Amazon S3 S3-Beispiele SDK für die Verwendung von Python (Boto3) - AWS SDK-Codebeispiele
GrundlagenAktionenSzenarienServerless-Beispiele

Weitere AWS SDK Beispiele sind im Repo AWS Doc SDK Examples GitHub verfügbar.

Die vorliegende Übersetzung wurde maschinell erstellt. Im Falle eines Konflikts oder eines Widerspruchs zwischen dieser übersetzten Fassung und der englischen Fassung (einschließlich infolge von Verzögerungen bei der Übersetzung) ist die englische Fassung maßgeblich.

Amazon S3 S3-Beispiele SDK für die Verwendung von Python (Boto3)

Die folgenden Codebeispiele zeigen Ihnen, wie Sie Aktionen ausführen und allgemeine Szenarien implementieren, indem Sie Amazon S3 verwenden. AWS SDK for Python (Boto3)

Basics sind Codebeispiele, die Ihnen zeigen, wie Sie die wesentlichen Operationen innerhalb eines Service ausführen.

Aktionen sind Codeauszüge aus größeren Programmen und müssen im Kontext ausgeführt werden. Aktionen zeigen Ihnen zwar, wie Sie einzelne Servicefunktionen aufrufen, aber Sie können Aktionen im Kontext der zugehörigen Szenarien sehen.

Szenarien sind Codebeispiele, die Ihnen zeigen, wie Sie bestimmte Aufgaben ausführen, indem Sie mehrere Funktionen innerhalb eines Dienstes oder in Kombination mit anderen aufrufen AWS-Services.

Jedes Beispiel enthält einen Link zum vollständigen Quellcode, in dem Sie Anweisungen zum Einrichten und Ausführen des Codes im Kontext finden.

Erste Schritte

Die folgenden Codebeispiele veranschaulichen die ersten Schritte mit Amazon S3.

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

import boto3


def hello_s3():
    """
    Use the AWS SDK for Python (Boto3) to create an Amazon Simple Storage Service
    (Amazon S3) resource and list the buckets in your account.
    This example uses the default settings specified in your shared credentials
    and config files.
    """
    s3_resource = boto3.resource("s3")
    print("Hello, Amazon S3! Let's list your buckets:")
    for bucket in s3_resource.buckets.all():
        print(f"\t{bucket.name}")


if __name__ == "__main__":
    hello_s3()

  • APIEinzelheiten finden Sie unter ListBucketsPython (Boto3) API -Referenz.AWS SDK

Grundlagen

Wie das aussehen kann, sehen Sie am nachfolgenden Beispielcode:

  • Erstellen Sie einen Bucket und laden Sie eine Datei in ihn hoch.

  • Laden Sie ein Objekt aus einem Bucket herunter.

  • Kopieren Sie ein Objekt in einen Unterordner eines Buckets.

  • Listen Sie die Objekte in einem Bucket auf.

  • Löschen Sie die Bucket-Objekte und den Bucket.

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

import io
import os
import uuid

import boto3
from boto3.s3.transfer import S3UploadFailedError
from botocore.exceptions import ClientError


def do_scenario(s3_resource):
    print("-" * 88)
    print("Welcome to the Amazon S3 getting started demo!")
    print("-" * 88)

    bucket_name = f"amzn-s3-demo-bucket-{uuid.uuid4()}"
    bucket = s3_resource.Bucket(bucket_name)
    try:
        bucket.create(
            CreateBucketConfiguration={
                "LocationConstraint": s3_resource.meta.client.meta.region_name
            }
        )
        print(f"Created demo bucket named {bucket.name}.")
    except ClientError as err:
        print(f"Tried and failed to create demo bucket {bucket_name}.")
        print(f"\t{err.response['Error']['Code']}:{err.response['Error']['Message']}")
        print(f"\nCan't continue the demo without a bucket!")
        return

    file_name = None
    while file_name is None:
        file_name = input("\nEnter a file you want to upload to your bucket: ")
        if not os.path.exists(file_name):
            print(f"Couldn't find file {file_name}. Are you sure it exists?")
            file_name = None

    obj = bucket.Object(os.path.basename(file_name))
    try:
        obj.upload_file(file_name)
        print(
            f"Uploaded file {file_name} into bucket {bucket.name} with key {obj.key}."
        )
    except S3UploadFailedError as err:
        print(f"Couldn't upload file {file_name} to {bucket.name}.")
        print(f"\t{err}")

    answer = input(f"\nDo you want to download {obj.key} into memory (y/n)? ")
    if answer.lower() == "y":
        data = io.BytesIO()
        try:
            obj.download_fileobj(data)
            data.seek(0)
            print(f"Got your object. Here are the first 20 bytes:\n")
            print(f"\t{data.read(20)}")
        except ClientError as err:
            print(f"Couldn't download {obj.key}.")
            print(
                f"\t{err.response['Error']['Code']}:{err.response['Error']['Message']}"
            )

    answer = input(
        f"\nDo you want to copy {obj.key} to a subfolder in your bucket (y/n)? "
    )
    if answer.lower() == "y":
        dest_obj = bucket.Object(f"demo-folder/{obj.key}")
        try:
            dest_obj.copy({"Bucket": bucket.name, "Key": obj.key})
            print(f"Copied {obj.key} to {dest_obj.key}.")
        except ClientError as err:
            print(f"Couldn't copy {obj.key} to {dest_obj.key}.")
            print(
                f"\t{err.response['Error']['Code']}:{err.response['Error']['Message']}"
            )

    print("\nYour bucket contains the following objects:")
    try:
        for o in bucket.objects.all():
            print(f"\t{o.key}")
    except ClientError as err:
        print(f"Couldn't list the objects in bucket {bucket.name}.")
        print(f"\t{err.response['Error']['Code']}:{err.response['Error']['Message']}")

    answer = input(
        "\nDo you want to delete all of the objects as well as the bucket (y/n)? "
    )
    if answer.lower() == "y":
        try:
            bucket.objects.delete()
            bucket.delete()
            print(f"Emptied and deleted bucket {bucket.name}.\n")
        except ClientError as err:
            print(f"Couldn't empty and delete bucket {bucket.name}.")
            print(
                f"\t{err.response['Error']['Code']}:{err.response['Error']['Message']}"
            )

    print("Thanks for watching!")
    print("-" * 88)


if __name__ == "__main__":
    do_scenario(boto3.resource("s3"))

Aktionen

Das folgende Codebeispiel zeigt die Verwendung. CopyObject

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    def copy(self, dest_object):
        """
        Copies the object to another bucket.

        :param dest_object: The destination object initialized with a bucket and key.
                            This is a Boto3 Object resource.
        """
        try:
            dest_object.copy_from(
                CopySource={"Bucket": self.object.bucket_name, "Key": self.object.key}
            )
            dest_object.wait_until_exists()
            logger.info(
                "Copied object from %s:%s to %s:%s.",
                self.object.bucket_name,
                self.object.key,
                dest_object.bucket_name,
                dest_object.key,
            )
        except ClientError:
            logger.exception(
                "Couldn't copy object from %s/%s to %s/%s.",
                self.object.bucket_name,
                self.object.key,
                dest_object.bucket_name,
                dest_object.key,
            )
            raise

Kopieren Sie ein Objekt mithilfe einer bedingten Anforderung.

class S3ConditionalRequests:
    """Encapsulates S3 conditional request operations."""

    def __init__(self, s3_client):
        self.s3 = s3_client

    @classmethod
    def from_client(cls):
        """
        Instantiates this class from a Boto3 client.
        """
        s3_client = boto3.client("s3")
        return cls(s3_client)


    def copy_object_conditional(
        self,
        source_key: str,
        dest_key: str,
        source_bucket: str,
        dest_bucket: str,
        condition_type: str,
        condition_value: str,
    ):
        """
        Copies an object from one Amazon S3 bucket to another with a conditional request.

        :param source_key: The key of the source object to copy.
        :param dest_key: The key of the destination object.
        :param source_bucket: The source bucket of the object.
        :param dest_bucket: The destination bucket of the object.
        :param condition_type: The type of condition to apply, e.g.
        'CopySourceIfMatch', 'CopySourceIfNoneMatch', 'CopySourceIfModifiedSince', 'CopySourceIfUnmodifiedSince'.
        :param condition_value: The value to use for the condition.
        """
        try:
            self.s3.copy_object(
                Bucket=dest_bucket,
                Key=dest_key,
                CopySource={"Bucket": source_bucket, "Key": source_key},
                **{condition_type: condition_value},
            )
            print(
                f"\tConditional copy successful for key {dest_key} in bucket {dest_bucket}."
            )
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            if error_code == "PreconditionFailed":
                print("\tConditional copy failed: Precondition failed")
            elif error_code == "304":  # Not modified error code.
                print("\tConditional copy failed: Object not modified")
            else:
                logger.error(f"Unexpected error: {error_code}")
                raise

  • APIEinzelheiten finden Sie unter CopyObjectPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. CreateBucket

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Erstellen Sie einen Bucket mit Standardeinstellungen.

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def create(self, region_override=None):
        """
        Create an Amazon S3 bucket in the default Region for the account or in the
        specified Region.

        :param region_override: The Region in which to create the bucket. If this is
                                not specified, the Region configured in your shared
                                credentials is used.
        """
        if region_override is not None:
            region = region_override
        else:
            region = self.bucket.meta.client.meta.region_name
        try:
            self.bucket.create(CreateBucketConfiguration={"LocationConstraint": region})

            self.bucket.wait_until_exists()
            logger.info("Created bucket '%s' in region=%s", self.bucket.name, region)
        except ClientError as error:
            logger.exception(
                "Couldn't create bucket named '%s' in region=%s.",
                self.bucket.name,
                region,
            )
            raise error

Erstellen Sie einen versionierten Bucket mit einer Lebenszyklus-Konfiguration.

def create_versioned_bucket(bucket_name, prefix):
    """
    Creates an Amazon S3 bucket, enables it for versioning, and configures a lifecycle
    that expires noncurrent object versions after 7 days.

    Adding a lifecycle configuration to a versioned bucket is a best practice.
    It helps prevent objects in the bucket from accumulating a large number of
    noncurrent versions, which can slow down request performance.

    Usage is shown in the usage_demo_single_object function at the end of this module.

    :param bucket_name: The name of the bucket to create.
    :param prefix: Identifies which objects are automatically expired under the
                   configured lifecycle rules.
    :return: The newly created bucket.
    """
    try:
        bucket = s3.create_bucket(
            Bucket=bucket_name,
            CreateBucketConfiguration={
                "LocationConstraint": s3.meta.client.meta.region_name
            },
        )
        logger.info("Created bucket %s.", bucket.name)
    except ClientError as error:
        if error.response["Error"]["Code"] == "BucketAlreadyOwnedByYou":
            logger.warning("Bucket %s already exists! Using it.", bucket_name)
            bucket = s3.Bucket(bucket_name)
        else:
            logger.exception("Couldn't create bucket %s.", bucket_name)
            raise

    try:
        bucket.Versioning().enable()
        logger.info("Enabled versioning on bucket %s.", bucket.name)
    except ClientError:
        logger.exception("Couldn't enable versioning on bucket %s.", bucket.name)
        raise

    try:
        expiration = 7
        bucket.LifecycleConfiguration().put(
            LifecycleConfiguration={
                "Rules": [
                    {
                        "Status": "Enabled",
                        "Prefix": prefix,
                        "NoncurrentVersionExpiration": {"NoncurrentDays": expiration},
                    }
                ]
            }
        )
        logger.info(
            "Configured lifecycle to expire noncurrent versions after %s days "
            "on bucket %s.",
            expiration,
            bucket.name,
        )
    except ClientError as error:
        logger.warning(
            "Couldn't configure lifecycle on bucket %s because %s. "
            "Continuing anyway.",
            bucket.name,
            error,
        )

    return bucket

  • APIEinzelheiten finden Sie unter CreateBucketPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. DeleteBucket

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def delete(self):
        """
        Delete the bucket. The bucket must be empty or an error is raised.
        """
        try:
            self.bucket.delete()
            self.bucket.wait_until_not_exists()
            logger.info("Bucket %s successfully deleted.", self.bucket.name)
        except ClientError:
            logger.exception("Couldn't delete bucket %s.", self.bucket.name)
            raise

  • APIEinzelheiten finden Sie unter DeleteBucketPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. DeleteBucketCors

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def delete_cors(self):
        """
        Delete the CORS rules from the bucket.

        :param bucket_name: The name of the bucket to update.
        """
        try:
            self.bucket.Cors().delete()
            logger.info("Deleted CORS from bucket '%s'.", self.bucket.name)
        except ClientError:
            logger.exception("Couldn't delete CORS from bucket '%s'.", self.bucket.name)
            raise

  • APIEinzelheiten finden Sie unter DeleteBucketCorsPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. DeleteBucketLifecycle

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def delete_lifecycle_configuration(self):
        """
        Remove the lifecycle configuration from the specified bucket.
        """
        try:
            self.bucket.LifecycleConfiguration().delete()
            logger.info(
                "Deleted lifecycle configuration for bucket '%s'.", self.bucket.name
            )
        except ClientError:
            logger.exception(
                "Couldn't delete lifecycle configuration for bucket '%s'.",
                self.bucket.name,
            )
            raise

Das folgende Codebeispiel zeigt, wie man es benutzt. DeleteBucketPolicy

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def delete_policy(self):
        """
        Delete the security policy from the bucket.
        """
        try:
            self.bucket.Policy().delete()
            logger.info("Deleted policy for bucket '%s'.", self.bucket.name)
        except ClientError:
            logger.exception(
                "Couldn't delete policy for bucket '%s'.", self.bucket.name
            )
            raise

Das folgende Codebeispiel zeigt, wie man es benutzt. DeleteObject

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Löschen Sie ein Objekt.

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    def delete(self):
        """
        Deletes the object.
        """
        try:
            self.object.delete()
            self.object.wait_until_not_exists()
            logger.info(
                "Deleted object '%s' from bucket '%s'.",
                self.object.key,
                self.object.bucket_name,
            )
        except ClientError:
            logger.exception(
                "Couldn't delete object '%s' from bucket '%s'.",
                self.object.key,
                self.object.bucket_name,
            )
            raise

Setzen Sie ein Objekt auf eine vorherige Version zurück, indem Sie spätere Versionen des Objekts löschen.

def rollback_object(bucket, object_key, version_id):
    """
    Rolls back an object to an earlier version by deleting all versions that
    occurred after the specified rollback version.

    Usage is shown in the usage_demo_single_object function at the end of this module.

    :param bucket: The bucket that holds the object to roll back.
    :param object_key: The object to roll back.
    :param version_id: The version ID to roll back to.
    """
    # Versions must be sorted by last_modified date because delete markers are
    # at the end of the list even when they are interspersed in time.
    versions = sorted(
        bucket.object_versions.filter(Prefix=object_key),
        key=attrgetter("last_modified"),
        reverse=True,
    )

    logger.debug(
        "Got versions:\n%s",
        "\n".join(
            [
                f"\t{version.version_id}, last modified {version.last_modified}"
                for version in versions
            ]
        ),
    )

    if version_id in [ver.version_id for ver in versions]:
        print(f"Rolling back to version {version_id}")
        for version in versions:
            if version.version_id != version_id:
                version.delete()
                print(f"Deleted version {version.version_id}")
            else:
                break

        print(f"Active version is now {bucket.Object(object_key).version_id}")
    else:
        raise KeyError(
            f"{version_id} was not found in the list of versions for " f"{object_key}."
        )

Reaktivieren Sie ein gelöschtes Objekt, indem Sie die aktive Löschmarke des Objekts entfernen.

def revive_object(bucket, object_key):
    """
    Revives a versioned object that was deleted by removing the object's active
    delete marker.
    A versioned object presents as deleted when its latest version is a delete marker.
    By removing the delete marker, we make the previous version the latest version
    and the object then presents as *not* deleted.

    Usage is shown in the usage_demo_single_object function at the end of this module.

    :param bucket: The bucket that contains the object.
    :param object_key: The object to revive.
    """
    # Get the latest version for the object.
    response = s3.meta.client.list_object_versions(
        Bucket=bucket.name, Prefix=object_key, MaxKeys=1
    )

    if "DeleteMarkers" in response:
        latest_version = response["DeleteMarkers"][0]
        if latest_version["IsLatest"]:
            logger.info(
                "Object %s was indeed deleted on %s. Let's revive it.",
                object_key,
                latest_version["LastModified"],
            )
            obj = bucket.Object(object_key)
            obj.Version(latest_version["VersionId"]).delete()
            logger.info(
                "Revived %s, active version is now %s  with body '%s'",
                object_key,
                obj.version_id,
                obj.get()["Body"].read(),
            )
        else:
            logger.warning(
                "Delete marker is not the latest version for %s!", object_key
            )
    elif "Versions" in response:
        logger.warning("Got an active version for %s, nothing to do.", object_key)
    else:
        logger.error("Couldn't get any version info for %s.", object_key)

Erstellen Sie einen Lambda-Handler, der eine Löschmarke aus einem S3-Objekt entfernt. Dieser Handler kann verwendet werden, um irrelevante Löschmarkierungen in einem versionierten Bucket effizient zu bereinigen.

import logging
from urllib import parse
import boto3
from botocore.exceptions import ClientError

logger = logging.getLogger(__name__)
logger.setLevel("INFO")

s3 = boto3.client("s3")


def lambda_handler(event, context):
    """
    Removes a delete marker from the specified versioned object.

    :param event: The S3 batch event that contains the ID of the delete marker
                  to remove.
    :param context: Context about the event.
    :return: A result structure that Amazon S3 uses to interpret the result of the
             operation. When the result code is TemporaryFailure, S3 retries the
             operation.
    """
    # Parse job parameters from Amazon S3 batch operations
    invocation_id = event["invocationId"]
    invocation_schema_version = event["invocationSchemaVersion"]

    results = []
    result_code = None
    result_string = None

    task = event["tasks"][0]
    task_id = task["taskId"]

    try:
        obj_key = parse.unquote(task["s3Key"], encoding="utf-8")
        obj_version_id = task["s3VersionId"]
        bucket_name = task["s3BucketArn"].split(":")[-1]

        logger.info(
            "Got task: remove delete marker %s from object %s.", obj_version_id, obj_key
        )

        try:
            # If this call does not raise an error, the object version is not a delete
            # marker and should not be deleted.
            response = s3.head_object(
                Bucket=bucket_name, Key=obj_key, VersionId=obj_version_id
            )
            result_code = "PermanentFailure"
            result_string = (
                f"Object {obj_key}, ID {obj_version_id} is not " f"a delete marker."
            )

            logger.debug(response)
            logger.warning(result_string)
        except ClientError as error:
            delete_marker = error.response["ResponseMetadata"]["HTTPHeaders"].get(
                "x-amz-delete-marker", "false"
            )
            if delete_marker == "true":
                logger.info(
                    "Object %s, version %s is a delete marker.", obj_key, obj_version_id
                )
                try:
                    s3.delete_object(
                        Bucket=bucket_name, Key=obj_key, VersionId=obj_version_id
                    )
                    result_code = "Succeeded"
                    result_string = (
                        f"Successfully removed delete marker "
                        f"{obj_version_id} from object {obj_key}."
                    )
                    logger.info(result_string)
                except ClientError as error:
                    # Mark request timeout as a temporary failure so it will be retried.
                    if error.response["Error"]["Code"] == "RequestTimeout":
                        result_code = "TemporaryFailure"
                        result_string = (
                            f"Attempt to remove delete marker from  "
                            f"object {obj_key} timed out."
                        )
                        logger.info(result_string)
                    else:
                        raise
            else:
                raise ValueError(
                    f"The x-amz-delete-marker header is either not "
                    f"present or is not 'true'."
                )
    except Exception as error:
        # Mark all other exceptions as permanent failures.
        result_code = "PermanentFailure"
        result_string = str(error)
        logger.exception(error)
    finally:
        results.append(
            {
                "taskId": task_id,
                "resultCode": result_code,
                "resultString": result_string,
            }
        )
    return {
        "invocationSchemaVersion": invocation_schema_version,
        "treatMissingKeysAs": "PermanentFailure",
        "invocationId": invocation_id,
        "results": results,
    }

  • APIEinzelheiten finden Sie unter DeleteObjectPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. DeleteObjects

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Löschen Sie eine Reihe von Objekten mithilfe einer Liste von Objektschlüsseln.

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    @staticmethod
    def delete_objects(bucket, object_keys):
        """
        Removes a list of objects from a bucket.
        This operation is done as a batch in a single request.

        :param bucket: The bucket that contains the objects. This is a Boto3 Bucket
                       resource.
        :param object_keys: The list of keys that identify the objects to remove.
        :return: The response that contains data about which objects were deleted
                 and any that could not be deleted.
        """
        try:
            response = bucket.delete_objects(
                Delete={"Objects": [{"Key": key} for key in object_keys]}
            )
            if "Deleted" in response:
                logger.info(
                    "Deleted objects '%s' from bucket '%s'.",
                    [del_obj["Key"] for del_obj in response["Deleted"]],
                    bucket.name,
                )
            if "Errors" in response:
                logger.warning(
                    "Could not delete objects '%s' from bucket '%s'.",
                    [
                        f"{del_obj['Key']}: {del_obj['Code']}"
                        for del_obj in response["Errors"]
                    ],
                    bucket.name,
                )
        except ClientError:
            logger.exception("Couldn't delete any objects from bucket %s.", bucket.name)
            raise
        else:
            return response

Löschen Sie alle Objekte im Bucket.

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    @staticmethod
    def empty_bucket(bucket):
        """
        Remove all objects from a bucket.

        :param bucket: The bucket to empty. This is a Boto3 Bucket resource.
        """
        try:
            bucket.objects.delete()
            logger.info("Emptied bucket '%s'.", bucket.name)
        except ClientError:
            logger.exception("Couldn't empty bucket '%s'.", bucket.name)
            raise

Löschen Sie ein versioniertes Objekt dauerhaft, indem Sie alle seine Versionen löschen.

def permanently_delete_object(bucket, object_key):
    """
    Permanently deletes a versioned object by deleting all of its versions.

    Usage is shown in the usage_demo_single_object function at the end of this module.

    :param bucket: The bucket that contains the object.
    :param object_key: The object to delete.
    """
    try:
        bucket.object_versions.filter(Prefix=object_key).delete()
        logger.info("Permanently deleted all versions of object %s.", object_key)
    except ClientError:
        logger.exception("Couldn't delete all versions of %s.", object_key)
        raise

  • APIEinzelheiten finden Sie unter DeleteObjectsPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. GetBucketAcl

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def get_acl(self):
        """
        Get the ACL of the bucket.

        :return: The ACL of the bucket.
        """
        try:
            acl = self.bucket.Acl()
            logger.info(
                "Got ACL for bucket %s. Owner is %s.", self.bucket.name, acl.owner
            )
        except ClientError:
            logger.exception("Couldn't get ACL for bucket %s.", self.bucket.name)
            raise
        else:
            return acl

  • APIEinzelheiten finden Sie unter GetBucketAclPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. GetBucketCors

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def get_cors(self):
        """
        Get the CORS rules for the bucket.

        :return The CORS rules for the specified bucket.
        """
        try:
            cors = self.bucket.Cors()
            logger.info(
                "Got CORS rules %s for bucket '%s'.", cors.cors_rules, self.bucket.name
            )
        except ClientError:
            logger.exception(("Couldn't get CORS for bucket %s.", self.bucket.name))
            raise
        else:
            return cors

  • APIEinzelheiten finden Sie unter GetBucketCorsPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. GetBucketLifecycleConfiguration

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def get_lifecycle_configuration(self):
        """
        Get the lifecycle configuration of the bucket.

        :return: The lifecycle rules of the specified bucket.
        """
        try:
            config = self.bucket.LifecycleConfiguration()
            logger.info(
                "Got lifecycle rules %s for bucket '%s'.",
                config.rules,
                self.bucket.name,
            )
        except:
            logger.exception(
                "Couldn't get lifecycle rules for bucket '%s'.", self.bucket.name
            )
            raise
        else:
            return config.rules

Das folgende Codebeispiel zeigt, wie man es benutzt. GetBucketPolicy

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def get_policy(self):
        """
        Get the security policy of the bucket.

        :return: The security policy of the specified bucket, in JSON format.
        """
        try:
            policy = self.bucket.Policy()
            logger.info(
                "Got policy %s for bucket '%s'.", policy.policy, self.bucket.name
            )
        except ClientError:
            logger.exception("Couldn't get policy for bucket '%s'.", self.bucket.name)
            raise
        else:
            return json.loads(policy.policy)

  • APIEinzelheiten finden Sie unter GetBucketPolicyPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. GetObject

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    def get(self):
        """
        Gets the object.

        :return: The object data in bytes.
        """
        try:
            body = self.object.get()["Body"].read()
            logger.info(
                "Got object '%s' from bucket '%s'.",
                self.object.key,
                self.object.bucket_name,
            )
        except ClientError:
            logger.exception(
                "Couldn't get object '%s' from bucket '%s'.",
                self.object.key,
                self.object.bucket_name,
            )
            raise
        else:
            return body

Ruft ein Objekt mithilfe einer bedingten Anfrage ab.

class S3ConditionalRequests:
    """Encapsulates S3 conditional request operations."""

    def __init__(self, s3_client):
        self.s3 = s3_client

    @classmethod
    def from_client(cls):
        """
        Instantiates this class from a Boto3 client.
        """
        s3_client = boto3.client("s3")
        return cls(s3_client)



    def get_object_conditional(
        self,
        object_key: str,
        source_bucket: str,
        condition_type: str,
        condition_value: str,
    ):
        """
        Retrieves an object from Amazon S3 with a conditional request.

        :param object_key: The key of the object to retrieve.
        :param source_bucket: The source bucket of the object.
        :param condition_type: The type of condition: 'IfMatch', 'IfNoneMatch', 'IfModifiedSince', 'IfUnmodifiedSince'.
        :param condition_value: The value to use for the condition.
        """
        try:
            response = self.s3.get_object(
                Bucket=source_bucket,
                Key=object_key,
                **{condition_type: condition_value},
            )
            sample_bytes = response["Body"].read(20)
            print(
                f"\tConditional read successful. Here are the first 20 bytes of the object:\n"
            )
            print(f"\t{sample_bytes}")
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            if error_code == "PreconditionFailed":
                print("\tConditional read failed: Precondition failed")
            elif error_code == "304":  # Not modified error code.
                print("\tConditional read failed: Object not modified")
            else:
                logger.error(f"Unexpected error: {error_code}")
                raise

  • APIEinzelheiten finden Sie unter GetObjectPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. GetObjectAcl

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    def get_acl(self):
        """
        Gets the ACL of the object.

        :return: The ACL of the object.
        """
        try:
            acl = self.object.Acl()
            logger.info(
                "Got ACL for object %s owned by %s.",
                self.object.key,
                acl.owner["DisplayName"],
            )
        except ClientError:
            logger.exception("Couldn't get ACL for object %s.", self.object.key)
            raise
        else:
            return acl

  • APIEinzelheiten finden Sie unter GetObjectAclPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. GetObjectLegalHold

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Sperren Sie ein Objekt für rechtlich unbedenklich.

def get_legal_hold(s3_client, bucket: str, key: str) -> None:
    """
    Get the legal hold status of a specific file in a bucket.

    Args:
        s3_client: Boto3 S3 client.
        bucket: The name of the bucket containing the file.
        key: The key of the file to get the legal hold status of.
    """
    print()
    logger.info("Getting legal hold status of file [%s] in bucket [%s]", key, bucket)
    try:
        response = s3_client.get_object_legal_hold(Bucket=bucket, Key=key)
        legal_hold_status = response["LegalHold"]["Status"]
        logger.debug(
            "Legal hold status of file [%s] in bucket [%s] is [%s]",
            key,
            bucket,
            legal_hold_status,
        )
    except Exception as e:
        logger.error(
            "Failed to get legal hold status of file [%s] in bucket [%s]: %s",
            key,
            bucket,
            e,
        )

Das folgende Codebeispiel zeigt, wie man es benutzt. GetObjectLockConfiguration

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Holen Sie sich die Objektsperren-Konfiguration.

def is_object_lock_enabled(s3_client, bucket: str) -> bool:
    """
    Check if object lock is enabled for a bucket.

    Args:
        s3_client: Boto3 S3 client.
        bucket: The name of the bucket to check.

    Returns:
        True if object lock is enabled, False otherwise.
    """
    try:
        response = s3_client.get_object_lock_configuration(Bucket=bucket)
        return (
            "ObjectLockConfiguration" in response
            and response["ObjectLockConfiguration"]["ObjectLockEnabled"] == "Enabled"
        )
    except s3_client.exceptions.ClientError as e:
        if e.response["Error"]["Code"] == "ObjectLockConfigurationNotFoundError":
            return False
        else:
            raise

Das folgende Codebeispiel zeigt, wie man es benutzt. HeadBucket

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def exists(self):
        """
        Determine whether the bucket exists and you have access to it.

        :return: True when the bucket exists; otherwise, False.
        """
        try:
            self.bucket.meta.client.head_bucket(Bucket=self.bucket.name)
            logger.info("Bucket %s exists.", self.bucket.name)
            exists = True
        except ClientError:
            logger.warning(
                "Bucket %s doesn't exist or you don't have access to it.",
                self.bucket.name,
            )
            exists = False
        return exists

  • APIEinzelheiten finden Sie unter HeadBucketPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. ListBuckets

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    @staticmethod
    def list(s3_resource):
        """
        Get the buckets in all Regions for the current account.

        :param s3_resource: A Boto3 S3 resource. This is a high-level resource in Boto3
                            that contains collections and factory methods to create
                            other high-level S3 sub-resources.
        :return: The list of buckets.
        """
        try:
            buckets = list(s3_resource.buckets.all())
            logger.info("Got buckets: %s.", buckets)
        except ClientError:
            logger.exception("Couldn't get buckets.")
            raise
        else:
            return buckets

  • APIEinzelheiten finden Sie unter ListBucketsPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. ListObjectsV2

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    @staticmethod
    def list(bucket, prefix=None):
        """
        Lists the objects in a bucket, optionally filtered by a prefix.

        :param bucket: The bucket to query. This is a Boto3 Bucket resource.
        :param prefix: When specified, only objects that start with this prefix are listed.
        :return: The list of objects.
        """
        try:
            if not prefix:
                objects = list(bucket.objects.all())
            else:
                objects = list(bucket.objects.filter(Prefix=prefix))
            logger.info(
                "Got objects %s from bucket '%s'", [o.key for o in objects], bucket.name
            )
        except ClientError:
            logger.exception("Couldn't get objects for bucket '%s'.", bucket.name)
            raise
        else:
            return objects

  • APIEinzelheiten finden Sie unter ListObjectsV2 in der AWS SDKPython-Referenz (Boto3) API.

Das folgende Codebeispiel zeigt die Verwendung. PutBucketAcl

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def grant_log_delivery_access(self):
        """
        Grant the AWS Log Delivery group write access to the bucket so that
        Amazon S3 can deliver access logs to the bucket. This is the only recommended
        use of an S3 bucket ACL.
        """
        try:
            acl = self.bucket.Acl()
            # Putting an ACL overwrites the existing ACL. If you want to preserve
            # existing grants, append new grants to the list of existing grants.
            grants = acl.grants if acl.grants else []
            grants.append(
                {
                    "Grantee": {
                        "Type": "Group",
                        "URI": "http://acs.amazonaws.com/groups/s3/LogDelivery",
                    },
                    "Permission": "WRITE",
                }
            )
            acl.put(AccessControlPolicy={"Grants": grants, "Owner": acl.owner})
            logger.info("Granted log delivery access to bucket '%s'", self.bucket.name)
        except ClientError:
            logger.exception("Couldn't add ACL to bucket '%s'.", self.bucket.name)
            raise

  • APIEinzelheiten finden Sie unter PutBucketAclPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. PutBucketCors

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def put_cors(self, cors_rules):
        """
        Apply CORS rules to the bucket. CORS rules specify the HTTP actions that are
        allowed from other domains.

        :param cors_rules: The CORS rules to apply.
        """
        try:
            self.bucket.Cors().put(CORSConfiguration={"CORSRules": cors_rules})
            logger.info(
                "Put CORS rules %s for bucket '%s'.", cors_rules, self.bucket.name
            )
        except ClientError:
            logger.exception("Couldn't put CORS rules for bucket %s.", self.bucket.name)
            raise

  • APIEinzelheiten finden Sie unter PutBucketCorsPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. PutBucketLifecycleConfiguration

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def put_lifecycle_configuration(self, lifecycle_rules):
        """
        Apply a lifecycle configuration to the bucket. The lifecycle configuration can
        be used to archive or delete the objects in the bucket according to specified
        parameters, such as a number of days.

        :param lifecycle_rules: The lifecycle rules to apply.
        """
        try:
            self.bucket.LifecycleConfiguration().put(
                LifecycleConfiguration={"Rules": lifecycle_rules}
            )
            logger.info(
                "Put lifecycle rules %s for bucket '%s'.",
                lifecycle_rules,
                self.bucket.name,
            )
        except ClientError:
            logger.exception(
                "Couldn't put lifecycle rules for bucket '%s'.", self.bucket.name
            )
            raise

Das folgende Codebeispiel zeigt, wie man es benutzt. PutBucketPolicy

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def put_policy(self, policy):
        """
        Apply a security policy to the bucket. Policies control users' ability
        to perform specific actions, such as listing the objects in the bucket.

        :param policy: The policy to apply to the bucket.
        """
        try:
            self.bucket.Policy().put(Policy=json.dumps(policy))
            logger.info("Put policy %s for bucket '%s'.", policy, self.bucket.name)
        except ClientError:
            logger.exception("Couldn't apply policy to bucket '%s'.", self.bucket.name)
            raise

  • APIEinzelheiten finden Sie unter PutBucketPolicyPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. PutObject

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    def put(self, data):
        """
        Upload data to the object.

        :param data: The data to upload. This can either be bytes or a string. When this
                     argument is a string, it is interpreted as a file name, which is
                     opened in read bytes mode.
        """
        put_data = data
        if isinstance(data, str):
            try:
                put_data = open(data, "rb")
            except IOError:
                logger.exception("Expected file name or binary data, got '%s'.", data)
                raise

        try:
            self.object.put(Body=put_data)
            self.object.wait_until_exists()
            logger.info(
                "Put object '%s' to bucket '%s'.",
                self.object.key,
                self.object.bucket_name,
            )
        except ClientError:
            logger.exception(
                "Couldn't put object '%s' to bucket '%s'.",
                self.object.key,
                self.object.bucket_name,
            )
            raise
        finally:
            if getattr(put_data, "close", None):
                put_data.close()

Laden Sie ein Objekt mithilfe einer bedingten Anfrage hoch.

class S3ConditionalRequests:
    """Encapsulates S3 conditional request operations."""

    def __init__(self, s3_client):
        self.s3 = s3_client

    @classmethod
    def from_client(cls):
        """
        Instantiates this class from a Boto3 client.
        """
        s3_client = boto3.client("s3")
        return cls(s3_client)



    def put_object_conditional(self, object_key: str, source_bucket: str, data: bytes):
        """
        Uploads an object to Amazon S3 with a conditional request. Prevents overwrite
        using an IfNoneMatch condition for the object key.

        :param object_key: The key of the object to upload.
        :param source_bucket: The source bucket of the object.
        :param data: The data to upload.
        """
        try:
            self.s3.put_object(
                Bucket=source_bucket, Key=object_key, Body=data, IfNoneMatch="*"
            )
            print(
                f"\tConditional write successful for key {object_key} in bucket {source_bucket}."
            )
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            if error_code == "PreconditionFailed":
                print("\tConditional write failed: Precondition failed")
            else:
                logger.error(f"Unexpected error: {error_code}")
                raise

  • APIEinzelheiten finden Sie unter PutObjectPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. PutObjectAcl

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen. 

class ObjectWrapper:
    """Encapsulates S3 object actions."""

    def __init__(self, s3_object):
        """
        :param s3_object: A Boto3 Object resource. This is a high-level resource in Boto3
                          that wraps object actions in a class-like structure.
        """
        self.object = s3_object
        self.key = self.object.key


    def put_acl(self, email):
        """
        Applies an ACL to the object that grants read access to an AWS user identified
        by email address.

        :param email: The email address of the user to grant access.
        """
        try:
            acl = self.object.Acl()
            # Putting an ACL overwrites the existing ACL, so append new grants
            # if you want to preserve existing grants.
            grants = acl.grants if acl.grants else []
            grants.append(
                {
                    "Grantee": {"Type": "AmazonCustomerByEmail", "EmailAddress": email},
                    "Permission": "READ",
                }
            )
            acl.put(AccessControlPolicy={"Grants": grants, "Owner": acl.owner})
            logger.info("Granted read access to %s.", email)
        except ClientError:
            logger.exception("Couldn't add ACL to object '%s'.", self.object.key)
            raise

  • APIEinzelheiten finden Sie unter PutObjectAclPython (Boto3) API -Referenz.AWS SDK

Das folgende Codebeispiel zeigt, wie man es benutzt. PutObjectLegalHold

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Verschaffen Sie einem Objekt eine gesetzliche Sperre.

def set_legal_hold(s3_client, bucket: str, key: str) -> None:
    """
    Set a legal hold on a specific file in a bucket.

    Args:
        s3_client: Boto3 S3 client.
        bucket: The name of the bucket containing the file.
        key: The key of the file to set the legal hold on.
    """
    print()
    logger.info("Setting legal hold on file [%s] in bucket [%s]", key, bucket)
    try:
        before_status = "OFF"
        after_status = "ON"
        s3_client.put_object_legal_hold(
            Bucket=bucket, Key=key, LegalHold={"Status": after_status}
        )
        logger.debug(
            "Legal hold set successfully on file [%s] in bucket [%s]", key, bucket
        )
        _print_legal_hold_update(bucket, key, before_status, after_status)
    except Exception as e:
        logger.error(
            "Failed to set legal hold on file [%s] in bucket [%s]: %s", key, bucket, e
        )

Das folgende Codebeispiel zeigt, wie man es benutzt. PutObjectLockConfiguration

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Setzen Sie die Objektsperren-Konfiguration.

        s3_client.put_object_lock_configuration(
            Bucket=bucket,
            ObjectLockConfiguration={"ObjectLockEnabled": "Disabled", "Rule": {}},
        )

Das folgende Codebeispiel zeigt, wie man es benutzt. PutObjectRetention

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Legen Sie eine Objektreservierung an.

            s3_client.put_object_retention(
                Bucket=bucket,
                Key=key,
                VersionId=version_id,
                Retention={"Mode": "GOVERNANCE", "RetainUntilDate": far_future_date},
                BypassGovernanceRetention=True,
            )

Szenarien

Das folgende Codebeispiel zeigt, wie Sie ein URL für Amazon S3 vorsigniertes Objekt erstellen und ein Objekt hochladen.

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Generieren Sie eine vorsignierte URL Datei, die eine S3-Aktion für eine begrenzte Zeit ausführen kann. Verwenden Sie das Requests-Paket, um eine Anfrage mit dem URL zu stellen.

import argparse
import logging
import boto3
from botocore.exceptions import ClientError
import requests

logger = logging.getLogger(__name__)


def generate_presigned_url(s3_client, client_method, method_parameters, expires_in):
    """
    Generate a presigned Amazon S3 URL that can be used to perform an action.

    :param s3_client: A Boto3 Amazon S3 client.
    :param client_method: The name of the client method that the URL performs.
    :param method_parameters: The parameters of the specified client method.
    :param expires_in: The number of seconds the presigned URL is valid for.
    :return: The presigned URL.
    """
    try:
        url = s3_client.generate_presigned_url(
            ClientMethod=client_method, Params=method_parameters, ExpiresIn=expires_in
        )
        logger.info("Got presigned URL: %s", url)
    except ClientError:
        logger.exception(
            "Couldn't get a presigned URL for client method '%s'.", client_method
        )
        raise
    return url


def usage_demo():
    logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")

    print("-" * 88)
    print("Welcome to the Amazon S3 presigned URL demo.")
    print("-" * 88)

    parser = argparse.ArgumentParser()
    parser.add_argument("bucket", help="The name of the bucket.")
    parser.add_argument(
        "key",
        help="For a GET operation, the key of the object in Amazon S3. For a "
        "PUT operation, the name of a file to upload.",
    )
    parser.add_argument("action", choices=("get", "put"), help="The action to perform.")
    args = parser.parse_args()

    s3_client = boto3.client("s3")
    client_action = "get_object" if args.action == "get" else "put_object"
    url = generate_presigned_url(
        s3_client, client_action, {"Bucket": args.bucket, "Key": args.key}, 1000
    )

    print("Using the Requests package to send a request to the URL.")
    response = None
    if args.action == "get":
        response = requests.get(url)
        if response.status_code == 200:
            with open(args.key.split("/")[-1], 'wb') as object_file:
                object_file.write(response.content)
    elif args.action == "put":
        print("Putting data to the URL.")
        try:
            with open(args.key, "rb") as object_file:
                object_text = object_file.read()
            response = requests.put(url, data=object_text)
        except FileNotFoundError:
            print(
                f"Couldn't find {args.key}. For a PUT operation, the key must be the "
                f"name of a file that exists on your computer."
            )

    if response is not None:
        print(f"Status: {response.status_code}\nReason: {response.reason}")

    print("-" * 88)


if __name__ == "__main__":
    usage_demo()

Generieren Sie eine vorsignierte POST Anfrage zum Hochladen einer Datei.

class BucketWrapper:
    """Encapsulates S3 bucket actions."""

    def __init__(self, bucket):
        """
        :param bucket: A Boto3 Bucket resource. This is a high-level resource in Boto3
                       that wraps bucket actions in a class-like structure.
        """
        self.bucket = bucket
        self.name = bucket.name


    def generate_presigned_post(self, object_key, expires_in):
        """
        Generate a presigned Amazon S3 POST request to upload a file.
        A presigned POST can be used for a limited time to let someone without an AWS
        account upload a file to a bucket.

        :param object_key: The object key to identify the uploaded object.
        :param expires_in: The number of seconds the presigned POST is valid.
        :return: A dictionary that contains the URL and form fields that contain
                 required access data.
        """
        try:
            response = self.bucket.meta.client.generate_presigned_post(
                Bucket=self.bucket.name, Key=object_key, ExpiresIn=expires_in
            )
            logger.info("Got presigned POST URL: %s", response["url"])
        except ClientError:
            logger.exception(
                "Couldn't get a presigned POST URL for bucket '%s' and object '%s'",
                self.bucket.name,
                object_key,
            )
            raise
        return response

Das folgende Codebeispiel zeigt, wie Sie die Amazon Textract Textract-Ausgabe mithilfe einer interaktiven Anwendung untersuchen können.

SDKfür Python (Boto3)

Zeigt, wie Sie AWS SDK for Python (Boto3) mit Amazon Textract Text-, Formular- und Tabellenelemente in einem Dokumentbild erkennen können. Das Eingabe-Image und die Amazon-Textract-Ausgabe werden in einer Tkinter-Anwendung angezeigt, mit der Sie die erkannten Elemente untersuchen können.

  • Senden Sie ein Dokument-Image an Amazon Textract und untersuchen Sie die Ausgabe erkannter Elemente.

  • Senden Sie Images direkt an Amazon Textract oder über einen Amazon Simple Storage Service (Amazon S3)-Bucket.

  • Verwenden Sie asynchronAPIs, um einen Job zu starten, der nach Abschluss des Jobs eine Benachrichtigung zu einem Amazon Simple Notification Service (AmazonSNS) -Thema veröffentlicht.

  • Fragen Sie eine Amazon Simple Queue Service (AmazonSQS) -Warteschlange nach einer Auftragsabschlussnachricht ab und zeigen Sie die Ergebnisse an.

Den vollständigen Quellcode und Anweisungen zur Einrichtung und Ausführung finden Sie im vollständigen Beispiel unter GitHub.

In diesem Beispiel verwendete Dienste

  • Amazon S3

  • Amazon SNS

  • Amazon SQS

  • Amazon Textract

Das folgende Codebeispiel zeigt, wie Amazon Comprehend verwendet wird, um Entitäten in Text zu erkennen, der von Amazon Textract aus einem in Amazon S3 gespeicherten Bild extrahiert wurde.

SDKfür Python (Boto3)

Zeigt, wie das AWS SDK for Python (Boto3) in einem Jupyter-Notizbuch verwendet wird, um Entitäten in Text zu erkennen, der aus einem Bild extrahiert wurde. In diesem Beispiel extrahiert Amazon Textract Text aus einem Bild, das in Amazon Simple Storage Service (Amazon S3) und Amazon Comprehend gespeichert ist, um Entitäten im extrahierten Text zu erkennen.

Dieses Beispiel ist ein Jupyter Notebook und muss in einer Umgebung ausgeführt werden, die Notebooks hosten kann. Anweisungen zur Ausführung des Beispiels mit Amazon SageMaker AI finden Sie in den Anweisungen in der Datei TextractAndComprehendNotebook.ipynb.

Den vollständigen Quellcode und Anweisungen zur Einrichtung und Ausführung finden Sie im vollständigen Beispiel unter. GitHub

In diesem Beispiel verwendete Dienste

  • Amazon Comprehend

  • Amazon S3

  • Amazon Textract

Das folgende Codebeispiel zeigt, wie Sie eine App erstellen, die Amazon Rekognition verwendet, um Objekte nach Kategorien in Bildern zu erkennen.

SDKfür Python (Boto3)

Zeigt Ihnen, wie Sie mit AWS SDK for Python (Boto3) dem eine Webanwendung erstellen, mit der Sie Folgendes tun können:

  • Laden Sie Fotos in einen Bucket von Amazon Simple Storage Service (Amazon S3) hoch.

  • Verwenden Sie Amazon Rekognition, um die Fotos zu analysieren und zu markieren.

  • Verwenden Sie Amazon Simple Email Service (AmazonSES), um E-Mail-Berichte zur Bildanalyse zu versenden.

Dieses Beispiel enthält zwei Hauptkomponenten: eine eingeschriebene Webseite JavaScript , die mit React erstellt wurde, und einen in Python geschriebenen REST Dienst, der mit Flask- RESTful erstellt wurde.

Sie können die React-Webseite verwenden, um Folgendes auszuführen:

  • Zeigen Sie eine Liste der Bilder an, die in Ihrem S3-Bucket gespeichert sind.

  • Laden Sie Bilder von Ihrem Computer in Ihren S3-Bucket hoch.

  • Zeigen Sie Bilder und Markierungen an, die Elemente identifizieren, welche im Bild erkannt werden.

  • Rufen Sie einen Bericht über alle Bilder in Ihrem S3-Bucket ab und senden Sie eine E-Mail mit dem Bericht.

Die Webseite ruft den REST Dienst auf. Der Service sendet Anforderungen an AWS , um die folgenden Aktionen durchzuführen:

  • Die Liste der Bilder abrufen und in Ihrem S3-Bucket filtern.

  • Fotos in Ihren S3-Bucket hochladen.

  • Verwenden Sie Amazon Rekognition, um einzelne Fotos zu analysieren und eine Liste von Markierungen zu erhalten, die die auf dem Foto erkannten Elemente identifizieren.

  • Analysieren Sie alle Fotos in Ihrem S3-Bucket und verwenden Sie AmazonSES, um einen Bericht per E-Mail zu versenden.

Den vollständigen Quellcode und Anweisungen zur Einrichtung und Ausführung finden Sie im vollständigen Beispiel unter GitHub.

In diesem Beispiel verwendete Dienste

  • Amazon Rekognition

  • Amazon S3

  • Amazon SES

Das folgende Codebeispiel zeigt, wie Vorbedingungen zu Amazon S3 S3-Anfragen hinzugefügt werden.

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Führen Sie ein interaktives Szenario aus, das bedingte Amazon S3 S3-Anfragen demonstriert.

"""
Purpose

Shows how to use AWS SDK for Python (Boto3) to get started using conditional requests for
Amazon Simple Storage Service (Amazon S3).

"""

import logging
import random
import sys
import datetime

import boto3
from botocore.exceptions import ClientError

from s3_conditional_requests import S3ConditionalRequests

# Add relative path to include demo_tools in this code example without need for setup.
sys.path.append("../../../..")
import demo_tools.question as q  # noqa

# Constants
FILE_CONTENT = "This is a test file for S3 conditional requests."
RANDOM_SUFFIX = str(random.randint(100, 999))

logger = logging.getLogger(__name__)


class ConditionalRequestsScenario:
    """Runs a scenario that shows how to use S3 Conditional Requests."""

    def __init__(self, conditional_requests, s3_client):
        """
        :param conditional_requests: An object that wraps S3 conditional request actions.
        :param s3_client: A Boto3 S3 client for setup and cleanup operations.
        """
        self.conditional_requests = conditional_requests
        self.s3_client = s3_client

    def setup_scenario(self, source_bucket: str, dest_bucket: str, object_key: str):
        """
        Sets up the scenario by creating a source and destination bucket.
        Prompts the user to provide a bucket name prefix.

        :param source_bucket: The name of the source bucket.
        :param dest_bucket: The name of the destination bucket.
        :param object_key: The name of a test file to add to the source bucket.
        """

        # Create the buckets.
        try:
            self.s3_client.create_bucket(Bucket=source_bucket)
            self.s3_client.create_bucket(Bucket=dest_bucket)
            print(
                f"Created source bucket: {source_bucket} and destination bucket: {dest_bucket}"
            )
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            logger.error(f"Error creating buckets: {error_code}")
            raise

        # Upload test file into the source bucket.
        try:
            print(f"Uploading file {object_key} to bucket {source_bucket}")
            response = self.s3_client.put_object(
                Bucket=source_bucket, Key=object_key, Body=FILE_CONTENT
            )
            object_etag = response["ETag"]
            return object_etag

        except Exception as e:
            logger.error(
                f"Failed to upload file {object_key} to bucket {source_bucket}: {e}"
            )


    def cleanup_scenario(self, source_bucket: str, dest_bucket: str):
        """
        Cleans up the scenario by deleting the source and destination buckets.

        :param source_bucket: The name of the source bucket.
        :param dest_bucket: The name of the destination bucket.
        """
        self.cleanup_bucket(source_bucket)
        self.cleanup_bucket(dest_bucket)

    def cleanup_bucket(self, bucket_name: str):
        """
        Cleans up the bucket by deleting all objects and then the bucket itself.

        :param bucket_name: The name of the bucket.
        """
        try:
            # Get list of all objects in the bucket.
            list_response = self.s3_client.list_objects_v2(Bucket=bucket_name)
            objs = list_response.get("Contents", [])
            for obj in objs:
                key = obj["Key"]
                self.s3_client.delete_object(Bucket=bucket_name, Key=key)
            self.s3_client.delete_bucket(Bucket=bucket_name)
            print(f"Cleaned up bucket: {bucket_name}.")
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            if error_code == "NoSuchBucket":
                logger.info(f"Bucket {bucket_name} does not exist, skipping cleanup.")
            else:
                logger.error(f"Error deleting bucket: {error_code}")
                raise


    def display_buckets(self, source_bucket: str, dest_bucket: str):
        """
        Display a list of the objects in the test buckets.

        :param source_bucket: The name of the source bucket.
        :param dest_bucket: The name of the destination bucket.
        """
        self.list_bucket_contents(source_bucket)
        self.list_bucket_contents(dest_bucket)

    def list_bucket_contents(self, bucket_name):
        """
        Display a list of the objects in the bucket.

        :param bucket_name: The name of the bucket.
        """
        try:
            # Get list of all objects in the bucket.
            print(f"\t Items in bucket {bucket_name}")
            list_response = self.s3_client.list_objects_v2(Bucket=bucket_name)
            objs = list_response.get("Contents", [])
            if not objs:
                print("\t\tNo objects found.")
            for obj in objs:
                key = obj["Key"]
                print(f"\t\t object: {key} ETag {obj['ETag']}")
            return objs
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            if error_code == "NoSuchBucket":
                logger.info(f"Bucket {bucket_name} does not exist.")
            else:
                logger.error(f"Error listing bucket and objects: {error_code}")
                raise


    def display_menu(
        self, source_bucket: str, dest_bucket: str, object_key: str, etag: str
    ):
        """
        Displays the menu of conditional request options for the user.

        :param source_bucket: The name of the source bucket.
        :param dest_bucket: The name of the destination bucket.
        :param object_key: The key of the test object in the source bucket.
        :param etag: The etag of the test object in the source bucket.
        """

        actions = [
            "Print list of bucket items.",
            "Perform a conditional read.",
            "Perform a conditional copy.",
            "Perform a conditional write.",
            "Clean up and exit.",
        ]

        conditions = [
            "If-Match: using the object's ETag. This condition should succeed.",
            "If-None-Match: using the object's ETag. This condition should fail.",
            "If-Modified-Since: using yesterday's date. This condition should succeed.",
            "If-Unmodified-Since: using yesterday's date. This condition should fail.",
        ]

        condition_types = [
            "IfMatch",
            "IfNoneMatch",
            "IfModifiedSince",
            "IfUnmodifiedSince",
        ]
        copy_condition_types = [
            "CopySourceIfMatch",
            "CopySourceIfNoneMatch",
            "CopySourceIfModifiedSince",
            "CopySourceIfUnmodifiedSince",
        ]

        yesterday_date = datetime.datetime.utcnow() - datetime.timedelta(days=1)

        choice = 0
        while choice != 4:
            print("-" * 88)
            print("Choose an action to explore some example conditional requests.")
            choice = q.choose("Which action would you like to take? ", actions)
            if choice == 0:
                print("Listing the objects and buckets.")
                self.display_buckets(source_bucket, dest_bucket)
            elif choice == 1:
                print("Perform a conditional read.")
                condition_type = q.choose("Enter the condition type : ", conditions)
                if condition_type == 0 or condition_type == 1:
                    self.conditional_requests.get_object_conditional(
                        object_key, source_bucket, condition_types[condition_type], etag
                    )
                elif condition_type == 2 or condition_type == 3:
                    self.conditional_requests.get_object_conditional(
                        object_key,
                        source_bucket,
                        condition_types[condition_type],
                        yesterday_date,
                    )
            elif choice == 2:
                print("Perform a conditional copy.")
                condition_type = q.choose("Enter the condition type : ", conditions)
                dest_key = q.ask("Enter an object key: ", q.non_empty)
                if condition_type == 0 or condition_type == 1:
                    self.conditional_requests.copy_object_conditional(
                        object_key,
                        dest_key,
                        source_bucket,
                        dest_bucket,
                        copy_condition_types[condition_type],
                        etag,
                    )
                elif condition_type == 2 or condition_type == 3:
                    self.conditional_requests.copy_object_conditional(
                        object_key,
                        dest_key,
                        copy_condition_types[condition_type],
                        yesterday_date,
                    )
            elif choice == 3:
                print(
                    "Perform a conditional write using IfNoneMatch condition on the object key."
                )
                print("If the key is a duplicate, the write will fail.")
                object_key = q.ask("Enter an object key: ", q.non_empty)
                self.conditional_requests.put_object_conditional(
                    object_key, source_bucket, b"Conditional write example data."
                )
            elif choice == 4:
                print("Proceeding to cleanup.")


    def run_scenario(self):
        """
        Runs the interactive scenario.
        """
        print("-" * 88)
        print("Welcome to the Amazon S3 conditional requests example.")
        print("-" * 88)

        print(
            f"""\
        This example demonstrates the use of conditional requests for S3 operations.
        You can use conditional requests to add preconditions to S3 read requests to return or copy
        an object based on its Entity tag (ETag), or last modified date. 
        You can use a conditional write requests to prevent overwrites by ensuring 
        there is no existing object with the same key. 
        
        This example will allow you to perform conditional reads
        and writes that will succeed or fail based on your selected options.
        
        Sample buckets and a sample object will be created as part of the example.
        """
        )

        bucket_prefix = q.ask("Enter a bucket name prefix: ", q.non_empty)
        source_bucket_name = f"{bucket_prefix}-source-{RANDOM_SUFFIX}"
        dest_bucket_name = f"{bucket_prefix}-dest-{RANDOM_SUFFIX}"
        object_key = "test-upload-file.txt"

        try:
            etag = self.setup_scenario(source_bucket_name, dest_bucket_name, object_key)
            self.display_menu(source_bucket_name, dest_bucket_name, object_key, etag)
        finally:
            self.cleanup_scenario(source_bucket_name, dest_bucket_name)

        print("-" * 88)
        print("Thanks for watching.")
        print("-" * 88)


if __name__ == "__main__":
    scenario = ConditionalRequestsScenario(
        S3ConditionalRequests.from_client(), boto3.client("s3")
    )
    scenario.run_scenario()

Eine Wrapper-Klasse, die die bedingten Anforderungsoperationen definiert.


import boto3
import logging

from botocore.exceptions import ClientError

# Configure logging
logger = logging.getLogger(__name__)


class S3ConditionalRequests:
    """Encapsulates S3 conditional request operations."""

    def __init__(self, s3_client):
        self.s3 = s3_client

    @classmethod
    def from_client(cls):
        """
        Instantiates this class from a Boto3 client.
        """
        s3_client = boto3.client("s3")
        return cls(s3_client)



    def get_object_conditional(
        self,
        object_key: str,
        source_bucket: str,
        condition_type: str,
        condition_value: str,
    ):
        """
        Retrieves an object from Amazon S3 with a conditional request.

        :param object_key: The key of the object to retrieve.
        :param source_bucket: The source bucket of the object.
        :param condition_type: The type of condition: 'IfMatch', 'IfNoneMatch', 'IfModifiedSince', 'IfUnmodifiedSince'.
        :param condition_value: The value to use for the condition.
        """
        try:
            response = self.s3.get_object(
                Bucket=source_bucket,
                Key=object_key,
                **{condition_type: condition_value},
            )
            sample_bytes = response["Body"].read(20)
            print(
                f"\tConditional read successful. Here are the first 20 bytes of the object:\n"
            )
            print(f"\t{sample_bytes}")
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            if error_code == "PreconditionFailed":
                print("\tConditional read failed: Precondition failed")
            elif error_code == "304":  # Not modified error code.
                print("\tConditional read failed: Object not modified")
            else:
                logger.error(f"Unexpected error: {error_code}")
                raise



    def put_object_conditional(self, object_key: str, source_bucket: str, data: bytes):
        """
        Uploads an object to Amazon S3 with a conditional request. Prevents overwrite
        using an IfNoneMatch condition for the object key.

        :param object_key: The key of the object to upload.
        :param source_bucket: The source bucket of the object.
        :param data: The data to upload.
        """
        try:
            self.s3.put_object(
                Bucket=source_bucket, Key=object_key, Body=data, IfNoneMatch="*"
            )
            print(
                f"\tConditional write successful for key {object_key} in bucket {source_bucket}."
            )
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            if error_code == "PreconditionFailed":
                print("\tConditional write failed: Precondition failed")
            else:
                logger.error(f"Unexpected error: {error_code}")
                raise


    def copy_object_conditional(
        self,
        source_key: str,
        dest_key: str,
        source_bucket: str,
        dest_bucket: str,
        condition_type: str,
        condition_value: str,
    ):
        """
        Copies an object from one Amazon S3 bucket to another with a conditional request.

        :param source_key: The key of the source object to copy.
        :param dest_key: The key of the destination object.
        :param source_bucket: The source bucket of the object.
        :param dest_bucket: The destination bucket of the object.
        :param condition_type: The type of condition to apply, e.g.
        'CopySourceIfMatch', 'CopySourceIfNoneMatch', 'CopySourceIfModifiedSince', 'CopySourceIfUnmodifiedSince'.
        :param condition_value: The value to use for the condition.
        """
        try:
            self.s3.copy_object(
                Bucket=dest_bucket,
                Key=dest_key,
                CopySource={"Bucket": source_bucket, "Key": source_key},
                **{condition_type: condition_value},
            )
            print(
                f"\tConditional copy successful for key {dest_key} in bucket {dest_bucket}."
            )
        except ClientError as e:
            error_code = e.response["Error"]["Code"]
            if error_code == "PreconditionFailed":
                print("\tConditional copy failed: Precondition failed")
            elif error_code == "304":  # Not modified error code.
                print("\tConditional copy failed: Object not modified")
            else:
                logger.error(f"Unexpected error: {error_code}")
                raise

Das folgende Codebeispiel zeigt, wie versionierte S3-Objekte in Batches mit einer Lambda-Funktion verwaltet werden.

SDKfür Python (Boto3)

Zeigt, wie versionierte Objekte von Amazon Simple Storage Service (Amazon S3) stapelweise bearbeitet werden, indem Jobs erstellt werden, die AWS Lambda Funktionen aufrufen, um die Verarbeitung durchzuführen. In diesem Beispiel wird ein versionsfähiger Bucket erstellt, der die Strophen des Gedichts You Are Old, Father William von Lewis Carroll hochlädt und Amazon-S3-Batch-Aufträge verwendet, um das Gedicht auf verschiedene Arten zu verdrehen.

Lernen Sie Folgendes:

  • Erstellen Sie Lambda-Funktionen, die mit versionierten Objekten arbeiten.

  • Erstellen Sie ein Manifest von zu aktualisierenden Objekten.

  • Erstellen Sie Batch-Aufträge, die Lambda-Funktionen zum Aktualisieren von Objekten aufrufen.

  • Löschen Sie Lambda-Funktionen.

  • Leeren und löschen Sie einen versionierten Bucket.

Dieses Beispiel lässt sich am besten auf ansehen. GitHub Den vollständigen Quellcode und Anweisungen zur Einrichtung und Ausführung finden Sie im vollständigen Beispiel unter GitHub.

In diesem Beispiel verwendete Dienste

  • Amazon S3

Das folgende Codebeispiel zeigt, wie Sie große Dateien zu und von Amazon S3 hochladen oder herunterladen können.

Weitere Informationen finden Sie unter Hochladen eines Objekts mit Multipart-Upload.

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Erstellen Sie Funktionen, die Dateien mit mehreren der verfügbaren Transfer-Manager-Einstellungen übertragen. Verwenden Sie eine Callback-Klasse, um während der Dateiübertragung Callback-Fortschritte zu schreiben.

import sys
import threading

import boto3
from boto3.s3.transfer import TransferConfig


MB = 1024 * 1024
s3 = boto3.resource("s3")


class TransferCallback:
    """
    Handle callbacks from the transfer manager.

    The transfer manager periodically calls the __call__ method throughout
    the upload and download process so that it can take action, such as
    displaying progress to the user and collecting data about the transfer.
    """

    def __init__(self, target_size):
        self._target_size = target_size
        self._total_transferred = 0
        self._lock = threading.Lock()
        self.thread_info = {}

    def __call__(self, bytes_transferred):
        """
        The callback method that is called by the transfer manager.

        Display progress during file transfer and collect per-thread transfer
        data. This method can be called by multiple threads, so shared instance
        data is protected by a thread lock.
        """
        thread = threading.current_thread()
        with self._lock:
            self._total_transferred += bytes_transferred
            if thread.ident not in self.thread_info.keys():
                self.thread_info[thread.ident] = bytes_transferred
            else:
                self.thread_info[thread.ident] += bytes_transferred

            target = self._target_size * MB
            sys.stdout.write(
                f"\r{self._total_transferred} of {target} transferred "
                f"({(self._total_transferred / target) * 100:.2f}%)."
            )
            sys.stdout.flush()


def upload_with_default_configuration(
    local_file_path, bucket_name, object_key, file_size_mb
):
    """
    Upload a file from a local folder to an Amazon S3 bucket, using the default
    configuration.
    """
    transfer_callback = TransferCallback(file_size_mb)
    s3.Bucket(bucket_name).upload_file(
        local_file_path, object_key, Callback=transfer_callback
    )
    return transfer_callback.thread_info


def upload_with_chunksize_and_meta(
    local_file_path, bucket_name, object_key, file_size_mb, metadata=None
):
    """
    Upload a file from a local folder to an Amazon S3 bucket, setting a
    multipart chunk size and adding metadata to the Amazon S3 object.

    The multipart chunk size controls the size of the chunks of data that are
    sent in the request. A smaller chunk size typically results in the transfer
    manager using more threads for the upload.

    The metadata is a set of key-value pairs that are stored with the object
    in Amazon S3.
    """
    transfer_callback = TransferCallback(file_size_mb)

    config = TransferConfig(multipart_chunksize=1 * MB)
    extra_args = {"Metadata": metadata} if metadata else None
    s3.Bucket(bucket_name).upload_file(
        local_file_path,
        object_key,
        Config=config,
        ExtraArgs=extra_args,
        Callback=transfer_callback,
    )
    return transfer_callback.thread_info


def upload_with_high_threshold(local_file_path, bucket_name, object_key, file_size_mb):
    """
    Upload a file from a local folder to an Amazon S3 bucket, setting a
    multipart threshold larger than the size of the file.

    Setting a multipart threshold larger than the size of the file results
    in the transfer manager sending the file as a standard upload instead of
    a multipart upload.
    """
    transfer_callback = TransferCallback(file_size_mb)
    config = TransferConfig(multipart_threshold=file_size_mb * 2 * MB)
    s3.Bucket(bucket_name).upload_file(
        local_file_path, object_key, Config=config, Callback=transfer_callback
    )
    return transfer_callback.thread_info


def upload_with_sse(
    local_file_path, bucket_name, object_key, file_size_mb, sse_key=None
):
    """
    Upload a file from a local folder to an Amazon S3 bucket, adding server-side
    encryption with customer-provided encryption keys to the object.

    When this kind of encryption is specified, Amazon S3 encrypts the object
    at rest and allows downloads only when the expected encryption key is
    provided in the download request.
    """
    transfer_callback = TransferCallback(file_size_mb)
    if sse_key:
        extra_args = {"SSECustomerAlgorithm": "AES256", "SSECustomerKey": sse_key}
    else:
        extra_args = None
    s3.Bucket(bucket_name).upload_file(
        local_file_path, object_key, ExtraArgs=extra_args, Callback=transfer_callback
    )
    return transfer_callback.thread_info


def download_with_default_configuration(
    bucket_name, object_key, download_file_path, file_size_mb
):
    """
    Download a file from an Amazon S3 bucket to a local folder, using the
    default configuration.
    """
    transfer_callback = TransferCallback(file_size_mb)
    s3.Bucket(bucket_name).Object(object_key).download_file(
        download_file_path, Callback=transfer_callback
    )
    return transfer_callback.thread_info


def download_with_single_thread(
    bucket_name, object_key, download_file_path, file_size_mb
):
    """
    Download a file from an Amazon S3 bucket to a local folder, using a
    single thread.
    """
    transfer_callback = TransferCallback(file_size_mb)
    config = TransferConfig(use_threads=False)
    s3.Bucket(bucket_name).Object(object_key).download_file(
        download_file_path, Config=config, Callback=transfer_callback
    )
    return transfer_callback.thread_info


def download_with_high_threshold(
    bucket_name, object_key, download_file_path, file_size_mb
):
    """
    Download a file from an Amazon S3 bucket to a local folder, setting a
    multipart threshold larger than the size of the file.

    Setting a multipart threshold larger than the size of the file results
    in the transfer manager sending the file as a standard download instead
    of a multipart download.
    """
    transfer_callback = TransferCallback(file_size_mb)
    config = TransferConfig(multipart_threshold=file_size_mb * 2 * MB)
    s3.Bucket(bucket_name).Object(object_key).download_file(
        download_file_path, Config=config, Callback=transfer_callback
    )
    return transfer_callback.thread_info


def download_with_sse(
    bucket_name, object_key, download_file_path, file_size_mb, sse_key
):
    """
    Download a file from an Amazon S3 bucket to a local folder, adding a
    customer-provided encryption key to the request.

    When this kind of encryption is specified, Amazon S3 encrypts the object
    at rest and allows downloads only when the expected encryption key is
    provided in the download request.
    """
    transfer_callback = TransferCallback(file_size_mb)

    if sse_key:
        extra_args = {"SSECustomerAlgorithm": "AES256", "SSECustomerKey": sse_key}
    else:
        extra_args = None
    s3.Bucket(bucket_name).Object(object_key).download_file(
        download_file_path, ExtraArgs=extra_args, Callback=transfer_callback
    )
    return transfer_callback.thread_info

Demonstrieren Sie die Funktionen des Transfer-Managers und melden Sie Ergebnisse.

import hashlib
import os
import platform
import shutil
import time

import boto3
from boto3.s3.transfer import TransferConfig
from botocore.exceptions import ClientError
from botocore.exceptions import ParamValidationError
from botocore.exceptions import NoCredentialsError

import file_transfer

MB = 1024 * 1024
# These configuration attributes affect both uploads and downloads.
CONFIG_ATTRS = (
    "multipart_threshold",
    "multipart_chunksize",
    "max_concurrency",
    "use_threads",
)
# These configuration attributes affect only downloads.
DOWNLOAD_CONFIG_ATTRS = ("max_io_queue", "io_chunksize", "num_download_attempts")


class TransferDemoManager:
    """
    Manages the demonstration. Collects user input from a command line, reports
    transfer results, maintains a list of artifacts created during the
    demonstration, and cleans them up after the demonstration is completed.
    """

    def __init__(self):
        self._s3 = boto3.resource("s3")
        self._chore_list = []
        self._create_file_cmd = None
        self._size_multiplier = 0
        self.file_size_mb = 30
        self.demo_folder = None
        self.demo_bucket = None
        self._setup_platform_specific()
        self._terminal_width = shutil.get_terminal_size(fallback=(80, 80))[0]

    def collect_user_info(self):
        """
        Collect local folder and Amazon S3 bucket name from the user. These
        locations are used to store files during the demonstration.
        """
        while not self.demo_folder:
            self.demo_folder = input(
                "Which file folder do you want to use to store " "demonstration files? "
            )
            if not os.path.isdir(self.demo_folder):
                print(f"{self.demo_folder} isn't a folder!")
                self.demo_folder = None

        while not self.demo_bucket:
            self.demo_bucket = input(
                "Which Amazon S3 bucket do you want to use to store "
                "demonstration files? "
            )
            try:
                self._s3.meta.client.head_bucket(Bucket=self.demo_bucket)
            except ParamValidationError as err:
                print(err)
                self.demo_bucket = None
            except ClientError as err:
                print(err)
                print(
                    f"Either {self.demo_bucket} doesn't exist or you don't "
                    f"have access to it."
                )
                self.demo_bucket = None

    def demo(
        self, question, upload_func, download_func, upload_args=None, download_args=None
    ):
        """Run a demonstration.

        Ask the user if they want to run this specific demonstration.
        If they say yes, create a file on the local path, upload it
        using the specified upload function, then download it using the
        specified download function.
        """
        if download_args is None:
            download_args = {}
        if upload_args is None:
            upload_args = {}
        question = question.format(self.file_size_mb)
        answer = input(f"{question} (y/n)")
        if answer.lower() == "y":
            local_file_path, object_key, download_file_path = self._create_demo_file()

            file_transfer.TransferConfig = self._config_wrapper(
                TransferConfig, CONFIG_ATTRS
            )
            self._report_transfer_params(
                "Uploading", local_file_path, object_key, **upload_args
            )
            start_time = time.perf_counter()
            thread_info = upload_func(
                local_file_path,
                self.demo_bucket,
                object_key,
                self.file_size_mb,
                **upload_args,
            )
            end_time = time.perf_counter()
            self._report_transfer_result(thread_info, end_time - start_time)

            file_transfer.TransferConfig = self._config_wrapper(
                TransferConfig, CONFIG_ATTRS + DOWNLOAD_CONFIG_ATTRS
            )
            self._report_transfer_params(
                "Downloading", object_key, download_file_path, **download_args
            )
            start_time = time.perf_counter()
            thread_info = download_func(
                self.demo_bucket,
                object_key,
                download_file_path,
                self.file_size_mb,
                **download_args,
            )
            end_time = time.perf_counter()
            self._report_transfer_result(thread_info, end_time - start_time)

    def last_name_set(self):
        """Get the name set used for the last demo."""
        return self._chore_list[-1]

    def cleanup(self):
        """
        Remove files from the demo folder, and uploaded objects from the
        Amazon S3 bucket.
        """
        print("-" * self._terminal_width)
        for local_file_path, s3_object_key, downloaded_file_path in self._chore_list:
            print(f"Removing {local_file_path}")
            try:
                os.remove(local_file_path)
            except FileNotFoundError as err:
                print(err)

            print(f"Removing {downloaded_file_path}")
            try:
                os.remove(downloaded_file_path)
            except FileNotFoundError as err:
                print(err)

            if self.demo_bucket:
                print(f"Removing {self.demo_bucket}:{s3_object_key}")
                try:
                    self._s3.Bucket(self.demo_bucket).Object(s3_object_key).delete()
                except ClientError as err:
                    print(err)

    def _setup_platform_specific(self):
        """Set up platform-specific command used to create a large file."""
        if platform.system() == "Windows":
            self._create_file_cmd = "fsutil file createnew {} {}"
            self._size_multiplier = MB
        elif platform.system() == "Linux" or platform.system() == "Darwin":
            self._create_file_cmd = f"dd if=/dev/urandom of={{}} " f"bs={MB} count={{}}"
            self._size_multiplier = 1
        else:
            raise EnvironmentError(
                f"Demo of platform {platform.system()} isn't supported."
            )

    def _create_demo_file(self):
        """
        Create a file in the demo folder specified by the user. Store the local
        path, object name, and download path for later cleanup.

        Only the local file is created by this method. The Amazon S3 object and
        download file are created later during the demonstration.

        Returns:
        A tuple that contains the local file path, object name, and download
        file path.
        """
        file_name_template = "TestFile{}-{}.demo"
        local_suffix = "local"
        object_suffix = "s3object"
        download_suffix = "downloaded"
        file_tag = len(self._chore_list) + 1

        local_file_path = os.path.join(
            self.demo_folder, file_name_template.format(file_tag, local_suffix)
        )

        s3_object_key = file_name_template.format(file_tag, object_suffix)

        downloaded_file_path = os.path.join(
            self.demo_folder, file_name_template.format(file_tag, download_suffix)
        )

        filled_cmd = self._create_file_cmd.format(
            local_file_path, self.file_size_mb * self._size_multiplier
        )

        print(
            f"Creating file of size {self.file_size_mb} MB "
            f"in {self.demo_folder} by running:"
        )
        print(f"{'':4}{filled_cmd}")
        os.system(filled_cmd)

        chore = (local_file_path, s3_object_key, downloaded_file_path)
        self._chore_list.append(chore)
        return chore

    def _report_transfer_params(self, verb, source_name, dest_name, **kwargs):
        """Report configuration and extra arguments used for a file transfer."""
        print("-" * self._terminal_width)
        print(f"{verb} {source_name} ({self.file_size_mb} MB) to {dest_name}")
        if kwargs:
            print("With extra args:")
            for arg, value in kwargs.items():
                print(f'{"":4}{arg:<20}: {value}')

    @staticmethod
    def ask_user(question):
        """
        Ask the user a yes or no question.

        Returns:
        True when the user answers 'y' or 'Y'; otherwise, False.
        """
        answer = input(f"{question} (y/n) ")
        return answer.lower() == "y"

    @staticmethod
    def _config_wrapper(func, config_attrs):
        def wrapper(*args, **kwargs):
            config = func(*args, **kwargs)
            print("With configuration:")
            for attr in config_attrs:
                print(f'{"":4}{attr:<20}: {getattr(config, attr)}')
            return config

        return wrapper

    @staticmethod
    def _report_transfer_result(thread_info, elapsed):
        """Report the result of a transfer, including per-thread data."""
        print(f"\nUsed {len(thread_info)} threads.")
        for ident, byte_count in thread_info.items():
            print(f"{'':4}Thread {ident} copied {byte_count} bytes.")
        print(f"Your transfer took {elapsed:.2f} seconds.")


def main():
    """
    Run the demonstration script for s3_file_transfer.
    """
    demo_manager = TransferDemoManager()
    demo_manager.collect_user_info()

    # Upload and download with default configuration. Because the file is 30 MB
    # and the default multipart_threshold is 8 MB, both upload and download are
    # multipart transfers.
    demo_manager.demo(
        "Do you want to upload and download a {} MB file "
        "using the default configuration?",
        file_transfer.upload_with_default_configuration,
        file_transfer.download_with_default_configuration,
    )

    # Upload and download with multipart_threshold set higher than the size of
    # the file. This causes the transfer manager to use standard transfers
    # instead of multipart transfers.
    demo_manager.demo(
        "Do you want to upload and download a {} MB file "
        "as a standard (not multipart) transfer?",
        file_transfer.upload_with_high_threshold,
        file_transfer.download_with_high_threshold,
    )

    # Upload with specific chunk size and additional metadata.
    # Download with a single thread.
    demo_manager.demo(
        "Do you want to upload a {} MB file with a smaller chunk size and "
        "then download the same file using a single thread?",
        file_transfer.upload_with_chunksize_and_meta,
        file_transfer.download_with_single_thread,
        upload_args={
            "metadata": {
                "upload_type": "chunky",
                "favorite_color": "aqua",
                "size": "medium",
            }
        },
    )

    # Upload using server-side encryption with customer-provided
    # encryption keys.
    # Generate a 256-bit key from a passphrase.
    sse_key = hashlib.sha256("demo_passphrase".encode("utf-8")).digest()
    demo_manager.demo(
        "Do you want to upload and download a {} MB file using "
        "server-side encryption?",
        file_transfer.upload_with_sse,
        file_transfer.download_with_sse,
        upload_args={"sse_key": sse_key},
        download_args={"sse_key": sse_key},
    )

    # Download without specifying an encryption key to show that the
    # encryption key must be included to download an encrypted object.
    if demo_manager.ask_user(
        "Do you want to try to download the encrypted "
        "object without sending the required key?"
    ):
        try:
            _, object_key, download_file_path = demo_manager.last_name_set()
            file_transfer.download_with_default_configuration(
                demo_manager.demo_bucket,
                object_key,
                download_file_path,
                demo_manager.file_size_mb,
            )
        except ClientError as err:
            print(
                "Got expected error when trying to download an encrypted "
                "object without specifying encryption info:"
            )
            print(f"{'':4}{err}")

    # Remove all created and downloaded files, remove all objects from
    # S3 storage.
    if demo_manager.ask_user(
        "Demonstration complete. Do you want to remove local files " "and S3 objects?"
    ):
        demo_manager.cleanup()


if __name__ == "__main__":
    try:
        main()
    except NoCredentialsError as error:
        print(error)
        print(
            "To run this example, you must have valid credentials in "
            "a shared credential file or set in environment variables."
        )

Wie das aussehen kann, sehen Sie am nachfolgenden Beispielcode:

  • Erstellen Sie einen versionierten S3 Bucket.

  • Rufen Sie alle Versionen eines Objekts ab.

  • Setzen Sie eine Richtlinie auf eine frühere Version zurück.

  • Löschen Sie ein versioniertes Objekt und stellen Sie es wieder her.

  • Löschen Sie alle Versionen eines Objekts dauerhaft.

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Sie sehen das vollständige Beispiel und erfahren, wie Sie das AWS -Code-Beispiel-Repository einrichten und ausführen.

Erstellen Sie Funktionen, die S3-Aktionen umschließen.

def create_versioned_bucket(bucket_name, prefix):
    """
    Creates an Amazon S3 bucket, enables it for versioning, and configures a lifecycle
    that expires noncurrent object versions after 7 days.

    Adding a lifecycle configuration to a versioned bucket is a best practice.
    It helps prevent objects in the bucket from accumulating a large number of
    noncurrent versions, which can slow down request performance.

    Usage is shown in the usage_demo_single_object function at the end of this module.

    :param bucket_name: The name of the bucket to create.
    :param prefix: Identifies which objects are automatically expired under the
                   configured lifecycle rules.
    :return: The newly created bucket.
    """
    try:
        bucket = s3.create_bucket(
            Bucket=bucket_name,
            CreateBucketConfiguration={
                "LocationConstraint": s3.meta.client.meta.region_name
            },
        )
        logger.info("Created bucket %s.", bucket.name)
    except ClientError as error:
        if error.response["Error"]["Code"] == "BucketAlreadyOwnedByYou":
            logger.warning("Bucket %s already exists! Using it.", bucket_name)
            bucket = s3.Bucket(bucket_name)
        else:
            logger.exception("Couldn't create bucket %s.", bucket_name)
            raise

    try:
        bucket.Versioning().enable()
        logger.info("Enabled versioning on bucket %s.", bucket.name)
    except ClientError:
        logger.exception("Couldn't enable versioning on bucket %s.", bucket.name)
        raise

    try:
        expiration = 7
        bucket.LifecycleConfiguration().put(
            LifecycleConfiguration={
                "Rules": [
                    {
                        "Status": "Enabled",
                        "Prefix": prefix,
                        "NoncurrentVersionExpiration": {"NoncurrentDays": expiration},
                    }
                ]
            }
        )
        logger.info(
            "Configured lifecycle to expire noncurrent versions after %s days "
            "on bucket %s.",
            expiration,
            bucket.name,
        )
    except ClientError as error:
        logger.warning(
            "Couldn't configure lifecycle on bucket %s because %s. "
            "Continuing anyway.",
            bucket.name,
            error,
        )

    return bucket



def rollback_object(bucket, object_key, version_id):
    """
    Rolls back an object to an earlier version by deleting all versions that
    occurred after the specified rollback version.

    Usage is shown in the usage_demo_single_object function at the end of this module.

    :param bucket: The bucket that holds the object to roll back.
    :param object_key: The object to roll back.
    :param version_id: The version ID to roll back to.
    """
    # Versions must be sorted by last_modified date because delete markers are
    # at the end of the list even when they are interspersed in time.
    versions = sorted(
        bucket.object_versions.filter(Prefix=object_key),
        key=attrgetter("last_modified"),
        reverse=True,
    )

    logger.debug(
        "Got versions:\n%s",
        "\n".join(
            [
                f"\t{version.version_id}, last modified {version.last_modified}"
                for version in versions
            ]
        ),
    )

    if version_id in [ver.version_id for ver in versions]:
        print(f"Rolling back to version {version_id}")
        for version in versions:
            if version.version_id != version_id:
                version.delete()
                print(f"Deleted version {version.version_id}")
            else:
                break

        print(f"Active version is now {bucket.Object(object_key).version_id}")
    else:
        raise KeyError(
            f"{version_id} was not found in the list of versions for " f"{object_key}."
        )



def revive_object(bucket, object_key):
    """
    Revives a versioned object that was deleted by removing the object's active
    delete marker.
    A versioned object presents as deleted when its latest version is a delete marker.
    By removing the delete marker, we make the previous version the latest version
    and the object then presents as *not* deleted.

    Usage is shown in the usage_demo_single_object function at the end of this module.

    :param bucket: The bucket that contains the object.
    :param object_key: The object to revive.
    """
    # Get the latest version for the object.
    response = s3.meta.client.list_object_versions(
        Bucket=bucket.name, Prefix=object_key, MaxKeys=1
    )

    if "DeleteMarkers" in response:
        latest_version = response["DeleteMarkers"][0]
        if latest_version["IsLatest"]:
            logger.info(
                "Object %s was indeed deleted on %s. Let's revive it.",
                object_key,
                latest_version["LastModified"],
            )
            obj = bucket.Object(object_key)
            obj.Version(latest_version["VersionId"]).delete()
            logger.info(
                "Revived %s, active version is now %s  with body '%s'",
                object_key,
                obj.version_id,
                obj.get()["Body"].read(),
            )
        else:
            logger.warning(
                "Delete marker is not the latest version for %s!", object_key
            )
    elif "Versions" in response:
        logger.warning("Got an active version for %s, nothing to do.", object_key)
    else:
        logger.error("Couldn't get any version info for %s.", object_key)



def permanently_delete_object(bucket, object_key):
    """
    Permanently deletes a versioned object by deleting all of its versions.

    Usage is shown in the usage_demo_single_object function at the end of this module.

    :param bucket: The bucket that contains the object.
    :param object_key: The object to delete.
    """
    try:
        bucket.object_versions.filter(Prefix=object_key).delete()
        logger.info("Permanently deleted all versions of object %s.", object_key)
    except ClientError:
        logger.exception("Couldn't delete all versions of %s.", object_key)
        raise

Laden Sie die Strophe eines Gedichts in ein versioniertes Objekt hoch und führen Sie eine Reihe von Aktionen dafür aus.

def usage_demo_single_object(obj_prefix="demo-versioning/"):
    """
    Demonstrates usage of versioned object functions. This demo uploads a stanza
    of a poem and performs a series of revisions, deletions, and revivals on it.

    :param obj_prefix: The prefix to assign to objects created by this demo.
    """
    with open("father_william.txt") as file:
        stanzas = file.read().split("\n\n")

    width = get_terminal_size((80, 20))[0]
    print("-" * width)
    print("Welcome to the usage demonstration of Amazon S3 versioning.")
    print(
        "This demonstration uploads a single stanza of a poem to an Amazon "
        "S3 bucket and then applies various revisions to it."
    )
    print("-" * width)
    print("Creating a version-enabled bucket for the demo...")
    bucket = create_versioned_bucket("bucket-" + str(uuid.uuid1()), obj_prefix)

    print("\nThe initial version of our stanza:")
    print(stanzas[0])

    # Add the first stanza and revise it a few times.
    print("\nApplying some revisions to the stanza...")
    obj_stanza_1 = bucket.Object(f"{obj_prefix}stanza-1")
    obj_stanza_1.put(Body=bytes(stanzas[0], "utf-8"))
    obj_stanza_1.put(Body=bytes(stanzas[0].upper(), "utf-8"))
    obj_stanza_1.put(Body=bytes(stanzas[0].lower(), "utf-8"))
    obj_stanza_1.put(Body=bytes(stanzas[0][::-1], "utf-8"))
    print(
        "The latest version of the stanza is now:",
        obj_stanza_1.get()["Body"].read().decode("utf-8"),
        sep="\n",
    )

    # Versions are returned in order, most recent first.
    obj_stanza_1_versions = bucket.object_versions.filter(Prefix=obj_stanza_1.key)
    print(
        "The version data of the stanza revisions:",
        *[
            f"    {version.version_id}, last modified {version.last_modified}"
            for version in obj_stanza_1_versions
        ],
        sep="\n",
    )

    # Rollback two versions.
    print("\nRolling back two versions...")
    rollback_object(bucket, obj_stanza_1.key, list(obj_stanza_1_versions)[2].version_id)
    print(
        "The latest version of the stanza:",
        obj_stanza_1.get()["Body"].read().decode("utf-8"),
        sep="\n",
    )

    # Delete the stanza
    print("\nDeleting the stanza...")
    obj_stanza_1.delete()
    try:
        obj_stanza_1.get()
    except ClientError as error:
        if error.response["Error"]["Code"] == "NoSuchKey":
            print("The stanza is now deleted (as expected).")
        else:
            raise

    # Revive the stanza
    print("\nRestoring the stanza...")
    revive_object(bucket, obj_stanza_1.key)
    print(
        "The stanza is restored! The latest version is again:",
        obj_stanza_1.get()["Body"].read().decode("utf-8"),
        sep="\n",
    )

    # Permanently delete all versions of the object. This cannot be undone!
    print("\nPermanently deleting all versions of the stanza...")
    permanently_delete_object(bucket, obj_stanza_1.key)
    obj_stanza_1_versions = bucket.object_versions.filter(Prefix=obj_stanza_1.key)
    if len(list(obj_stanza_1_versions)) == 0:
        print("The stanza has been permanently deleted and now has no versions.")
    else:
        print("Something went wrong. The stanza still exists!")

    print(f"\nRemoving {bucket.name}...")
    bucket.delete()
    print(f"{bucket.name} deleted.")
    print("Demo done!")

Serverless-Beispiele

Das folgende Codebeispiel zeigt, wie eine Lambda-Funktion implementiert wird, die ein Ereignis empfängt, das durch das Hochladen eines Objekts in einen S3-Bucket ausgelöst wird. Die Funktion ruft den S3-Bucket-Namen und den Objektschlüssel aus dem Event-Parameter ab und ruft Amazon S3 API auf, um den Inhaltstyp des Objekts abzurufen und zu protokollieren.

SDKfür Python (Boto3)
Anmerkung

Es gibt noch mehr dazu. GitHub Das vollständige Beispiel sowie eine Anleitung zum Einrichten und Ausführen finden Sie im Repository mit Serverless-Beispielen.

Nutzen eines S3-Ereignisses mit Lambda unter Verwendung von Python

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0
import json
import urllib.parse
import boto3

print('Loading function')

s3 = boto3.client('s3')


def lambda_handler(event, context):
    #print("Received event: " + json.dumps(event, indent=2))

    # Get the object from the event and show its content type
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = urllib.parse.unquote_plus(event['Records'][0]['s3']['object']['key'], encoding='utf-8')
    try:
        response = s3.get_object(Bucket=bucket, Key=key)
        print("CONTENT TYPE: " + response['ContentType'])
        return response['ContentType']
    except Exception as e:
        print(e)
        print('Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(key, bucket))
        raise e