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<h3 id="Spark笔记"><a href="#Spark笔记" class="headerlink" title="Spark笔记"></a>Spark笔记</h3><h3 id="1-数据结构方式"><a href="#1-数据结构方式" class="headerlink" title="1.数据结构方式"></a>1.数据结构方式</h3><p> RDD是Spark处理数据的数据结构，可以通过两种方式加载数据创建RDD</p>
<ul>
<li>从程序中parallelize一种现有的数据：如Array</li>
<li>从外部读取文件：CSV，Hive等</li>
</ul>
<h3 id="2-RDD操作类型"><a href="#2-RDD操作类型" class="headerlink" title="2.RDD操作类型"></a>2.RDD操作类型</h3><p>2.1 RDD的计算方式是lazy加载，即用的时候再计算。</p>
<p>2.2 如果一个变量需要经常使用，可以持久化persist</p>
<p>2.3 封装函数有多种方式：</p>
<ul>
<li>封装静态方法，创建<strong>object</strong></li>
<li>封装方法以及变量参数等等，创建<strong>class</strong></li>
</ul>
<p>2.3 常用转换方法</p>
<div class="table-container">
<table>
<thead>
<tr>
<th style="text-align:left">Transformation</th>
<th style="text-align:left">Meaning</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left"><strong>map</strong>(<em>func</em>)</td>
<td style="text-align:left">Return a new distributed dataset formed by passing each element of the source through a function <em>func</em>.</td>
</tr>
<tr>
<td style="text-align:left"><strong>filter</strong>(<em>func</em>)</td>
<td style="text-align:left">Return a new dataset formed by selecting those elements of the source on which <em>func</em>returns true.</td>
</tr>
<tr>
<td style="text-align:left"><strong>flatMap</strong>(<em>func</em>)</td>
<td style="text-align:left">Similar to map, but each input item can be mapped to 0 or more output items (so <em>func</em> should return a Seq rather than a single item).</td>
</tr>
<tr>
<td style="text-align:left"><strong>mapPartitions</strong>(<em>func</em>)</td>
<td style="text-align:left">Similar to map, but runs separately on each partition (block) of the RDD, so <em>func</em> must be of type Iterator<t> => Iterator<u> when running on an RDD of type T.</u></t></td>
</tr>
<tr>
<td style="text-align:left"><strong>mapPartitionsWithIndex</strong>(<em>func</em>)</td>
<td style="text-align:left">Similar to mapPartitions, but also provides <em>func</em> with an integer value representing the index of the partition, so <em>func</em> must be of type (Int, Iterator<t>) => Iterator<u> when running on an RDD of type T.</u></t></td>
</tr>
<tr>
<td style="text-align:left"><strong>sample</strong>(<em>withReplacement</em>, <em>fraction</em>, <em>seed</em>)</td>
<td style="text-align:left">Sample a fraction <em>fraction</em> of the data, with or without replacement, using a given random number generator seed.</td>
</tr>
<tr>
<td style="text-align:left"><strong>union</strong>(<em>otherDataset</em>)</td>
<td style="text-align:left">Return a new dataset that contains the union of the elements in the source dataset and the argument.</td>
</tr>
<tr>
<td style="text-align:left"><strong>intersection</strong>(<em>otherDataset</em>)</td>
<td style="text-align:left">Return a new RDD that contains the intersection of elements in the source dataset and the argument.</td>
</tr>
<tr>
<td style="text-align:left"><strong>distinct</strong>([<em>numPartitions</em>]))</td>
<td style="text-align:left">Return a new dataset that contains the distinct elements of the source dataset.</td>
</tr>
<tr>
<td style="text-align:left"><strong>groupByKey</strong>([<em>numPartitions</em>])</td>
<td style="text-align:left">When called on a dataset of (K, V) pairs, returns a dataset of (K, Iterable<v>) pairs. <strong>Note:</strong> If you are grouping in order to perform an aggregation (such as a sum or average) over each key, using <code>reduceByKey</code> or <code>aggregateByKey</code> will yield much better performance. <strong>Note:</strong> By default, the level of parallelism in the output depends on the number of partitions of the parent RDD. You can pass an optional <code>numPartitions</code> argument to set a different number of tasks.</v></td>
</tr>
<tr>
<td style="text-align:left"><strong>reduceByKey</strong>(<em>func</em>, [<em>numPartitions</em>])</td>
<td style="text-align:left">When called on a dataset of (K, V) pairs, returns a dataset of (K, V) pairs where the values for each key are aggregated using the given reduce function <em>func</em>, which must be of type (V,V) => V. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument.</td>
</tr>
<tr>
<td style="text-align:left"><strong>aggregateByKey</strong>(<em>zeroValue</em>)(<em>seqOp</em>, <em>combOp</em>, [<em>numPartitions</em>])</td>
<td style="text-align:left">When called on a dataset of (K, V) pairs, returns a dataset of (K, U) pairs where the values for each key are aggregated using the given combine functions and a neutral “zero” value. Allows an aggregated value type that is different than the input value type, while avoiding unnecessary allocations. Like in <code>groupByKey</code>, the number of reduce tasks is configurable through an optional second argument.</td>
</tr>
<tr>
<td style="text-align:left"><strong>sortByKey</strong>([<em>ascending</em>], [<em>numPartitions</em>])</td>
<td style="text-align:left">When called on a dataset of (K, V) pairs where K implements Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending or descending order, as specified in the boolean <code>ascending</code> argument.</td>
</tr>
<tr>
<td style="text-align:left"><strong>join</strong>(<em>otherDataset</em>, [<em>numPartitions</em>])</td>
<td style="text-align:left">When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (V, W)) pairs with all pairs of elements for each key. Outer joins are supported through <code>leftOuterJoin</code>, <code>rightOuterJoin</code>, and <code>fullOuterJoin</code>.</td>
</tr>
<tr>
<td style="text-align:left"><strong>cogroup</strong>(<em>otherDataset</em>, [<em>numPartitions</em>])</td>
<td style="text-align:left">When called on datasets of type (K, V) and (K, W), returns a dataset of (K, (Iterable<v>, Iterable<w>)) tuples. This operation is also called <code>groupWith</code>.</w></v></td>
</tr>
<tr>
<td style="text-align:left"><strong>cartesian</strong>(<em>otherDataset</em>)</td>
<td style="text-align:left">When called on datasets of types T and U, returns a dataset of (T, U) pairs (all pairs of elements).</td>
</tr>
<tr>
<td style="text-align:left"><strong>pipe</strong>(<em>command</em>, <em>[envVars]</em>)</td>
<td style="text-align:left">Pipe each partition of the RDD through a shell command, e.g. a Perl or bash script. RDD elements are written to the process’s stdin and lines output to its stdout are returned as an RDD of strings.</td>
</tr>
<tr>
<td style="text-align:left"><strong>coalesce</strong>(<em>numPartitions</em>)</td>
<td style="text-align:left">Decrease the number of partitions in the RDD to numPartitions. Useful for running operations more efficiently after filtering down a large dataset.</td>
</tr>
<tr>
<td style="text-align:left"><strong>repartition</strong>(<em>numPartitions</em>)</td>
<td style="text-align:left">Reshuffle the data in the RDD randomly to create either more or fewer partitions and balance it across them. This always shuffles all data over the network.</td>
</tr>
<tr>
<td style="text-align:left"><strong>repartitionAndSortWithinPartitions</strong>(<em>partitioner</em>)</td>
<td style="text-align:left">Repartition the RDD according to the given partitioner and, within each resulting partition, sort records by their keys. This is more efficient than calling <code>repartition</code> and then sorting within each partition because it can push the sorting down into the shuffle machinery.</td>
</tr>
</tbody>
</table>
</div>
<h3 id="3-创建DataFrame的三种方式"><a href="#3-创建DataFrame的三种方式" class="headerlink" title="3.创建DataFrame的三种方式"></a>3.创建DataFrame的三种方式</h3><ul>
<li><p>使用toDF函数</p>
</li>
<li><p>使用createDataFrame函数</p>
</li>
<li>通过文件直接创建</li>
</ul>
<h3 id="4-scala的vector和spark包中vector不一样"><a href="#4-scala的vector和spark包中vector不一样" class="headerlink" title="4.scala的vector和spark包中vector不一样"></a>4.scala的vector和spark包中vector不一样</h3><h3 id="5-Spark优化：（美团Spark）"><a href="#5-Spark优化：（美团Spark）" class="headerlink" title="5.Spark优化：（美团Spark）"></a>5.Spark优化：（美团Spark）</h3><p>基础版：<a href="https://tech.meituan.com/2016/04/29/spark-tuning-basic.html" target="_blank" rel="external">https://tech.meituan.com/2016/04/29/spark-tuning-basic.html</a></p>
<p>高级版：<a href="https://tech.meituan.com/2016/05/12/spark-tuning-pro.html" target="_blank" rel="external">https://tech.meituan.com/2016/05/12/spark-tuning-pro.html</a></p>
<h3 id="6-Spark保留运行环境（用于查错）"><a href="#6-Spark保留运行环境（用于查错）" class="headerlink" title="6.Spark保留运行环境（用于查错）"></a>6.Spark保留运行环境（用于查错）</h3><figure class="highlight xml"><table><tr><td class="gutter"><pre><div class="line">1</div></pre></td><td class="code"><pre><div class="line">conf.spark.yarn.preserve.staging.files=true</div></pre></td></tr></table></figure>
<h3 id="7-宽依赖和窄依赖"><a href="#7-宽依赖和窄依赖" class="headerlink" title="7.宽依赖和窄依赖"></a>7.宽依赖和窄依赖</h3><ul>
<li><strong>窄依赖</strong>：指父RDD的每个分区只被一个子RDD分区使用，子RDD分区通常只对应常数个父RDD分区。（map、filter、union操作）。</li>
<li><strong>宽依赖</strong>：指父RDD的每个分区都有可能被多个子RDD分区使用，子RDD分区通常对应父RDD所有分区。（groupByKey、partitionBy等操作）</li>
<li>比较：<strong>宽依赖通常对应着shuffle操作</strong>，需要在运行的过程中将同一个RDD分区传入到不同的RDD分区中，中间可能涉及多个节点之间数据的传输。</li>
</ul>
<h3 id="8-ORC格式和PARQUET格式文件对比"><a href="#8-ORC格式和PARQUET格式文件对比" class="headerlink" title="8.ORC格式和PARQUET格式文件对比"></a>8.ORC格式和PARQUET格式文件对比</h3><blockquote>
<p>impala暂时不支持orc格式的表查询</p>
</blockquote>
<h3 id="9-left-anti-join（某个字段过滤用）"><a href="#9-left-anti-join（某个字段过滤用）" class="headerlink" title="9.left anti join（某个字段过滤用）"></a>9.left anti join（某个字段过滤用）</h3><ul>
<li>left semi join —> exists</li>
<li>left anti join —> not exists</li>
</ul>
<h3 id="10-Shuffle过程数据倾斜"><a href="#10-Shuffle过程数据倾斜" class="headerlink" title="10.Shuffle过程数据倾斜"></a>10.Shuffle过程数据倾斜</h3><blockquote>
<p>和Hive中类似，数据的倾斜都发生在shuffle过程中，下面以hive的shuffle进行总结。发生倾斜的根本原因在于，shuffle之后，key的分布不均匀，使得大量的key集中在某个reduce节点，导致此节点过于“忙碌”，在其他节点都处理完之后，任务的结整需要等待此节点处理完，使得整个任务被此节点堵塞。</p>
<p>要解决此问题，主要可以分为两大块：</p>
<ul>
<li>一是尽量不shuffle；</li>
<li>二是shuffle之后，在reduce节点上的key分布尽量均匀。</li>
</ul>
</blockquote>
<p>方案总结如下：</p>
<hr>
<p>解决方案：MapJoin，添加随机前缀，使用列桶表</p>
<ul>
<li>mapjoin</li>
</ul>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div></pre></td><td class="code"><pre><div class="line">-- mapjoin配置</div><div class="line">set hive.auto.convert.join = true;</div><div class="line">set hive.mapjoin.smalltable.filesize=25000000;</div></pre></td></tr></table></figure>
<ul>
<li>手动分割成两部分进行join</li>
</ul>
<figure class="highlight plain"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div></pre></td><td class="code"><pre><div class="line">select t1.* </div><div class="line">from t1 join t2 on t1.key=t2.key</div><div class="line"></div><div class="line">拆成以下SQL:</div><div class="line"></div><div class="line">select t1.* </div><div class="line">from t1 join t2 on t1.key=t2.key</div><div class="line">where t1.key=A</div><div class="line">union all </div><div class="line">select t1.*</div><div class="line">from t1 join t2 on t1.key=t2.key</div><div class="line">where t1.key<>A</div></pre></td></tr></table></figure>
<ul>
<li>当小表不是很小，不太方便用mapjoin，大表添加N中随机前缀，小表膨胀N倍数据</li>
<li>使用Skewed Table 或者 List Bucketing Table</li>
</ul>
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<h2 id="Scala笔记"><a href="#Scala笔记" class="headerlink" title="Scala笔记"></a>Scala笔记</h2><h3 id="1-四种操作符的区别和联系"><a href="#1-四种操作符的区别和联系" class="headerlink" title="1.四种操作符的区别和联系"></a>1.四种操作符的区别和联系</h3><ul>
<li><p>:: 该方法成为cons，表时构造，向队列头部加入元素。x::list表示向list头部加入元素。（列表构造：</p>
<figure class="highlight scala"><table><tr><td class="gutter"><pre><div class="line">1</div></pre></td><td class="code"><pre><div class="line"><span class="number">2</span>::<span class="number">1</span>::<span class="number">2</span>::<span class="string">"bar"</span>::<span class="string">"foo"</span> 表示<span class="type">List</span>[<span class="type">Any</span>]= (<span class="number">2</span>,<span class="number">1</span>,<span class="number">2</span>,bar,foo)</div></pre></td></tr></table></figure>
</li>
<li><p>:+和+:表示分别在尾部加入元素和在头部加入元素。</p>
</li>
<li><p>++ 表示连接两个集合</p>
</li>
<li><p>::: 该方法只能用于连接两个list类型的集合</p>
</li>
</ul>
<h3 id="2-日期操作-经常用到，所以记录下"><a href="#2-日期操作-经常用到，所以记录下" class="headerlink" title="2.日期操作(经常用到，所以记录下)"></a>2.日期操作(经常用到，所以记录下)</h3><ul>
<li><p>获取今天0点时间戳</p>
<figure class="highlight scala"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div></pre></td><td class="code"><pre><div class="line"><span class="keyword">val</span> dateFormat = <span class="keyword">new</span> <span class="type">SimpleDateFormat</span>(<span class="string">"yyyy-MM-dd"</span>)</div><div class="line"><span class="keyword">val</span> cur = dateFormat.parse(dateFormat.format(<span class="keyword">new</span> <span class="type">Date</span>())).getTime</div></pre></td></tr></table></figure>
</li>
<li><p>日期格式转时间戳</p>
<figure class="highlight scala"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div></pre></td><td class="code"><pre><div class="line"><span class="keyword">val</span> dateFormat = <span class="keyword">new</span> <span class="type">SimpleDateFormat</span>(<span class="string">"yyyy-MM-dd"</span>)</div><div class="line"><span class="comment">//val dateFormat = new SimpleDateFormat("yyyy-MM-dd hh:MM:ss")</span></div><div class="line"><span class="keyword">val</span> timestamp = dateFormat.parse(dateFormat.format(<span class="keyword">new</span> <span class="type">Date</span>())).getTime</div></pre></td></tr></table></figure>
</li>
<li><p>时间戳转日期</p>
<figure class="highlight scala"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div></pre></td><td class="code"><pre><div class="line"><span class="keyword">val</span> dateFormat = <span class="keyword">new</span> <span class="type">SimpleDateFormat</span>(<span class="string">"yyyy-MM-dd"</span>)</div><div class="line"><span class="keyword">val</span> date = dateFormat.format(<span class="keyword">new</span> <span class="type">Date</span>())</div></pre></td></tr></table></figure>
</li>
</ul>
<h3 id="3-删除目录或文件"><a href="#3-删除目录或文件" class="headerlink" title="3.删除目录或文件"></a>3.删除目录或文件</h3><figure class="highlight scala"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div><div class="line">16</div><div class="line">17</div><div class="line">18</div></pre></td><td class="code"><pre><div class="line"><span class="keyword">import</span> java.io.<span class="type">File</span></div><div class="line"><span class="function"><span class="keyword">def</span> <span class="title">dirDel</span></span>(path: <span class="type">File</span>) {</div><div class="line"> <span class="keyword">if</span> (!path.exists())</div><div class="line"> <span class="keyword">return</span></div><div class="line"> <span class="keyword">else</span> <span class="keyword">if</span> (path.isFile()) {</div><div class="line"> path.delete()</div><div class="line"> <span class="keyword">return</span></div><div class="line"> }</div><div class="line"> <span class="keyword">val</span> file: <span class="type">Array</span>[<span class="type">File</span>] = path.listFiles()</div><div class="line"> <span class="keyword">for</span> (d <- file) {</div><div class="line"> dirDel(d)</div><div class="line"> }</div><div class="line"> path.delete()</div><div class="line"> }</div><div class="line"> <span class="keyword">if</span>(<span class="type">Files</span>.exists(<span class="type">Paths</span>.get(path))) {</div><div class="line"> <span class="comment">// 先删除目录里的文件</span></div><div class="line"> dirDel(<span class="type">Paths</span>.get(path).toFile)</div><div class="line"> }</div></pre></td></tr></table></figure>
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<h3 id="Redis"><a href="#Redis" class="headerlink" title="Redis"></a>Redis</h3><h3 id="1、为什么使用Redis数据库"><a href="#1、为什么使用Redis数据库" class="headerlink" title="1、为什么使用Redis数据库"></a>1、为什么使用Redis数据库</h3><ul>
<li><strong>性能极高</strong> – Redis能读的速度是110000次/s,写的速度是81000次/s 。</li>
<li>丰富的数据类型 – Redis支持二进制案例的 Strings, Lists, Hashes, Sets 及 Ordered Sets 数据类型操作。</li>
<li>原子 – Redis的所有操作都是原子性的，意思就是要么成功执行要么失败完全不执行。单个操作是原子性的。多个操作也支持事务，即原子性，通过MULTI和EXEC指令包起来。</li>
<li>丰富的特性 – Redis还支持 publish/subscribe, 通知, key 过期等等特性。</li>
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<h3 id="2-数据类型"><a href="#2-数据类型" class="headerlink" title="2.数据类型"></a>2.数据类型</h3><ul>
<li><p>string</p>
</li>
<li><p>hash：键值对的集合 </p>
<figure class="highlight plain"><figcaption><span>hashset1 key1 val1 key2 val2```</span></figcaption><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div></pre></td><td class="code"><pre><div class="line"></div><div class="line">```HGET hashset1 key1</div></pre></td></tr></table></figure>
</li>
<li><p>list</p>
<figure class="highlight plain"><figcaption><span>list1 val1```</span></figcaption><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div></pre></td><td class="code"><pre><div class="line"></div><div class="line">* set</div><div class="line"></div><div class="line"> ```sadd set1 val1</div></pre></td></tr></table></figure>
<figure class="highlight plain"><figcaption><span>set1```</span></figcaption><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div></pre></td><td class="code"><pre><div class="line"></div><div class="line">* zset（有序集合，带分数）</div><div class="line"></div><div class="line"> ```zadd key score memeber</div></pre></td></tr></table></figure>
</li>
</ul>
<h3 id="3-set和hset区别"><a href="#3-set和hset区别" class="headerlink" title="3.set和hset区别"></a>3.set和hset区别</h3><ul>
<li>set 就是普通的已key-value 方式存储数据，可以设置过期时间。时间复杂度为 O(1)</li>
<li>hset 则是以hash 散列表的形式存储。超时时间只能设置在 大 key 上，单个 filed 则不可以设置超时</li>
</ul>
<hr>
<p>使用场景对比：<strong>set 存储单个大文本非结构化数据，hset 则存储结构化数据</strong>，一个 hash 存储一条数据，一个 filed 则存储 一条数据中的一个属性，value 则是属性对应的值。</p>
<h3 id="4-Scan操作，keys-操作，线上谨慎使用"><a href="#4-Scan操作，keys-操作，线上谨慎使用" class="headerlink" title="4.Scan操作，keys()操作，线上谨慎使用"></a>4.Scan操作，keys()操作，线上谨慎使用</h3><p>scan操作取N条出来进行scan，并保持prefix</p>
<figure class="highlight java"><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div><div class="line">3</div><div class="line">4</div><div class="line">5</div><div class="line">6</div><div class="line">7</div><div class="line">8</div><div class="line">9</div><div class="line">10</div><div class="line">11</div><div class="line">12</div><div class="line">13</div><div class="line">14</div><div class="line">15</div></pre></td><td class="code"><pre><div class="line"><span class="function"><span class="keyword">public</span> HashSet<String> <span class="title">getKeysByScan</span><span class="params">(String pattern)</span> </span>{</div><div class="line"> ScanParams scanParams = <span class="keyword">new</span> ScanParams().count(<span class="number">1000</span>).match(pattern);</div><div class="line"> HashSet<String> allKeys = <span class="keyword">new</span> HashSet<>();</div><div class="line"> cluster.getClusterNodes().values().forEach((pool) -> {</div><div class="line"> String cur = ScanParams.SCAN_POINTER_START;</div><div class="line"> <span class="keyword">do</span> {</div><div class="line"> <span class="keyword">try</span> (Jedis jedis = pool.getResource()) {</div><div class="line"> ScanResult<String> scanResult = jedis.scan(cur, scanParams);</div><div class="line"> allKeys.addAll(scanResult.getResult());</div><div class="line"> cur = scanResult.getStringCursor();</div><div class="line"> }</div><div class="line"> } <span class="keyword">while</span> (!cur.equals(ScanParams.SCAN_POINTER_START));</div><div class="line"> });</div><div class="line"> <span class="keyword">return</span> allKeys;</div><div class="line"> }</div></pre></td></tr></table></figure>
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<h2 id="Flink笔记"><a href="#Flink笔记" class="headerlink" title="Flink笔记"></a>Flink笔记</h2><h3 id="1-数据集类型"><a href="#1-数据集类型" class="headerlink" title="1.数据集类型"></a>1.数据集类型</h3><ul>
<li>有界数据集：具有时间边界，在处理过程中数据一定会在某个时间范围内起始和结束。提供DataSet API</li>
<li>无界数据集： 数据从一开始就一直持续产生的。提供DataStream API</li>
</ul>
<h3 id="2-Flink编程接口"><a href="#2-Flink编程接口" class="headerlink" title="2.Flink编程接口"></a>2.Flink编程接口</h3><ul>
<li>Flink SQL</li>
<li>Table API：在内存中的DataSet和DataStream基础上加上Schema信息，将数据类型<strong>抽象成表结构</strong></li>
<li>DataStream API和DataSet API</li>
<li>Stateful Stream Process API</li>
</ul>
<h3 id="3-程序结构"><a href="#3-程序结构" class="headerlink" title="3.程序结构"></a>3.程序结构</h3><ul>
<li><p>设定运行环境：</p>
<ul>
<li><figure class="highlight plain"><figcaption><span>env = StreamExecutionEnvironment.getExecutionEnvironment```</span></figcaption><table><tr><td class="gutter"><pre><div class="line">1</div><div class="line">2</div></pre></td><td class="code"><pre><div class="line"></div><div class="line">* ```/*设置并行度为5*/val env = StreamExecutionEnvironment.createLocalEnvironment(5)</div></pre></td></tr></table></figure>
</li>
<li><figure class="highlight scala"><table><tr><td class="gutter"><pre><div class="line">1</div></pre></td><td class="code"><pre><div class="line"><span class="keyword">val</span> env = <span class="type">StreamExecutionEnvironment</span>.createRemoteEnvironment(<span class="string">"JobManagerHost"</span>,<span class="number">6021</span>,<span class="number">5</span>,<span class="string">"/user/application.jar"</span>)</div></pre></td></tr></table></figure>
</li>
</ul>
</li>
<li><p>初始化数据：</p>
<p>将外部数据转换成DataStream<t>或者DataSet<t></t></t></p>
</li>
<li><p>执行转换逻辑：</p>
<ul>
<li>复杂的逻辑通过实现MapFunction接口，然后调用map()方法将实现类传入</li>
<li>匿名函数</li>
<li>RichFunction接口</li>
</ul>
</li>
<li><p>分区key指定</p>
</li>
<li><p>根据第一个字段分区，根据第二个字段求和</p>
<p><code>val result = DataStream.keyBy(0).sum(1)</code></p>