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/*!
\mainpage

<h2>Introduction to Avro C++</h2>

<p>Avro is a data serialization system. See
<a href="http://hadoop.apache.org/avro/docs/current/">http://avro.apache.org/docs/current/</a>
for background information.</p>
<p>Avro C++ is a C++ library which implementats parts of the <a href="http://avro.apache.org/docs/current/spec.html"> Avro Specification</a>. The library includes the following functionality:</p>
<ul>
	<li>Assembling schemas programmatically.
    <li>A schema parser, which can parse Avro schema (written in JSON) into a Schema object.
    <li>Encoders and decoders to encode data into Avro format and decode it back using primitive functions. There are multiple implementations of encoders and decoders.
    <ul>
        <li>A binary encoder, which encodes into binary Avro data.
        <li>A JSON encoder, which encodes into JSON Avro data.
        <li>A validating encoder, an encoder proxy, which validates the call sequence to the encoder before sending the calls to another encoder.
        <li>A binary decoder, which decodes binay Avro data.
        <li>A JSON decoder, which decodes JSON Avro data.
        <li>A validating decoder, a decoder proxy, which validates the call sequence to the decoder before sending the calls to another decoder.
        <li>A resolving decoder, which accepts calls for according to a reader's schema but decodes data corresponding to a different (writer's) schema doing schema resolution according to resolution rules in the Avro specification.
    </ul>
    <li>Streams for storing and reading data, which Encoders and Decoders use.
    <li>Support for Avro DataFile.
    <li>A code generator, which generates C++ classes fnd functions to encode and decode them. The code generator produces a C++ header file from a given schema file.
</ul>
Presently there is no support for the following specified in Avro specification.
<ul>
    <li>Avro RPC
</ul>

<b>Note:</b> Prior to Avro release 1.5, some of the functionality mentioned above was avilable through a somewhat different API and set tools. They are partially incompatible to the present ones. They continue to be available but will be deprecated and discontinued sometime in the future. The documentation on that API can be found at <a href="http://avro.apache.org/docs/1.4.0/api/cpp/html/index.html">http://avro.apache.org/docs/1.4.0/api/cpp/html/index.html</a> 

<h2>Installing Avro C++</h2>
<h3>Supported platforms and pre-requisites</h3>
One should be able to build Avro C++ on (1) any UNIX flavor including cygwin for Windows and (2) natively on Windows using Visual Studio. We have tested it on (1) Linux systems (Ubuntu and RHEL) and Cygwin and Visual Studio 2010 Express edition.

In order to build Avro C++, one needs the following:
<ul>
    <li>A C++ compiler and runtime libraries.
    <li>Boost library version 1.38 or later. Apart from the header-only libraries of Boost, Avro C++ requires filesystem, system and program_options libraries. Please see <a href="http://www.boost.org/">http://www.boost.org</a> or your platform's documentation for details on how to set up Boost for your platform. 
    <li>CMake build tool version 2.6 or later. Please see <a href="http://www.cmake.org">http://www.cmake.org</a> or your platform's documentation for details on how to set up CMake for your system.
    <li>Python. If not already present, please consult your platform-specific documentation on how to install Python on your system.
</ul>

For Ubuntu Linux, for example, you can have these by doing
<tt>apt-get install</tt> for the following packages:
\ul
\li cmake
\li g++
\li libboost-dev
\li libboost-filesystem-dev
\li libboost-system-dev
\li libboost-program-options-dev

For Windows native builds, you need to install the following:
\ul
\li cmake
\li boost distribution from Boost consulting
\li Visual studio

<h3>Installing Avro C++</h3>
<ol>
    <li>Download the latest Avro distribution. Avro distribution is a compressed tarball.
Please see the main documentation if you want to build anything more than Avro C++. 
</ol>

<h4>On Unix systems and on Cygwin</h4>
<ol>
    <li>Expand the tarball into a directory.
    <li>Change to <tt>lang/c++</tt> subdirectory.
    <li>Type <tt>./build.sh test</tt>. This builds Avro C++ and runs tests on it.
    <li>Type <tt>./build.sh install</tt>. This installs Avro C++ under /usr/local on your system.
</ol>

<h4>On native Windows</h4>
<ol>
    <li>Ensure that Cmake's bin directory and Boost's lib directory are in the path.
    <li>Expand the tarball into a directory.
    <li>Change to <tt>lang/c++</tt> subdirectory.
    <li>Create a subdirectory, say, build.win, and change to that directory.
    <li>Type <tt>cmake -G "Visual Studio 10"</tt>. It creates, among other things, Avro-cpp.sln file.
    <li>Open the solution file using Visual Studio and build the projects from within the Visual Studio.
    <li>To run all unit tests, build the special project named "RUN_TESTS".
    <li>After building all the projects, you can also execute the unit tests from command line. <tt>ctest -C release</tt> or <tt>ctest -C debug</tt>.

</ol>

<h2>Getting started with Avro C++</h2>

<p>Although Avro does not require use of code generation, that is the easiest
way to get started with the Avro C++ library.
The code generator reads a schema, and generates a C++
header file that defines one or more C++ <tt>struct</tt>s to represent
the data for the schema and functions to encode and decode those
<tt>struct</tt>s. Even if you wish to write custom code to encode and decode
your objects using the core functionality of Avro C++, the generated code
can serve as an example of how to use the code functionality.

<p>
Let's walk through an example, using a simple schema. Use the
schema that represents an complex number:</p>

<b>File: cpx.json</b>

\includelineno cpx.json
<p>
<b>Note:</b> All the example code given here can be found under
<tt>examples</tt> directory of the distribution.

<p>
Assume this JSON representation of the schema is stored in a file
called <tt>cpx.json</tt>. To generate the code issue the command:.
<pre>
avrogencpp -i cpx.json -o cpx.hh -n c
</pre>
The <tt>-i</tt> flag specifies the input schema file and <tt>-o</tt> flag
specifies the output header file to generate. The generated C++ code will be
in the namespace specifed with <tt>-n</tt> flag.

<p>
The generated file, among other things will have the following:

<pre>

...
namespace c {
...

struct cpx {
    double re;
    double im;
};


...

}

</pre>
<tt>cpx</tt> is a C++ representation of the Avro schema <tt>cpx</tt>.

Now let's see how we can use the code generated to encode data into avro and decode it back.

<b>File: generated.cc</b>

\includelineno generated.cc

In line 9, we construct a memory output stream. By this we indicate that we
want to send the encoded Avro data into memory. In line 10, we construct a
binary encoder, whereby we mean the output should be encoded using the Avro
binary standard. In line 11, we attach the output stream to the encoder. At any given time an incoder can write to only one output stream.
<p>
In line 14, we write the contents of c1 into the output stream using the
encoder. Now the output stream contains the binary representation of
the object. The rest of the code verifies that the data is indeed in the stream.
<p>
In line 17, we construct a memory input stream from the contents of the
output stream. Thus the input stream has the binary representation of the
object. In line 18 and 19, we construct a binary decoder and attach the
input stream to it. Line 22 decodes the contents of the stream into another
object c2. Now c1 and c2 should have identical contents, which one can readily
verify from the output of the program, which should be:

<pre>
(1, 2.13)
</pre>

Now, if you want to encode the data using Avro JSON encoding, you should use
avro::jsonEncoder() instead of avro::binaryEncoder() in line 10
and avro::jsonDecoder() instead of avro::binaryDecoder() in line 18.
<p>

On the other hand, if you want to write the contents to a file instead of
memory, you should use avro::fileOutputStream() instead of
avro::memoryOutputStream() in ine 9 and avro::fileInputStream()
instead of avro::memoryInputStream() in line 17.
<p>

<h2>Reading a JSON schema</h2>

<p>The section above demonstrated pretty much all that's needed to
know to get started reading and writing objects using the Avro C++
code generator. The following sections will cover some more
information.</p>
<p>The library provides some utilities to read a schema that is
stored in a JSON file:</p>

<b>File: schemaload.cc</b>

\includelineno schemaload.cc

<p>
This reads the file, and parses the JSON schema into an in-meory schema
object of type avro::ValidSchema. If, for some reason, the schema is not valid,
the <tt>cpxSchema</tt> object will not be set, and an exception will be
thrown. 
</p>
If you always use code Avro generator you don't really need the in-memory
schema objects. But if you use custom objects and routines to encode or decode
avro data, you will need the schema objects. Other uses of schema objects
are generic data objects and schema resolution described in the following
sections.

<h2>Custom encoding and decoding</h2>

Suppose you want to encode objects of type std::complex<double> from
C++ standard library using the schema defined in cpx.json.
Since std::complex<double> was not generated by Avro, it does't know how to encode or decode objects of that
type. You have to tell Avro how to do that.

The recommended way to tell Avro how to encode or decode is to specialize
Avro's codec_traits template. For std::complex<double>, here is what you'd do:

<b>File: custom.cc</b>

\includelineno custom.cc

Please notice that the main function is pretty much similar to that we used
for the generated class. Once <tt>codec_traits</tt> for a specific type is
supplied, you do not really need to do anything special for your custom types.

<p>

But wait, how does Avro know that complex<double> represents the data for
the schema in <tt>cpx.json</tt>? It doesn't. In fact, if you have used
<tt>std::complex<float></tt> instead of <tt>std::complex<double></tt> program
would have worked. But the data in the memory would not have been corresponding
to the schema in <tt>cpx.json</tt>.

<p>

In order to ensure that you indeed use the correct type, you can use
the validating encoders and decoder. Here is how:

<b>File: validating.cc</b>

\includelineno validating.cc

Here, instead of using the plain binary encoder, you use a validating encoder
backed by a binary encoder. Similarly, instead of using the plain binary
decoder, you use a validating decoder backed by a binary decoder. Now,
if you use <tt>std::complex<float></tt> intead of <tt>std::complex<double></tt>
the validating encoder and decoder will throw exception stating that
you are trying to encode or decode <tt>float</tt> instead of <tt>double</tt>.
<p>
You can use any encoder behind the validating encoder and any decoder
behind the validating decoder. But in practice, only the binary encoder
and the binary decoder have no knowledge of the underlying schema.
All other encoders (JSON encoder) and decoders (JSON decoder,
resolving decoder) do know about the schema and they validate internally. So,
fronting them with a validating encoder or validating decoder is wasteful.

<h2>Generic data objects</h2>

A third way to encode and decode data is to use Avro's generic datum.
Avro's generic datum allows you to read any arbitray data corresponding to
an arbitrary schema into a generic object. One need not know anything
about the schema or data at complie time. 

Here is an example how one can use the generic datum.

<b>File: generic.cc</b>

\includelineno generic.cc

In this example, we encode the data using generated code and decode it with
generic datum. Then we examine the contents of the generic datum and extract
them. Please see \ref avro::GenericDatum for more details on how to use it.

<h2>Reading data with a schema different from that of the writer</h2>
It is possible to read the data written according to one schema
using a different schema, provided the reader's schema and the writer's
schema are compatible according to the Avro's Schema resolution rules. 
<p>
For example, you have a reader which is interested only in the imaginary part
of a complex number while the writer writes both the real and imaginary parts.
It is possible to do automatic schema resolution between the writer's schema
and schema as shown below.

<b>File: imaginary.json</b>

\includelineno imaginary.json

<pre>
avrogencpp -i imaginary.json -o imaginary.hh -n i
</pre>

<b>File: resolving.cc</b>

\includelineno resolving.cc

In this example, writer and reader deal with different schemas,
both are recornd with the same name cpx. The writer schema has two fields and
the reader's has just one. We generated code for writer's schema in a namespace
<tt>c</tt> and the reader's in <tt>i</tt>.

<p>
Please notice how the reading part of the example at line 42 reads as if
the stream contains the data corresponding to its schema. The schema resolution
is automatically done by the resolving decoder.

<p>
In this example, we have used a simple (somewhat artificial) projection (where the set of fields in
the reader's schema is a subset of set of fields in the writer's). But more
complex resolutions are allowed by Avro specification.

<h2>Using Avro data files</h2>
Avro specification specifies a format for data files. Avro C++ implements
the sepcification. The code below demonstrates how one can use the
Avro data file to store and retrieve a collection of objects
corresponding to a given schema.

<b>File: datafile.cc</b>

\includelineno datafile.cc

Please see DataFile.hh for more details.
*/