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<h1 class="post-title" itemprop="name headline">生产实践 | 实时压测方案沉淀</h1>
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<h1 id="生产实践-Flink-直播（三）-如何建设当前正在直播-xx-数？"><a href="#生产实践-Flink-直播（三）-如何建设当前正在直播-xx-数？" class="headerlink" title="生产实践 | Flink + 直播（三）| 如何建设当前正在直播 xx 数？"></a>生产实践 | Flink + 直播（三）| 如何建设当前正在直播 xx 数？</h1><blockquote>
<p>本系列每篇文章都是从一些实际的 case 出发，分析一些生产环境中经常会遇到的问题，抛砖引玉，以帮助小伙伴们解决一些实际问题。本篇文章主要介绍直播间生产侧指标的建设过程，如果对小伙伴有帮助的话，欢迎点赞 + 再看~</p>
</blockquote>
<h2 id="整体架构"><a href="#整体架构" class="headerlink" title="整体架构"></a>整体架构</h2><p>本文主要介绍<strong>生产侧指标的建设</strong>，比如当前正在直播直播间数，或者主播数等。在介绍生产侧指标的建设过程之前，我们先回顾下上一节的<strong>架构</strong>图。</p>
<p><img src="/blog-img/apache-flink:realtime-live-stream-3/tec-arc.png" alt="架构"></p>
<p>而本篇要介绍的<strong>生产侧指标</strong>的数据链路主要对应以下几个模块。</p>
<ul>
<li>数据源：读取直播生产，比如开播，关播等 kafka 数据源日志；</li>
<li>数据处理：使用生产侧数据源 + 实时画像维表 + flink 建设生产侧实时指标；</li>
<li>数据汇：将处理完成的指标数据写入到 kafka 中。</li>
</ul>
<p>我用另一张图进行了标注，图中<strong>标红</strong>模块为生产侧指标的数据链路涉及到的模块。</p>
<p><img src="/blog-img/apache-flink:realtime-live-stream-3/metric-prod-tec-arc.png" alt="生产侧架构"></p>
<p>其中直播间实时画像维表的介绍已经在上节进行了介绍，感兴趣的话可以点击以下链接，跳转到上节进行阅读~</p>
<p>本小节就不针对<strong>生产侧指标的建设</strong>中所有涉及指标的建设过程进行详细介绍了，我们主要以<strong>当前分钟正在开播直播间数</strong>作为<strong>生产侧指标建设</strong>的一个代表性案例，介绍这个指标的整个建设过程。<br>来为大家还原生产侧指标的业务过程以及技术方案。</p>
<h2 id="Question"><a href="#Question" class="headerlink" title="Question"></a>Question</h2><p>仍然从几个问题入手，介绍<strong>当前分钟正在开播直播间数</strong>的建设过程。</p>
<ul>
<li><strong>当前分钟正在开播直播间数</strong>的定义什么？业务过程是怎么样的？举例？</li>
<li>怎样去建设这个指标？整体的指标计算流程？</li>
</ul>
<h2 id="1-聊聊定义？"><a href="#1-聊聊定义？" class="headerlink" title="1.聊聊定义？"></a>1.聊聊定义？</h2><p>当前分钟正在开播直播间数，其定义就是整个平台中，当前分钟正在开播的直播间数 + 单层维度下钻的当前分钟正在开播的直播间数。</p>
<p>举例：</p>
<p>现在的时间点是 2020-11-11 12:42，真实直播的直播间数为 3000 个（平台维度下钻：IOS 平台为 1500，安卓平台为 1500）</p>
<p>到了 12:43 时，有 200 个直播间进行了关播（其中 100 个为 IOS，100 个为安卓），有 100 个直播间开播（全部为 IOS），则当前正在直播的直播间数为 2900（平台维度下钻：IOS 平台为 1500，安卓平台为 1400）。</p>
<p>其中 2020-11-11 12:42 的 3000 以及 2020-11-11 12:43 的 2900 以及按照平台下钻的数值就为当前时间正在开播的直播间数。</p>
<p>因此根据上述定义和分析，我们可以直接将数据源和数据汇的 schema 定义下来，主体信息如下。</p>
<h3 id="数据源-schema"><a href="#数据源-schema" class="headerlink" title="数据源 schema"></a>数据源 schema</h3><table>
<thead>
<tr>
<th>字段</th>
<th>备注</th>
</tr>
</thead>
<tbody><tr>
<td>live_stream_id</td>
<td>直播间 id</td>
</tr>
<tr>
<td>author_id</td>
<td>主播 id</td>
</tr>
<tr>
<td>start_or_end</td>
<td>开播还是关播</td>
</tr>
<tr>
<td>timestamp</td>
<td>时间戳</td>
</tr>
<tr>
<td>…</td>
<td>…</td>
</tr>
</tbody></table>
<h3 id="数据汇-schema"><a href="#数据汇-schema" class="headerlink" title="数据汇 schema"></a>数据汇 schema</h3><table>
<thead>
<tr>
<th>字段</th>
<th>备注</th>
</tr>
</thead>
<tbody><tr>
<td>timestamp</td>
<td>时间戳，汇总到分钟粒度</td>
</tr>
<tr>
<td>metric_name</td>
<td>指标名，举例：开播直播间数</td>
</tr>
<tr>
<td>metric_value</td>
<td>指标值，举例：3000（开播直播间数）</td>
</tr>
<tr>
<td>dim_name</td>
<td>维度名，举例：平台，版本</td>
</tr>
<tr>
<td>dim_value</td>
<td>维度值，举例：IOS，8.1</td>
</tr>
<tr>
<td>…</td>
<td>…</td>
</tr>
</tbody></table>
<blockquote>
<p>Notes:</p>
<p><strong>metric_name 和 metric_value</strong>：</p>
<p>这两个字段是为了之后进行指标扩充时进行的设计。比如后续如果需要加入开播主播数，开播时长等指标，不用修改数据汇 schema，只需要加一种 metric_name，就可以使用原有 schema 进行数据产出。</p>
<p><strong>dim_name 和 dim_value</strong>：</p>
<p>目前我们建设的指标只提供了进行单维度下钻的能力，所以设计了 dim_name 和 dim_value 两个字段，可满足用户查看平台为 IOS 的当前开播直播间数或者使用开播软件版本为 8.1 的当前开播直播间数。<br>如果后续业务场景需要多维下钻能力，可以在字段上面进行扩充。或者也可以提供明细数据在 OLAP 中进行多维下钻。</p>
</blockquote>
<h2 id="2-怎样建设？"><a href="#2-怎样建设？" class="headerlink" title="2.怎样建设？"></a>2.怎样建设？</h2><p>对于当前分钟正在开播直播间数来说，其计算方式很简单，就是下面这个数学公式：</p>
<p><strong>当前分钟正在开播直播间数</strong> = <strong>上一分钟正在开播直播间数</strong> + <strong>当前分钟开播直播间数</strong> - <strong>当前分钟关播直播间数</strong></p>
<p>可以从上面的公式可以看出，对于当前分钟正在开播直播间数的计算来说，是依赖上下文信息的，即<strong>上一分钟正在开播直播间数</strong>，这也就是我们所说的<strong>状态</strong>。</p>
<h3 id="指标处理逻辑"><a href="#指标处理逻辑" class="headerlink" title="指标处理逻辑"></a>指标处理逻辑</h3><p>从获取到数据源，到产出指标的整体处理逻辑如下图所示。这里就不进行赘述了。</p>
<p><img src="/blog-img/apache-flink:realtime-live-stream-3/metric-current-live-live-stream-number-life-cycle.png" alt="技术架构"></p>
<p>其中标为<strong>粉色</strong>的模块为任务中的<strong>状态</strong>，即任务中一直存储的当前分钟正在开播直播间数。</p>
<h3 id="状态"><a href="#状态" class="headerlink" title="状态"></a>状态</h3><p>上述指标涉及到了，状态，那么我这里讲一下我对<strong>状态</strong>的理解。如有错误，请在文末讨论中进行指出，我会和大家讨论。</p>
<p>状态其实就是一个记录上下文信息的东西，如果当前的计算过程依赖到上次计算的结果，那么上次计算的结果就是状态。举几个🌰；</p>
<ul>
<li><p><strong>流处理</strong>：如本节介绍的<strong>当前分钟正在开播直播间数</strong>的计算，就是依赖上一分钟的正在开播直播间数（状态）进行的计算。<br>可能有小伙伴会说，我不依赖上一分钟，我从头开始计算可以不？答案是可以的，但是从头开始计算，也需要将所有历史数据进行存储，这些历史数据其实也就是状态，只不过我们将其优化为了上一分钟开播直播间数。</p>
</li>
<li><p><strong>批处理</strong>：今天的全量表 = 昨天全量表（状态） + 今天的增量表。</p>
</li>
<li><p><strong>数据库存储</strong>：最常见的 mysql 主键自增，unique key 等。<br>为什么新插入一条数据主键会自增？因为 mysql 存储了主键的上一个值（状态）。<br>为什么插入相同数据时，由于 unique key 会导致报错，就是因为 mysql 存储了所有 unique key 的字段的数据（状态）。</p>
</li>
<li><p><strong>生活</strong>：当前的手机电量 = 上一分钟的手机电量（状态） + （充电/用电量）。<br>为什么你越来越喜欢你的另一半？因为你对她的感觉 = 前一秒你对她的感觉（状态） + 当前这一秒她亲了你一下。</p>
</li>
</ul>
<p>生活中随处可见状态，即使你不是程序员，我相信也都可以理解状态的概念。</p>
<h3 id="指标计算代码示例"><a href="#指标计算代码示例" class="headerlink" title="指标计算代码示例"></a>指标计算代码示例</h3><p>按照最简单的实现方式举例如下。</p>
<figure class="highlight java"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">public</span> <span class="class"><span class="keyword">class</span> <span class="title">LiveStreamRealtimeMetricProdProcessorJob</span> </span>{</span><br><span class="line"></span><br><span class="line"> <span class="function"><span class="keyword">public</span> <span class="keyword">static</span> <span class="keyword">void</span> <span class="title">main</span><span class="params">(String[] args)</span> <span class="keyword">throws</span> Exception </span>{</span><br><span class="line"> <span class="keyword">final</span> StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();</span><br><span class="line"></span><br><span class="line"> DataStream<SourceModel> source = SourceFactory.getSourceDataStream(...);</span><br><span class="line"></span><br><span class="line"> DataStream<SinkModel> result = source</span><br><span class="line"> .keyBy(<span class="keyword">new</span> KeySelector<SourceModel, Long>() {</span><br><span class="line"> <span class="meta">@Override</span></span><br><span class="line"> <span class="function"><span class="keyword">public</span> Long <span class="title">getKey</span><span class="params">(SourceModel commonModel)</span> <span class="keyword">throws</span> Exception </span>{</span><br><span class="line"> <span class="keyword">return</span> commonModel.getLiveStreamId() % <span class="number">1000</span>;</span><br><span class="line"> }</span><br><span class="line"> })</span><br><span class="line"> .timeWindow(Time.seconds(<span class="number">60</span>))</span><br><span class="line"> .process(<span class="keyword">new</span> ProcessWindowFunction<SourceModel, SinkModel, Long, TimeWindow>() {</span><br><span class="line"></span><br><span class="line"> <span class="keyword">private</span> ValueState<Long> playingLiveStreamNumberValueState;</span><br><span class="line"></span><br><span class="line"> <span class="meta">@Override</span></span><br><span class="line"> <span class="function"><span class="keyword">public</span> <span class="keyword">void</span> <span class="title">open</span><span class="params">(Configuration parameters)</span> <span class="keyword">throws</span> Exception </span>{</span><br><span class="line"> <span class="keyword">super</span>.open(parameters);</span><br><span class="line"> <span class="keyword">this</span>.playingLiveStreamNumberValueState = getRuntimeContext().getState(...);</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line"> <span class="meta">@Override</span></span><br><span class="line"> <span class="function"><span class="keyword">public</span> <span class="keyword">void</span> <span class="title">process</span><span class="params">(Long bucket, Context context, Iterable<SourceModel> iterable,</span></span></span><br><span class="line"><span class="function"><span class="params"> Collector<SinkModel> collector)</span> <span class="keyword">throws</span> Exception </span>{</span><br><span class="line"> Long playingLiveStreamNumber = <span class="keyword">this</span>.playingLiveStreamNumberValueState.value();</span><br><span class="line"></span><br><span class="line"> <span class="keyword">if</span> (<span class="keyword">null</span> == playingLiveStreamNumber) {</span><br><span class="line"> playingLiveStreamNumber = <span class="number">0L</span>;</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line"> List<SourceModel> sourceModels = (List<SourceModel>) iterable;</span><br><span class="line"></span><br><span class="line"> <span class="keyword">for</span> (SourceModel sourceModel : sourceModels) {</span><br><span class="line"> <span class="keyword">if</span> (BizType.I == sourceModel.getBizType()) {</span><br><span class="line"> playingLiveStreamNumber++;</span><br><span class="line"> } <span class="keyword">else</span> {</span><br><span class="line"> playingLiveStreamNumber--;</span><br><span class="line"> }</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line"> <span class="keyword">this</span>.playingLiveStreamNumberValueState.update(playingLiveStreamNumber);</span><br><span class="line"></span><br><span class="line"> collector.collect(</span><br><span class="line"> SinkModel.builder().build()</span><br><span class="line"> );</span><br><span class="line"> }</span><br><span class="line"> });</span><br><span class="line"></span><br><span class="line"> SinkFactory.setSinkDataStream(...);</span><br><span class="line"></span><br><span class="line"> env.execute();</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line"> <span class="meta">@Data</span></span><br><span class="line"> <span class="meta">@Builder</span></span><br><span class="line"> <span class="keyword">static</span> <span class="class"><span class="keyword">class</span> <span class="title">SourceModel</span> </span>{</span><br><span class="line"> <span class="comment">// 直播间id</span></span><br><span class="line"> <span class="keyword">private</span> Long liveStreamId;</span><br><span class="line"> <span class="comment">// 开播时间，关播时间</span></span><br><span class="line"> <span class="keyword">private</span> Long time;</span><br><span class="line"> <span class="comment">// 主播id</span></span><br><span class="line"> <span class="keyword">private</span> Long authorId;</span><br><span class="line"> <span class="comment">// binlog 时间戳</span></span><br><span class="line"> <span class="keyword">private</span> <span class="keyword">long</span> binlogTimestamp;</span><br><span class="line"> <span class="comment">// 开播，关播</span></span><br><span class="line"> <span class="keyword">private</span> BizType bizType;</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line"> <span class="class"><span class="keyword">enum</span> <span class="title">BizType</span> </span>{</span><br><span class="line"> I, <span class="comment">// 开播</span></span><br><span class="line"> D, <span class="comment">// 关播</span></span><br><span class="line"> ;</span><br><span class="line"> }</span><br><span class="line"></span><br><span class="line"> <span class="meta">@Data</span></span><br><span class="line"> <span class="meta">@Builder</span></span><br><span class="line"> <span class="keyword">static</span> <span class="class"><span class="keyword">class</span> <span class="title">SinkModel</span> </span>{</span><br><span class="line"> <span class="comment">// 时间戳，汇总到分钟粒度</span></span><br><span class="line"> <span class="keyword">private</span> Long timestamp;</span><br><span class="line"> <span class="comment">// 指标名</span></span><br><span class="line"> <span class="keyword">private</span> String metricName;</span><br><span class="line"> <span class="comment">// 指标值</span></span><br><span class="line"> <span class="keyword">private</span> <span class="keyword">double</span> metricValue;</span><br><span class="line"> <span class="comment">// 维度名</span></span><br><span class="line"> <span class="keyword">private</span> String dimName;</span><br><span class="line"> <span class="comment">// 维度值</span></span><br><span class="line"> <span class="keyword">private</span> String dimValue;</span><br><span class="line"> }</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
<h2 id="总结"><a href="#总结" class="headerlink" title="总结"></a>总结</h2><p>本文衔接上文，主要介绍直播间<strong>生产侧指标的建设</strong>，以<strong>当前分钟正在开播直播间数</strong>为代表举例。提出定义以及建设过程相关的问题，以这两个个问题出发，引出了以下两小节。</p>
<p>第一节简单介绍了当前分钟正在开播直播间数的定义。</p>
<p>第二节主要介绍了当前分钟正在开播直播间数的建设逻辑以及过程，并对<strong>状态</strong>这个概念进行了一个拓展介绍。</p>
<p>最后一节对本文进行了总结。</p>
<p>如果你也有相同的指标建设需求，或者存在一些指标建设过程中的问题，欢迎关注博主公众号，或者添加博主微信，互相交流~</p>
<p>记得点赞 + 再看喔~</p>
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