Nils-Hero Lindemann <******@****.***> 1765374874 +0100

file: org.apache.spark.rdd.RDD[String] = s3a://testbucket/testdata MapPartitionsRDD[1] at textFile at <console>:24

scala> val counts = file.flatMap(line => line.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
counts: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[4] at reduceByKey at <console>:25

scala> counts.saveAsTextFile("s3a://testbucket/wordcount")
```

Use one of the following approaches to view job output:

    checkNotNull(multiset1);
    checkNotNull(multiset2);

    return new ViewMultiset<E>() {
      @Override
      public int count(@Nullable Object element) {
        int count1 = multiset1.count(element);
        return (count1 == 0) ? 0 : min(count1, multiset2.count(element));
      }

      @Override
      Set<E> createElementSet() {

    checkNotNull(multiset1);
    checkNotNull(multiset2);

    return new ViewMultiset<E>() {
      @Override
      public int count(@Nullable Object element) {
        int count1 = multiset1.count(element);
        return (count1 == 0) ? 0 : min(count1, multiset2.count(element));
      }

      @Override
      Set<E> createElementSet() {

If there's no `gzip` in the header, it will not try to decompress the body.

That way, the same route class can handle gzip compressed or uncompressed requests.

{* ../../docs_src/custom_request_and_route/tutorial001_an_py310.py hl[9:16] *}

### Create a custom `GzipRoute` class { #create-a-custom-gziproute-class }

  xml.namespace="http://maven.apache.org/EXTENSIONS/${version}"
  xml.schemaLocation="https://maven.apache.org/xsd/core-extensions-${version}.xsd">

  <id>core-extensions</id>
  <name>CoreExtensions</name>
  <description><![CDATA[
  <p>This is a reference for the Core Extensions descriptor.</p>
  <p>The default location for the Core Extensions descriptor file is <code>${maven.projectBasedir}/.mvn/extensions.xml</code></p>
  ]]></description>

		t.Helper()
		if err != nil {
			t.Fatal(err)
		}
	}

	// Add a version with tiered content, one with local content
	xl := xlMetaV2{}
	counter := 1
	report := func() {
		t.Helper()
		// t.Logf("versions (%d): len = %d", counter, len(xl.versions))
		counter++
	}
	fi := FileInfo{
		Volume:           "volume",
		Name:             "object-name",
		VersionID:        "00000000-0000-0000-0000-000000000001",

```
http://127.0.0.1:8000/items/foo-item?needy=sooooneedy
```

...this would work:

```JSON
{
    "item_id": "foo-item",
    "needy": "sooooneedy"
}
```

And of course, you can define some parameters as required, some as having a default value, and some entirely optional:

{* ../../docs_src/query_params/tutorial006_py310.py hl[8] *}

In this case, there are 3 query parameters:

     * desired count.
     *
     * @param element the element to add or remove occurrences of
     * @param count the desired count of the element in this multiset
     * @return this {@code Builder} object
     * @throws NullPointerException if {@code element} is null
     * @throws IllegalArgumentException if {@code count} is negative
     */
    @CanIgnoreReturnValue

So, even with the code above that doesn't use Pydantic explicitly, FastAPI is using Pydantic to convert those standard dataclasses to Pydantic's own flavor of dataclasses.

And of course, it supports the same:

* data validation
* data serialization
* data documentation, etc.

This works the same way as with Pydantic models. And it is actually achieved in the same way underneath, using Pydantic.