## **4. Run Sample Applications**

After installing Hive, Hadoop and Spark successfully, we can now proceed to run some sample applications to see if they are configured appropriately.  We can use Spark Pi and Spark WordCount programs to validate our Spark installation. We can also explore how to run Spark jobs from the command line and Spark shell.

### **4.1 Spark Pi**

    // <plugin/> block in a Maven POM. We have to do some wiggling to pull the sources of information
    // together and this really shows the problem of constructing a sensible default configuration, but
    // it's all encapsulated here so it appears normalized to the POM builder.

    // We are going to take the project packaging and find all plugin in the default lifecycle and create

In that case, you can simply swap the standard `dataclasses` with `pydantic.dataclasses`, which is a drop-in replacement:

{* ../../docs_src/dataclasses_/tutorial003_py310.py hl[1,4,7:10,13:16,22:24,27] *}

1. We still import `field` from standard `dataclasses`.

2. `pydantic.dataclasses` is a drop-in replacement for `dataclasses`.

3. The `Author` dataclass includes a list of `Item` dataclasses.

 * the final model of the project being assembled. There is no
 * overriding going on amongst the models being used in this test:
 * each model in the lineage is providing a value that is not present
 * anywhere else in the lineage. We are just making sure that values
 * down in the lineage are bubbling up where they should.
 *
 */
@Deprecated
@SuppressWarnings("checkstyle:UnusedLocalVariable")

// Copyright 2021 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// Test to make sure that if we use R15 after it is clobbered by
// a global variable access while dynamic linking, we get an error.
// See issue 43661.

TEXT ·a1(SB), 0, $0-0
	CMPL runtime·writeBarrier(SB), $0
	MOVL $0, R15
	RET
TEXT ·a2(SB), 0, $0-0
	CMPL runtime·writeBarrier(SB), $0

# limitations under the License.
# ==============================================================================
"""
Top-level module of TensorFlow. By convention, we refer to this module as
`tf` instead of `tensorflow`, following the common practice of importing
TensorFlow via the command `import tensorflow as tf`.

The primary function of this module is to import all of the public TensorFlow

    <suppress checks="InterfaceIsTypeCheck"
              files=".*[/\\]core-api[/\\]src[/\\]main[/\\]java[/\\]org[/\\]gradle[/\\]api[/\\]attributes[/\\]Usage.+"/>

    <!-- We like it explicit -->
    <suppress checks="ExplicitInitializationCheck"
              files=".*"/>

    <!-- Ignore code from maven subproject under org.apache -->
    <suppress checks="RegexpSingleline"

				if err != nil {
					host = r.Host
				}
				// Make sure to skip matching minio.<domain>` this is
				// specifically meant for operator/k8s deployment
				// The reason we need to skip this is for a special
				// usecase where we need to make sure that
				// minio.<namespace>.svc.<cluster_domain> is ignored
				// by the bucketDNS style to ensure that path style
				// is available and honored at this domain.
				//

A PR should have a specific use case that it is solving.

* If the PR is for a feature, it should have docs.
    * Unless it's a feature we want to discourage, like support for a corner case that we don't want users to use.
* The docs should include a source example file, not write Python directly in Markdown.

        ComparableVersion upper = new ComparableVersion("1.0.0-ALPHA1");
        // Lower case is equal to upper case. This is *NOT* what Semver 1.0
        // specifies. Here we are explicitly deviating from Semver 1.0.
        assertTrue(upper.compareTo(lower) == 0, "expected 1.0.0-ALPHA1 == 1.0.0-alpha1");
        assertTrue(lower.compareTo(upper) == 0, "expected 1.0.0-alpha1 == 1.0.0-ALPHA1");
    }