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<!DOCTYPE document PUBLIC "-//APACHE//DTD Documentation V2.0//EN"
   "http://forrest.apache.org/dtd/document-v20.dtd" [
  <!ENTITY % avro-entities PUBLIC "-//Apache//ENTITIES Avro//EN"
	   "../../../../build/avro.ent">
  %avro-entities;
]>
<document>
  <header>
    <title>Apache Avro&#153; &AvroVersion; Getting Started (Java)</title>
  </header>
  <body>
    <p>
      This is a short guide for getting started with Apache Avro&#153; using
      Java.  This guide only covers using Avro for data serialization; see
      Patrick Hunt's <a href="https://github.com/phunt/avro-rpc-quickstart">Avro
      RPC Quick Start</a> for a good introduction to using Avro for RPC.
    </p>
    <section id="download_install">
      <title>Download</title>
      <p>
        Avro implementations for C, C++, C#, Java, PHP, Python, and Ruby can be
        downloaded from the <a
        href="http://avro.apache.org/releases.html">Apache Avro&#153;
        Releases</a> page.  This guide uses Avro &AvroVersion;, the latest
        version at the time of writing.  For the examples in this guide,
        download <em>avro-&AvroVersion;.jar</em> and
        <em>avro-tools-&AvroVersion;.jar</em>.  The Avro Java implementation
        also depends on the <a href="http://jackson.codehaus.org/">Jackson</a>
        JSON library.  From the Jackson <a
        href="http://wiki.fasterxml.com/JacksonDownload">download page</a>,
        download the core-asl and mapper-asl jars.  Add
        <em>avro-&AvroVersion;.jar</em> and the Jackson jars to your project's
        classpath (avro-tools will be used for code generation).
      </p>
      <p>
        Alternatively, if you are using Maven, add the following dependency to
        your POM:
      </p>
      <source>
&#60;dependency&#62;
  &#60;groupId&#62;org.apache.avro&#60;/groupId&#62;
  &#60;artifactId&#62;avro&#60;/artifactId&#62;
  &#60;version&#62;&AvroVersion;&#60;/version&#62;
&#60;/dependency&#62;
      </source>
      <p>
        As well as the Avro Maven plugin (for performing code generation):
      </p>
      <source>
&#60;plugin&#62;
  &#60;groupId&#62;org.apache.avro&#60;/groupId&#62;
  &#60;artifactId&#62;avro-maven-plugin&#60;/artifactId&#62;
  &#60;version&#62;&AvroVersion;&#60;/version&#62;
  &#60;executions&#62;
    &#60;execution&#62;
      &#60;phase&#62;generate-sources&#60;/phase&#62;
      &#60;goals&#62;
        &#60;goal&#62;schema&#60;/goal&#62;
      &#60;/goals&#62;
      &#60;configuration&#62;
        &#60;sourceDirectory&#62;${project.basedir}/src/main/avro/&#60;/sourceDirectory&#62;
        &#60;outputDirectory&#62;${project.basedir}/src/main/java/&#60;/outputDirectory&#62;
      &#60;/configuration&#62;
    &#60;/execution&#62;
  &#60;/executions&#62;
&#60;/plugin&#62;
&#60;plugin&#62;
  &#60;groupId&#62;org.apache.maven.plugins&#60;/groupId&#62;
  &#60;artifactId&#62;maven-compiler-plugin&#60;/artifactId&#62;
  &#60;configuration&#62;
    &#60;source&#62;1.6&#60;/source&#62;
    &#60;target&#62;1.6&#60;/target&#62;
  &#60;/configuration&#62;
&#60;/plugin&#62;
      </source>
      <p>
        You may also build the required Avro jars from source.  Building Avro is
        beyond the scope of this guide; see the <a
        href="https://cwiki.apache.org/AVRO/Build+Documentation">Build
        Documentation</a> page in the wiki for more information.
      </p>
    </section>

    <section>
      <title>Defining a schema</title>
      <p>
        Avro schemas are defined using JSON.  Schemas are composed of <a
        href="spec.html#schema_primitive">primitive types</a>
        (<code>null</code>, <code>boolean</code>, <code>int</code>,
        <code>long</code>, <code>float</code>, <code>double</code>,
        <code>bytes</code>, and <code>string</code>) and <a
        href="spec.html#schema_complex">complex types</a> (<code>record</code>,
        <code>enum</code>, <code>array</code>, <code>map</code>,
        <code>union</code>, and <code>fixed</code>).  You can learn more about
        Avro schemas and types from the specification, but for now let's start
        with a simple schema example, <em>user.avsc</em>:
      </p>
      <source>
{"namespace": "example.avro",
 "type": "record",
 "name": "User",
 "fields": [
     {"name": "name", "type": "string"},
     {"name": "favorite_number",  "type": ["int", "null"]},
     {"name": "favorite_color", "type": ["string", "null"]}
 ]
}
      </source>
      <p>
        This schema defines a record representing a hypothetical user.  (Note
        that a schema file can only contain a single schema definition.)  At
        minimum, a record definition must include its type (<code>"type":
        "record"</code>), a name (<code>"name": "User"</code>), and fields, in
        this case <code>name</code>, <code>favorite_number</code>, and
        <code>favorite_color</code>.  We also define a namespace
        (<code>"namespace": "example.avro"</code>), which together with the name
        attribute defines the "full name" of the schema
        (<code>example.avro.User</code> in this case).

      </p>
      <p>
        Fields are defined via an array of objects, each of which defines a name
        and type (other attributes are optional, see the <a
        href="spec.html#schema_record">record specification</a> for more
        details).  The type attribute of a field is another schema object, which
        can be either a primitive or complex type.  For example, the
        <code>name</code> field of our User schema is the primitive type
        <code>string</code>, whereas the <code>favorite_number</code> and
        <code>favorite_color</code> fields are both <code>union</code>s,
        represented by JSON arrays.  <code>union</code>s are a complex type that
        can be any of the types listed in the array; e.g.,
        <code>favorite_number</code> can either be an <code>int</code> or
        <code>null</code>, essentially making it an optional field.
      </p>
    </section>

    <section>
      <title>Serializing and deserializing with code generation</title>
      <section>
        <title>Compiling the schema</title>
        <p>
          Code generation allows us to automatically create classes based on our
          previously-defined schema.  Once we have defined the relevant classes,
          there is no need to use the schema directly in our programs.  We use the
          avro-tools jar to generate code as follows:
        </p>
        <source>
java -jar /path/to/avro-tools-&AvroVersion;.jar compile schema &#60;schema file&#62; &#60;destination&#62;
        </source>
        <p>
          This will generate the appropriate source files in a package based on
          the schema's namespace in the provided destination folder.  For
          instance, to generate a <code>User</code> class in package
          <code>example.avro</code> from the schema defined above, run
        </p>
        <source>
java -jar /path/to/avro-tools-&AvroVersion;.jar compile schema user.avsc .
        </source>
        <p>
          Note that if you using the Avro Maven plugin, there is no need to
          manually invoke the schema compiler; the plugin automatically
          performs code generation on any .avsc files present in the configured
          source directory.
        </p>
      </section>
      <section>
        <title>Creating Users</title>
        <p>
          Now that we've completed the code generation, let's create some
          <code>User</code>s, serialize them to a data file on disk, and then
          read back the file and deserialize the <code>User</code> objects.
        </p>
        <p>
          First let's create some <code>User</code>s and set their fields.
        </p>
        <source>
User user1 = new User();
user1.setName("Alyssa");
user1.setFavoriteNumber(256);
// Leave favorite color null

// Alternate constructor
User user2 = new User("Ben", 7, "red");

// Construct via builder
User user3 = User.newBuilder()
             .setName("Charlie")
             .setFavoriteColor("blue")
             .setFavoriteNumber(null)
             .build();
        </source>
        <p>
          As shown in this example, Avro objects can be created either by
          invoking a constructor directly or by using a builder.  Unlike
          constructors, builders will automatically set any default values
          specified in the schema.  Additionally, builders validate the data as
          it set, whereas objects constructed directly will not cause an error
          until the object is serialized.  However, using constructors directly
          generally offers better performance, as builders create a copy of the
          datastructure before it is written.
        </p>
        <p>
          Note that we do not set <code>user1</code>'s favorite color. Since
          that record is of type <code>["string", "null"]</code>, we can either
          set it to a <code>string</code> or leave it <code>null</code>; it is
          essentially optional.  Similarly, we set <code>user3</code>'s favorite
          number to null (using a builder requires setting all fields, even if
          they are null).
        </p>
      </section>
      <section>
        <title>Serializing</title>
      <p>
        Now let's serialize our <code>User</code>s to disk.
      </p>
      <source>
// Serialize user1, user2 and user3 to disk
DatumWriter&#60;User&#62; userDatumWriter = new SpecificDatumWriter&#60;User&#62;(User.class);
DataFileWriter&#60;User&#62; dataFileWriter = new DataFileWriter&#60;User&#62;(userDatumWriter);
dataFileWriter.create(user1.getSchema(), new File("users.avro"));
dataFileWriter.append(user1);
dataFileWriter.append(user2);
dataFileWriter.append(user3);
dataFileWriter.close();
      </source>
      <p>
        We create a <code>DatumWriter</code>, which converts Java objects into
        an in-memory serialized format.  The <code>SpecificDatumWriter</code>
        class is used with generated classes and extracts the schema from the
        specified generated type.
      </p>
      <p>
        Next we create a <code>DataFileWriter</code>, which writes the
        serialized records, as well as the schema, to the file specified in the
        <code>dataFileWriter.create</code> call.  We write our users to the file
        via calls to the <code>dataFileWriter.append</code> method.  When we are
        done writing, we close the data file.
      </p>
      </section>
      <section>
        <title>Deserializing</title>
        <p>
          Finally, let's deserialize the data file we just created.
        </p>
        <source>
// Deserialize Users from disk
DatumReader&#60;User&#62; userDatumReader = new SpecificDatumReader&#60;User&#62;(User.class);
DataFileReader&#60;User&#62; dataFileReader = new DataFileReader&#60;User&#62;(file, userDatumReader);
User user = null;
while (dataFileReader.hasNext()) {
// Reuse user object by passing it to next(). This saves us from
// allocating and garbage collecting many objects for files with
// many items.
user = dataFileReader.next(user);
System.out.println(user);
}
        </source>
        <p>
          This snippet will output:
        </p>
        <source>
{"name": "Alyssa", "favorite_number": 256, "favorite_color": null}
{"name": "Ben", "favorite_number": 7, "favorite_color": "red"}
{"name": "Charlie", "favorite_number": null, "favorite_color": "blue"}
        </source>
        <p>
          Deserializing is very similar to serializing.  We create a
          <code>SpecificDatumReader</code>, analogous to the
          <code>SpecificDatumWriter</code> we used in serialization, which
          converts in-memory serialized items into instances of our generated
          class, in this case <code>User</code>.  We pass the
          <code>DatumReader</code> and the previously created <code>File</code>
          to a <code>DataFileReader</code>, analogous to the
          <code>DataFileWriter</code>, which reads the data file on disk.
        </p>
        <p>
          Next we use the <code>DataFileReader</code> to iterate through the
          serialized <code>User</code>s and print the deserialized object to
          stdout.  Note how we perform the iteration: we create a single
          <code>User</code> object which we store the current deserialized user
          in, and pass this record object to every call of
          <code>dataFileReader.next</code>.  This is a performance optimization
          that allows the <code>DataFileReader</code> to reuse the same
          <code>User</code> object rather than allocating a new
          <code>User</code> for every iteration, which can be very expensive in
          terms of object allocation and garbage collection if we deserialize a
          large data file.  While this technique is the standard way to iterate
          through a data file, it's also possible to use <code>for (User user :
          dataFileReader)</code> if performance is not a concern.
        </p>
      </section>
      <section>
        <title>Compiling and running the example code</title>
        <p>
          This example code is included as a Maven project in the
          <em>examples/java-example</em> directory in the Avro docs.  From this
          directory, execute the following commands to build and run the
          example:
        </p>
        <source>
$ mvn compile # includes code generation via Avro Maven plugin
$ mvn -q exec:java -Dexec.mainClass=example.SpecificMain
        </source>
      </section>
    </section>

    <section>
      <title>Serializing and deserializing without code generation</title>
      <p>
        Data in Avro is always stored with its corresponding schema, meaning we
        can always read a serialized item regardless of whether we know the
        schema ahead of time.  This allows us to perform serialization and
        deserialization without code generation.
      </p>
      <p>
        Let's go over the same example as in the previous section, but without
        using code generation: we'll create some users, serialize them to a data
        file on disk, and then read back the file and deserialize the users
        objects.
      </p>
      <section>
        <title>Creating users</title>
        <p>
          First, we use a <code>Parser</code> to read our schema definition and
          create a <code>Schema</code> object.
        </p>
        <source>
Schema schema = new Schema.Parser().parse(new File("user.avsc"));
        </source>
        <p>
          Using this schema, let's create some users.
        </p>
        <source>
GenericRecord user1 = new GenericData.Record(schema);
user1.put("name", "Alyssa");
user1.put("favorite_number", 256);
// Leave favorite color null

GenericRecord user2 = new GenericData.Record(schema);
user2.put("name", "Ben");
user2.put("favorite_number", 7);
user2.put("favorite_color", "red");
        </source>
        <p>
          Since we're not using code generation, we use
          <code>GenericRecord</code>s to represent users.
          <code>GenericRecord</code> uses the schema to verify that we only
          specify valid fields.  If we try to set a non-existent field (e.g.,
          <code>user1.put("favorite_animal", "cat")</code>), we'll get an
          <code>AvroRuntimeException</code> when we run the program.
        </p>
        <p>
          Note that we do not set <code>user1</code>'s favorite color.  Since
          that record is of type <code>["string", "null"]</code>, we can either
          set it to a <code>string</code> or leave it <code>null</code>; it is
          essentially optional.
        </p>
      </section>
      <section>
        <title>Serializing</title>
        <p>
          Now that we've created our user objects, serializing and deserializing
          them is almost identical to the example above which uses code
          generation.  The main difference is that we use generic instead of
          specific readers and writers.
        </p>
        <p>
          First we'll serialize our users to a data file on disk.
        </p>
        <source>
// Serialize user1 and user2 to disk
File file = new File("users.avro");
DatumWriter&#60;GenericRecord&#62; datumWriter = new GenericDatumWriter&#60;GenericRecord&#62;(schema);
DataFileWriter&#60;GenericRecord&#62; dataFileWriter = new DataFileWriter&#60;GenericRecord&#62;(datumWriter);
dataFileWriter.create(schema, file);
dataFileWriter.append(user1);
dataFileWriter.append(user2);
dataFileWriter.close();
        </source>
        <p>
          We create a <code>DatumWriter</code>, which converts Java objects into
          an in-memory serialized format.  Since we are not using code
          generation, we create a <code>GenericDatumWriter</code>.  It requires
          the schema both to determine how to write the
          <code>GenericRecord</code>s and to verify that all non-nullable fields
          are present.
        </p>
        <p>
          As in the code generation example, we also create a
          <code>DataFileWriter</code>, which writes the serialized records, as
          well as the schema, to the file specified in the
          <code>dataFileWriter.create</code> call.  We write our users to the
          file via calls to the <code>dataFileWriter.append</code> method.  When
          we are done writing, we close the data file.
        </p>
      </section>
      <section>
        <title>Deserializing</title>
        <p>
          Finally, we'll deserialize the data file we just created.
        </p>
        <source>
// Deserialize users from disk
DatumReader&#60;GenericRecord&#62; datumReader = new GenericDatumReader&#60;GenericRecord&#62;(schema);
DataFileReader&#60;GenericRecord&#62; dataFileReader = new DataFileReader&#60;GenericRecord&#62;(file, datumReader);
GenericRecord user = null;
while (dataFileReader.hasNext()) {
// Reuse user object by passing it to next(). This saves us from
// allocating and garbage collecting many objects for files with
// many items.
user = dataFileReader.next(user);
System.out.println(user);
        </source>
        <p>This outputs:</p>
        <source>
{"name": "Alyssa", "favorite_number": 256, "favorite_color": null}
{"name": "Ben", "favorite_number": 7, "favorite_color": "red"}
        </source>
        <p>
          Deserializing is very similar to serializing.  We create a
          <code>GenericDatumReader</code>, analogous to the
          <code>GenericDatumWriter</code> we used in serialization, which
          converts in-memory serialized items into <code>GenericRecords</code>.
          We pass the <code>DatumReader</code> and the previously created
          <code>File</code> to a <code>DataFileReader</code>, analogous to the
          <code>DataFileWriter</code>, which reads the data file on disk.
        </p>
        <p>
          Next, we use the <code>DataFileReader</code> to iterate through the
          serialized users and print the deserialized object to stdout.  Note
          how we perform the iteration: we create a single
          <code>GenericRecord</code> object which we store the current
          deserialized user in, and pass this record object to every call of
          <code>dataFileReader.next</code>.  This is a performance optimization
          that allows the <code>DataFileReader</code> to reuse the same record
          object rather than allocating a new <code>GenericRecord</code> for
          every iteration, which can be very expensive in terms of object
          allocation and garbage collection if we deserialize a large data file.
          While this technique is the standard way to iterate through a data
          file, it's also possible to use <code>for (GenericRecord user :
          dataFileReader)</code> if performance is not a concern.
        </p>
      </section>
      <section>
        <title>Compiling and running the example code</title>
        <p>
          This example code is included as a Maven project in the
          <em>examples/java-example</em> directory in the Avro docs.  From this
          directory, execute the following commands to build and run the
          example:
        </p>
        <source>
$ mvn compile
$ mvn -q exec:java -Dexec.mainClass=example.GenericMain
        </source>
      </section>
    </section>
  </body>
</document>
