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# 通过 Java 使用 CreateDataset API 和 AWS CLI
创建数据集。数据集存储通过应用 `queryAction`（SQL 查询）或 `containerAction`（执行容器化应用程序）从数据存储中检索的数据。此操作创建数据集的骨架。该数据集可以通过调用 `CreateDatasetContent` 手动填充，也可以根据您指定的 `trigger` 自动填充。有关更多信息，请参阅[CreateDataset](https://docs.aws.amazon.com/iotanalytics/latest/APIReference/API_CreateDataset.html)和[https://docs.aws.amazon.com/iotanalytics/latest/APIReference/API_CreateDatasetContent.html](https://docs.aws.amazon.com/iotanalytics/latest/APIReference/API_CreateDatasetContent.html)。
**Topics**
+ [示例 1 - 创建 SQL 数据集 (java)](#automate-example1)
+ [示例 2 - 创建具有增量时段的 SQL 数据集 (Java)](#automate-example2)
+ [示例 3 - 创建自带计划触发器的容器数据集 (Java)](#automate-example3)
+ [示例 4 - 创建以 SQL 数据集作为触发器的容器数据集 (Java)](#automate-example4)
+ [示例 5 - 创建 SQL 数据集 (CLI)](#automate-example5)
+ [示例 6 - 创建具有增量时段的 SQL 数据集 (CLI)](#automate-example6)
## 示例 1 - 创建 SQL 数据集 (java)
```
CreateDatasetRequest request = new CreateDatasetRequest();
request.setDatasetName(dataSetName);
DatasetAction action = new DatasetAction();
//Create Action
action.setActionName("SQLAction1");
action.setQueryAction(new SqlQueryDatasetAction().withSqlQuery("select * from DataStoreName"));
// Add Action to Actions List
List actions = new ArrayList();
actions.add(action);
//Create Trigger
DatasetTrigger trigger = new DatasetTrigger();
trigger.setSchedule(new Schedule().withExpression("cron(0 12 * * ? *)"));
//Add Trigger to Triggers List
List triggers = new ArrayList();
triggers.add(trigger);
// Add Triggers and Actions to CreateDatasetRequest object
request.setActions(actions);
request.setTriggers(triggers);
// Add RetentionPeriod to CreateDatasetRequest object
request.setRetentionPeriod(new RetentionPeriod().withNumberOfDays(10));
final CreateDatasetResult result = iot.createDataset(request);
```
成功时的输出：
```
{DatasetName: , DatasetArn: , RetentionPeriod: {unlimited: true} or {numberOfDays: 10, unlimited: false}}
```
## 示例 2 - 创建具有增量时段的 SQL 数据集 (Java)
```
CreateDatasetRequest request = new CreateDatasetRequest();
request.setDatasetName(dataSetName);
DatasetAction action = new DatasetAction();
//Create Filter for DeltaTime
QueryFilter deltaTimeFilter = new QueryFilter();
deltaTimeFilter.withDeltaTime(
new DeltaTime()
.withOffsetSeconds(-1 * EstimatedDataDelayInSeconds)
.withTimeExpression("from_unixtime(timestamp)"));
//Create Action
action.setActionName("SQLActionWithDeltaTime");
action.setQueryAction(new SqlQueryDatasetAction()
.withSqlQuery("SELECT * from DataStoreName")
.withFilters(deltaTimeFilter));
// Add Action to Actions List
List actions = new ArrayList();
actions.add(action);
//Create Trigger
DatasetTrigger trigger = new DatasetTrigger();
trigger.setSchedule(new Schedule().withExpression("cron(0 12 * * ? *)"));
//Add Trigger to Triggers List
List triggers = new ArrayList();
triggers.add(trigger);
// Add Triggers and Actions to CreateDatasetRequest object
request.setActions(actions);
request.setTriggers(triggers);
// Add RetentionPeriod to CreateDatasetRequest object
request.setRetentionPeriod(new RetentionPeriod().withNumberOfDays(10));
final CreateDatasetResult result = iot.createDataset(request);
```
成功时的输出：
```
{DatasetName: , DatasetArn: , RetentionPeriod: {unlimited: true} or {numberOfDays: 10, unlimited: false}}
```
## 示例 3 - 创建自带计划触发器的容器数据集 (Java)
```
CreateDatasetRequest request = new CreateDatasetRequest();
request.setDatasetName(dataSetName);
DatasetAction action = new DatasetAction();
//Create Action
action.setActionName("ContainerActionDataset");
action.setContainerAction(new ContainerDatasetAction()
.withImage(ImageURI)
.withExecutionRoleArn(ExecutionRoleArn)
.withResourceConfiguration(
new ResourceConfiguration()
.withComputeType(new ComputeType().withAcu(1))
.withVolumeSizeInGB(1))
.withVariables(new Variable()
.withName("VariableName")
.withStringValue("VariableValue"));
// Add Action to Actions List
List actions = new ArrayList();
actions.add(action);
//Create Trigger
DatasetTrigger trigger = new DatasetTrigger();
trigger.setSchedule(new Schedule().withExpression("cron(0 12 * * ? *)"));
//Add Trigger to Triggers List
List triggers = new ArrayList();
triggers.add(trigger);
// Add Triggers and Actions to CreateDatasetRequest object
request.setActions(actions);
request.setTriggers(triggers);
// Add RetentionPeriod to CreateDatasetRequest object
request.setRetentionPeriod(new RetentionPeriod().withNumberOfDays(10));
final CreateDatasetResult result = iot.createDataset(request);
```
成功时的输出：
```
{DatasetName: , DatasetArn: , RetentionPeriod: {unlimited: true} or {numberOfDays: 10, unlimited: false}}
```
## 示例 4 - 创建以 SQL 数据集作为触发器的容器数据集 (Java)
```
CreateDatasetRequest request = new CreateDatasetRequest();
request.setDatasetName(dataSetName);
DatasetAction action = new DatasetAction();
//Create Action
action.setActionName("ContainerActionDataset");
action.setContainerAction(new ContainerDatasetAction()
.withImage(ImageURI)
.withExecutionRoleArn(ExecutionRoleArn)
.withResourceConfiguration(
new ResourceConfiguration()
.withComputeType(new ComputeType().withAcu(1))
.withVolumeSizeInGB(1))
.withVariables(new Variable()
.withName("VariableName")
.withStringValue("VariableValue"));
// Add Action to Actions List
List actions = new ArrayList();
actions.add(action);
//Create Trigger
DatasetTrigger trigger = new DatasetTrigger()
.withDataset(new TriggeringDataset()
.withName(TriggeringSQLDataSetName));
//Add Trigger to Triggers List
List triggers = new ArrayList();
triggers.add(trigger);
// Add Triggers and Actions to CreateDatasetRequest object
request.setActions(actions);
request.setTriggers(triggers);
final CreateDatasetResult result = iot.createDataset(request);
```
成功时的输出：
```
{DatasetName: , DatasetArn: }
```
## 示例 5 - 创建 SQL 数据集 (CLI)
```
aws iotanalytics --endpoint --region create-dataset --dataset-name="" --actions="[{\"actionName\":\"\", \"queryAction\":{\"sqlQuery\":\"\"}}]" --retentionPeriod numberOfDays=10
```
成功时的输出：
```
{
"datasetName": "",
"datasetArn": "",
"retentionPeriod": {unlimited: true} or {numberOfDays: 10, unlimited: false}
}
```
## 示例 6 - 创建具有增量时段的 SQL 数据集 (CLI)
增量时段是一系列用户定义的不重叠且连续的时间间隔。通过使用增量时段，您可以使用在上次分析后到达数据存储的新数据创建数据集内容，并对新数据执行分析。您可以通过在数据集的`filters`部分`deltaTime`中设置来创建增量窗口 ([CreateDataset](https://docs.aws.amazon.com/iotanalytics/latest/userguide/api.html#cli-iotanalytics-createdataset))。`queryAction`通常，您还希望设置时间间隔触发器以自动创建数据集内容 (`triggers:schedule:expression`)。基本上，这使您可以筛选在特定时间窗口中到达的消息，这样来自以前时间窗口的消息中包含的数据不会重复计数。
在该示例中，我们创建一个新数据集，它仅使用在上次传送后到达的数据每隔 15 分钟自动创建新的数据集内容。我们指定 3 分钟（180 秒）的 `deltaTime` 偏差，以允许到达指定数据存储的消息延迟 3 分钟。因此，如果数据集内容是在上午 10:30 创建的，则使用的数据（包含在数据集内容中）的时间戳在上午 10:12 到上午 10:27 之间（即上午 10:30 - 15 分钟 - 3 分钟到上午 10:30 - 3 分钟）。
```
aws iotanalytics --endpoint --region create-dataset --cli-input-json file://delta-window.json
```
`delta-window.json` 文件包含以下内容。
```
{
"datasetName": "delta_window_example",
"actions": [
{
"actionName": "delta_window_action",
"queryAction": {
"sqlQuery": "SELECT temperature, humidity, timestamp FROM my_datastore",
"filters": [
{
"deltaTime": {
"offsetSeconds": -180,
"timeExpression": "from_unixtime(timestamp)"
}
}
]
}
}
],
"triggers": [
{
"schedule": {
"expression": "cron(0/15 * * * ? *)"
}
}
]
}
```
成功时的输出：
```
{
"datasetName": "",
"datasetArn": "",
}
```