Boto3 s3

Boto3 s3 DEFAULT




Creates a copy of an object that is already stored in Amazon S3.


You can store individual objects of up to 5 TB in Amazon S3. You create a copy of your object up to 5 GB in size in a single atomic action using this API. However, to copy an object greater than 5 GB, you must use the multipart upload Upload Part - Copy API. For more information, see Copy Object Using the REST Multipart Upload API .

All copy requests must be authenticated. Additionally, you must have read access to the source object and write access to the destination bucket. For more information, see REST Authentication . Both the Region that you want to copy the object from and the Region that you want to copy the object to must be enabled for your account.

A copy request might return an error when Amazon S3 receives the copy request or while Amazon S3 is copying the files. If the error occurs before the copy action starts, you receive a standard Amazon S3 error. If the error occurs during the copy operation, the error response is embedded in the response. This means that a response can contain either a success or an error. Design your application to parse the contents of the response and handle it appropriately.

If the copy is successful, you receive a response with information about the copied object.


If the request is an HTTP 1.1 request, the response is chunk encoded. If it were not, it would not contain the content-length, and you would need to read the entire body.

The copy request charge is based on the storage class and Region that you specify for the destination object. For pricing information, see Amazon S3 pricing .


Amazon S3 transfer acceleration does not support cross-Region copies. If you request a cross-Region copy using a transfer acceleration endpoint, you get a 400 error. For more information, see Transfer Acceleration .


When copying an object, you can preserve all metadata (default) or specify new metadata. However, the ACL is not preserved and is set to private for the user making the request. To override the default ACL setting, specify a new ACL when generating a copy request. For more information, see Using ACLs .

To specify whether you want the object metadata copied from the source object or replaced with metadata provided in the request, you can optionally add the header. When you grant permissions, you can use the condition key to enforce certain metadata behavior when objects are uploaded. For more information, see Specifying Conditions in a Policy in the Amazon S3 User Guide . For a complete list of Amazon S3-specific condition keys, see Actions, Resources, and Condition Keys for Amazon S3 .

** Headers**

To only copy an object under certain conditions, such as whether the matches or whether the object was modified before or after a specified date, use the following request parameters:

    If both the and headers are present in the request and evaluate as follows, Amazon S3 returns and copies the data:

    • condition evaluates to true
    • condition evaluates to false

    If both the and headers are present in the request and evaluate as follows, Amazon S3 returns the response code:

    • condition evaluates to false
    • condition evaluates to true


    All headers with the prefix, including , must be signed.

    Server-side encryption

    When you perform a CopyObject operation, you can optionally use the appropriate encryption-related headers to encrypt the object using server-side encryption with Amazon Web Services managed encryption keys (SSE-S3 or SSE-KMS) or a customer-provided encryption key. With server-side encryption, Amazon S3 encrypts your data as it writes it to disks in its data centers and decrypts the data when you access it. For more information about server-side encryption, see Using Server-Side Encryption .

    If a target object uses SSE-KMS, you can enable an S3 Bucket Key for the object. For more information, see Amazon S3 Bucket Keys in the Amazon S3 User Guide .

    Access Control List (ACL)-Specific Request Headers

    When copying an object, you can optionally use headers to grant ACL-based permissions. By default, all objects are private. Only the owner has full access control. When adding a new object, you can grant permissions to individual Amazon Web Services accounts or to predefined groups defined by Amazon S3. These permissions are then added to the ACL on the object. For more information, see Access Control List (ACL) Overview and Managing ACLs Using the REST API .

    Storage Class Options

    You can use the action to change the storage class of an object that is already stored in Amazon S3 using the parameter. For more information, see Storage Classes in the Amazon S3 User Guide .


    By default, identifies the current version of an object to copy. If the current version is a delete marker, Amazon S3 behaves as if the object was deleted. To copy a different version, use the subresource.

    If you enable versioning on the target bucket, Amazon S3 generates a unique version ID for the object being copied. This version ID is different from the version ID of the source object. Amazon S3 returns the version ID of the copied object in the response header in the response.

    If you do not enable versioning or suspend it on the target bucket, the version ID that Amazon S3 generates is always null.

    If the source object's storage class is GLACIER, you must restore a copy of this object before you can use it as a source object for the copy operation. For more information, see RestoreObject .

    The following operations are related to :

    For more information, see Copying Objects .

    See also: AWS API Documentation

    Request Syntax

    • ACL (string) --

      The canned ACL to apply to the object.

      This action is not supported by Amazon S3 on Outposts.

    • Bucket (string) --


      The name of the destination bucket.

      When using this action with an access point, you must direct requests to the access point hostname. The access point hostname takes the form AccessPointName -AccountId .s3-accesspoint.*Region* When using this action with an access point through the Amazon Web Services SDKs, you provide the access point ARN in place of the bucket name. For more information about access point ARNs, see Using access points in the Amazon S3 User Guide .

      When using this action with Amazon S3 on Outposts, you must direct requests to the S3 on Outposts hostname. The S3 on Outposts hostname takes the form AccessPointName -AccountId .*outpostID* .s3-outposts.*Region* When using this action using S3 on Outposts through the Amazon Web Services SDKs, you provide the Outposts bucket ARN in place of the bucket name. For more information about S3 on Outposts ARNs, see Using S3 on Outposts in the Amazon S3 User Guide .

    • CacheControl (string) -- Specifies caching behavior along the request/reply chain.
    • ContentDisposition (string) -- Specifies presentational information for the object.
    • ContentEncoding (string) -- Specifies what content encodings have been applied to the object and thus what decoding mechanisms must be applied to obtain the media-type referenced by the Content-Type header field.
    • ContentLanguage (string) -- The language the content is in.
    • ContentType (string) -- A standard MIME type describing the format of the object data.
    • CopySource (str or dict) -- [REQUIRED] The name of the source bucket, key name of the source object, and optional version ID of the source object. You can either provide this value as a string or a dictionary. The string form is {bucket}/{key} or {bucket}/{key}?versionId={versionId} if you want to copy a specific version. You can also provide this value as a dictionary. The dictionary format is recommended over the string format because it is more explicit. The dictionary format is: {'Bucket': 'bucket', 'Key': 'key', 'VersionId': 'id'}. Note that the VersionId key is optional and may be omitted. To specify an S3 access point, provide the access point ARN for the key in the copy source dictionary. If you want to provide the copy source for an S3 access point as a string instead of a dictionary, the ARN provided must be the full S3 access point object ARN (i.e. {accesspoint_arn}/object/{key})
    • CopySourceIfMatch (string) -- Copies the object if its entity tag (ETag) matches the specified tag.
    • CopySourceIfModifiedSince (datetime) -- Copies the object if it has been modified since the specified time.
    • CopySourceIfNoneMatch (string) -- Copies the object if its entity tag (ETag) is different than the specified ETag.
    • CopySourceIfUnmodifiedSince (datetime) -- Copies the object if it hasn't been modified since the specified time.
    • Expires (datetime) -- The date and time at which the object is no longer cacheable.
    • GrantFullControl (string) --

      Gives the grantee READ, READ_ACP, and WRITE_ACP permissions on the object.

      This action is not supported by Amazon S3 on Outposts.

    • GrantRead (string) --

      Allows grantee to read the object data and its metadata.

      This action is not supported by Amazon S3 on Outposts.

    • GrantReadACP (string) --

      Allows grantee to read the object ACL.

      This action is not supported by Amazon S3 on Outposts.

    • GrantWriteACP (string) --

      Allows grantee to write the ACL for the applicable object.

      This action is not supported by Amazon S3 on Outposts.

    • Key (string) --


      The key of the destination object.

    • Metadata (dict) --

      A map of metadata to store with the object in S3.

    • MetadataDirective (string) -- Specifies whether the metadata is copied from the source object or replaced with metadata provided in the request.
    • TaggingDirective (string) -- Specifies whether the object tag-set are copied from the source object or replaced with tag-set provided in the request.
    • ServerSideEncryption (string) -- The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).
    • StorageClass (string) -- By default, Amazon S3 uses the STANDARD Storage Class to store newly created objects. The STANDARD storage class provides high durability and high availability. Depending on performance needs, you can specify a different Storage Class. Amazon S3 on Outposts only uses the OUTPOSTS Storage Class. For more information, see Storage Classes in the Amazon S3 User Guide .
    • WebsiteRedirectLocation (string) -- If the bucket is configured as a website, redirects requests for this object to another object in the same bucket or to an external URL. Amazon S3 stores the value of this header in the object metadata.
    • SSECustomerAlgorithm (string) -- Specifies the algorithm to use to when encrypting the object (for example, AES256).
    • SSECustomerKey (string) -- Specifies the customer-provided encryption key for Amazon S3 to use in encrypting data. This value is used to store the object and then it is discarded; Amazon S3 does not store the encryption key. The key must be appropriate for use with the algorithm specified in the header.
    • SSECustomerKeyMD5 (string) --

      Specifies the 128-bit MD5 digest of the encryption key according to RFC 1321. Amazon S3 uses this header for a message integrity check to ensure that the encryption key was transmitted without error.

      Please note that this parameter is automatically populated if it is not provided. Including this parameter is not required

    • SSEKMSKeyId (string) -- Specifies the Amazon Web Services KMS key ID to use for object encryption. All GET and PUT requests for an object protected by Amazon Web Services KMS will fail if not made via SSL or using SigV4. For information about configuring using any of the officially supported Amazon Web Services SDKs and Amazon Web Services CLI, see Specifying the Signature Version in Request Authentication in the Amazon S3 User Guide .
    • SSEKMSEncryptionContext (string) -- Specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.
    • BucketKeyEnabled (boolean) --

      Specifies whether Amazon S3 should use an S3 Bucket Key for object encryption with server-side encryption using AWS KMS (SSE-KMS). Setting this header to causes Amazon S3 to use an S3 Bucket Key for object encryption with SSE-KMS.

      Specifying this header with a COPY action doesn’t affect bucket-level settings for S3 Bucket Key.

    • CopySourceSSECustomerAlgorithm (string) -- Specifies the algorithm to use when decrypting the source object (for example, AES256).
    • CopySourceSSECustomerKey (string) -- Specifies the customer-provided encryption key for Amazon S3 to use to decrypt the source object. The encryption key provided in this header must be one that was used when the source object was created.
    • CopySourceSSECustomerKeyMD5 (string) --

      Specifies the 128-bit MD5 digest of the encryption key according to RFC 1321. Amazon S3 uses this header for a message integrity check to ensure that the encryption key was transmitted without error.

      Please note that this parameter is automatically populated if it is not provided. Including this parameter is not required

    • RequestPayer (string) -- Confirms that the requester knows that they will be charged for the request. Bucket owners need not specify this parameter in their requests. For information about downloading objects from requester pays buckets, see Downloading Objects in Requestor Pays Buckets in the Amazon S3 User Guide .
    • Tagging (string) -- The tag-set for the object destination object this value must be used in conjunction with the . The tag-set must be encoded as URL Query parameters.
    • ObjectLockMode (string) -- The Object Lock mode that you want to apply to the copied object.
    • ObjectLockRetainUntilDate (datetime) -- The date and time when you want the copied object's Object Lock to expire.
    • ObjectLockLegalHoldStatus (string) -- Specifies whether you want to apply a Legal Hold to the copied object.
    • ExpectedBucketOwner (string) -- The account ID of the expected destination bucket owner. If the destination bucket is owned by a different account, the request will fail with an HTTP error.
    • ExpectedSourceBucketOwner (string) -- The account ID of the expected source bucket owner. If the source bucket is owned by a different account, the request will fail with an HTTP error.
    Return type



    Response Syntax


    Response Structure

    • (dict) --

      • CopyObjectResult(dict) --

        Container for all response elements.

        • ETag(string) --

          Returns the ETag of the new object. The ETag reflects only changes to the contents of an object, not its metadata.

        • LastModified(datetime) --

          Creation date of the object.

      • Expiration(string) --

        If the object expiration is configured, the response includes this header.

      • CopySourceVersionId(string) --

        Version of the copied object in the destination bucket.

      • VersionId(string) --

        Version ID of the newly created copy.

      • ServerSideEncryption(string) --

        The server-side encryption algorithm used when storing this object in Amazon S3 (for example, AES256, aws:kms).

      • SSECustomerAlgorithm(string) --

        If server-side encryption with a customer-provided encryption key was requested, the response will include this header confirming the encryption algorithm used.

      • SSECustomerKeyMD5(string) --

        If server-side encryption with a customer-provided encryption key was requested, the response will include this header to provide round-trip message integrity verification of the customer-provided encryption key.

      • SSEKMSKeyId(string) --

        If present, specifies the ID of the Amazon Web Services Key Management Service (Amazon Web Services KMS) symmetric customer managed key that was used for the object.

      • SSEKMSEncryptionContext(string) --

        If present, specifies the Amazon Web Services KMS Encryption Context to use for object encryption. The value of this header is a base64-encoded UTF-8 string holding JSON with the encryption context key-value pairs.

      • BucketKeyEnabled(boolean) --

        Indicates whether the copied object uses an S3 Bucket Key for server-side encryption with Amazon Web Services KMS (SSE-KMS).

      • RequestCharged(string) --

        If present, indicates that the requester was successfully charged for the request.



      The following example copies an object from one bucket to another.


      Expected Output:

      1. Zelda videos
      2. Pathofexile trade
      3. Schwinn bikes
      4. Song in my soul chords
      5. Peanuts gif

      Creating and Using Amazon S3 Buckets¶

      This Python example shows you how to:

      • Obtain and display a list of Amazon S3 buckets in your account.
      • Create an Amazon S3 bucket.
      • Upload an object to a specified bucket.

      The Scenario¶

      In this example, Python code is used to obtain a list of existing Amazon S3 buckets, create a bucket, and upload a file to a specified bucket. The code uses the AWS SDK for Python to get information from and upload files to an Amazon S3 bucket using these methods of the Amazon S3 client class:

      All the example code for the Amazon Web Services (AWS) SDK for Python is available here on GitHub.

      Prerequisite Tasks¶

      To set up and run this example, you must first complete this task:

      • Configure your AWS credentials, as described in Quickstart.

      Display a List of Amazon S3 Buckets¶

      List all the buckets owned by the authenticated sender of the request.

      The example below shows how to:


      importboto3# Create an S3 clients3=boto3.client('s3')# Call S3 to list current bucketsresponse=s3.list_buckets()# Get a list of all bucket names from the responsebuckets=[bucket['Name']forbucketinresponse['Buckets']]# Print out the bucket listprint("Bucket List: %s"%buckets)

      Create an Amazon S3 Bucket¶

      The example below shows how to:



      Upload a File to an Amazon S3 Bucket¶

      The example below shows how to:


      importboto3# Create an S3 clients3=boto3.client('s3')filename='file.txt'bucket_name='my-bucket'# Uploads the given file using a managed uploader, which will split up large# files automatically and upload parts in parallel.s3.upload_file(filename,bucket_name,filename)
      AWS Boto3 Python Crash Course with AWS S3 - Getting Started with Boto3 and AWS S3

      Uploading files¶

      The AWS SDK for Python provides a pair of methods to upload a file to an S3 bucket.

      The method accepts a file name, a bucket name, and an object name. The method handles large files by splitting them into smaller chunks and uploading each chunk in parallel.

      importloggingimportboto3frombotocore.exceptionsimportClientErrorimportosdefupload_file(file_name,bucket,object_name=None):"""Upload a file to an S3 bucket :param file_name: File to upload :param bucket: Bucket to upload to :param object_name: S3 object name. If not specified then file_name is used :return: True if file was uploaded, else False """# If S3 object_name was not specified, use file_nameifobject_nameisNone:object_name=os.path.basename(file_name)# Upload the files3_client=boto3.client('s3')try:response=s3_client.upload_file(file_name,bucket,object_name)exceptClientErrorase:logging.error(e)returnFalsereturnTrue

      The method accepts a readable file-like object. The file object must be opened in binary mode, not text mode.


      The and methods are provided by the S3 , , and classes. The method functionality provided by each class is identical. No benefits are gained by calling one class's method over another's. Use whichever class is most convenient.

      The Callback parameter¶

      Both and accept an optional parameter. The parameter references a class that the Python SDK invokes intermittently during the transfer operation.

      Invoking a Python class executes the class's method. For each invocation, the class is passed the number of bytes transferred up to that point. This information can be used to implement a progress monitor.

      The following setting instructs the Python SDK to create an instance of the class. During the upload, the instance's method will be invoked intermittently.


      An example implementation of the class is shown below.

      importosimportsysimportthreadingclassProgressPercentage(object):def__init__(self,filename):self._filename=filenameself._size=float(os.path.getsize(filename))self._seen_so_far=0self._lock=threading.Lock()def__call__(self,bytes_amount):# To simplify, assume this is hooked up to a single filenamewithself._lock:self._seen_so_far+=bytes_amountpercentage=(self._seen_so_far/self._size)*100sys.stdout.write("\r%s%s / %s (%.2f%%)"%(self._filename,self._seen_so_far,self._size,percentage))sys.stdout.flush()

      S3 boto3

      An Introduction to boto’s S3 interface¶

      This tutorial focuses on the boto interface to the Simple Storage Service from Amazon Web Services. This tutorial assumes that you have already downloaded and installed boto.

      Creating a Connection¶

      The first step in accessing S3 is to create a connection to the service. There are two ways to do this in boto. The first is:

      >>> fromboto.s3.connectionimportS3Connection>>> conn=S3Connection('<aws access key>','<aws secret key>')

      At this point the variable conn will point to an S3Connection object. In this example, the AWS access key and AWS secret key are passed in to the method explicitly. Alternatively, you can set the environment variables:

      • AWS_ACCESS_KEY_ID - Your AWS Access Key ID
      • AWS_SECRET_ACCESS_KEY - Your AWS Secret Access Key

      and then call the constructor without any arguments, like this:

      >>> conn=S3Connection()

      There is also a shortcut function in the boto package, called connect_s3 that may provide a slightly easier means of creating a connection:

      >>> importboto>>> conn=boto.connect_s3()

      In either case, conn will point to an S3Connection object which we will use throughout the remainder of this tutorial.

      Creating a Bucket¶

      Once you have a connection established with S3, you will probably want to create a bucket. A bucket is a container used to store key/value pairs in S3. A bucket can hold an unlimited amount of data so you could potentially have just one bucket in S3 for all of your information. Or, you could create separate buckets for different types of data. You can figure all of that out later, first let’s just create a bucket. That can be accomplished like this:

      >>> bucket=conn.create_bucket('mybucket')Traceback (most recent call last): File "<stdin>", line 1, in ? File "boto/", line 285, in create_bucketraiseS3CreateError(response.status,response.reason)boto.exception.S3CreateError: S3Error[409]: Conflict

      Whoa. What happened there? Well, the thing you have to know about buckets is that they are kind of like domain names. It’s one flat name space that everyone who uses S3 shares. So, someone has already create a bucket called “mybucket” in S3 and that means no one else can grab that bucket name. So, you have to come up with a name that hasn’t been taken yet. For example, something that uses a unique string as a prefix. Your AWS_ACCESS_KEY (NOT YOUR SECRET KEY!) could work but I’ll leave it to your imagination to come up with something. I’ll just assume that you found an acceptable name.

      The create_bucket method will create the requested bucket if it does not exist or will return the existing bucket if it does exist.

      Creating a Bucket In Another Location¶

      The example above assumes that you want to create a bucket in the standard US region. However, it is possible to create buckets in other locations. To do so, first import the Location object from the boto.s3.connection module, like this:

      >>> fromboto.s3.connectionimportLocation>>> print'\n'.join(iforiindir(Location)ifi[0].isupper())APNortheastAPSoutheastAPSoutheast2DEFAULTEUEUCentral1SAEastUSWestUSWest2

      As you can see, the Location object defines a number of possible locations. By default, the location is the empty string which is interpreted as the US Classic Region, the original S3 region. However, by specifying another location at the time the bucket is created, you can instruct S3 to create the bucket in that location. For example:

      >>> conn.create_bucket('mybucket',location=Location.EU)

      will create the bucket in the EU region (assuming the name is available).

      Storing Data¶

      Once you have a bucket, presumably you will want to store some data in it. S3 doesn’t care what kind of information you store in your objects or what format you use to store it. All you need is a key that is unique within your bucket.

      The Key object is used in boto to keep track of data stored in S3. To store new data in S3, start by creating a new Key object:

      >>> fromboto.s3.keyimportKey>>> k=Key(bucket)>>> k.key='foobar'>>> k.set_contents_from_string('This is a test of S3')

      The net effect of these statements is to create a new object in S3 with a key of “foobar” and a value of “This is a test of S3”. To validate that this worked, quit out of the interpreter and start it up again. Then:

      >>> importboto>>> c=boto.connect_s3()>>> b=c.get_bucket('mybucket')# substitute your bucket name here>>> fromboto.s3.keyimportKey>>> k=Key(b)>>> k.key='foobar'>>> k.get_contents_as_string()'This is a test of S3'

      So, we can definitely store and retrieve strings. A more interesting example may be to store the contents of a local file in S3 and then retrieve the contents to another local file.

      >>> k=Key(b)>>> k.key='myfile'>>> k.set_contents_from_filename('foo.jpg')>>> k.get_contents_to_filename('bar.jpg')

      There are a couple of things to note about this. When you send data to S3 from a file or filename, boto will attempt to determine the correct mime type for that file and send it as a Content-Type header. The boto package uses the standard mimetypes package in Python to do the mime type guessing. The other thing to note is that boto does stream the content to and from S3 so you should be able to send and receive large files without any problem.

      When fetching a key that already exists, you have two options. If you’re uncertain whether a key exists (or if you need the metadata set on it, you can call . However, if you’re sure a key already exists within a bucket, you can skip the check for a key on the server.

      >>> importboto>>> c=boto.connect_s3()>>> b=c.get_bucket('mybucket')# substitute your bucket name here# Will hit the API to check if it exists.>>> possible_key=b.get_key('mykey')# substitute your key name here# Won't hit the API.>>> key_we_know_is_there=b.get_key('mykey',validate=False)

      Storing Large Data¶

      At times the data you may want to store will be hundreds of megabytes or more in size. S3 allows you to split such files into smaller components. You upload each component in turn and then S3 combines them into the final object. While this is fairly straightforward, it requires a few extra steps to be taken. The example below makes use of the FileChunkIO module, so if it isn’t already installed.

      >>> importmath,os>>> importboto>>> fromfilechunkioimportFileChunkIO# Connect to S3>>> c=boto.connect_s3()>>> b=c.get_bucket('mybucket')# Get file info>>> source_path='path/to/your/file.ext'>>> source_size=os.stat(source_path).st_size# Create a multipart upload request>>> mp=b.initiate_multipart_upload(os.path.basename(source_path))# Use a chunk size of 50 MiB (feel free to change this)>>> chunk_size=52428800>>> chunk_count=int(math.ceil(source_size/float(chunk_size)))# Send the file parts, using FileChunkIO to create a file-like object# that points to a certain byte range within the original file. We# set bytes to never exceed the original file size.>>> foriinrange(chunk_count):>>> offset=chunk_size*i>>> bytes=min(chunk_size,source_size-offset)>>> withFileChunkIO(source_path,'r',offset=offset, bytes=bytes) as fp:>>> mp.upload_part_from_file(fp,part_num=i+1)# Finish the upload>>> mp.complete_upload()

      It is also possible to upload the parts in parallel using threads. The script that ships with Boto provides an example of doing so using a thread pool.

      Note that if you forget to call either or you will be left with an incomplete upload and charged for the storage consumed by the uploaded parts. A call to can help to show lost multipart upload parts.

      Accessing A Bucket¶

      Once a bucket exists, you can access it by getting the bucket. For example:

      >>> mybucket=conn.get_bucket('mybucket')# Substitute in your bucket name>>> mybucket.list()...listing of keys in the bucket...

      By default, this method tries to validate the bucket’s existence. You can override this behavior by passing .:

      >>> nonexistent=conn.get_bucket('i-dont-exist-at-all',validate=False)

      Changed in version 2.25.0.


      If is passed, no request is made to the service (no charge/communication delay). This is only safe to do if you are sure the bucket exists.

      If the default is passed, a request is made to the service to ensure the bucket exists. Prior to Boto v2.25.0, this fetched a list of keys (but with a max limit set to , always returning an empty list) in the bucket (& included better error messages), at an increased expense. As of Boto v2.25.0, this now performs a HEAD request (less expensive but worse error messages).

      If you were relying on parsing the error message before, you should call something like:


      If the bucket does not exist, a will commonly be thrown. If you’d rather not deal with any exceptions, you can use the method.:

      >>> nonexistent=conn.lookup('i-dont-exist-at-all')>>> ifnonexistentisNone:... print"No such bucket!"...No such bucket!

      Deleting A Bucket¶

      Removing a bucket can be done using the method. For example:

      >>> conn.delete_bucket('mybucket')# Substitute in your bucket name

      The bucket must be empty of keys or this call will fail & an exception will be raised. You can remove a non-empty bucket by doing something like:

      >>> full_bucket=conn.get_bucket('bucket-to-delete')# It's full of keys. Delete them all.>>> forkeyinfull_bucket.list():... key.delete()...# The bucket is empty now. Delete it.>>> conn.delete_bucket('bucket-to-delete')


      This method can cause data loss! Be very careful when using it.

      Additionally, be aware that using the above method for removing all keys and deleting the bucket involves a request for each key. As such, it’s not particularly fast & is very chatty.

      Listing All Available Buckets¶

      In addition to accessing specific buckets via the create_bucket method you can also get a list of all available buckets that you have created.

      >>> rs=conn.get_all_buckets()

      This returns a ResultSet object (see the SQS Tutorial for more info on ResultSet objects). The ResultSet can be used as a sequence or list type object to retrieve Bucket objects.

      >>> len(rs)11>>> forbinrs:...<listing of available buckets>>>> b=rs[0]

      Setting / Getting the Access Control List for Buckets and Keys¶

      The S3 service provides the ability to control access to buckets and keys within s3 via the Access Control List (ACL) associated with each object in S3. There are two ways to set the ACL for an object:

      1. Create a custom ACL that grants specific rights to specific users. At the moment, the users that are specified within grants have to be registered users of Amazon Web Services so this isn’t as useful or as general as it could be.
      2. Use a “canned” access control policy. There are four canned policies defined:
        1. private: Owner gets FULL_CONTROL. No one else has any access rights.
        2. public-read: Owners gets FULL_CONTROL and the anonymous principal is granted READ access.
        3. public-read-write: Owner gets FULL_CONTROL and the anonymous principal is granted READ and WRITE access.
        4. authenticated-read: Owner gets FULL_CONTROL and any principal authenticated as a registered Amazon S3 user is granted READ access.

      To set a canned ACL for a bucket, use the set_acl method of the Bucket object. The argument passed to this method must be one of the four permissable canned policies named in the list CannedACLStrings contained in For example, to make a bucket readable by anyone:

      >>> b.set_acl('public-read')

      You can also set the ACL for Key objects, either by passing an additional argument to the above method:

      >>> b.set_acl('public-read','foobar')

      where ‘foobar’ is the key of some object within the bucket b or you can call the set_acl method of the Key object:

      >>> k.set_acl('public-read')

      You can also retrieve the current ACL for a Bucket or Key object using the get_acl object. This method parses the AccessControlPolicy response sent by S3 and creates a set of Python objects that represent the ACL.

      >>> acp=b.get_acl()>>> acp<boto.acl.Policy instance at 0x2e6940>>>> acp.acl<boto.acl.ACL instance at 0x2e69e0>>>> acp.acl.grants[<boto.acl.Grant instance at 0x2e6a08>]>>> forgrantinacp.acl.grants:... printgrant.permission,grant.display_name,grant.email_address, <boto.user.User instance at 0x2e6a30>

      The Python objects representing the ACL can be found in the module of boto.

      Both the Bucket object and the Key object also provide shortcut methods to simplify the process of granting individuals specific access. For example, if you want to grant an individual user READ access to a particular object in S3 you could do the following:

      >>> key=b.lookup('mykeytoshare')>>> key.add_email_grant('READ','[email protected]')

      The email address provided should be the one associated with the users AWS account. There is a similar method called add_user_grant that accepts the canonical id of the user rather than the email address.

      Setting/Getting/Deleting CORS Configuration on a Bucket¶

      Cross-origin resource sharing (CORS) defines a way for client web applications that are loaded in one domain to interact with resources in a different domain. With CORS support in Amazon S3, you can build rich client-side web applications with Amazon S3 and selectively allow cross-origin access to your Amazon S3 resources.

      To create a CORS configuration and associate it with a bucket:

      >>> fromboto.s3.corsimportCORSConfiguration>>> cors_cfg=CORSConfiguration()>>> cors_cfg.add_rule(['PUT','POST','DELETE'],'',allowed_header='*',max_age_seconds=3000,expose_header='x-amz-server-side-encryption')>>> cors_cfg.add_rule('GET','*')

      The above code creates a CORS configuration object with two rules.

      • The first rule allows cross-origin PUT, POST, and DELETE requests from the origin. The rule also allows all headers in preflight OPTIONS request through the Access-Control-Request-Headers header. In response to any preflight OPTIONS request, Amazon S3 will return any requested headers.
      • The second rule allows cross-origin GET requests from all origins.

      To associate this configuration with a bucket:

      >>> importboto>>> c=boto.connect_s3()>>> bucket=c.lookup('mybucket')>>> bucket.set_cors(cors_cfg)

      To retrieve the CORS configuration associated with a bucket:

      >>> cors_cfg=bucket.get_cors()

      And, finally, to delete all CORS configurations from a bucket:

      >>> bucket.delete_cors()

      Transitioning Objects¶

      S3 buckets support transitioning objects to various storage classes. This is done using lifecycle policies. You can currently transitions objects to Infrequent Access, Glacier, or just plain Expire. All of these options are capable of being applied after a number of days or after a given date. Lifecycle configurations are assigned to buckets and require these parameters:

      • The object prefix that identifies the objects you are targeting. (or none)
      • The action you want S3 to perform on the identified objects.
      • The date or number of days when you want S3 to perform these actions.

      For example, given a bucket , we can first retrieve the bucket:

      >>> importboto>>> c=boto.connect_s3()>>> bucket=c.get_bucket('s3-lifecycle-boto-demo')

      Then we can create a lifecycle object. In our example, we want all objects under to transition to Standard IA 30 days after the object is created, glacier 90 days after creation, and be deleted 120 days after creation.

      >>> fromboto.s3.lifecycleimportLifecycle,Transitions,Rule>>> transitions=Transitions()>>> transitions.add_transition(days=30,storage_class='STANDARD_IA')>>> transitions.add_transition(days=90,storage_class='GLACIER')>>> expiration=Expiration(days=120)>>> rule=Rule(id='ruleid',prefix='logs/',status='Enabled',expiration=expiration,transition=transitions)>>> lifecycle=Lifecycle()>>> lifecycle.append(rule)

      We can now configure the bucket with this lifecycle policy:

      >>> bucket.configure_lifecycle(lifecycle)True

      You can also retrieve the current lifecycle policy for the bucket:

      >>> current=bucket.get_lifecycle_config()>>> printcurrent[0].transition>>> printcurrent[0].expiration[<Transition: in: 90 days, GLACIER>, <Transition: in: 30 days, STANDARD_IA>]<Expiration: in: 120 days>

      Note: We have deprecated directly accessing transition properties from the lifecycle object. You must index into the transition array first.

      When an object transitions, the storage class will be updated. This can be seen when you list the objects in a bucket:

      >>> forkeyinbucket.list():... printkey,key.storage_class...<Key: s3-lifecycle-boto-demo,logs/testlog1.log> STANDARD_IA<Key: s3-lifecycle-boto-demo,logs/testlog2.log> GLACIER

      You can also use the prefix argument to the method:

      >>> printlist(b.list(prefix='logs/testlog1.log'))[0].storage_class>>> printlist(b.list(prefix='logs/testlog2.log'))[0].storage_classu'STANDARD_IA'u'GLACIER'

      Restoring Objects from Glacier¶

      Once an object has been transitioned to Glacier, you can restore the object back to S3. To do so, you can use the method of the key object. The method takes an integer that specifies the number of days to keep the object in S3.

      >>> importboto>>> c=boto.connect_s3()>>> bucket=c.get_bucket('s3-glacier-boto-demo')>>> key=bucket.get_key('logs/testlog1.log')>>> key.restore(days=5)

      It takes about 4 hours for a restore operation to make a copy of the archive available for you to access. While the object is being restored, the attribute will be set to :

      >>> key=bucket.get_key('logs/testlog1.log')>>> printkey.ongoing_restoreTrue

      When the restore is finished, this value will be and the expiry date of the object will be non :

      >>> key=bucket.get_key('logs/testlog1.log')>>> printkey.ongoing_restoreFalse>>> printkey.expiry_date"Fri, 21 Dec 2012 00:00:00 GMT"


      If there is no restore operation either in progress or completed, the attribute will be .

      Once the object is restored you can then download the contents:

      >>> key.get_contents_to_filename('testlog1.log')
      How to Upload Files to Aws S3 using Python and Boto3 -- AWS S3 Tutorial For Beginners #Aws S3[2021]

      Working with Amazon S3 buckets with Boto3

      Amazon Simple Storage Service, or S3, offers space to store, protect, and share data with finely-tuned access control. When working with Python, one can easily interact with S3 with the Boto3 package. In this post, I will put together a cheat sheet of Python commands that I use a lot when working with S3. I hope you will find it useful.

      Let’s kick off with a few words about the S3 data structures. On your own computer, you store files in folders. On S3, the folders are called buckets. Inside buckets, you can store objects, such as .csv files. You can refer to buckets by their name, while to objects — by their key. To make the code chunks more tractable, we will use emojis. Here’s the key to symbols:

      🗑 — a bucket’s name, e.g. “mybucket”🔑 — an object’s key, e.g. "myfile_s3_name.csv"📄 - a file's name on your computer, e.g. "myfile_local_name.csv"

      Both 🗑 and 🔑 can either denote a name already existing on S3 or a name you want to give a newly created bucket or object. 📄 denotes a file you have or want to have somewhere locally on your machine.

      Setting up a client

      To access any AWS service with Boto3, we have to connect to it with a client. Here, we create an S3 client. We specify the region in which our data lives. We also have to pass the access key and the password, which we can generate in the AWS console, as described here.

      Buckets: listing, creating & deleting

      To list the buckets existing on S3, delete one or create a new one, we simply use the , and functions, respectively.

      Objects: listing, downloading, uploading & deleting

      Within a bucket, there reside objects. We can list them with . The argument sets the maximum number of objects listed; it’s like calling on the results before printing them. We can also list only objects whose keys (names) start with a specific prefix using the argument.
      We can use to upload a file called 📄 to S3 under the name 🔑. Similarly, will save a file called 🔑 on S3 locally under the name 📄.
      To get some metadata about an object, such as creation or modification time, permission rights or size, we can call .
      Deleting an object works the same way as deleting a bucket: we just need to pass the bucket name and object key to .

      Loading multiple files into a single data frame

      Oftentimes, data are spread across several files. For instance, you can have sales data for different stores or regions in different CSV files with matching column names. For analytics or modeling, we might want to have all these data in a single pandas data frame. The following code chunk will do just that: download all data files in 🗑 whose name starts with “some_prefix” and put it into a single data frame.

      Making objects public or private with access control lists (ACLs)

      One way to manage access rights on S3 is with access control lists or ACLs. By default, all files are private, which is the best (and safest!) practice. You can specify a file to be , in which case, everyone can access it, or , making yourself the only authorized person, among others. Look here for the exhaustive list of access options.
      You can set a file’s ACL both when it’s already on S3 using as well as upon upload via passing appropriate to .

      Accessing private files with pre-signed URLs

      You can also grant anyone short-time access to a private file by generating a temporary pre-signed URL using the function. This will yield a string that can be inserted right into pandas’ , for instance, to download the data. You can specify how long this temporary access link will be valid via the argument. Here, we create a link valid for 1 hour (3600 seconds).

      Thanks for reading!

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