUpdateTime) * refillRate`
- Tokens are never added beyond the maximum `burstCapacity`
4. **Burst Handling**:
- The bucket can temporarily allow higher rates up to `burstCapacity`
- This accommodates traffic spikes while maintaining a long-term average rate
**Implementation Details:**
- This library uses a sharded approach with multiple buckets to reduce contention
- Each request ID is consistently hashed to the same bucket
- The bucket stores both token count and timestamp in a single uint64 for efficiency
### AIMDTokenBucketLimiter
The AIMD (Additive Increase, Multiplicative Decrease) algorithm provides dynamic rate adjustments inspired by TCP congestion control. It gradually increases token rates during successful operations and quickly reduces rates when encountering failures.
```go
limiter, err := rate.NewAIMDTokenBucketLimiter(
numBuckets, // Number of buckets (automatically rounded to nearest power of 2 if not already a power of 2)
burstCapacity, // Maximum tokens per bucket
rateMin, // Minimum token refill rate
rateMax, // Maximum token refill rate
rateInit, // Initial token refill rate
rateAdditiveIncrease, // Amount to increase rate on success
rateMultiplicativeDecrease, // Factor to decrease rate on failure
rateUnit, // Time unit for rate calculations
)
```
#### Parameters:
- `numBuckets`: Number of token buckets (automatically rounded up to the nearest power of two if not already a power of two)
- `burstCapacity`: Maximum number of tokens that can be consumed at once
- `rateMin`: Minimum token refill rate
- `rateMax`: Maximum token refill rate
- `rateInit`: Initial token refill rate
- `rateAdditiveIncrease`: Amount to increase rate by on success
- `rateMultiplicativeDecrease`: Factor to decrease rate by on failure
- `rateUnit`: Time unit for rate calculations (e.g., time.Second)
#### Input Validation:
The constructor performs comprehensive validation on all parameters and returns descriptive errors:
- `rateInit` must be a positive, finite number (not NaN, infinity, zero, or negative)
- `rateUnit` must represent a positive duration
- `rateMin` must be a positive, finite number
- `rateMax` must be a positive, finite number
- `rateMin` must be less than or equal to `rateMax`
- `rateInit` must be between `rateMin` and `rateMax` (inclusive)
- `rateAdditiveIncrease` must be a positive, finite number
- `rateMultiplicativeDecrease` must be a finite number greater than or equal to 1.0
- The product of `rateInit`, `rateMin`, `rateMax`, or `rateAdditiveIncrease` with `rateUnit` must not overflow when converted to nanoseconds
#### AIMD Algorithm Explained
The AIMD algorithm dynamically adjusts token refill rates based on success or failure feedback, similar to how TCP adjusts its congestion window:
```
Rate
▲
│
│
│
rateMax ─────────────────────────────────────────
│ ╱ │
│ ╱ │
│ ╱ │ ╱
│ ╱ │ ╱
│ ╱ │ ╱
│ ╱ │ ╱
│ ╱ │ ╱
│ ╱ │ ╱
│ ╱ ▼ Multiplicative
│ ╱ Decrease
│ ╱ (rate / 2)
│ ╱
rateInit
│ ↑
│ Additive
│ Increase
│ (+1)
rateMin ─────────────────────────────────────────
│
└─────────────────────────────────────────────► Time
Success Failure
```
**How it works:**
1. **Initialization**:
- Each bucket starts with a rate of `rateInit` tokens per time unit
- Token buckets are created with `burstCapacity` tokens
2. **Adaptive Rate Adjustment**:
- When operations succeed, the rate increases linearly by `rateAdditiveIncrease`
- `newRate = currentRate + rateAdditiveIncrease`
- When operations fail, the rate decreases multiplicatively by `rateMultiplicativeDecrease`
- `newRate = rateMin + (currentRate - rateMin) / rateMultiplicativeDecrease`
- Rates are bounded between `rateMin` (lower bound) and `rateMax` (upper bound)
3. **Practical Applications**:
- Gracefully adapts to changing capacity of backend systems
- Quickly backs off when systems are overloaded (multiplicative decrease)
- Slowly probes for increased capacity when systems are healthy (additive increase)
- Maintains stability even under varying load conditions
**Implementation Details:**
- Each ID has its own dedicated rate that adjusts independently
- The actual token bucket mechanics remain the same as TokenBucketLimiter
- Rate changes are applied atomically using lock-free operations
- Rate information is stored alongside token information in the bucket
## Performance
The library is optimized for high performance and efficiency:
- Core operations complete in 1-15ns with zero allocations
- Efficient memory usage through packed representations
- Minimal contention in multi-threaded environments
### Benchmarks
Run the benchmarks on your machine with:
```shell
# Run all benchmarks with memory allocation stats
go test -bench=. -benchmem
# Run specific benchmark
go test -bench=BenchmarkTokenBucketCheck -benchmem
```
Here are the results from an Apple M1 Pro:
```
# Core operations
BenchmarkTokenBucketCheck-10 203429409 5.919 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketTakeToken-10 165718064 7.240 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketParallel-10 841380494 1.419 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketContention-10 655109293 1.827 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketWithRefill-10 165840525 7.110 ns/op 0 B/op 0 allocs/op
# Bucket creation (different sizes)
BenchmarkTokenBucketCreateSmall-10 16929116 71.00 ns/op 192 B/op 2 allocs/op
BenchmarkTokenBucketCreateMedium-10 664831 1820 ns/op 8256 B/op 2 allocs/op
BenchmarkTokenBucketCreateLarge-10 45537 25471 ns/op 131137 B/op 2 allocs/op
# Internals
BenchmarkTokenBucketPacked-10 1000000000 0.3103 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketUnpack-10 1000000000 0.3103 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketIndex-10 265611426 4.510 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketRefill-10 591397021 2.023 ns/op 0 B/op 0 allocs/op
# Real-world scenarios
BenchmarkTokenBucketManyIDs-10 139485549 8.590 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketDynamicID-10 93835521 12.87 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketRealWorldRequestRate-10 853565757 1.401 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketHighContention-10 579507058 2.068 ns/op 0 B/op 0 allocs/op
BenchmarkTokenBucketWithSystemClock-10 459682273 2.605 ns/op 0 B/op 0 allocs/op
```
## Advanced Usage
### Choosing the Optimal Number of Buckets
The `numBuckets` parameter is automatically rounded up to the nearest power of two if not already a power of two (e.g., 256, 1024, 4096) for efficient hashing and is a critical factor in the limiter's performance and fairness.
#### How Bucket Selection Works
When you call `TakeToken(id)` or similar methods, the library:
1. Hashes the ID using Go's built-in hash/maphash algorithm (non-cryptographic, high performance)
2. Uses the hash value & (numBuckets-1) to determine the bucket index
3. Takes/checks a token from that specific bucket
```go
// Simplified version of the internal hashing mechanism
func index(id []byte) int {
return int(maphash.Bytes(seed, id) & (numBuckets - 1))
}
```
#### Understanding Collision Probability
A collision occurs when two different IDs map to the same bucket. When this happens:
- Those IDs share the same rate limit, which may be undesirable
- Higher contention can occur on that bucket when accessed concurrently
The probability of collision depends on:
1. Number of buckets (`numBuckets`)
2. Number of distinct IDs being rate limited
The birthday paradox formula gives us the probability of *at least one* collision:
| Number of Distinct IDs | Buckets=1024 | Buckets=4096 | Buckets=16384 | Buckets=65536 |
|------------------------|--------------|--------------|---------------|---------------|
| 10 | 4.31% | 1.09% | 0.27% | 0.07% |
| 100 | 99.33% | 70.43% | 26.12% | 7.28% |
| 1,000 | 100.00% | 100.00% | 100.00% | 99.95% |
| 10,000 | 100.00% | 100.00% | 100.00% | 100.00% |
| 100,000 | 100.00% | 100.00% | 100.00% | 100.00% |
However, the probability of collision doesn't tell the whole story. What matters more is the **percentage of IDs involved in collisions** and the **expected number of collisions**:
| Configuration | Load Factor | Expected Collisions | % of IDs in Collisions |
|---------------|------------|---------------------|------------------------|
| 100 IDs in 65,536 buckets | 0.0015 | ~0.08 | ~0.15% |
| 1,000 IDs in 65,536 buckets | 0.0153 | ~7.6 | ~1.5% |
| 10,000 IDs in 65,536 buckets | 0.1526 | ~763 | ~7.3% |
#### Guidelines for Choosing `numBuckets`
It's important to understand that even though the probability of having *at least one* collision approaches 100% quickly as the number of IDs increases, the **percentage of IDs affected by collisions** grows much more slowly.
Based on the percentage of IDs involved in collisions:
- For **small** systems (≤100 IDs):
- 4,096 buckets: ~2.4% of IDs will be involved in collisions
- 16,384 buckets: ~0.6% of IDs will be involved in collisions
- For **medium** systems (101-1,000 IDs):
- 16,384 buckets: ~6% of IDs will be involved in collisions
- 65,536 buckets: ~1.5% of IDs will be involved in collisions
- For **large** systems (1,001-10,000 IDs):
- 65,536 buckets: ~7.3% of IDs will be involved in collisions
- 262,144 buckets (2^18): ~1.9% of IDs will be involved in collisions
- For **very large** systems (>10,000 IDs):
- 1,048,576 buckets (2^20): ~4.7% of IDs among 100,000 will be involved in collisions
**General Rule of Thumb**: To keep collision impact below 5% of IDs, maintain a load factor (IDs/buckets) below 0.1.
**Memory Impact**:
- Each bucket uses 8 bytes (uint64)
- 65,536 buckets = 512 KB of memory
- 1,048,576 buckets = 8 MB of memory
For systems with many distinct IDs where even low collision percentages are unacceptable, consider:
- Sharding rate limiters by logical partitions (e.g., user type, region)
- Using multiple rate limiters with different hashing algorithms
- Implementing a consistent hashing approach
> **Note**: While having some collisions is usually acceptable for rate limiting (it just means some IDs might share limits), the key is to keep the percentage of affected IDs low enough for your use case.
Memory usage scales linearly with `numBuckets`, so there's a trade-off between collision probability and memory consumption.
```go
// Example for a system with ~5,000 distinct IDs
limiter, err := rate.NewTokenBucketLimiter(
65536, // numBuckets (will remain 2^16 since it's already a power of 2) to maintain <10% collision probability
10, // burstCapacity
100, // refillRate
time.Second,
)
```
> **Note**: If you observe uneven rate limiting across different IDs, consider increasing the number of buckets to reduce collision probability.
### Custom Rate Limiting Scenarios
#### High-Volume API Rate Limiting
```go
// API rate limiting with 100 requests per second, 10 burst capacity
limiter, _ := rate.NewTokenBucketLimiter(1024, 10, 100, time.Second)
// Take a token for a specific API key
apiKey := []byte("customer-api-key")
if limiter.TakeToken(apiKey) {
// Process API request
} else {
// Return 429 Too Many Requests
}
```
#### Adaptive Backend Protection with AIMD
```go
// Create AIMD limiter for dynamic backend protection
limiter, _ := rate.NewAIMDTokenBucketLimiter(
4096, // numBuckets
20, // burstCapacity
1.0, // rateMin
1000.0, // rateMax
100.0, // rateInit
10.0, // rateAdditiveIncrease
2.0, // rateMultiplicativeDecrease
time.Second,
)
func handleRequest(backendID []byte) {
if limiter.TakeToken(backendID) {
err := callBackendService()
if err == nil {
// Backend is healthy, increase rate
limiter.IncreaseRate(backendID)
} else if isOverloadError(err) {
// Backend is overloaded, decrease rate
limiter.DecreaseRate(backendID)
}
} else {
// Circuit breaking, return error immediately
}
}
```
## Design Philosophy
The library is designed with these principles in mind:
1. **Performance First**: Every operation is optimized for minimal CPU and memory usage
2. **Thread Safety**: All operations are thread-safe for concurrent environments without lock contention
3. **Memory Efficiency**: Uses packed representation and custom implementations for efficient storage
4. **Simplicity**: Provides simple interfaces for common rate limiting patterns
5. **Flexibility**: Multiple strategies to handle different rate limiting requirements
## Thread-Safety Guarantees
The `rate` package is designed for high-concurrency environments with the following guarantees:
### Atomic Operations
- All token bucket operations use lock-free atomic operations
- Token count updates and timestamp operations use atomic Compare-And-Swap (CAS)
- No mutexes or traditional locks are used, eliminating lock contention
- Bucket selection uses thread-safe hash calculations to map IDs to buckets
### Concurrent Access Patterns
- **Read Operations**: Multiple goroutines can safely check token availability concurrently
- **Write Operations**: Multiple goroutines can safely take tokens and update rates concurrently
- **Creation**: Limiter creation is thread-safe and can be performed from any goroutine
- **Refill**: Token refill happens automatically during token operations without requiring explicit locks
### Memory Ordering
- All atomic operations enforce appropriate memory ordering guarantees
- Reads and writes are properly synchronized across multiple cores/CPUs
- Operations follow Go's memory model for concurrent access
### Contention Handling
- Multiple buckets distribute load to minimize atomic operation contention
- Rate updates for one ID don't block operations on other IDs
- The implementation uses a sharded approach where each ID maps to a specific bucket
### Limitations
- The library doesn't guarantee perfect fairness across all IDs
- When multiple IDs hash to the same bucket (collision), they share the same rate limit
- Very high contention on a single ID might experience CAS retry loops
## Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
## License
This project is licensed under the [MIT License](LICENSE).