r = s3.select_object_content(
    Bucket='mycsvbucket',
    Key='sampledata/TotalPopulation.csv.gz',
    ExpressionType='SQL',
    Expression="select * from s3object s where s.Location like '%United States%'",
    InputSerialization={
        'CSV': {
            "FileHeaderInfo": "USE",
        },
        'CompressionType': 'GZIP',
    },
    OutputSerialization={'CSV': {}},
)

edTemporaryFile" class="external-link" target="_blank">`SpooledTemporaryFile`</a> (ein <a href="https://docs.python.org/3/glossary.html#term-file-like-object" class="external-link" target="_blank">dateiartiges</a> Objekt). Das ist das tatsächliche Python-Objekt, das Sie direkt anderen Funktionen oder Bibliotheken übergeben können, welche ein „file-like“-Objekt erwarten.

`UploadFile` hat die folgenden `async`hronen Methoden. Sie alle rufen die entsprechenden Methoden des darunterliegenden...

 * Futures#transform(ListenableFuture, com.google.common.base.Function, Executor) Futures.transform}
 * (or {@link FluentFuture#transform(com.google.common.base.Function, Executor)
 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.

 * Futures#transform(ListenableFuture, com.google.common.base.Function, Executor) Futures.transform}
 * (or {@link FluentFuture#transform(com.google.common.base.Function, Executor)
 * FluentFuture.transform}), but you will often find it easier to use a framework. Frameworks
 * automate the process, often adding features like monitoring, debugging, and cancellation.

		if e.ConditionRHS.In.ListExpr != nil {
			return e.ConditionRHS.In.ListExpr.aggregateRow(r, tableAlias)
		}
		return nil
	case e.ConditionRHS.Like != nil:
		err = e.ConditionRHS.Like.Pattern.aggregateRow(r, tableAlias)
		if err != nil {
			return err
		}
		return e.ConditionRHS.Like.EscapeChar.aggregateRow(r, tableAlias)
	default:
		return errInvalidASTNode
	}
}

## Architecture Standards

**Experimental!**

We'd like to capture our architectural decisions about the build tool as [Architectural Decision Records (ADRs)](https://adr.github.io/).
For now we just have this global repository of ADRs.
If we see fit, we can break these out to per-platform ones, or keep a hybrid approach to having global and platform-specific ADSs.

Our aim is to keep the process lightweight and approachable.

    return PlainTextResponse(message, status_code=400)


@app.get("/items/{item_id}")
async def read_item(item_id: int):
    if item_id == 3:
        raise HTTPException(status_code=418, detail="Nope! I don't like 3.")

func (s *Scanner) MaxTokenSize(n int) {
	if n < utf8.UTFMax || n > 1e9 {
		panic("bad max token size")
	}
	if n < len(s.buf) {
		s.buf = make([]byte, n)
	}
	s.maxTokenSize = n
}

// ErrOrEOF is like Err, but returns EOF. Used to test a corner case.
func (s *Scanner) ErrOrEOF() error {
	return s.err

 * and distributions as well as local repositories as additional test inputs
 * to test functionality that requires rea distributions (like the wrapper)
 * or separately published libraries (like the Tooling API Jar).
 */
@DisableCachingByDefault(because = "Abstract super-class, not to be instantiated directly")
abstract class DistributionTest : Test() {

    /**

#   and will also try using public build cache results to speed up
#   your builds. This usually saves a lot of time, especially when
#   re-running tests. However, note that:
#
#    - New environments like new CUDA versions, changes to manylinux,
#      compilers, etc. can cause undefined behavior such as build failures
#      or tests passing incorrectly.
#    - Automatic LLVM updates are known to extend build time even with