I’m a big fan of Amazon Web Services (AWS). I’m using them in almost all of my projects. One of their most popular services is Simple Storage Service (S3). It is a storage for binary objects (files) with unique names, accessible through HTTP or RESTful API.

Using S3 is very simple. You create a “bucket” with a unique name, upload your “object” into the bucket through their web interface or through RESTful API, and then download it again (either through HTTP or the API.)

Amazon ships the Java SDK that wraps their RESTful API. However, this SDK is not object-oriented at all. It is purely imperative and procedural—it just mirrors the API.

For example, in order to download an existing object doc.txt from bucket test-1 , you have to do something like this:

AWSCredentials creds = new BasicAWSCredentials ( key , secret ); AmazonS3 aws = new AmazonS3Client ( creds ); S3Object obj = aws . getObject ( new GetObjectRequest ( "test-1" , "doc.txt" ) ); InputStream input = obj . getObjectContent (); String content = IOUtils . toString ( input , "UTF-8" ); input . close ();

As always, procedural programming has its inevitable disadvantages. To overcome them all, I designed jcabi-s3, which is a small object-oriented adapter for Amazon SDK. This is how the same object-reading task can be accomplished with jcabi-s3:

Region region = new Region . Simple ( key , secret ); Bucket bucket = region . bucket ( "test-1" ); Ocket ocket = bucket . ocket ( "doc.txt" ); String content = new Ocket . Text ( ocket ). read ();

Why is this approach better? Well, there are a number of obvious advantages.

S3 Object is an Object in Java

S3 object get its representative in Java. It is not a collection of procedures to be called in order to get its properties (as with AWS SDK). Rather, it is a Java object with certain behaviors. I called them “ockets” (similar to “buckets”), in order to avoid clashes with java.lang.Object .

Ocket is an interface, that exposes the behavior of a real AWS S3 object: read, write, check existence. There is also a convenient decorator Ocket.Text that simplifies working with binary objects:

Ocket . Text ocket = new Ocket . Text ( ocket_from_s3 ); if ( ocket . exists ()) { System . out . print ( ocket . read ()); } else { ocket . write ( "Hello, world!" ); }

Now, you can pass an object to another class, instead of giving it your AWS credentials, bucket name, and object name. You simply pass a Java object, which encapsulates all AWS interaction details.

Extendability Through Decoration

Since jcabi-s3 exposes all entities as interfaces, they can easily be extended through encapsulation (Decorator Pattern).

For example, you want your code to retry S3 object read operations a few times before giving up and throwing an IOException (by the way, this is a very good practice when working with web services). So, you want all your S3 reading operations to be redone a few times if first attempts fail.

You define a new decorator class, say, RetryingOcket , which encapsulates an original Ocket :

public RetryingOcket implements Ocket { private final Ocket origin ; public RetryingOcket ( Ocket ocket ) { this . origin = ocket ; } @Override public void read ( OutputStream stream ) throws IOException { int attempt = 0 ; while ( true ) { try { this . origin . read ( stream ); } catch ( IOException ex ) { if ( attempt ++ > 3 ) { throw ex ; } } } } // same for other methods }

Now, everywhere where Ocket is expected you send an instance of RetryingOcket that wraps your original object:

foo . process ( new RetryingOcket ( ocket ));

Method foo.process() won’t see a difference, since it is the same Ocket interface it is expecting.

By the way, this retry functionality is implemented out-of-the-box in jcabi-s3, in com.jcabi.s3.retry package.

Easy Mocking

Again, due to the fact that all entities in jcabi-s3 are interfaces, they are very easy to mock. For example, your class expects an S3 object, reads its data and calculates the MD5 hash (I’m using DigestUtils from commons-codec):

import com.jcabi.s3.Ocket ; import org.apache.commons.codec.digest.DigestUtils ; public class S3Md5Hash { private final Ocket ocket ; public S3Md5Hash ( Ocket okt ) { this . ocket = okt ; } public hash () throws IOException { ByteArrayOutputStream baos = new ByteArrayOutputStream (); this . ocket . read ( baos ); return DigestUtils . md5hex ( baos . toByteArray ()); } }

Here is how simple a unit test will look (try to create a unit test for a class using AWS SDK and you will see the difference):

import com.jcabi.s3.Ocket ; import org.junit.Test ; public class S3Md5HashTest { @Test public void generatesHash () { Ocket ocket = Mockito . mock ( Ocket . class ); Mockito . doAnswer ( new Answer < Void >() { public Void answer ( final InvocationOnMock inv ) throws IOException { OutputStream . class . cast ( inv . getArguments ()[ 0 ]). write ( ' ' ); } } ). when ( ocket ). read ( Mockito . any ( OutputStream . class )); String hash = new S5Md5Hash ( ocket ); Assert . assertEquals ( hash , "7215ee9c7d9dc229d2921a40e899ec5f" ); } }

I’m using JUnit and Mockito in this test.

Immutability

All classes in jcabi-s3 are annotated with @Immutable and are truly immutable.

The library ships as a JAR dependency in Maven Central (get its latest versions in Maven Central):

<dependency> <groupId> com.jcabi </groupId> <artifactId> jcabi-s3 </artifactId> </dependency>

As always, your comments and criticism are welcome as GitHub issues.