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======================================
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An Introduction to boto's S3 interface
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======================================
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This tutorial focuses on the boto interface to the Simple Storage Service
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from Amazon Web Services. This tutorial assumes that you have already
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downloaded and installed boto.
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The first step in accessing S3 is to create a connection to the service.
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There are two ways to do this in boto. The first is:
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>>> from boto.s3.connection import S3Connection
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>>> conn = S3Connection('<aws access key>', '<aws secret key>')
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At this point the variable conn will point to an S3Connection object. In
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this example, the AWS access key and AWS secret key are passed in to the
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method explicitely. Alternatively, you can set the environment variables:
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AWS_ACCESS_KEY_ID - Your AWS Access Key ID
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AWS_SECRET_ACCESS_KEY - Your AWS Secret Access Key
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and then call the constructor without any arguments, like this:
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>>> conn = S3Connection()
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There is also a shortcut function in the boto package, called connect_s3
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that may provide a slightly easier means of creating a connection:
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>>> conn = boto.connect_s3()
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In either case, conn will point to an S3Connection object which we will
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use throughout the remainder of this tutorial.
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Once you have a connection established with S3, you will probably want to
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create a bucket. A bucket is a container used to store key/value pairs
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in S3. A bucket can hold an unlimited amount of data so you could potentially
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have just one bucket in S3 for all of your information. Or, you could create
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separate buckets for different types of data. You can figure all of that out
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later, first let's just create a bucket. That can be accomplished like this:
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>>> bucket = conn.create_bucket('mybucket')
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Traceback (most recent call last):
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File "<stdin>", line 1, in ?
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File "boto/connection.py", line 285, in create_bucket
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raise S3CreateError(response.status, response.reason)
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boto.exception.S3CreateError: S3Error[409]: Conflict
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Whoa. What happended there? Well, the thing you have to know about
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buckets is that they are kind of like domain names. It's one flat name
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space that everyone who uses S3 shares. So, someone has already create
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a bucket called "mybucket" in S3 and that means no one else can grab that
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bucket name. So, you have to come up with a name that hasn't been taken yet.
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For example, something that uses a unique string as a prefix. Your
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AWS_ACCESS_KEY (NOT YOUR SECRET KEY!) could work but I'll leave it to
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your imagination to come up with something. I'll just assume that you
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found an acceptable name.
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The create_bucket method will create the requested bucket if it does not
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exist or will return the existing bucket if it does exist.
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Creating a Bucket In Another Location
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-------------------------------------
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The example above assumes that you want to create a bucket in the
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standard US region. However, it is possible to create buckets in
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other locations. To do so, first import the Location object from the
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boto.s3.connection module, like this:
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>>> from boto.s3.connection import Location
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['DEFAULT', 'EU', 'USWest', 'APSoutheast', '__doc__', '__module__']
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As you can see, the Location object defines three possible locations;
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DEFAULT, EU, USWest, and APSoutheast. By default, the location is the
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empty string which is interpreted as the US Classic Region, the
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original S3 region. However, by specifying another location at the
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time the bucket is created, you can instruct S3 to create the bucket
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in that location. For example:
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>>> conn.create_bucket('mybucket', location=Location.EU)
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will create the bucket in the EU region (assuming the name is available).
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Once you have a bucket, presumably you will want to store some data
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in it. S3 doesn't care what kind of information you store in your objects
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or what format you use to store it. All you need is a key that is unique
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The Key object is used in boto to keep track of data stored in S3. To store
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new data in S3, start by creating a new Key object:
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>>> from boto.s3.key import Key
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>>> k.set_contents_from_string('This is a test of S3')
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The net effect of these statements is to create a new object in S3 with a
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key of "foobar" and a value of "This is a test of S3". To validate that
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this worked, quit out of the interpreter and start it up again. Then:
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>>> c = boto.connect_s3()
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>>> b = c.create_bucket('mybucket') # substitute your bucket name here
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>>> from boto.s3.key import Key
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>>> k.get_contents_as_string()
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'This is a test of S3'
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So, we can definitely store and retrieve strings. A more interesting
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example may be to store the contents of a local file in S3 and then retrieve
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the contents to another local file.
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>>> k.set_contents_from_filename('foo.jpg')
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>>> k.get_contents_to_filename('bar.jpg')
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There are a couple of things to note about this. When you send data to
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S3 from a file or filename, boto will attempt to determine the correct
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mime type for that file and send it as a Content-Type header. The boto
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package uses the standard mimetypes package in Python to do the mime type
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guessing. The other thing to note is that boto does stream the content
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to and from S3 so you should be able to send and receive large files without
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Listing All Available Buckets
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-----------------------------
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In addition to accessing specific buckets via the create_bucket method
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you can also get a list of all available buckets that you have created.
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>>> rs = conn.get_all_buckets()
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This returns a ResultSet object (see the SQS Tutorial for more info on
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ResultSet objects). The ResultSet can be used as a sequence or list type
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object to retrieve Bucket objects.
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<listing of available buckets>
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Setting / Getting the Access Control List for Buckets and Keys
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--------------------------------------------------------------
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The S3 service provides the ability to control access to buckets and keys
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within s3 via the Access Control List (ACL) associated with each object in
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S3. There are two ways to set the ACL for an object:
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1. Create a custom ACL that grants specific rights to specific users. At the
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moment, the users that are specified within grants have to be registered
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users of Amazon Web Services so this isn't as useful or as general as it
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2. Use a "canned" access control policy. There are four canned policies
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a. private: Owner gets FULL_CONTROL. No one else has any access rights.
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b. public-read: Owners gets FULL_CONTROL and the anonymous principal is granted READ access.
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c. public-read-write: Owner gets FULL_CONTROL and the anonymous principal is granted READ and WRITE access.
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d. authenticated-read: Owner gets FULL_CONTROL and any principal authenticated as a registered Amazon S3 user is granted READ access.
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To set a canned ACL for a bucket, use the set_acl method of the Bucket object.
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The argument passed to this method must be one of the four permissable
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canned policies named in the list CannedACLStrings contained in acl.py.
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For example, to make a bucket readable by anyone:
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>>> b.set_acl('public-read')
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You can also set the ACL for Key objects, either by passing an additional
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argument to the above method:
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>>> b.set_acl('public-read', 'foobar')
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where 'foobar' is the key of some object within the bucket b or you can
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call the set_acl method of the Key object:
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>>> k.set_acl('public-read')
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You can also retrieve the current ACL for a Bucket or Key object using the
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get_acl object. This method parses the AccessControlPolicy response sent
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by S3 and creates a set of Python objects that represent the ACL.
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>>> acp = b.get_acl()
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<boto.acl.Policy instance at 0x2e6940>
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<boto.acl.ACL instance at 0x2e69e0>
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[<boto.acl.Grant instance at 0x2e6a08>]
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>>> for grant in acp.acl.grants:
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... print grant.permission, grant.display_name, grant.email_address, grant.id
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FULL_CONTROL <boto.user.User instance at 0x2e6a30>
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The Python objects representing the ACL can be found in the acl.py module
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Both the Bucket object and the Key object also provide shortcut
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methods to simplify the process of granting individuals specific
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access. For example, if you want to grant an individual user READ
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access to a particular object in S3 you could do the following:
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>>> key = b.lookup('mykeytoshare')
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>>> key.add_email_grant('READ', 'foo@bar.com')
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The email address provided should be the one associated with the users
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AWS account. There is a similar method called add_user_grant that accepts the
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canonical id of the user rather than the email address.
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Setting/Getting Metadata Values on Key Objects
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----------------------------------------------
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S3 allows arbitrary user metadata to be assigned to objects within a bucket.
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To take advantage of this S3 feature, you should use the set_metadata and
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get_metadata methods of the Key object to set and retrieve metadata associated
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with an S3 object. For example:
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>>> k.key = 'has_metadata'
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>>> k.set_metadata('meta1', 'This is the first metadata value')
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>>> k.set_metadata('meta2', 'This is the second metadata value')
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>>> k.set_contents_from_filename('foo.txt')
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This code associates two metadata key/value pairs with the Key k. To retrieve
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>>> k = b.get_key('has_metadata)
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>>> k.get_metadata('meta1')
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'This is the first metadata value'
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>>> k.get_metadata('meta2')
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'This is the second metadata value'
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docs/source/s3_tut.rst
