Methods to reduce memory allocation in Go include multiplexing objects, pre-allocating capacity, and reducing escape. 1. Use sync.Pool to cache temporary objects, multiplex the space of slices and maps, and avoid duplicate allocation; 2. Specify the capacity of slices and maps during initialization to reduce the overhead caused by dynamic expansion; 3. Avoid local variables escaping on the heap, reduce address fetching of small objects, and use compiler escape analysis to optimize stack allocation; 4. Pay attention to variable escape problems caused by closures and goroutines, and try to avoid modifying external variables or passing large structure references.
In Go, the key is to understand which operations trigger allocation and consciously optimize. Although Go's garbage collection mechanism is already very efficient, frequent memory allocations can still lead to performance bottlenecks, especially in scenarios where high-frequency calls or large-scale data processing are being performed.

Reuse objects: Reduce unnecessary new creations
Object creation in Go (such as make
, new
or struct literals) triggers memory allocation. If these operations occur in a cycle or high-frequency function, it will bring considerable pressure when accumulated.

Suggested practices:
- Use
sync.Pool
to cache temporary objects, such as buffers, structures, etc. - For slices and mappings, try to reuse the existing space, rather than creating new ones every time.
- If it is a temporary variable in HTTP request processing, it can be uniformly initialized and multiplexed in the handler.
For example:

var bufPool = sync.Pool{ New: func() interface{} { return make([]byte, 1024) }, } func getBuffer() []byte { return bufPool.Get().([]byte) } func putBuffer(buf []byte) { bufPool.Put(buf) }
This prevents new buffers from being allocated for each request.
Pre-allocated capacity: Avoid additional overhead caused by dynamic capacity expansion
Go's slicing and mapping grow dynamically, but if you know the approximate amount of data in advance, it is best to specify the capacity at initialization, which can avoid multiple memory reallocations.
FAQ:
- Continuous
append
without knowing the capacity, resulting in the underlying array being expanded multiple times. - The initial capacity is not set before inserting large amounts of data into the map.
Optimization method:
- When initializing slices, use
make([]T, 0, cap)
to reserve space. - When creating a map, set the initial size by
make(map[key]value, cap)
.
For example:
// Suppose we know that we want to save 100 elements s := make([]int, 0, 100) for i := 0; i < 100; i { s = append(s, i) }
This is less than not specifying capacity once or even multiple memory copies.
Reduce escape: Make variables allocated on the stack as much as possible
The Go compiler decides whether variables are allocated on the stack or the heap at compile time. Allocation and release on the stack are almost cost-free, while allocations on the heap require GC recycling.
How to reduce escape:
- Avoid returning or passing local variables into goroutines.
- Try not to address small objects (unless you really need to share them).
- Use
-gcflags="-m"
to view the escape analysis results.
For example:
func badFunc() *int { x := 10 return &x // This will cause x to escape to the heap}
Change to:
func goodFunc() int { x := 10 return x // x allocated on stack}
Pay attention to the side effects of closures and goroutines
When a closure captures an external variable, it may cause the variables that should have been on the stack to escape to the heap. The same is true for goroutine, if you pass a variable into a function that is executed asynchronously, it may also cause additional allocation.
Notes:
- Try to avoid modifying external variables in closures.
- Do not capture large structures as closure environment variables.
- If concurrent processing is necessary, consider passing a copy instead of a reference.
For example:
func startWorker(data []byte) { go func() { // data is captured and may escape process(data) }() }
If data
is large and has a long life cycle, it is recommended to control its scope or reuse its memory.
Basically that's it. Many times, Go's performance optimization is not "how fast", but "less allocation". Pay attention to these details and many performance problems can be avoided naturally.
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