Overview

iotdb-kairosdb is a RESTful service for using IoTDB by KairosDB RESTful APIs.

Usage

Configurations are in conf/config.properties

To start the RESTful service:

run > ./start-rest-service.sh

Doc

IKR部署与测试用例

1 测试环境部署
2 测试用例

1 测试环境部署

1.1 测试环境要求

Ubuntu 16.04
Java 8
Maven
Git

1.2 IoTDB部署

下载 IoTDB

$ git clone https://github.com/apache/incubator-iotdb.git

安装 IoTDB

$ cd incubator-iotdb
$ mvn clean install -Dmaven.test.skip=true

后台启动 IoTDB

$ nohup ./iotdb/iotdb/bin/start-server.sh &

关闭 IoTDB[仅用于当需要操作关闭IoTDB时]
```
$ ./iotdb/iotdb/bin/stop-server.sh
```

1.3 IKR 部署

如果 IKR 和 IoTDB在同一台机器上，步骤 1 和 2 可以省略

在 IKR 的工作目录下，下载 IoTDB

$ git clone https://github.com/apache/incubator-iotdb.git

安装IoTDB

$ mvn clean install -Dmaven.test.skip=true

下载 IKR
```
$ git clone https://github.com/thulab/iotdb-kairosdb.git

$ cd iotdb-kairosdb

4. 配置 IKR

$ vim conf/config.properties

配置HOST和PORT，对应IoTDB所在的IP和端口
5. 后台启动 IKR

$ nohup ./start-rest-service.sh &

5. 关闭 IKR[仅用于当需要操作关闭IKR时]

$ ./stop-rest-service-daemon.sh


## 2 测试用例
### 2.1 写入测试用例
1. 编写测试 JSON 文件作为写入请求的body

$ vim insert.json

输入以下 JSON :

[
{
“name”: “archive_file_tracked”,
“datapoints”: [
[1359788400000, 123.3],
[1359788300000, 13.2 ],
[1359788410000, 23.1 ]
],
“tags”: {
“host”: “server1”,
“data_center”: “DC1”
}
},
{
“name”: “archive_file_search”,
“timestamp”: 1359786400000,
“value”: 321,
“tags”: {
“host”: “server2”
}
}
]

2. 向 IKR 服务发送写入请求

$ curl -XPOST -H’Content-Type: application/json’ -d @insert.json http://[host]:[port]/api/v1/datapoints


3. 通过http请求查询写入数据，检查数据是否正确写入
编写查询JSON 文件

$ vim query.json

输入以下 JSON :

{
“start_absolute” : 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “archive_file_tracked”
},
{
“name”: “archive_file_search”
}
]
}

向 IKR 服务发送查询请求

curl -XPOST -H’Content-Type: application/json’ -d @query.json http://[host]:[port]/api/v1/datapoints/query

返回结果：

{“queries”:[{“sample_size”:3,”results”:[{“name”:”archive_file_tracked”,”group_by”:[{“name”:”type”,”type”:”number”}],”tags”:{“host”:[“server1”],”data_center”:[“DC1”]},”values”:[[1359788300000,13.2],[1359788400000,123.3],[1359788410000,23.1]]}]},{“sample_size”:1,”results”:[{“name”:”archive_file_search”,”group_by”:[{“name”:”type”,”type”:”number”}],”tags”:{“host”:[“server2”]},”values”:[[1359786400000,”321”]]}]}]}

为了便于阅读，将以上JSON字符串格式化后为:

{
“queries”: [
{
“sample_size”: 3,
“results”: [
{
“name”: “archive_file_tracked”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“host”: [
“server1”
],
“data_center”: [
“DC1”
]
},
“values”: [
[
1359788300000,
13.2
],
[
1359788400000,
123.3
],
[
1359788410000,
23.1
]
]
}
]
},
{
“sample_size”: 1,
“results”: [
{
“name”: “archive_file_search”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“host”: [
“server2”
]
},
“values”: [
[
1359786400000,
“321”
]
]
}
]
}
]
}

查询结果与写入数据一致，说明写入成功
### 2.2 查询测试
#### 2.2.1 基本查询测试用例
1. 准备查询测试用的测试数据，写入 JSON 为

[
{
“name”: “test_query”,
“datapoints”: [
[1400000000000, 12.3],
[1400000001000, 13.2],
[1400000002000, 23.1],
[1400000003000, 24.0],
[1400000004000, 24.1],
[1400000009000, 24.6],
[1400000010000, 24.7],
[1400000011000, 24.8],
[1400000012000, 24.9],
[1400000013000, 25.0],
[1400000014000, 25.1],
[1400000015000, 25.2],
[1400000016000, 25.3],
[1400000017000, 25.4],
[1400000023000, 26.0],
[1400000024000, 26.1],
[1400000025000, 26.2],
[1400000026000, 26.3],
[1400000027000, 26.4]
],
“tags”: {
“host”: “server1”,
“data_center”: “DC1”
}
},
{
“name”: “test_query”,
“datapoints”: [
[1400000005000, 24.2],
[1400000006000, 24.3],
[1400000007000, 24.4],
[1400000008000, 24.5],
[1400000018000, 25.5],
[1400000019000, 25.6],
[1400000020000, 25.7],
[1400000021000, 25.8],
[1400000022000, 25.9]
],
“tags”: {
“host”: “server2”,
“data_center”: “DC1”
}
}
]

写入方法与写入测试相同：

$ curl -XPOST -H’Content-Type: application/json’ -d @insert.json http://[host]:[port]/api/v1/datapoints


2. 基本查询测试用例
基本查询又叫简单查询，该查询的JSON中指定了查询的时间范围和查询的metric以及tag。查询结果返回是原始数据。
编写测试 JSON 文件

$ vim query.json

输入以下 JSON :

{
“start_absolute” : 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”
}]
}

向 IKR 服务发送查询请求

curl -XPOST -H’Content-Type: application/json’ -d @query.json http://[host]:[port]/api/v1/datapoints/query

返回结果：

格式化后为：

{
“queries”: [
{
“sample_size”:28,
“results”:[
{
“name”:”test_query”,
“group_by”:[
{
“name”:”type”,
“type”:”number”
}],
“tags”:{
“host”:[“server1”,”server2”],
“data_center”:[“DC1”]
},
“values”:[
[1400000000000,12.3],
[1400000001000,13.2],
[1400000002000,23.1],
[1400000003000,24.0],
[1400000004000,24.1],
[1400000005000,24.2],
[1400000006000,24.3],
[1400000007000,24.4],
[1400000008000,24.5],
[1400000009000,24.6],
[1400000010000,24.7],
[1400000011000,24.8],
[1400000012000,24.9],
[1400000013000,25.0],
[1400000014000,25.1],
[1400000015000,25.2],
[1400000016000,25.3],
[1400000017000,25.4],
[1400000018000,25.5],
[1400000019000,25.6],
[1400000020000,25.7],
[1400000021000,25.8],
[1400000022000,25.9],
[1400000023000,26.0],
[1400000024000,26.1],
[1400000025000,26.2],
[1400000026000,26.3],
[1400000027000,26.4]
]
}]
}]
}

#### 2.2.2 聚合查询测试用例
聚合查询是在基本查询的基础上加入aggregators字段进行的分析型复杂查询。以下聚合查询测试用例同样使用基本查询测试中写入的测试数据。
#### 2.2.2.1 均值聚合查询测试用例（avg）
创建 avg_query.json

$ vim avg_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“tags”: {
“host”: [
“server2”
]
},
“aggregators”: [
{
“name”: “avg”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @avg_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

{“queries”:[{“sample_size”:9,”results”:[{“name”:”test_query”,”group_by”:[{“name”:”type”,”type”:”number”}],”tags”:{“host”:[“server2”],”data_center”:[“DC1”]},”values”:[[1400000005000,24.25],[1400000007000,24.45],[1400000018000,25.5],[1400000019000,25.65],[1400000021000,25.85]]}]}]}

格式化后为：

{
“queries”: [
{
“sample_size”: 9,
“results”: [
{
“name”: “test_query”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“host”: [
“server2”
],
“data_center”: [
“DC1”
]
},
“values”: [
[
1400000005000,
24.25
],
[
1400000007000,
24.45
],
[
1400000018000,
25.5
],
[
1400000019000,
25.65
],
[
1400000021000,
25.85
]
]
}
]
}
]
}

#### 2.2.2.2 方差聚合查询测试用例（dev）
创建 dev_query.json

$ vim dev_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “dev”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
},
“return_type”:”value”
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @dev_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.3 计数聚合查询测试用例（count）
创建 count_query.json

$ vim count_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “count”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @count_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.4 首值聚合查询测试用例（first）
创建 first_query.json

$ vim first_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “first”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @first_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.5 尾值聚合查询测试用例（last）
创建 last_query.json

$ vim last_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “last”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @last_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.6 最大值聚合查询测试用例（max）
创建 max_query.json

$ vim max_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “max”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @max_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.7 最小值聚合查询测试用例（min）
创建 min_query.json

$ vim min_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “min”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @min_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.8 求和值聚合查询测试用例（sum）
创建 sum_query.json

$ vim sum_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “sum”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @sum_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.9 一阶差分聚合查询测试用例（diff）
创建 diff_query.json

$ vim diff_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “diff”
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @diff_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

{“queries”:[{“sample_size”:28,”results”:[{“name”:”test_query”,”group_by”:[{“name”:”type”,”type”:”number”}],”tags”:{“host”:[“server1”,”server2”],”data_center”:[“DC1”]},”values”:[[1400000001000,0.8999999999999986],[1400000002000,9.900000000000002],[1400000003000,0.8999999999999986],[1400000004000,0.10000000000000142],[1400000005000,0.09999999999999787],[1400000006000,0.10000000000000142],[1400000007000,0.09999999999999787],[1400000008000,0.10000000000000142],[1400000009000,0.10000000000000142],[1400000010000,0.09999999999999787],[1400000011000,0.10000000000000142],[1400000012000,0.09999999999999787],[1400000013000,0.10000000000000142],[1400000014000,0.10000000000000142],[1400000015000,0.09999999999999787],[1400000016000,0.10000000000000142],[1400000017000,0.09999999999999787],[1400000018000,0.10000000000000142],[1400000019000,0.10000000000000142],[1400000020000,0.09999999999999787],[1400000021000,0.10000000000000142],[1400000022000,0.09999999999999787],[1400000023000,0.10000000000000142],[1400000024000,0.10000000000000142],[1400000025000,0.09999999999999787],[1400000026000,0.10000000000000142],[1400000027000,0.09999999999999787]]}]}]}

格式化后为：

{
“queries”: [
{
“sample_size”: 28,
“results”: [
{
“name”: “test_query”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“host”: [
“server1”,
“server2”
],
“data_center”: [
“DC1”
]
},
“values”: [
[
1400000001000,
0.8999999999999986
],
[
1400000002000,
9.900000000000002
],
[
1400000003000,
0.8999999999999986
],
[
1400000004000,
0.10000000000000142
],
[
1400000005000,
0.09999999999999787
],
[
1400000006000,
0.10000000000000142
],
[
1400000007000,
0.09999999999999787
],
[
1400000008000,
0.10000000000000142
],
[
1400000009000,
0.10000000000000142
],
[
1400000010000,
0.09999999999999787
],
[
1400000011000,
0.10000000000000142
],
[
1400000012000,
0.09999999999999787
],
[
1400000013000,
0.10000000000000142
],
[
1400000014000,
0.10000000000000142
],
[
1400000015000,
0.09999999999999787
],
[
1400000016000,
0.10000000000000142
],
[
1400000017000,
0.09999999999999787
],
[
1400000018000,
0.10000000000000142
],
[
1400000019000,
0.10000000000000142
],
[
1400000020000,
0.09999999999999787
],
[
1400000021000,
0.10000000000000142
],
[
1400000022000,
0.09999999999999787
],
[
1400000023000,
0.10000000000000142
],
[
1400000024000,
0.10000000000000142
],
[
1400000025000,
0.09999999999999787
],
[
1400000026000,
0.10000000000000142
],
[
1400000027000,
0.09999999999999787
]
]
}
]
}
]
}

#### 2.2.2.10 除法聚合查询测试用例（div）
创建 div_query.json

$ vim div_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “div”,
“divisor”: “2”
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @div_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

{
“queries”: [
{
“sample_size”: 28,
“results”: [
{
“name”: “test_query”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“host”: [
“server1”,
“server2”
],
“data_center”: [
“DC1”
]
},
“values”: [
[
1400000000000,
6.15
],
[
1400000001000,
6.6
],
[
1400000002000,
11.55
],
[
1400000003000,
12
],
[
1400000004000,
12.05
],
[
1400000005000,
12.1
],
[
1400000006000,
12.15
],
[
1400000007000,
12.2
],
[
1400000008000,
12.25
],
[
1400000009000,
12.3
],
[
1400000010000,
12.35
],
[
1400000011000,
12.4
],
[
1400000012000,
12.45
],
[
1400000013000,
12.5
],
[
1400000014000,
12.55
],
[
1400000015000,
12.6
],
[
1400000016000,
12.65
],
[
1400000017000,
12.7
],
[
1400000018000,
12.75
],
[
1400000019000,
12.8
],
[
1400000020000,
12.85
],
[
1400000021000,
12.9
],
[
1400000022000,
12.95
],
[
1400000023000,
13
],
[
1400000024000,
13.05
],
[
1400000025000,
13.1
],
[
1400000026000,
13.15
],
[
1400000027000,
13.2
]
]
}
]
}
]
}

#### 2.2.2.11 值过滤聚合查询测试用例（filter）
创建 filter_query.json

$ vim filter_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “filter”,
“filter_op”: “lt”,
“threshold”: “25”
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @filter_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.12 另存为聚合查询测试用例（save_as）
创建 save_as_query.json

$ vim save_as_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “save_as”,
“metric_name”: “test_save_as”
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @save_as_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

{
“queries”: [
{
“sample_size”: 28,
“results”: [
{
“name”: “test_query”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“host”: [
“server1”,
“server2”
],
“data_center”: [
“DC1”
]
},
“values”: [
[
1400000000000,
12.3
],
[
1400000001000,
13.2
],
[
1400000002000,
23.1
],
[
1400000003000,
24
],
[
1400000004000,
24.1
],
[
1400000005000,
24.2
],
[
1400000006000,
24.3
],
[
1400000007000,
24.4
],
[
1400000008000,
24.5
],
[
1400000009000,
24.6
],
[
1400000010000,
24.7
],
[
1400000011000,
24.8
],
[
1400000012000,
24.9
],
[
1400000013000,
25
],
[
1400000014000,
25.1
],
[
1400000015000,
25.2
],
[
1400000016000,
25.3
],
[
1400000017000,
25.4
],
[
1400000018000,
25.5
],
[
1400000019000,
25.6
],
[
1400000020000,
25.7
],
[
1400000021000,
25.8
],
[
1400000022000,
25.9
],
[
1400000023000,
26
],
[
1400000024000,
26.1
],
[
1400000025000,
26.2
],
[
1400000026000,
26.3
],
[
1400000027000,
26.4
]
]
}
]
}
]
}

然后查询 test_save_as

$ curl -XPOST -H’Content-Type: application/json’ —data “{“start_absolute”:1,”end_relative”:{“value”:”5”,”unit”:”days”},”time_zone”:”Asia/Kabul”,”metrics”:[{“name”:”test_save_as”}]}” http://[host]:[port]/api/v1/datapoints/query

得到返回：

{“queries”:[{“sample_size”:28,”results”:[{“name”:”test_save_as”,”group_by”:[{“name”:”type”,”type”:”number”}],”tags”:{“saved_from”:[“test_query”]},”values”:[[1400000000000,12.3],[1400000001000,13.2],[1400000002000,23.1],[1400000003000,24.0],[1400000004000,24.1],[1400000005000,24.2],[1400000006000,24.3],[1400000007000,24.4],[1400000008000,24.5],[1400000009000,24.6],[1400000010000,24.7],[1400000011000,24.8],[1400000012000,24.9],[1400000013000,25.0],[1400000014000,25.1],[1400000015000,25.2],[1400000016000,25.3],[1400000017000,25.4],[1400000018000,25.5],[1400000019000,25.6],[1400000020000,25.7],[1400000021000,25.8],[1400000022000,25.9],[1400000023000,26.0],[1400000024000,26.1],[1400000025000,26.2],[1400000026000,26.3],[1400000027000,26.4]]}]}]}

格式化后为：

{
“queries”: [
{
“sample_size”: 28,
“results”: [
{
“name”: “test_save_as”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“saved_from”: [
“test_query”
]
},
“values”: [
[
1400000000000,
12.3
],
[
1400000001000,
13.2
],
[
1400000002000,
23.1
],
[
1400000003000,
24
],
[
1400000004000,
24.1
],
[
1400000005000,
24.2
],
[
1400000006000,
24.3
],
[
1400000007000,
24.4
],
[
1400000008000,
24.5
],
[
1400000009000,
24.6
],
[
1400000010000,
24.7
],
[
1400000011000,
24.8
],
[
1400000012000,
24.9
],
[
1400000013000,
25
],
[
1400000014000,
25.1
],
[
1400000015000,
25.2
],
[
1400000016000,
25.3
],
[
1400000017000,
25.4
],
[
1400000018000,
25.5
],
[
1400000019000,
25.6
],
[
1400000020000,
25.7
],
[
1400000021000,
25.8
],
[
1400000022000,
25.9
],
[
1400000023000,
26
],
[
1400000024000,
26.1
],
[
1400000025000,
26.2
],
[
1400000026000,
26.3
],
[
1400000027000,
26.4
]
]
}
]
}
]
}

#### 2.2.2.13 变化率聚合查询测试用例（rate）
> 变化率：相邻两个值单位时间内的变化幅度
> sampling中的value字段不起作用，只由unit决定变化率的单位
创建 rate_query.json

$ vim rate_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “rate”,
“sampling”: {
“value”: 1,
“unit”: “seconds”
}
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @rate_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：

#### 2.2.2.14 采样率聚合查询测试用例（sampler）
> 采样率 = 当前数据点的值 * （单位时间(unit) / （当前点的时间戳 - 前一个点的时间戳））
>
> 返回数据点数 = 原始数据点数 - 1 （不计算第一个数据点）
创建 sampler_query.json

$ vim sampler_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “sampler”,
“unit”: “minutes”
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @sampler_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

{“queries”:[{“sample_size”:28,”results”:[{“name”:”test_query”,”group_by”:[{“name”:”type”,”type”:”number”}],”tags”:{“host”:[“server1”,”server2”],”data_center”:[“DC1”]},”values”:[[1400000001000,792.0],[1400000002000,1386.0],[1400000003000,1440.0],[1400000004000,1446.0],[1400000005000,1452.0],[1400000006000,1458.0],[1400000007000,1464.0],[1400000008000,1470.0],[1400000009000,1476.0],[1400000010000,1482.0],[1400000011000,1488.0],[1400000012000,1494.0],[1400000013000,1500.0],[1400000014000,1506.0],[1400000015000,1512.0],[1400000016000,1518.0],[1400000017000,1524.0],[1400000018000,1530.0],[1400000019000,1536.0],[1400000020000,1542.0],[1400000021000,1548.0],[1400000022000,1554.0],[1400000023000,1560.0],[1400000024000,1566.0],[1400000025000,1572.0],[1400000026000,1578.0],[1400000027000,1584.0]]}]}]}

格式化后为：

{
“queries”: [
{
“sample_size”: 28,
“results”: [
{
“name”: “test_query”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“host”: [
“server1”,
“server2”
],
“data_center”: [
“DC1”
]
},
“values”: [
[
1400000001000,
792
],
[
1400000002000,
1386
],
[
1400000003000,
1440
],
[
1400000004000,
1446
],
[
1400000005000,
1452
],
[
1400000006000,
1458
],
[
1400000007000,
1464
],
[
1400000008000,
1470
],
[
1400000009000,
1476
],
[
1400000010000,
1482
],
[
1400000011000,
1488
],
[
1400000012000,
1494
],
[
1400000013000,
1500
],
[
1400000014000,
1506
],
[
1400000015000,
1512
],
[
1400000016000,
1518
],
[
1400000017000,
1524
],
[
1400000018000,
1530
],
[
1400000019000,
1536
],
[
1400000020000,
1542
],
[
1400000021000,
1548
],
[
1400000022000,
1554
],
[
1400000023000,
1560
],
[
1400000024000,
1566
],
[
1400000025000,
1572
],
[
1400000026000,
1578
],
[
1400000027000,
1584
]
]
}
]
}
]
}

#### 2.2.2.15 百分位数聚合查询测试用例（percentile）
创建 percentile_query.json

$ vim percentile_query.json

输入以下内容

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“aggregators”: [
{
“name”: “percentile”,
“sampling”: {
“value”: “5”,
“unit”: “seconds”
},
“percentile”: “0.75”
}
]
}
]
}

执行以下命令

$ curl -XPOST -H’Content-Type: application/json’ -d @percentile_query.json http://[host]:[port]/api/v1/datapoints/query

得到返回：

格式化后为：


### 2.3 删除功能测试用例
#### 2.3.1 删除数据点
1. 准备测试数据，写入 JSON 为

写入过程与插入测试相同：

$ curl -XPOST -H’Content-Type: application/json’ -d @insert.json http://[host]:[port]/api/v1/datapoints

2. 编写测试 JSON 文件

$ vim delete.json

输入以下 JSON :

{
“start_absolute” : 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”,
“tags”: {
“host”: [ “server2” ]
}
}
]
}

该JSON表示删除metric为test_query，且host为server2的所以数据
3. 向 IKR 服务发送删除请求

$ curl -XPOST -H’Content-Type: application/json’ -d @delete.json http://[host]:[port]/api/v1/datapoints/delete


4. 查询数据

$ curl -XPOST -H’Content-Type: application/json’ -d @query.json http://[host]:[port]/api/v1/datapoints/query

其中的 query.json 为：

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”
}]
}

得到结果：

{“queries”:[{“sample_size”:19,”results”:[{“name”:”test_query”,”group_by”:[{“name”:”type”,”type”:”number”}],”tags”:{“host”:[“server1”],”data_center”:[“DC1”]},”values”:[[1400000000000,12.3],[1400000001000,13.2],[1400000002000,23.1],[1400000003000,24.0],[1400000004000,24.1],[1400000009000,24.6],[1400000010000,24.7],[1400000011000,24.8],[1400000012000,24.9],[1400000013000,25.0],[1400000014000,25.1],[1400000015000,25.2],[1400000016000,25.3],[1400000017000,25.4],[1400000023000,26.0],[1400000024000,26.1],[1400000025000,26.2],[1400000026000,26.3],[1400000027000,26.4]]}]}]}

格式化后为：

{
“queries”:[
{
“sample_size”:19,
“results”:[
{
“name”:”test_query”,
“group_by”: [
{
“name”:”type”,
“type”:”number”
}],
“tags”:
{
“host”:[“server1”],
“data_center”:[“DC1”]
},
“values”: [
[1400000000000,12.3],
[1400000001000,13.2],
[1400000002000,23.1],
[1400000003000,24.0],
[1400000004000,24.1],
[1400000009000,24.6],
[1400000010000,24.7],
[1400000011000,24.8],
[1400000012000,24.9],
[1400000013000,25.0],
[1400000014000,25.1],
[1400000015000,25.2],
[1400000016000,25.3],
[1400000017000,25.4],
[1400000023000,26.0],
[1400000024000,26.1],
[1400000025000,26.2],
[1400000026000,26.3],
[1400000027000,26.4]
]
}]
}]
}

返回结果中没有host是server2的数据，说明删除成功。
#### 2.3.2 删除metric
1. 向 IKR 服务发送删除metric请求

$ curl -XDELETE http://[host]:[port]/api/v1/metric/[metric_name]

将[metric_name]替换为 test_query，以删除test_query这整个metric

$ curl -XDELETE http://[host]:[port]/api/v1/metric/test_query


2. 查询数据

$ curl -XPOST -H’Content-Type: application/json’ -d @query.json http://[host]:[port]/api/v1/datapoints/query

其中的 query.json 为：

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “days”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “test_query”
}]
}

得到结果：

{“queries”:[{“sample_size”:0,”results”:[{“name”:”test_query”,”group_by”:[],”tags”:{},”values”:[]}]}]}

格式化后为：

{
“queries”: [
{
“sample_size”: 0,
“results”: [
{
“name”: “test_query”,
“group_by”: [],
“tags”: {},
“values”: []
}
]
}
]
}


返回结果中没有任何数据，说明成功删除了metric。
### 2.4 Metadata 功能测试用例
1. 写入数据

$ curl -XPOST -H’Content-Type: application/json’ —data “t_value” http://[host]:[port]/api/v1/metadata/t_service/t_service_key/t_key

$ curl -XPOST -H’Content-Type: application/json’ —data “t_value2” http://[host]:[port]/api/v1/metadata/t_service/t_service_key/t_key2

$ curl -XPOST -H’Content-Type: application/json’ —data “t_value3” http://[host]:[port]/api/v1/metadata/t_service/t_service_key2/t_key3

$ curl -XPOST -H’Content-Type: application/json’ —data “t_value4” http://[host]:[port]/api/v1/metadata/t_service/t_service_key2/t_key4

$ curl -XPOST -H’Content-Type: application/json’ —data “t_value5” http://[host]:[port]/api/v1/metadata/t_service2/t_service_key3/t_key5

执行以上命令后，我们存入的数据应如下表所示：
service | service key | key | value
---|---|---|---
t_service | t_service_key | t_key | t_value
t_service | t_service_key | t_key2 | t_value2
t_service | t_service_key2 | t_key3 | t_value3
t_service | t_service_key2 | t_key4 | t_value4
t_service | t_service_key3 | t_key5 | t_value5
2. 读取数据

$ curl http://[host]:[port]/api/v1/metadata/t_service/t_service_key/t_key
t_value

$ curl http://[host]:[port]/api/v1/metadata/t_service/t_service_key/t_key2
t_value2

$ curl http://[host]:[port]/api/v1/metadata/t_service/t_service_key2/t_key3
t_value3

$ curl http://[host]:[port]/api/v1/metadata/t_service/t_service_key2/t_key4
t_value4

$ curl http://[host]:[port]/api/v1/metadata/t_service2/t_service_key3/t_key5
t_value5

$ curl http://[host]:[port]/api/v1/metadata/t_service/t_service_key
{“results”:[“t_key”,”t_key2”]}

$ curl http://[host]:[port]/api/v1/metadata/t_service/t_service_key2
{“results”:[“t_key3”,”t_key4”]}

$ curl http://[host]:[port]/api/v1/metadata/t_service2/t_service_key3
{“results”:[“t_key5”]}

$ curl http://[host]:[port]/api/v1/metadata/t_service
{“results”:[“t_service_key”,”t_service_key2”]}

$ curl http://[host]:[port]/api/v1/metadata/t_service2
{“results”:[“t_service_key3”]}

如上执行命令即可得到相应的值
3. 修改数据：修改数据和插入数据同理
4. 删除数据

$ curl -XDELETE http://[host]:[port]/api/v1/metadata/t_service/t_service_key/t_key2

执行以上命令删除对应的value，然后查询：

$ curl http://[host]:[port]/api/v1/metadata/t_service/t_service_key/t_key2

结果返回为空，即已经成功删除
### 2.5 Roll-up功能测试用例
####  2.5.1 准备数据
使用查询测试数据作为Roll-up功能的测试数据，需要特别注意的是rollup任务只对从rollup任务创建时刻开始，到当前时刻内的数据进行汇总。因此以下JSON中的时间戳需要根据执行时的真实时间修改，否则将有可能查询不到rollup汇总的结果数据：

vim insert.json

写入 JSON 为

写入过程与插入测试相同：

$ curl -XPOST -H’Content-Type: application/json’ -d @insert.json http://[host]:[port]/api/v1/datapoints

####  2.5.2 创建Roll-up任务
1. 编写创建Roll-up任务的JSON文件

$ vim create_rollup.json

输入以下 JSON :

{
“name”: “MyRollup1”,
“execution_interval”: {
“value”: 2,
“unit”: “seconds”
},
“rollups”: [
{
“save_as”: “rollup1”,
“query”: {
“start_relative”: {
“value”: “10”,
“unit”: “years”
},
“end_relative”: {
“value”: “1”,
“unit”: “seconds”
},
“metrics”: [
{
“name”: “test_query”,
“tags”: {},
“aggregators”: [
{
“name”: “sum”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}
}
]
}

2. 向 IKR 服务发送请求

$ curl -XPOST -H’Content-Type: application/json’ -d @create_rollup.json http://[host]:[port]/api/v1/rollups

得到类似以下结果(Response):

{“id”:”1557338016912”,”name”:”MyRollup1”,”attributes”:{“url”:”/api/v1/rollups/1557338016912”}}

其中id对应的value是创建rollup任务时的时间戳
查看IKR的日志输出，将定期输出类似以下内容：

2019-05-09 02:00:21,936 INFO cn.edu.tsinghua.iotdb.kairosdb.rollup.RollUp:73 - Roll-up id: 1557338016912, name: MyRollup1, execution_interval: 2 SECONDS

3. 查询rollup汇总数据

$ curl -XPOST -H’Content-Type: application/json’ -d @query.json http://[host]:[port]/api/v1/datapoints/query

其中的 query.json 为：

{
“start_absolute”: 1,
“end_relative”: {
“value”: “5”,
“unit”: “seconds”
},
“time_zone”: “Asia/Kabul”,
“metrics”: [
{
“name”: “rollup1”
}]
}

得到结果：

{“queries”:[{“sample_size”:15,”results”:[{“name”:”rollup1”,”group_by”:[{“name”:”type”,”type”:”number”}],”tags”:{“saved_from”:[“test_query”]},”values”:[[1400000000000,12.3],[1400000001000,36.3],[1400000003000,48.1],[1400000005000,48.5],[1400000007000,48.9],[1400000009000,49.3],[1400000011000,49.7],[1400000013000,50.1],[1400000015000,50.5],[1400000017000,50.9],[1400000019000,51.3],[1400000021000,51.7],[1400000023000,52.1],[1400000025000,52.5],[1400000027000,26.4]]}]}]}

格式化后：

{
“queries”: [
{
“sample_size”: 15,
“results”: [
{
“name”: “rollup1”,
“group_by”: [
{
“name”: “type”,
“type”: “number”
}
],
“tags”: {
“saved_from”: [
“test_query”
]
},
“values”: [
[
1400000000000,
12.3
],
[
1400000001000,
36.3
],
[
1400000003000,
48.1
],
[
1400000005000,
48.5
],
[
1400000007000,
48.9
],
[
1400000009000,
49.3
],
[
1400000011000,
49.7
],
[
1400000013000,
50.1
],
[
1400000015000,
50.5
],
[
1400000017000,
50.9
],
[
1400000019000,
51.3
],
[
1400000021000,
51.7
],
[
1400000023000,
52.1
],
[
1400000025000,
52.5
],
[
1400000027000,
26.4
]
]
}
]
}
]
}

可以看rollup任务的查询结果成功写入了rollup1这个metric中，且它有一个tag key是saved_from，tag value为test_query。
####  2.5.3 查询Roll-up任务
1. 查询所有rollup任务

$ curl http://[host]:[port]/api/v1/rollups

执行如上命令即可得到所有的rollup任务list
得到类似以下结果：

[{“id”:”1557338016912”,
“name”: “MyRollup1”,
“execution_interval”: {
“value”: 2,
“unit”: “seconds”
},
“rollups”: [
{
“save_as”: “rollup1”,
“query”: {
“start_relative”: {
“value”: “10”,
“unit”: “years”
},
“end_relative”: {
“value”: “1”,
“unit”: “seconds”
},
“metrics”: [
{
“name”: “test_query”,
“tags”: {

        },
        "aggregators": [
          {
            "name": "sum",
            "sampling": {
              "value": 2,
              "unit": "seconds"
            }
          }
        ]
      }
    ]
  }
}

]


2. 根据id查询rollup任务

$ curl http://[host]:[port]/api/v1/rollups/[id]

上面命令中的id是创建rollup任务时返回的id, 执行如上命令即可得到对于id的rollup任务JSON
得到类似以下结果：

{“id”:”1557338016912”,
“name”: “MyRollup1”,
“execution_interval”: {
“value”: 2,
“unit”: “seconds”
},
“rollups”: [
{
“save_as”: “rollup1”,
“query”: {
“start_relative”: {
“value”: “10”,
“unit”: “years”
},
“end_relative”: {
“value”: “1”,
“unit”: “seconds”
},
“metrics”: [
{
“name”: “test_query”,
“tags”: {

        },
        "aggregators": [
          {
            "name": "sum",
            "sampling": {
              "value": 2,
              "unit": "seconds"
            }
          }
        ]
      }
    ]
  }
}

]
}


####  2.5.4 更新Roll-up任务

http://[host]:[port]/api/v1/rollups/{id}


1. 编写更新Roll-up任务的JSON文件

$ vim update_rollup.json

输入以下 JSON :

{
“name”: “MyRollup1Update”,
“execution_interval”: {
“value”: 3,
“unit”: “seconds”
},
“rollups”: [
{
“save_as”: “rollup1”,
“query”: {
“start_relative”: {
“value”: “10”,
“unit”: “years”
},
“end_relative”: {
“value”: “1”,
“unit”: “seconds”
},
“metrics”: [
{
“name”: “test_query”,
“tags”: {},
“aggregators”: [
{
“name”: “sum”,
“sampling”: {
“value”: 2,
“unit”: “seconds”
}
}
]
}
]
}
}
]
}

2. 向 IKR 服务发送请求

$ curl -XPUT -H’Content-Type: application/json’ -d @update_rollup.json http://[host]:[port]/api/v1/rollups/[id]

得到类似以下结果(Response):

{“id”:”1557338016912”,”name”:”MyRollup1Update”,”attributes”:{“url”:”/api/v1/rollups/1557338016912”}}

其中id依然是之前创建的rollup任务的id
查看IKR的日志输出，将定期输出类似以下内容：

2019-05-09 11:12:10,953 INFO cn.edu.tsinghua.iotdb.kairosdb.rollup.RollUp:73 - Roll-up id: 1557371470757, name: MyRollup1Update, execution_interval: 3 SECONDS

输出间隔变为3秒，name变为MyRollup1Update，与更新的JSON中指定的一致，说明更新成功。
####  2.5.5 删除Roll-up任务

$ curl -XDELETE http://[host]:[port]/api/v1/rollups/[id]

执行如上命令即可删除对应id的rollup任务
观察IKR日志发现对应rollup任务的定时日志已经停止输出，说明rollup任务已经成功删除
更新和创建方法类似，区别是更新的URL中包含了更新的rollup任务对应的id，以及使用的请求方法是PUT
###  2.6 健康检查功能测试用例
1. 向 IKR 服务发送status健康检查请求

$ curl http://[host]:[port]/api/v1/health/status

正常情况下的返回结果：

[“JVM-Thread-Deadlock: OK”,”Datastore-Query: OK”]


2. 向 IKR 服务发送check健康检查请求

$ curl -w %{http_code} http://[host]:[port]/api/v1/health/check

返回结果为状态码：

204


###  2.7 查询指标名称功能测试用例
1. 向 IKR 服务发送查询所有metric name的请求

$ curl http://[host]:[port]/api/v1/metricnames


返回类似以下结果：

{“results”:[“archive_file_search”,”archive_file_tracked”,”rollup1”]}


2. 向 IKR 服务发送查询以指定字符串开头的metric name的请求

Mac

$ curl http://[host]:[port]/api/v1/metricnames\?prefix=[prefix]

Ubuntu

$ curl http://[host]:[port]/api/v1/metricnames/?prefix=[prefix]

将[prefix]替换为ar，表示查询以ar开头的metric

Mac

$ curl http://[host]:[port]/api/v1/metricnames\?prefix=ar

Ubuntu

$ curl http://[host]:[port]/api/v1/metricnames\?prefix=ar

返回类似以下结果：

{“results”:[“archive_file_search”,”archive_file_tracked”]}

返回结果中都是以ar开头的metric name，若没有符合要求的则返回的result数组为空：

{“results”:[]}
```