Netty学习笔记之一（Netty解析简单的Http Post Json 请求）

一，HTTP解码器可能会将一个HTTP请求解析成多个消息对象。

 ch.pipeline().addLast(new HttpServerCodec());
 ch.pipeline().addLast(new ParseRequestHandler());

经过HttpServerCodec解码之后，一个HTTP请求会导致：ParseRequestHandler的 channelRead()方法调用多次（测试时 "received message"输出了两次）

    @Override
    public void channelRead(ChannelHandlerContext ctx, Object msg)
            throws Exception {
        System.out.println("received message");

可以用HttpObjectAggregator 将多个消息转换为单一的一个FullHttpRequest，如下：

 ch.pipeline().addLast(new HttpServerCodec());
 ch.pipeline().addLast(new HttpObjectAggregator(65536));
 ch.pipeline().addLast(new ParseRequestHandler());

此时，一个HTTP消息(Object msg)是下面这样的。

HttpObjectAggregator$AggregatedFullHttpRequest(decodeResult: success, version: HTTP/1.1, content: CompositeByteBuf(ridx: 0, widx: 17, cap: 17, components=1))
POST / HTTP/1.1
Host: 127.0.0.1:8888
User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:46.0) Gecko/20100101 Firefox/46.0
Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8
Accept-Language: null
Accept-Encoding: gzip, deflate
Content-Type: application/x-www-form-urlencoded
Content-Length: 17
Cookie: _ga=GA1.1.457486782.1446782739
Connection: keep-alive

从上面可以看出，实体首部字段Content-Length是17，表明实体主体有17个字节。

而我发送的消息是这样的：

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alt="" width="754" height="228" />

HTTP POST 请求，请求体是JSON格式的数据。这里使用的是json-lib解析的 Json字符串。代码如下：

    //parse job type 0,1
    private String getJobType(FullHttpRequest request) throws IOException{
        ByteBuf jsonBuf = request.content();
        String jsonStr = jsonBuf.toString(CharsetUtil.UTF_8);
        JSONObject jsonObj = JSONObject.fromObject(jsonStr);
        String jobType = jsonObj.getString("jobType");
        return jobType;
    }

需要注意是：使用json-lib解析Json字符串时，需要其他的依赖包如下：

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" alt="" width="560" height="134" />

解析完成之后，需要把处理后的结果发送到下一个ChannelHandler，进行下一步的处理。

@Override
public void channelRead(ChannelHandlerContext ctx, Object msg)
    throws Exception {
        //do some process
 .....
        ctx.fireChannelRead(job);
}

注意，使用的是fireChannelRead()方法，而不是 ctx.writeAndFlush(...)。因为，writeAndFlush/write 是Outbound，它是把消息发送到上一个Handler，进而发送到remote peer，而这里是InBound。具体参考：

这里，通过 ctx.fireChannelRead(job); 将处理后的结果发送到下一个Channel处理。

 ch.pipeline().addLast(new HttpServerCodec());
 ch.pipeline().addLast(new HttpObjectAggregator(2048));
 ch.pipeline().addLast(new ParseRequestHandler());
 ch.pipeline().addLast(new OozieRequestHandler());

下一个Handler是OozieRequestHandler，它负责向Oozie Server提交作业，之后返回jobId给客户端(HttpServerCodec  Handler 负责底层传输细节)。

Netty构造一个http 响应的方法如下：

String jobId = doPost(jobConfig);
FullHttpResponse response = new DefaultFullHttpResponse(
                    HttpVersion.HTTP_1_1, HttpResponseStatus.OK,
                    Unpooled.wrappedBuffer(jobId.getBytes()));

response.headers().set(CONTENT_TYPE, "application/xml");

response.headers().setInt(CONTENT_LENGTH,
                    response.content().readableBytes());
            ctx.write(response).addListener(ChannelFutureListener.CLOSE);

整个完整代码可参考：https://github.com/hapjin/netty_schedule