return JobLogDbm.getInstance();
    }

    @Override
    protected <RESULT extends JobLog> RESULT createEntity(Map<String, Object> source, Class<? extends RESULT> entityType) {
        try {
            final RESULT result = entityType.newInstance();
            result.setEndTime(DfTypeUtil.toLong(source.get("endTime")));
            result.setJobName(DfTypeUtil.toString(source.get("jobName")));

    }

    @Test
    @DisplayName("Test managed resource wrapper")
    void testManagedResource() throws Exception {
        TestResource resource = new TestResource("test3");

        try (ResourceManager.ManagedResource<TestResource> managed = resourceManager.manage(resource)) {
            assertNotNull(managed.get());
            assertEquals("test3", managed.get().getName());

            byte[] buffer = new byte[10];

            Transport.readn(is, buffer, 0, 2); // Read all
            int bytesRead = Transport.readn(is, buffer, 0, 5); // Try to read more
            assertEquals(0, bytesRead);
        }
    }

    @Nested
    @DisplayName("Resource management tests")
    class ResourceManagementTests {

        @Test

要解決相同問題，使用針對 `RequestValidationError` 的自訂處理器來讀取 `body` 通常更簡單（[處理錯誤](../tutorial/handling-errors.md#use-the-requestvalidationerror-body)）。

但本範例仍然有效，並示範了如何與內部元件互動。

///

我們也可以用同樣的方法，在例外處理器中存取請求本文。

我們只需要在 `try`/`except` 區塊中處理請求即可：

{* ../../docs_src/custom_request_and_route/tutorial002_an_py310.py hl[14,16] *}

若發生例外，`Request` 實例依然在作用域內，因此在處理錯誤時我們仍可讀取並使用請求本文：

Use `CrawlingParameterUtil` to set/get `CrawlerContext` and `UrlQueue` in worker threads. Always clear in finally block with `CrawlingParameterUtil.clearAll()`.

### Resource Cleanup Pattern

Always use try-with-resources for `ResponseData` - temp files are auto-deleted on close.

## Log Message Guidelines

- Format parameters as `key=value` (e.g., `sessionId={}`, `url={}`)

    public void test_execute_withInvalidQuery() {
        QueryContext context = new QueryContext("test", false);
        Query invalidQuery = new TermQuery(new Term("field", "value"));
        float boost = 1.0f;

        try {
            fuzzyQueryCommand.execute(context, invalidQuery, boost);
            fail("Should throw InvalidQueryException");
        } catch (InvalidQueryException e) {

            range1.isConnected(range2) && !range1.intersection(range2).isEmpty();
        ImmutableRangeMap.Builder<Integer, Integer> builder = ImmutableRangeMap.builder();
        builder.put(range1, 1).put(range2, 2);
        try {
          ImmutableRangeMap<Integer, Integer> unused = builder.build();
          assertFalse(expectRejection);
        } catch (IllegalArgumentException e) {
          assertTrue(expectRejection);
        }
      }

Then we use the request directly, and extract the body as `bytes`. This means that FastAPI won't even try to parse the request payload as JSON.

And then in our code, we parse that YAML content directly, and then we are again using the same Pydantic model to validate the YAML content:

  shopt -s expand_aliases
else
  echo '==TFCI==: Error: Cannot find path to grealpath or gstat'
  echo 'TF CI scripts require GNU core utilties to be installed. Please make'
  echo 'sure they are present on your system and try again.'
  exit 1
fi

# "TFCI_MACOS_BAZEL_TEST_DIR_PATH" specifies the directory that Bazel should use
# when running tests. Each test will be executed in a separate subdirectory

    * Add `-prof gc` to the options to check whether the garbage collector runs during a microbenchmarks and skews
   your results. If so, try to force a GC between runs (`-gc true`) but watch out for the caveats.
    * Add `-prof perf` or `-prof perfasm` (both only available on Linux, see Disassembling below) to see hotspots.
    * Add `-prof async` to see hotspots.