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chore: retire the benchmark data

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Andy Pan 2024-04-15 17:50:28 +08:00
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README.md
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@ -293,47 +293,6 @@ pool.Reboot()
All tasks submitted to `ants` pool will not be guaranteed to be addressed in order, because those tasks scatter among a series of concurrent workers, thus those tasks would be executed concurrently. All tasks submitted to `ants` pool will not be guaranteed to be addressed in order, because those tasks scatter among a series of concurrent workers, thus those tasks would be executed concurrently.
## 🧲 Benchmarks
<div align="center"><img src="https://user-images.githubusercontent.com/7496278/51515466-c7ce9e00-1e4e-11e9-89c4-bd3785b3c667.png"/></div>
In this benchmark result, the first and second benchmarks performed test cases with 1M tasks, and the rest of the benchmarks performed test cases with 10M tasks, both in unlimited goroutines and `ants` pool, and the capacity of this `ants` goroutine pool was limited to 50K.
- BenchmarkGoroutine-4 represents the benchmarks with unlimited goroutines in Golang.
- BenchmarkPoolGroutine-4 represents the benchmarks with an `ants` pool.
### Benchmarks with Pool
![](https://user-images.githubusercontent.com/7496278/51515499-f187c500-1e4e-11e9-80e5-3df8f94fa70f.png)
In the above benchmark result, the first and second benchmarks performed test cases with 1M tasks, and the rest of the benchmarks performed test cases with 10M tasks, both in unlimited goroutines and `ants` pool and the capacity of this `ants` goroutine-pool was limited to 50K.
**As you can see, `ants` performs 2 times faster than goroutines without a pool (10M tasks) and it only consumes half the memory compared with goroutines without a pool. (both in 1M and 10M tasks)**
### Benchmarks with PoolWithFunc
![](https://user-images.githubusercontent.com/7496278/51515565-1e3bdc80-1e4f-11e9-8a08-452ab91d117e.png)
### Throughput (it is suitable for scenarios where tasks are submitted asynchronously without waiting for the final results)
#### 100K tasks
![](https://user-images.githubusercontent.com/7496278/51515590-36abf700-1e4f-11e9-91e4-7bd3dcb5f4a5.png)
#### 1M tasks
![](https://user-images.githubusercontent.com/7496278/51515596-44617c80-1e4f-11e9-89e3-01e19d2979a1.png)
#### 10M tasks
![](https://user-images.githubusercontent.com/7496278/52987732-537c2000-3437-11e9-86a6-177f00d7a1d6.png)
## 📊 Performance Summary
![](https://user-images.githubusercontent.com/7496278/63449727-3ae6d400-c473-11e9-81e3-8b3280d8288a.gif)
**In conclusion, `ants` performs 2~6 times faster than goroutines without a pool and the memory consumption is reduced by 10 to 20 times.**
## 👏 Contributors ## 👏 Contributors
Please read our [Contributing Guidelines](CONTRIBUTING.md) before opening a PR and thank you to all the developers who already made contributions to `ants`! Please read our [Contributing Guidelines](CONTRIBUTING.md) before opening a PR and thank you to all the developers who already made contributions to `ants`!

41
README_ZH.md
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@ -294,47 +294,6 @@ pool.Reboot()
`ants` 并不保证提交的任务被执行的顺序，执行的顺序也不是和提交的顺序保持一致，因为在 `ants` 是并发地处理所有提交的任务，提交的任务会被分派到正在并发运行的 workers 上去，因此那些任务将会被并发且无序地被执行。 `ants` 并不保证提交的任务被执行的顺序，执行的顺序也不是和提交的顺序保持一致，因为在 `ants` 是并发地处理所有提交的任务，提交的任务会被分派到正在并发运行的 workers 上去，因此那些任务将会被并发且无序地被执行。
## 🧲 Benchmarks
<div align="center"><img src="https://user-images.githubusercontent.com/7496278/51515466-c7ce9e00-1e4e-11e9-89c4-bd3785b3c667.png"/></div>
上图中的前两个 benchmark 测试结果是基于100w 任务量的条件，剩下的几个是基于 1000w 任务量的测试结果，`ants` 的默认池容量是 5w。
- BenchmarkGoroutine-4 代表原生 goroutine
- BenchmarkPoolGroutine-4 代表使用 goroutine 池 `ants`
### Benchmarks with Pool
![](https://user-images.githubusercontent.com/7496278/51515499-f187c500-1e4e-11e9-80e5-3df8f94fa70f.png)
**这里为了模拟大规模 goroutine 的场景，两次测试的并发次数分别是 100w 和 1000w，前两个测试分别是执行 100w 个并发任务不使用 Pool 和使用了`ants`的 Goroutine Pool 的性能，后两个则是 1000w 个任务下的表现，可以直观的看出在执行速度和内存使用上，`ants`的 Pool 都占有明显的优势。100w 的任务量，使用`ants`，执行速度与原生 goroutine 相当甚至略快，但只实际使用了不到 5w 个 goroutine 完成了全部任务，且内存消耗仅为原生并发的 40%；而当任务量达到 1000w，优势则更加明显了：用了 70w 左右的 goroutine 完成全部任务，执行速度比原生 goroutine 提高了 100%，且内存消耗依旧保持在不使用 Pool 的 40% 左右。**
### Benchmarks with PoolWithFunc
![](https://user-images.githubusercontent.com/7496278/51515565-1e3bdc80-1e4f-11e9-8a08-452ab91d117e.png)
**因为`PoolWithFunc`这个 Pool 只绑定一个任务函数，也即所有任务都是运行同一个函数，所以相较于`Pool`对原生 goroutine 在执行速度和内存消耗的优势更大，上面的结果可以看出，执行速度可以达到原生 goroutine 的 300%，而内存消耗的优势已经达到了两位数的差距，原生 goroutine 的内存消耗达到了`ants`的35倍且原生 goroutine 的每次执行的内存分配次数也达到了`ants`45倍，1000w 的任务量，`ants`的初始分配容量是 5w，因此它完成了所有的任务依旧只使用了 5w 个 goroutine！事实上，`ants`的 Goroutine Pool 的容量是可以自定义的，也就是说使用者可以根据不同场景对这个参数进行调优直至达到最高性能。**
### 吞吐量测试（适用于那种只管提交异步任务而无须关心结果的场景）
#### 10w 任务量
![](https://user-images.githubusercontent.com/7496278/51515590-36abf700-1e4f-11e9-91e4-7bd3dcb5f4a5.png)
#### 100w 任务量
![](https://user-images.githubusercontent.com/7496278/51515596-44617c80-1e4f-11e9-89e3-01e19d2979a1.png)
#### 1000w 任务量
![](https://user-images.githubusercontent.com/7496278/52987732-537c2000-3437-11e9-86a6-177f00d7a1d6.png)
## 📊 性能小结
![](https://user-images.githubusercontent.com/7496278/63449727-3ae6d400-c473-11e9-81e3-8b3280d8288a.gif)
**从该 demo 测试吞吐性能对比可以看出，使用`ants`的吞吐性能相较于原生 goroutine 可以保持在 2-6 倍的性能压制，而内存消耗则可以达到 10-20 倍的节省优势。**
## 👏 贡献者 ## 👏 贡献者
请在提 PR 之前仔细阅读 [Contributing Guidelines](CONTRIBUTING.md)，感谢那些为 `ants` 贡献过代码的开发者！ 请在提 PR 之前仔细阅读 [Contributing Guidelines](CONTRIBUTING.md)，感谢那些为 `ants` 贡献过代码的开发者！