mapreduce.fileoutputcommitter.algorithm.version=2 # Use the latest committer version
mapreduce.job.reduce.slowstart.completedmaps=0.99 # 99% map, then reduce
mapreduce.reduce.shuffle.input.buffer.percent=0.9 # Min % buffer in RAM
mapreduce.reduce.shuffle.merge.percent=0.9 # Minimum % merges in RAM
mapreduce.reduce.speculative=false # Disable speculation for reducing
mapreduce.task.io.sort.factor=999 # Threshold before writing to disk

		}
	}
}

// Test for reduceErrs, reduceErr reduces collection
// of errors into a single maximal error with in the list.
func TestReduceErrs(t *testing.T) {
	canceledErrs := make([]error, 0, 5)
	for i := range 5 {
		canceledErrs = append(canceledErrs, fmt.Errorf("error %d: %w", i, context.Canceled))
	}
	// List all of all test cases to validate various cases of reduce errors.
	testCases := []struct {
		errs        []error

/// warning

The supporting additional functions `fake_password_hasher` and `fake_save_user` are just to demo a possible flow of the data, but they of course are not providing any real security.

///

## Reduce duplication { #reduce-duplication }

Reducing code duplication is one of the core ideas in **FastAPI**.

and scales with the number of available CPU cores.
Decompression speed is typically at least 1GB/s.

This means that in cases where raw IO is below these numbers
compression will not only reduce disk usage but also help increase system throughput.
Typically, enabling compression on spinning disk systems
will increase speed when the content can be compressed.

## Get Started

### 1. Prerequisites

That way I could find the best ways to reduce code duplication as much as possible, to have completion everywhere, type and error checks, etc.

All in a way that provided the best development experience for all the developers.

## Requirements { #requirements }

#### Connection Timeouts
```java
// Increase timeout values
client.setConnectionTimeout(60000);  // 60 seconds
client.setSocketTimeout(120000);     // 120 seconds
```

#### Memory Issues
```java
// Reduce concurrent threads and batch sizes
crawler.crawlerContext.setNumOfThread(5);
crawler.crawlerContext.setMaxAccessCount(1000);
```

#### SSL/TLS Issues
```java
// Configure SSL settings

# Old config for backward compatibility
build:nvcc_clang --config=cuda_nvcc

# Debug config
build:dbg -c dbg
# Only include debug info for files under tensorflow/, excluding kernels, to
# reduce the size of the debug info in the binary. This is because if the debug
# sections in the ELF binary are too large, errors can occur. See
# https://github.com/tensorflow/tensorflow/issues/48919.

### Documentación de API Alternativa

Y ahora, ve a <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>.

Verás la documentación alternativa automática (proporcionada por <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>):

![ReDoc](https://fastapi.tiangolo.com/img/index/index-02-redoc-simple.png)

## Actualización del Ejemplo

        and `tf.data.Dataset.with_options()` respectively.
    *   New `tf.data.Dataset.reduce()` API allows users to reduce a finite
        dataset to a single element using a user-provided reduce function.
    *   New `tf.data.Dataset.window()` API allows users to create finite windows
        of input dataset; when combined with the `tf.data.Dataset.reduce()` API,
        this allows users to implement customized batching.

And now, go to <a href="http://127.0.0.1:8000/redoc" class="external-link" target="_blank">http://127.0.0.1:8000/redoc</a>.

You will see the alternative automatic documentation (provided by <a href="https://github.com/Rebilly/ReDoc" class="external-link" target="_blank">ReDoc</a>):

![ReDoc](https://fastapi.tiangolo.com/img/index/index-02-redoc-simple.png)

## Example upgrade { #example-upgrade }