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PREVIOUSUNTILLAST考虑到在某些业务场景下，所填充的值的时间不能大于该时间序列last的时间戳（从业务角度考虑，取历史数据不能取未来历史数据）
看下面的例子，或许更容易理解</p> <p>A点时间戳为1，B为5，C为20，D为30，N为8，M为38</p> <p>原始数据为<img src="https://user-images.githubusercontent.com/16079446/78784824-9f41ae00-79d8-11ea-9920-0825e081cae0.png" style="width:100%;max-width:800px;max-height:600px;margin-left:auto;margin-right:auto;display:block;"></p> <p><code>select temperature FROM root.ln.wf01.wt01 where time &gt;= 1 and time &lt;= 38</code></p> <table><thead><tr><th>Time</th> <th>root.ln.wf01.wt01.temperature</th></tr></thead> <tbody><tr><td>1</td> <td>21</td></tr> <tr><td>3</td> <td>23</td></tr> <tr><td>5</td> <td>25</td></tr> <tr><td>20</td> <td>26</td></tr> <tr><td>27</td> <td>29</td></tr> <tr><td>28</td> <td>30</td></tr> <tr><td>30</td> <td>40</td></tr></tbody></table> <p>当我们使用Previous插值方式时，即使D到M这一段是未来的数据，我们也会用D点的数据进行填充</p> <p><code>SELECT last_value(temperature) as last_temperature FROM root.ln.wf01.wt01 GROUP BY([8, 39), 5m) FILL (int32[previous])</code></p> <table><thead><tr><th>Time</th> <th>last_temperature</th></tr></thead> <tbody><tr><td>8</td> <td>25</td></tr> <tr><td>13</td> <td>25</td></tr> <tr><td>18</td> <td>26</td></tr> <tr><td>23</td> <td>29</td></tr> <tr><td>28</td> <td>40</td></tr> <tr><td>33</td> <td>40</td></tr> <tr><td>38</td> <td>40</td></tr></tbody></table> <p>当我们使用NONLASTPREVIOUS插值方式时，因为D到M这一段是未来的数据，我们不会进行插值，还是返回null</p> <p><code>SELECT last_value(temperature) as last_temperature FROM root.ln.wf01.wt01 GROUP BY([8, 39), 5m) FILL (int32[PREVIOUSUNTILLAST])</code></p> <table><thead><tr><th>Time</th> <th>last_temperature</th></tr></thead> <tbody><tr><td>8</td> <td>25</td></tr> <tr><td>13</td> <td>25</td></tr> <tr><td>18</td> <td>26</td></tr> <tr><td>23</td> <td>29</td></tr> <tr><td>28</td> <td>40</td></tr> <tr><td>33</td> <td>null</td></tr> <tr><td>38</td> <td>null</td></tr></tbody></table> <h2 id="核心查询逻辑"><a href="#核心查询逻辑" class="header-anchor">#</a> 核心查询逻辑</h2> <p>在<code>GroupByFillDataSet</code>中维护了两个主要变量</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>// the first value for each time series
private Object[] previousValue;
// last timestamp for each time series
private long[] lastTimeArray;
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br></div></div><h3 id="previousvalue"><a href="#previousvalue" class="header-anchor">#</a> <code>previousValue</code></h3> <p><code>previousValue</code>这个变量维护了当前时间窗口的前一个降采样值，在<code>GroupByFillDataSet</code>构造函数中调用了<code>initPreviousParis</code>方法对其进行初始化。</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>  private void initPreviousParis(QueryContext context, GroupByFillPlan groupByFillPlan)
          throws StorageEngineException, IOException, QueryProcessException {
    previousValue = new Object[paths.size()];
    for (int i = 0; i &lt; paths.size(); i++) {
      Path path = paths.get(i);
      TSDataType dataType = dataTypes.get(i);
      IFill fill = new PreviousFill(dataType, groupByEngineDataSet.getStartTime(), -1L);
      fill.constructReaders(path, groupByFillPlan.getAllMeasurementsInDevice(path.getDevice()), context);

      TimeValuePair timeValuePair = fill.getFillResult();
      if (timeValuePair == null || timeValuePair.getValue() == null) {
        previousValue[i] = null;
      } else {
        previousValue[i] = timeValuePair.getValue().getValue();
      }
    }
  }
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br><span class="line-number">13</span><br><span class="line-number">14</span><br><span class="line-number">15</span><br><span class="line-number">16</span><br><span class="line-number">17</span><br></div></div><p><code>initPreviousParis</code>方法主要为每个时间序列构造了一个单点补空值查询，<code>queryTime</code>设置为降采样时间窗口的起始值，<code>beforeRange</code>不作限制。</p> <h3 id="lasttimearray"><a href="#lasttimearray" class="header-anchor">#</a> <code>lastTimeArray</code></h3> <p><code>lastTimeArray</code>这个变量维护了每个时间序列的最近时间戳值，主要用于<code>PREVIOUSUNTILLAST</code>这一填充方式，在<code>GroupByFillDataSet</code>构造函数中调用了<code>initLastTimeArray</code>方法对其进行初始化。</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>  private void initLastTimeArray(QueryContext context)
      throws IOException, StorageEngineException, QueryProcessException {
    lastTimeArray = new long[paths.size()];
    Arrays.fill(lastTimeArray, Long.MAX_VALUE);
    for (int i = 0; i &lt; paths.size(); i++) {
      TimeValuePair lastTimeValuePair =
          LastQueryExecutor.calculateLastPairForOneSeries(paths.get(i), dataTypes.get(i), context);
      if (lastTimeValuePair.getValue() != null) {
        lastTimeArray[i] = lastTimeValuePair.getTimestamp();
      }
    }
  }
</code></pre> <div class="line-numbers-wrapper"><span class="line-number">1</span><br><span class="line-number">2</span><br><span class="line-number">3</span><br><span class="line-number">4</span><br><span class="line-number">5</span><br><span class="line-number">6</span><br><span class="line-number">7</span><br><span class="line-number">8</span><br><span class="line-number">9</span><br><span class="line-number">10</span><br><span class="line-number">11</span><br><span class="line-number">12</span><br></div></div><p><code>initPreviousParis</code>方法主要为每个时间序列构造了一个最近时间戳 Last 查询</p> <h3 id="填充过程"><a href="#填充过程" class="header-anchor">#</a> 填充过程</h3> <p>填充过程在<code>nextWithoutConstraint</code>方法中完成，主要逻辑如下：</p> <div class="language- line-numbers-mode"><pre class="language-text"><code>protected RowRecord nextWithoutConstraint() throws IOException {

    // 首先通过groupByEngineDataSet，获得原始的降采样查询结果行
    RowRecord rowRecord = groupByEngineDataSet.nextWithoutConstraint();

    // 接下来对每个时间序列判断需不需要填充
    for (int i = 0; i &lt; paths.size(); i++) {
      Field field = rowRecord.getFields().get(i);
      // 当前值为null，需要进行填充
      if (field.getDataType() == null) {
        // 当前一个值不为null 并且 (填充方式不是PREVIOUSUNTILLAST 或者 当前时间小于改时间序列的最近时间戳)
        if (previousValue[i] != null
            &amp;&amp; (!((PreviousFill) fillTypes.get(dataTypes.get(i))).isUntilLast()
            || rowRecord.getTimestamp() &lt;= lastTimeArray[i])) {
          rowRecord.getFields().set(i, Field.getField(previousValue[i], dataTypes.get(i)));
        }
      } else {
        // 当前值不为null，不需要填充，用当前值更新previousValue数组
        previousValue[i] = field.getObjectValue(field.getDataType());
      }
    }
    return rowRecord;
  }
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