Thanks a lot! You are so kind and nice :-D
I tried golang's heap profile and found it is very helpful. By the way,
what do you think about this article:
https://dgraph.io/blog/post/manual-memory-management-golang-jemalloc/
On Sunday, July 18, 2021 at 11:27:38 PM UTC+8 jake...@gmail.com wrote:
> Two other replies have mentioned Sync.Pool. I agree that Sync.Pool is a
> valuable tool.
>
> However, for the benefit of any beginning gophers who may read this
> thread, I wanted to point out that, in a situation like yours, I would want
> to try to reduce heap allocation in other ways first. Not that Sync.pool is
> a last resort exactly, but it does have non-trivial overhead, and is not a
> substitute for thinking about other ways to clean up heap allocations. For
> example, pulling allocations out of loops, or manual object re-use where it
> fits naturally into the code.
>
> On Friday, July 16, 2021 at 8:27:03 AM UTC-4 rmfr wrote:
>
>> I run it at an 8 cores 16GB machine and it occupies all cpu cores it
>> could.
>>
>> 1. It is ~95% cpu intensive and with ~5% network communications.
>>
>> 2. The codebase is huge and has more than 300 thousands of lines of code
>> (even didn't count the third party library yet).
>>
>> 3. The tool pprof tells nearly 50% percent of the time is spending on the
>> runtime, something related to gc, mallocgc, memclrNoHeapPointers, and so on.
>>
>> 4. It has ~100 million dynamic objects.
>>
>> Do you guys have some good advice to optimize the performance?
>>
>> One idea that occurs to me is to do something like sync.Pool to buffer
>> some most frequently allocated and freed objects. But the problem is I
>> didn't manage to find a golang tool to find such objects. The runtime
>> provides api to get the amount of objects but it didn't provide api to get
>> the detailed statistics of all objects. Please correct me if I'm wrong.
>> Thanks a lot :-)
>>
>
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