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fillCache(int)")]),a._v(" "),s("p",[a._v("该方法负责从阻塞队列中取出数据，并填充缓存，具体逻辑见下文注释")]),a._v(" "),s("div",{staticClass:"language- line-numbers-mode"},[s("pre",{pre:!0,attrs:{class:"language-text"}},[s("code",[a._v("private void fillCache(int seriesIndex) throws InterruptedException {\n // 从阻塞队列中拿数据，如果没有数据，则会阻塞等待队列中有数据\n\tBatchData batchData = blockingQueueArray[seriesIndex].take();\n\t// 如果是一个信号BatchData，则将相应时间序列的oMoreDataInQueue置为false\n\tif (batchData instanceof SignalBatchData) {\n\t noMoreDataInQueueArray[seriesIndex] = true;\n\t}\n\telse {\n\t // 将取出的BatchData放进cachedBatchDataArray缓存起来\n\t cachedBatchDataArray[seriesIndex] = batchData;\n\t\n\t // 这里加锁的原因与生产者任务那边一样，是为了保证对于blockingQueue满不满的判断是正确同步的\n\t synchronized (seriesReaderList.get(seriesIndex)) {\n\t // 只有当阻塞队列不满的时候，我们才需要判断是不是需要提交生产者任务，这里也保证了生产者任务会被提交，当且仅当阻塞队列不满\n\t if (blockingQueueArray[seriesIndex].remainingCapacity() > 0) {\n\t ManagedSeriesReader reader = seriesReaderList.get(seriesIndex);、\n\t // 如果该时间序列的reader并没有被查询管理器管理(即生产者任务由于队列满了，自行退出)，并且该reader里还有数据，我们需要再次提交该时间序列的生产者任务\n\t if (!reader.isManagedByQueryManager() && reader.hasRemaining()) {\n\t reader.setManagedByQueryManager(true);\n\t pool.submit(new ReadTask(reader, blockingQueueArray[seriesIndex]));\n\t }\n\t }\n\t }\n\t}\n}\n")])]),a._v(" 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cachedBatchDataArray[seriesIndex].currentTime();\n timeHeap.add(time);\n }\n}\n")])]),a._v(" 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不包含值过滤条件 + 结果集不按时间戳对齐")]),a._v(" "),s("h3",{attrs:{id:"org-apache-iotdb-db-query-dataset-nonalignenginedataset"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#org-apache-iotdb-db-query-dataset-nonalignenginedataset"}},[a._v("#")]),a._v(" org.apache.iotdb.db.query.dataset.NonAlignEngineDataSet")]),a._v(" "),s("p",[s("code",[a._v("NonAlignEngineDataSet")]),a._v("实现了没有值过滤条件，且不需要按照时间戳对齐的查询逻辑。这里的查询逻辑跟"),s("code",[a._v("RawQueryDataSetWithoutValueFilter")]),a._v("很类似，但是它的消费者逻辑更为简单，因为不需要做时间戳对齐的操作。并且每个生产者任务中也可以做更多的工作，不仅可以从Reader中取出BatchData，还可以进一步讲取出的BatchData格式化为结果集需要的输出，从而提高了程序的并行度。如此，消费者只需要从每个阻塞队列里取出数据，set进"),s("code",[a._v("TSQueryNonAlignDataSet")]),a._v("相应的位置即可。")]),a._v(" "),s("p",[a._v("具体的查询逻辑，在此就不再赘述了，可以参照"),s("code",[a._v("RawQueryDataSetWithoutValueFilter")]),a._v("的查询逻辑分析。")]),a._v(" "),s("h2",{attrs:{id:"包含值过滤条件-结果集按时间戳对齐"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#包含值过滤条件-结果集按时间戳对齐"}},[a._v("#")]),a._v(" 包含值过滤条件 + 结果集按时间戳对齐")]),a._v(" 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"),s("h4",{attrs:{id:"cacherowrecord"}},[s("a",{staticClass:"header-anchor",attrs:{href:"#cacherowrecord"}},[a._v("#")]),a._v(" cacheRowRecord()")]),a._v(" "),s("div",{staticClass:"language- line-numbers-mode"},[s("pre",{pre:!0,attrs:{class:"language-text"}},[s("code",[a._v("private boolean cacheRowRecord() throws IOException {\n // 判断有没有下一个符合条件的时间戳\n\twhile (timeGenerator.hasNext()) {\n\t boolean hasField = false;\n\t // 获得当前符合条件的时间戳\n\t long timestamp = timeGenerator.next();\n\t RowRecord rowRecord = new RowRecord(timestamp);\n\t for (int i = 0; i < seriesReaderByTimestampList.size(); i++) {\n\t // 根获得每个时间序列当前时间戳下的value\n\t IReaderByTimestamp reader = seriesReaderByTimestampList.get(i);\n\t Object value = reader.getValueInTimestamp(timestamp);\n\t // 如果该时间序列在当前时间戳下没有值，则置null\n\t if (value == null) {\n\t rowRecord.addField(null);\n\t } \n\t // 否则将hasField置为true\n\t else {\n\t hasField = true;\n\t rowRecord.addField(value, dataTypes.get(i));\n\t }\n\t }\n\t // 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