Reducing Garbage Collector Pressure in Golang

In high-performance Go applications, excessive memory allocation and deallocation can seriously affect performance, putting unnecessary pressure on the garbage collector (GC), resulting in increased latency and reduced efficiency. This article will introduce how to use object reuse technology and the sync.Pool feature to reduce GC pressure.

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This article was inspired by a LinkedIn post by Branko Pitulic, which highlighted the importance of optimizing memory usage in Go applications.

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1. Understanding questions

Go’s garbage collector is responsible for automatic memory management. However, when an application allocates and frees memory frequently (especially on the heap), the GC has to work harder, resulting in:

• CPU usage increased;
• Execution paused during GC cycle;
• Performance bottleneck in low latency systems.

The goal is to reduce the number of objects allocated on the heap by promoting memory reuse.

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2. Technology to reduce GC pressure

2.1 Object Reuse

Reuse objects whenever possible instead of creating new ones. A common pattern is to reuse slices and arrays.

Bad Practice:

<code class="language-go">func process() []byte {
    return make([]byte, 1024) // 每次都创建一个新的切片。
}</code>

Good Practice:

<code class="language-go">var buffer = make([]byte, 1024)

func process() []byte {
    return buffer // 重用现有的切片。
}</code>

Note: This approach works well in non-concurrent contexts where reuse is safe.

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2.2 Using sync.Pool

The

sync package provides the Pool type, which is an efficient object pool structure that enables reuse, thereby reducing memory allocation on the heap.

How

sync.Pool works:

• Used objects can be stored in the pool.
• When a new object is needed, the pool is checked before memory is allocated.
• If the pool is empty, create a new object.

Basic example:

<code class="language-go">package main

import (
    "fmt"
    "sync"
)

func main() {
    // 创建一个对象池。
    pool := sync.Pool{
        New: func() any {
            return make([]byte, 1024) // 创建一个新的1KB切片。
        },
    }

    // 从池中检索一个对象。
    buffer := pool.Get().([]byte)
    fmt.Printf("Buffer length: %d\n", len(buffer))

    // 通过将对象放回池中来重用它。
    pool.Put(buffer)

    // 从池中检索另一个对象。
    reusedBuffer := pool.Get().([]byte)
    fmt.Printf("Reused buffer length: %d\n", len(reusedBuffer))
}</code>

In this example:

1. Use the function to create a New to initialize the object. sync.Pool
Use to retrieve objects from the pool.
2. Get Use to return the object to the pool for supply.
3. Put
<.> 3. Use the best practice of

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sync.Pool Lightweight objects: Ponds are very suitable for small or medium -sized objects. For large objects, the storage cost may exceed the income.

concurrent:
1. can be used safely in multiple goroutine, although the performance under high load may be different.
Initialization:
2. Always define a function in the pool to ensure the correct creation object. sync.Pool Avoid excessive use of the pool:
Only used the pool for only timely reused objects.
3. New <.> 4. Common cases
4. <.> 4.1 The buffer pool for reading/writing operations

Applications with a large number of read/write operations (e.g., an HTTP server or message processor) can effectively reuse the buffer.

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Example:

<.> 4.2 Structural weight use

If your application frequently creates and discard the structure,

can help.

Example:

<code class="language-go">func process() []byte {
    return make([]byte, 1024) // 每次都创建一个新的切片。
}</code>

<.> 5. Finally precautions

Using can significantly improve application performance, especially in high throughput scenarios. But:

sync.Pool

Avoid premature optimization. Before using

, use tools such as to analyze performance to ensure that GC is really a real bottleneck.

The use of the pool is combined with the general best practice, such as reducing the variable scope and effective use of slices or arrays.

<code class="language-go">var buffer = make([]byte, 1024)

func process() []byte {
    return buffer // 重用现有的切片。
}</code>

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Understanding and applying these technologies will help you build more efficient and scalable systems in Go.

If you have any questions or more advanced examples, please ask at any time! ?

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