### **4.2 WordCount**

WordCount is a simple program that counts how often a word occurs in a text file. The code builds a dataset of (String, Int) pairs called counts, and saves the dataset to a file.

The following example submits WordCount code to the Scala shell. Select an input file for the Spark WordCount example. We can use any text file as input.

unnecessary redirection from ci/latest.txt to ci-cross/latest.txt. Users should know exactly where the builds are stored on Google Cloud storage buckets from now on. For example for 1.9 and 1.10, users can specify ci/latest-1.9 and ci/latest-1.10 as the CI build jobs what build images correctly updates those. The CI jobs for master update the ci-cross/latest location, so if you are looking for latest master builds, then the correct parameter to use would be ci-cross/latest. ([#63504](https://github.com/ku...

/// info

This requires installing `ujson` for example with `pip install ujson`.

///

/// warning

`ujson` is less careful than Python's built-in implementation in how it handles some edge-cases.

///

{* ../../docs_src/custom_response/tutorial001.py hl[2,7] *}

/// tip

It's possible that `ORJSONResponse` might be a faster alternative.

		// To maintain the invariance that lastRefreshedAt records the
		// timestamp of last successful refresh
		config.lastRefreshedAt = UTCNow()
		return nil
	default:
		return err
	}

	// Reset drivercache built using current config
	clear(config.drivercache)
	// Remove existing tier configs
	clear(config.Tiers)
	// Copy over the new tier configs
	maps.Copy(config.Tiers, newConfig.Tiers)
	config.lastRefreshedAt = UTCNow()

  }

  public void testPrecomputedOptimizations() {
    // These are testing behavior that's never promised by the API.
    // Some matchers are so efficient that it is a waste of effort to
    // build a precomputed version.
    CharMatcher m1 = is('x');
    assertSame(m1, m1.precomputed());
    assertEquals(m1.toString(), m1.precomputed().toString());

    CharMatcher m2 = anyOf("Az");

///

### <a href="https://www.starlette.io/" class="external-link" target="_blank">Starlette</a>

Starlette — це легкий фреймворк/набір інструментів <abbr title="The new standard for build asynchronous Python web">ASGI</abbr>, який ідеально підходить для створення високопродуктивних asyncio сервісів.

- Kubernetes is now built with Go 1.19.1 ([#112287](https://github.com/kubernetes/kubernetes/pull/112287), [@palnabarun](https://github.com/palnabarun)) [SIG Release and Testing]
- Kubernetes is now built with Go 1.19.2 ([#112900](https://github.com/kubernetes/kubernetes/pull/112900), [@xmudrii](https://github.com/xmudrii)) [SIG Release and Testing]

You could have a proxy that adds a path prefix to your application.

In these cases you can use `root_path` to configure your application.

The `root_path` is a mechanism provided by the ASGI specification (that FastAPI is built on, through Starlette).

The `root_path` is used to handle these specific cases.

And it's also used internally when mounting sub-applications.

Here we'll see an example using <a href="https://sqlmodel.tiangolo.com/" class="external-link" target="_blank">SQLModel</a>.

**SQLModel** is built on top of <a href="https://www.sqlalchemy.org/" class="external-link" target="_blank">SQLAlchemy</a> and Pydantic. It was made by the same author of **FastAPI** to be the perfect match for FastAPI applications that need to use **SQL databases**.

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }