# 模型查询 API
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模型查询 API 主要用于获取各种数据分析报告。
# 一、调用方法
请参见Open API文档中的调用方法描述。
# 二、通用参数
# 2.1 属性表达
几乎所有的 API 都会用到属性，例如按照某个属性进行过滤、分组或者聚合等等。属性包括事件属性，用户属性和用户分群属性，属性通常使用名称和表类型两个字段表达。例如表示 省份 这个事件属性的表达式如下:
"tableType":"event"
"columnName":"#province"
用户属性类似，例如表示 用户级别:
"tableType":"user"
"columnName":"user_level"
# 2.2 筛选表达式
筛选表达式同样适用于绝大多数 API，用于表示对某些事件或者用户的筛选操作，使用如下格式的 JSON 表示:
{
// 表示 filts 里的各个条件的关系是 或 还是 且
"relation": "and",
// 具体的条件列表，可以有多个
"filts": [{
// 条件的左值，是一个属性
"tableType":"event",
"columnName": "#os",
// 条件的比较符，这里表示等于
"comparator": "equal",
// 条件的比较值，根据不同的比较符可以有一个或者多个
"ftv": [
"ios"
]
},
{
"tableType":"user",
"columnName":"user_level",
"comparator": "equal",
"ftv": [
"5"
]
}]
}
目前支持的操作符如下:
- equal / notEqual
表示等于/不等于，对字符串、数值类型有效。如果 ftv 有多个，则相当于 In 或者 Not In。例如想筛选出来等级为 3 和 4 的用户:
{
"tableType":"user"
"columnName":"user_level"
"comparator": "equal",
"ftv": ["3","5"]
}
- isTrue / isFalse
只对布尔类型有效。
- isNull / notNull
某个属性是否有值，对字符串、数值类型有效。
- include / notInclude
表示包含或不包含某个子串:
{
"tableType":"user"
"columnName":"channel"
"comparator": "include",
"ftv": ["应用宝"]
}
- less / greater / range：表示小于/大于/小于且大于，其中 range 是前闭后闭的区间，对数值类型和时间类型有效。例如筛选物品剩余数量在 3 和 9 之间的所有事件:
{
"tableType":"event"
"columnName":"count_left"
"comparator": "range",
"ftv": [3, 9]
}
或者筛选所有最后登录时间在 2019-11-13 00:00~2019-11-23 00:00 之间的用户
{
"tableType":"user"
"columnName":"latest_login_time"
"comparator": "range",
"ftv":["2019-11-13 00:00","2019-11-23 00:00"]
}
- regexMatch / notRegexMatch
正则匹配或者正则不匹配，只对字符串类型有效。
- relativeCurrentBetween / relativeCurrentBefore
针对日期类型的操作符，分别表示 相对当前时间在过去 N 天到过去 M 天之间/相对当前时间在过去 N 天之前。例如想筛选所有注册时间相对当前时间在过去 3 天之前的用户:
{
"tableType":"user"
"columnName":"register_time"
"comparator": "relativeCurrentBefore",
"ftv": [3]
}
或者筛选所有注册时间相对当前时间在过去 9 天到过去 3 天之间的用户:
{
"tableType":"user"
"columnName":"register_time"
"comparator": "relativeCurrentBetween",
"ftv": [9, 3]
}
- relativeEventBefore / relativeEventAfter / relativeEventAbsolute
针对日期类型的操作符，分别表示 相对事件发生时刻在之前 N 长时间之内/相对事件发生时刻在之后 N 长时间之内/相对事件发生时刻在前后 N 长时间之内。例如想筛选用户上次登录时间相对事件发生时刻在之前 3 个小时之内的事件:
{
"tableType":"user"
"columnName":"latest_login_time"
"comparator": "relativeEventBefore",
"ftv": [3]
"timeUnit": "hour"
}
- arrayIncludeItem / arrayNotIncludeItem
列表类型的操作符，表示列表是否包含某些元素
- arrayItemPos
列表类型的操作符，表示列表第 n 个元素等于某个值
- arrayIsNull / arrayNotNull
列表类型的操作符，表示列表是否存在
# 三、行为分析
# 3.1 事件分析
# 3.1.1 普通分析
[POST /open/event-analyze?token=xxxxxxx]
- Request body (application/json)
{
// 项目Id
"projectId": 0,
"events": [
{
// normal 普通分析, customized 自定义公式
"type": "normal",
// 事件名称，特别的，可以使用 anyEvent 表示任意事件
"eventName": "buy_shopitem",
// （可选）分析的属性名称
"quota": "",
// 分析类型，聚合操作
"analysis": "TOTAL_TIMES",
"relation": "and",
"filts": [
{
"columnName": "user_level",
"comparator": "greater",
"ftv": [
"3"
],
"tableType": "user"
},
{
"columnName": "#os",
"comparator": "equal",
"ftv": [
"android"
],
"tableType": "event"
},
{
"columnName": "#lib",
"comparator": "equal",
"ftv": [
"android"
],
"tableType": "event"
}
]
}
],
"eventView": {
// 相对时间
"recentDay": "1-3"
// 起始时间，相对时间为空时有效
"startTime": "2019-11-24 00:00:00",
// 结束时间，相对时间为空时有效
"endTime": "2019-11-26 00:00:00",
// 分析的时间单位，可以是 minute/hour/day/week/month/total
"timeParticleSize": "day"
//（可选）分组属性，可以有零个或者多个
"groupBy": [
{
"columnName": "#screen_width",
// 自定义属性区间
"propertyRange": "2000,3000",
// 属性区间类型，对数值型属性进行分组时，可以为自定义分桶条件
"propertyRangeType": "user_defined",
"tableType": "event"
},
{
"columnName": "#screen_height",
"propertyRange": "2000",
"propertyRangeType": "user_defined",
"tableType": "event"
}
]
},
// 使用缓存，可选参数，默认为true
"useCache": true,
// 每分析对象的分组数上限，可选参数，默认为1000，最大为10000
"limit": 1000
}
- 关键参数说明
| 参数 | 描述 |
|---|---|
| analysis | 聚合方法类型, 取值稍后说明 |
| quota | 聚合的字段名，某些聚合方法需要，比如 SUM/MAX/MIN/AVG |
| propertyRangeType | 数值型属性如何划分成区间进行分组，取值稍后说明 |
聚合方法 analysis 取值：
| 值 | 描述 | 是否需要属性 |
|---|---|---|
| TOTAL_TIMES | 总次数 | 否 |
| TRIG_USER_NUM | 触发用户数 | 否 |
| PER_CAPITA_TIMES | 人均次数 | 否 |
| SUM | 数值总和 | 是 |
| AVG | 数值平均值 | 是 |
| PER_CAPITA_NUM | 人均值 | 是 |
| MAX | 数值最大值 | 是 |
| MIN | 数值最小值 | 是 |
| DISTINCT | 去重数 | 是 |
| TRUE | 为真数 | 是 |
| FALSE | 为假数 | 是 |
| IS_NOT_EMPTY | 不为空数 | 是 |
| IS_EMPTY | 为空数 | 是 |
| ARRAY_DISTINCT | 列表整体去重数 | 是 |
| ARRAY_SET_DISTINCT | 元素集合去重数 | 是 |
| ARRAY_ITEM_DISTINCT | 列表元素去重数 | 是 |
| MEDIAN | 中位数 | 是 |
propertyRangeType 取值：
| 值 | 描述 |
|---|---|
| def | 默认区间，由系统自动划分 |
| discrete | 每个值为一个独立的分组 |
| user_defined | 用户自定义。自定义内容设置在 propertyRange 中 |
- curl 示例
curl -X POST --header 'Content-Type: application/json' --header 'Accept: application/json' -d '{"eventView":{"comparedByTime":true,"comparedEndTime":"2018-10-18 00:00:00","comparedStartTime":"2018-09-16 00:00:00","endTime":"2018-11-02 00:00:00","groupBy":[{"columnName":"#province","tableType":"event"}],"startTime":"2018-10-01 00:00:00","timeParticleSize":"day"},"events":[{"analysis":"TOTAL_TIMES","eventName":"use_item","quota":"","type":"normal"},{"analysis":"PER_CAPITA_TIMES","eventName":"participate_activity","filts":[{"columnName":"#manufacturer","comparator":"equal","ftv":["iphone"],"tableType":"event"}],"quota":"","relation":"and","type":"normal"},{"analysis":"TOTAL_TIMES","eventName":"use_item","quota":"","type":"normal"},{"analysis":"PER_CAPITA_TIMES","eventName":"participate_activity","filts":[{"columnName":"#manufacturer","comparator":"equal","ftv":["iphone"],"tableType":"event"}],"quota":"","relation":"and","type":"normal"}],"projectId":102,"useCache":true}' 'http://dev-ta2:8992/open/event-analyze?token=bTOzKiTIozG4e19FgXphcA8dDV3DIY8RwdHTO7aSnBsRqSNaIk19BnBMecJDWibD'
- Response body (application/json)
返回内容代表了一张结果表格，表格标题是时间，表格内容每行代表了某个分组的分析结果。
| 分析对象 | 分组 | x[0] | x[1] | x[2] |
|---|---|---|---|---|
| y[0]."buy_shopitem.TOTAL_TIMES" | y[0]."buy_shopitem.TOTAL_TIMES"[0].group_cols | y[0]."buy_shopitem.TOTAL_TIMES"[0].values[0] | y[0]."buy_shopitem.TOTAL_TIMES"[0].values[1] | y[0]."buy_shopitem.TOTAL_TIMES"[0].values[2] |
| y[0]."buy_shopitem.TOTAL_TIMES" | y[0]."buy_shopitem.TOTAL_TIMES"[1].group_cols | y[0]."buy_shopitem.TOTAL_TIMES"[1].values[0] | y[0]."buy_shopitem.TOTAL_TIMES"[1].values[1] | y[0]."buy_shopitem.TOTAL_TIMES"[1].values[2] |
{
"return_code": 0,
"return_message": "success",
"data": {
"union_groups": [
[
"-∞~2000",
"2000~+∞"
],
[
"-∞~2000",
"-∞~2000"
]
]
"x": [
"2019-11-24",
"2019-11-25",
"2019-11-26"
],
"y": [
{
"buy_shopitem.TOTAL_TIMES": [
{
"group_cols": [
"-∞~2000",
"2000~+∞"
],
"values": [
"507",
"2127",
"1550"
],
"group_num": 2
},
{
"group_cols": [
"-∞~2000",
"-∞~2000"
],
"values": [
"663",
"398",
"694"
],
"group_num": 2
}
]
}
]
}
}
# 3.1.2 自定义公式
- 使用公式 Request (application/json)
{
"eventView": {
"endTime": "2019-11-27 00:00:00",
"groupBy": [
{
"columnName": "#screen_width",
"propertyRange": "2000,3000",
"propertyRangeType": "user_defined",
"tableType": "event"
},
{
"columnName": "#screen_height",
"propertyRange": "2000",
"propertyRangeType": "user_defined",
"tableType": "event"
}
],
"projectId": 0,
"recentDay": "1-3",
"startTime": "2019-11-25 00:00:00",
"timeParticleSize": "day"
},
"events": [
{
"customEvent": "consume_item.PER_CAPITA_TIMES+obtain_item.PER_CAPITA_TIMES",
// 自定义一个名字
"eventName": "公式demo",
"format": "float",
// customized 自定义公式
"type": "customized"
}
],
"useCache": true
}
关键参数说明：
- customEvent: 公式表达式，由分析项或数值常量的加减乘除构成。分析项有两种形式: eventName.columnName.analysis 或 eventName.analysis。analysis 取值见 3.1.1 普通分析关键参数说明。
- propertyRangeType: 数值型属性区间类型
- def: 默认区间
- discrete: 离散数字
- user_defined: 用户自定义
- propertyRange: 自定义数值型属性区间，对数值型属性进行分组时，可以自定义属性区间
- 使用公式 Response body (application/json)
返回内容代表了一张结果表格，表格标题是时间，表格内容每行代表了某个分组的分析结果。
| 分析对象 | 分组 | x[0] | x[1] | x[2] |
|---|---|---|---|---|
| y[0]."公式 demo" | y[0]."公式 demo"[0].group_cols | y[0]."公式 demo"[0].values[0] | y[0]."公式 demo"[0].values[1] | y[0]."公式 demo"[0].values[2] |
| y[0]."公式 demo" | y[0]."公式 demo"[1].group_cols | y[0]."公式 demo"[1].values[0] | y[0]."公式 demo"[1].values[1] | y[0]."公式 demo"[1].values[2] |
{
"data": {
"union_groups": [
[
"-∞~2000",
"2000~+∞"
],
[
"-∞~2000",
"-∞~2000"
]
],
"group_num": 2,
"x": [
"2019-11-25",
"2019-11-26",
"2019-11-27"
],
"y": [
{
"公式demo": [
{
"group_cols": [
"-∞~2000",
"2000~+∞"
],
"values": [
"411.42",
"538.65",
"429.31"
],
"group_num": 2
},
{
"group_cols": [
"-∞~2000",
"-∞~2000"
],
"values": [
"336.3",
"418.33",
"515.61"
],
"group_num": 2
}
]
}
],
"cols_format_List": [
"string",
"string"
],
"type": [
null,
null
]
},
"return_code": 0,
"return_message": "success"
}
# 3.2 留存分析
[POST /open/retention-analyze?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"events": [
{
// 第一个事件
"type": "first",
"eventName": "player_register",
"filts": [
{
"columnName": "user_level",
"comparator": "greater",
"ftv": [
"2"
],
"tableType": "user"
}
],
"relation": "and"
},
{
// 第二个事件
"type": "second",
"eventName": "obtain_diamond",
"filts": [
{
"columnName": "#os",
"comparator": "equal",
"ftv": [
"android"
],
"tableType": "event"
},
{
"columnName": "user_level",
"comparator": "greater",
"ftv": [
"2"
],
"tableType": "user"
}
],
"relation": "and"
},
{
// （可选）同时显示第三个指标
"type": "simultaneous_display",
"eventName": "consume_item",
"analysis": "PER_CAPITA_NUM",
"quota": "#vp@dailyTask",
"filts": [
{
"columnName": "user_level",
"comparator": "greater",
"ftv": [
"2"
],
"tableType": "user"
}
],
"relation": "and"
}
],
"eventView": {
// 结束时间
"endTime": "2019-11-26 00:00:00",
// 起始时间
"startTime": "2019-11-24 00:00:00",
// 统计类型: retention 留存, lost 流失
"statType": "retention",
// 时间单位
"timeParticleSize": "day",
// 留存期限
"unitNum": 3
},
"useCache": true,
"limit": 1000
}
同时展示聚合方法 analysis 取值：
| 值 | 描述 | 是否需要属性 |
|---|---|---|
| TOTAL_TIMES | 总次数 | 否 |
| TRIG_USER_NUM | 触发用户数 | 否 |
| PER_CAPITA_TIMES | 人均次数 | 否 |
| SUM | 数值总和 | 是 |
| PER_CAPITA_NUM | 人均值 | 是 |
| STAGE_ACC | 阶段累计总和 | 是 |
| STAGE_ACC_PCV | 阶段累计人均值 | 是 |
| TRUE | 为真数 | 是 |
| FALSE | 为假数 | 是 |
| IS_NOT_EMPTY | 不为空数 | 是 |
| IS_EMPTY | 为空数 | 是 |
- Response 200 (application/json)
返回内容代表了一张结果表格，表格标题是当日到 n 日后。表格内容每行代表了某天的指标，指标有以下三种类型：
| 类型 | 含义 |
|---|---|
| 0 | 留存 |
| 1 | 流失 |
| 2 | 同时展示 |
阶段平均的留存或流失用户数作为特殊的几行
| 类型 | 初始事件时间 | 分组 | 初始事件用户数 | 当日 | 1 日后 | 2 日后 | 3 日后 |
|---|---|---|---|---|---|---|---|
| 0 | 阶段均值 | state_avg.0[0].groupCols | state_avg.0[0].values[0] | state_avg.0[0].values[1] | state_avg.0[0].values[2] | state_avg.0[0].values[3] | state_avg.0[0].values[4] |
| 1 | 阶段均值 | state_avg.1[0].groupCols | state_avg.1[0].values[0] | state_avg.1[0].values[1] | state_avg.1[0].values[2] | state_avg.1[0].values[3] | state_avg.1[0].values[4] |
| 2 | 阶段均值 | state_avg.2[0].groupCols | state_avg.2[0].values[0] | state_avg.2[0].values[1] | state_avg.2[0].values[2] | state_avg.2[0].values[3] | state_avg.2[0].values[4] |
| 0 | y.0.2019-11-24 | y.0.2019-11-24[0].groupCols | y.0.2019-11-24[0].values[0] | y.0.2019-11-24[0].values[1] | y.0.2019-11-24[0].values[2] | y.0.2019-11-24[0].values[3] | y.0.2019-11-24[0].values[4] |
| 0 | y.0.2019-11-25 | y.0.2019-11-25[0].groupCols | y.0.2019-11-25[0].values[0] | y.0.2019-11-25[0].values[1] | y.0.2019-11-25[0].values[2] | y.0.2019-11-25[0].values[3] | y.0.2019-11-25[0].values[4] |
| 0 | y.0.2019-11-26 | y.0.2019-11-26[0].groupCols | y.0.2019-11-26[0].values[0] | y.0.2019-11-26[0].values[1] | y.0.2019-11-26[0].values[2] | y.0.2019-11-26[0].values[3] | y.0.2019-11-26[0].values[4] |
| 1 | y.1.2019-11-24 | y.1.2019-11-24[0].groupCols | y.1.2019-11-24[0].values[0] | y.1.2019-11-24[0].values[1] | y.1.2019-11-24[0].values[2] | y.1.2019-11-24[0].values[3] | y.1.2019-11-24[0].values[4] |
| 1 | y.1.2019-11-25 | y.1.2019-11-25[0].groupCols | y.1.2019-11-25[0].values[0] | y.1.2019-11-25[0].values[1] | y.1.2019-11-25[0].values[2] | y.1.2019-11-25[0].values[3] | y.1.2019-11-25[0].values[4] |
| 1 | y.1.2019-11-26 | y.1.2019-11-26[0].groupCols | y.1.2019-11-26[0].values[0] | y.1.2019-11-26[0].values[1] | y.1.2019-11-26[0].values[2] | y.1.2019-11-26[0].values[3] | y.1.2019-11-26[0].values[4] |
| 2 | y.2.2019-11-24 | y.2.2019-11-24[0].groupCols | y.2.2019-11-24[0].values[0] | y.2.2019-11-24[0].values[1] | y.2.2019-11-24[0].values[2] | y.2.2019-11-24[0].values[3] | y.2.2019-11-24[0].values[4] |
| 2 | y.2.2019-11-25 | y.2.2019-11-25[0].groupCols | y.2.2019-11-25[0].values[0] | y.2.2019-11-25[0].values[1] | y.2.2019-11-25[0].values[2] | y.2.2019-11-25[0].values[3] | y.2.2019-11-25[0].values[4] |
| 2 | y.2.2019-11-26 | y.2.2019-11-26[0].groupCols | y.2.2019-11-26[0].values[0] | y.2.2019-11-26[0].values[1] | y.2.2019-11-26[0].values[2] | y.2.2019-11-26[0].values[3] | y.2.2019-11-26[0].values[4] |
{
"data": {
"state_avg": {
"0": [
{
"groupCols": [],
"isTotal": 1,
"lastValidDateVerticalIndexs": [
"-",
"2",
"1",
"0"
],
"values": [
"-",
0.365,
0.0576,
0.0349,
"-"
]
}
],
"1": [
{
"groupCols": [],
"isTotal": 1,
"lastValidDateVerticalIndexs": [
"-",
"2",
"1",
"0"
],
"values": [
"-",
1,
0.9424,
0.9302,
"-"
]
}
],
"2": [
{
"groupCols": [],
"isTotal": 1,
"lastValidDateVerticalIndexs": [
"-",
"2",
"1",
"0"
],
"values": [
"-",
0,
0,
0,
"-"
]
}
]
},
"x": [
"2019-11-24",
"2019-11-25",
"2019-11-26"
],
"y": {
// 留存
"0": {
"2019-11-24": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
172,
62,
10,
6,
2
]
}
],
"2019-11-25": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
158,
58,
9,
2,
"-"
]
}
],
"2019-11-26": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
81,
30,
5,
"-",
"-"
]
}
]
},
// 流失
"1": {
"2019-11-24": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
172,
172,
162,
160,
160
]
}
],
"2019-11-25": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
158,
158,
149,
149,
"-"
]
}
],
"2019-11-26": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
81,
81,
76,
"-",
"-"
]
}
]
},
// 同时展示
"2": {
"2019-11-24": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
0.0,
0.0,
0.0,
0.0,
0.0
]
}
],
"2019-11-25": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
0.0,
0.0,
0.0,
0.0,
"-"
]
}
],
"2019-11-26": [
{
"groupCols": [],
"includeToday": true,
"isTotal": 1,
"values": [
0.0,
0.0,
0.0,
"-",
"-"
]
}
]
}
},
"z": [
"player_register",
"obtain_diamond",
"consume_item"
]
},
"return_code": 0,
"return_message": "success"
}
# 3.3 漏斗分析
[POST /open/funnel-analyze?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"eventView": {
"endTime": "2019-11-26 00:00:00",
"filts": [
{
"columnName": "user_level",
"comparator": "equal",
"ftv": [
"5"
],
"tableType": "user"
}
],
"groupBy": [
{
"columnName": "#country",
"tableType": "event"
}
],
"recentDay": "1-3",
"relation": "and",
"startTime": "2019-11-24 00:00:00",
"windows_gap": 1,
"windows_gap_tu": "day"
},
"events": [
{
"eventName": "obtain_item",
"filts": [
{
"columnName": "#province",
"comparator": "equal",
"ftv": [
"江苏省",
"上海市"
],
"tableType": "event"
}
],
"relation": "and"
},
{
"eventName": "consume_item",
"filts": [
{
"columnName": "#os",
"comparator": "equal",
"ftv": [
"ios"
],
"tableType": "event"
}
],
"relation": "and"
}
],
"useCache": true,
"limit": 1000
}
- Response body (application/json)
返回内容代表了一张结果表格，表格标题是漏斗步骤。表格内容每行通过了某个步骤后的用户数
| 分组 | 时间 | 步骤 1 | 步骤 2 |
|---|---|---|---|
| y[0].总体 | 阶段合计 | y[0].总体.col1[0] | y[0].总体.col1[1] |
| y[0].总体 | x[0] | y[0].总体.col2[0][0] | y[0].总体.col2[0][1] |
| y[0].总体 | x[1] | y[0].总体.col2[1][0] | y[0].总体.col2[1][1] |
| y[0].总体 | x[2] | y[0].总体.col2[2][0] | y[0].总体.col2[2][1] |
| y[1].中国 | 阶段合计 | y[1].总体.col1[0] | y[1].总体.col1[1] |
| y[1].中国 | x[0] | y[1].中国.col2[0][0] | y[1].中国.col2[0][1] |
| y[1].中国 | x[1] | y[1].中国.col2[1][0] | y[1].中国.col2[1][1] |
| y[1].中国 | x[2] | y[1].中国.col2[2][0] | y[1].中国.col2[2][1] |
{
"data": {
"x": [
"2019-11-24",
"2019-11-25",
"2019-11-26"
],
"y": [
{
"总体": {
"col1": [
14,
3
],
"col2": [
[
6,
1
],
[
6,
0
],
[
4,
2
],
[
0,
0
]
]
}
},
{
"中国": {
"col1": [
14,
3
],
"col2": [
[
6,
1
],
[
6,
0
],
[
4,
2
],
[
0,
0
]
]
}
}
],
"z": [
"obtain_item",
"consume_item"
]
},
"return_code": 0,
"return_message": "success"
}
# 3.4 分布分析
[POST /open/distribution-analyze?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"events": [{
"analysis": "AVG",
"eventName": "ta@v_test",
"filts": [
{
"columnName": "#ip",
"comparator": "equal",
"ftv": [
"159.226.30.7"
],
"tableType": "event"
}
],
"intervalType": "user_defined",
"quota": "#vp@strlenchannelname",
"quotaIntervalArr": [
3,
6
],
"relation": "and"
}],
"eventView": {
"endTime": "2019-11-26 00:00:00",
"groupBy": [
{
"columnName": "#province",
"tableType": "event"
}
],
"startTime": "2019-11-24 00:00:00",
"timeParticleSize": "week"
},
"useCache": true,
"limit": 1000
}
分布分析聚合方法 analysis 取值：
| 值 | 描述 | 是否需要属性 |
|---|---|---|
| TIMES | 次数 | 否 |
| NUMBER_OF_DAYS | 天数 | 否 |
| NUMBER_OF_HOURS | 小时数 | 否 |
| SUM | 数值总和 | 是 |
| AVG | 数值平均值 | 是 |
| MAX | 数值最大值 | 是 |
| MIN | 数值最小值 | 是 |
| DISTINCT | 去重数 | 是 |
| TRUE | 为真数 | 是 |
| FALSE | 为假数 | 是 |
| IS_NOT_EMPTY | 不为空数 | 是 |
| IS_EMPTY | 为空数 | 是 |
| ARRAY_DISTINCT | 列表整体去重数 | 是 |
| ARRAY_SET_DISTINCT | 元素集合去重数 | 是 |
| ARRAY_ITEM_DISTINCT | 列表元素去重数 | 是 |
| MEDIAN | 中位数 | 是 |
- Response body (application/json)
返回内容代表了一张结果表格，表格标题是分布区间，表格内容每行代表了一段时间在某个分组的分布人数。
| 时间 | 分组 | 总人数 | distribution_interval[0] | distribution_interval[1] | distribution_interval[2] |
|---|---|---|---|---|---|
| y.2019-11-18 | y.2019-11-18[0].groupCols | y.2019-11-18[0].totalUserNum | y.2019-11-18[0].values[0] | y.2019-11-18[0].values[1] | y.2019-11-18[0].values[2] |
| y.2019-11-18 | y.2019-11-18[1].groupCols | y.2019-11-18[1].totalUserNum | y.2019-11-18[1].values[0] | y.2019-11-18[1].values[1] | y.2019-11-18[1].values[2] |
| y.2019-11-25 | y.2019-11-25[0].groupCols | y.2019-11-25[0].totalUserNum | y.2019-11-25[0].values[0] | y.2019-11-25[0].values[1] | y.2019-11-25[0].values[2] |
| y.2019-11-25 | y.2019-11-25[1].groupCols | y.2019-11-25[1].totalUserNum | y.2019-11-25[1].values[0] | y.2019-11-25[1].values[1] | y.2019-11-25[1].values[2] |
{
"data": {
"distribution_interval": [
",3",
"3,6",
"6,"
]
"x": [
"2019-11-18",
"2019-11-25"
],
"y": {
"2019-11-18": [
{
"groupCols": [
null
],
"isTotal": 1,
"totalUserNum": 1,
"values": [
0,
1,
0
]
},
{
"groupCols": [
"北京市"
],
"isTotal": 0,
"totalUserNum": 1,
"values": [
0,
1,
0
]
}
],
"2019-11-25": [
{
"groupCols": [
null
],
"isTotal": 1,
"totalUserNum": 1,
"values": [
0,
1,
0
]
},
{
"groupCols": [
"北京市"
],
"isTotal": 0,
"totalUserNum": 1,
"values": [
0,
1,
0
]
}
]
}
},
"return_code": 0,
"return_message": "success"
}
# 3.5 路径分析
[POST /open/path-analyze?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"events": {
"by_fields": [
{
"event_name": "consume_item",
"field": "#country",
"table_type": "event"
}
],
"event_names": [
"consume_item",
"player_logout",
"player_register"
],
"source_event": {
"event_name": "consume_item",
"filter": {
"filts": [
{
"columnName": "#os",
"comparator": "equal",
"ftv": [
"ios"
],
"tableType": "event"
}
],
"relation": "and"
}
},
// 起始事件是initial_event, 结束事件是termination_event
"source_type": "initial_event",
"user_filter": {
"filts": [
{
"columnName": "user_level",
"comparator": "equal",
"ftv": [
"9"
],
"tableType": "user"
}
],
"relation": "and"
}
},
"eventView": {
"col_limit": 10,
"from_date": "2019-11-20 00:00:00",
"recent_day": "1-7",
"session_interval": 22,
"session_type": "minute",
"to_date": "2019-11-26 00:00:00"
},
"useCache": true
}
- Response body (application/json)
{
"data": {
"nodes": [
[
{
"times": 6,
"by_value": "中国",
"event_name": "consume_item",
"id": "0_中国^_^consume_item"
}
],
[
{
"times": 4,
"by_value": "中国",
"event_name": "consume_item",
"id": "1_中国^_^consume_item"
}
],
[
{
"times": 3,
"by_value": "中国",
"event_name": "consume_item",
"id": "2_中国^_^consume_item"
}
],
[
{
"times": 3,
"by_value": "中国",
"event_name": "consume_item",
"id": "3_中国^_^consume_item"
}
],
[
{
"times": 2,
"by_value": "中国",
"event_name": "consume_item",
"id": "4_中国^_^consume_item"
}
],
[
{
"times": 2,
"by_value": "中国",
"event_name": "consume_item",
"id": "5_中国^_^consume_item"
}
],
[
{
"times": 2,
"by_value": "中国",
"event_name": "consume_item",
"id": "6_中国^_^consume_item"
}
],
[
{
"times": 1,
"by_value": "中国",
"event_name": "consume_item",
"id": "7_中国^_^consume_item"
}
],
[
{
"times": 1,
"by_value": "中国",
"event_name": "consume_item",
"id": "8_中国^_^consume_item"
}
],
[
{
"times": 1,
"by_value": "中国",
"event_name": "consume_item",
"id": "9_中国^_^consume_item"
}
]
],
"links": [
[
{
"times": 4,
"source": "0_中国^_^consume_item",
"target": "1_中国^_^consume_item"
},
{
"times": 2,
"source": "0_中国^_^consume_item",
"is_wastage": true,
"target": "1_wastage"
}
],
[
{
"times": 3,
"source": "1_中国^_^consume_item",
"target": "2_中国^_^consume_item"
},
{
"times": 1,
"source": "1_中国^_^consume_item",
"is_wastage": true,
"target": "2_wastage"
}
],
[
{
"times": 3,
"source": "2_中国^_^consume_item",
"target": "3_中国^_^consume_item"
}
],
[
{
"times": 2,
"source": "3_中国^_^consume_item",
"target": "4_中国^_^consume_item"
},
{
"times": 1,
"source": "3_中国^_^consume_item",
"is_wastage": true,
"target": "4_wastage"
}
],
[
{
"times": 2,
"source": "4_中国^_^consume_item",
"target": "5_中国^_^consume_item"
}
],
[
{
"times": 2,
"source": "5_中国^_^consume_item",
"target": "6_中国^_^consume_item"
}
],
[
{
"times": 1,
"source": "6_中国^_^consume_item",
"target": "7_中国^_^consume_item"
},
{
"times": 1,
"source": "6_中国^_^consume_item",
"is_wastage": true,
"target": "7_wastage"
}
],
[
{
"times": 1,
"source": "7_中国^_^consume_item",
"target": "8_中国^_^consume_item"
}
],
[
{
"times": 1,
"source": "8_中国^_^consume_item",
"target": "9_中国^_^consume_item"
}
]
],
"event_name_desc_map": {
"anyEvent": "任意事件",
"consume_item": "消费道具",
"player_logout": "用户登出",
"player_register": "用户注册"
}
},
"return_code": 0,
"return_message": "success"
}
# 3.6 用户属性分析
[POST /open/user-prop-analyze?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"events": [{
"analysis": "AVG",
"filts": [
{
"columnName": "latest_login_time",
"comparator": "relativeCurrentBetween",
"ftv": [
"200",
"1"
],
"tableType": "user"
},
{
"columnName": "user_level",
"comparator": "less",
"ftv": [
"6"
],
"tableType": "user"
}
],
"quota": "diamond_num",
"relation": "and",
"tableType": "user"
}],
"eventView": {
"groupBy": [
{
"columnName": "user_level",
"tableType": "user"
}
]
},
"useCache": true,
"limit": 1000
}
- Response body (application/json)
返回内容代表了一张结果表格，表格内容每行代表了某个分组的分析结果。
| 分组 | 分析结果 |
|---|---|
| data_list[0].group_cols | data_list[0].values |
| data_list[1].group_cols | data_list[1].values |
| data_list[2].group_cols | data_list[2].values |
| data_list[3].group_cols | data_list[3].values |
| data_list[4].group_cols | data_list[4].values |
{
"data": {
"display_quota_name": "当前拥有钻石数.均值",
"data_list": [
{
"group_cols": [
"5"
],
"values": 905.762
},
{
"group_cols": [
"4"
],
"values": 869.983
},
{
"group_cols": [
"3"
],
"values": 333.965
},
{
"group_cols": [
"2"
],
"values": 171.515
},
{
"group_cols": [
"1"
],
"values": 153.568
}
],
"group_cols_sorted": [
[
"5",
"4",
"3",
"2",
"1"
]
],
"cols_format_List": [
"string"
],
"group_num": 5
},
"return_code": 0,
"return_message": "success"
}
# 四、用户列表查询
用户列表系列接口用于查询某一个特定分析报告中的具体用户列表，请求的大部分参数与分析报告相同，新增的参数会在每个接口里具体说明。
# 4.1 事件分析用户列表
[POST /open/event-user-list?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"eventView": {
"endTime": "2019-11-26 00:00:00",
"groupBy": [
{
"columnName": "#city",
"tableType": "event"
}
],
"recentDay": "1-3",
"startTime": "2019-11-24 00:00:00",
"timeParticleSize": "day"
},
"events": [
{
"analysis": "TRIG_USER_NUM",
"eventName": "consume_item",
"filts": [
{
"columnName": "user_level",
"comparator": "equal",
"ftv": [
"5"
],
"tableType": "user"
}
],
"quota": "#vp@dailyTask",
"relation": "and",
"type": "normal"
}
],
// 事件所在日期
"sliceDate": "2019-11-26",
// 事件所在分组
"sliceGroupVal": [
"北京市"
]
}
- Response body (application/json)
{
"data": {
"datalist": [
{
"#account_id": "e78107482",
"#distinct_id": "e145056682",
"user_level": 5,
"register_time": "2019-11-26 14:36:13",
"diamond_num": 1006,
"latest_login_time": "2019-11-26 15:45:16",
"channel": "app store",
"#user_id": 33474682
},
{
"#account_id": "d7819213",
"#distinct_id": "d14521393",
"user_level": 5,
"register_time": "2019-11-26 23:25:14",
"diamond_num": 858,
"first_recharge_time": "2019-11-26 23:29:56",
"latest_login_time": "2019-11-26 23:32:48",
"channel": "app store",
"#user_id": 3351093
}
],
"columMeta": {
"#account_id": "账户ID",
"#distinct_id": "访客ID",
"user_level": "用户等级",
"register_time": "注册时间",
"diamond_num": "当前拥有钻石数",
"first_recharge_time": "首次充值时间",
"latest_login_time": "最后登录时间",
"channel": "渠道"
}
},
"return_code": 0,
"return_message": "success"
}
# 4.2 留存分析用户列表
[POST /open/retention-user-list?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"eventView": {
"endTime": "2019-11-26 00:00:00",
"groupBy": [
{
"columnName": "#province",
"tableType": "event"
}
],
"recentDay": "1-3",
"startTime": "2019-11-24 00:00:00",
"statType": "retention",
"timeParticleSize": "day",
"unitNum": 7
},
"events": [
{
"eventName": "player_register",
"filts": [
{
"columnName": "#province",
"comparator": "equal",
"ftv": [
"江苏省",
"上海市"
],
"tableType": "event"
},
{
"columnName": "user_level",
"comparator": "greater",
"ftv": [
"2"
],
"tableType": "user"
}
],
"relation": "and",
"type": "first"
},
{
"eventName": "obtain_diamond",
"filts": [
{
"columnName": "#os",
"comparator": "equal",
"ftv": [
"android"
],
"tableType": "event"
},
{
"$ref": "$.events[0].filts[1]"
}
],
"relation": "and",
"type": "second"
}
],
"sliceDate": "2019-11-26",
// 时段下标: 0:初始事件用户数, 1:当日, 2:1日后, 3:2日后
"sliceInterval": 3
}
- Response body (application/json)
{
"data": {
"datalist": [
{
"#account_id": "v47739399",
"#distinct_id": "v88658799",
"user_level": 11,
"register_time": "2019-11-26 19:13:20",
"diamond_num": 1182,
"latest_login_time": "2019-11-26 20:16:19",
"channel": "华为应用市场",
"#user_id": 20459799
},
{
"#account_id": "i7819568",
"#distinct_id": "i14522048",
"user_level": 4,
"register_time": "2019-11-26 23:56:17",
"diamond_num": 1006,
"latest_login_time": "2019-11-26 23:59:59",
"channel": "360手机助手",
"#user_id": 3351248
},
{
"#account_id": "g7812426",
"#distinct_id": "g14508786",
"user_level": 14,
"register_time": "2019-11-26 17:54:13",
"diamond_num": 245,
"first_recharge_time": "2019-11-26 18:08:58",
"latest_login_time": "2019-11-26 20:16:19",
"channel": "小米应用商店",
"#user_id": 3348186
},
{
"#account_id": "a7812000",
"#distinct_id": "a14508000",
"user_level": 3,
"register_time": "2019-11-26 17:27:28",
"diamond_num": 1153,
"latest_login_time": "2019-11-26 18:45:58",
"channel": "app store",
"#user_id": 3348000
}
],
"columMeta": {
"#account_id": "账户ID",
"#distinct_id": "访客ID",
"user_level": "用户等级",
"register_time": "注册时间",
"diamond_num": "当前拥有钻石数",
"first_recharge_time": "首次充值时间",
"latest_login_time": "最后登录时间",
"channel": "渠道"
}
},
"return_code": 0,
"return_message": "success"
}
# 4.3 漏斗分析用户列表
[POST /open/funnel-user-list?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"eventView": {
"endTime": "2019-11-26 00:00:00",
"filts": [
{
"columnName": "user_level",
"comparator": "equal",
"ftv": [
"5"
],
"tableType": "user"
}
],
"groupBy": [
{
"columnName": "#province",
"tableType": "event"
}
],
"recentDay": "1-4",
"relation": "and",
"startTime": "2019-11-23 00:00:00",
"timeParticleSize": "day"
},
"events": [
{
"eventName": "obtain_item",
"filts": [
{
"columnName": "#province",
"comparator": "equal",
"ftv": [
"江苏省",
"上海市"
],
"tableType": "event"
}
],
"relation": "and"
}
],
// 漏斗步骤, 从1开始
"sliceFunnelStep": 1,
"sliceGroupVal": "上海市"
}
- Response body (application/json)
{
"data": {
"datalist": [
{
"#account_id": "u78082246",
"#distinct_id": "u145009846",
"user_level": 5,
"register_time": "2019-11-26 09:59:34",
"diamond_num": 1006,
"latest_login_time": "2019-11-26 11:14:12",
"channel": "小米应用商店",
"#user_id": 33463846
},
{
"#account_id": "k77655236",
"#distinct_id": "k144216836",
"user_level": 5,
"register_time": "2019-11-24 08:53:09",
"diamond_num": 1012,
"latest_login_time": "2019-11-24 10:02:38",
"channel": "豌豆荚",
"#user_id": 33280836
},
{
"#account_id": "a77648226",
"#distinct_id": "a144203826",
"user_level": 5,
"register_time": "2019-11-24 08:21:19",
"diamond_num": 1006,
"latest_login_time": "2019-11-24 09:32:31",
"channel": "应用宝",
"#user_id": 33277826
}
],
"columMeta": {
"#account_id": "账户ID",
"#distinct_id": "访客ID",
"user_level": "用户等级",
"register_time": "注册时间",
"diamond_num": "当前拥有钻石数",
"first_recharge_time": "首次充值时间",
"latest_login_time": "最后登录时间",
"channel": "渠道"
}
},
"return_code": 0,
"return_message": "success"
}
# 4.4 分布分析用户列表
[POST /open/distribution-user-list?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"events": [{
"analysis": "TIMES",
"eventName": "consume_item",
"filts": [
{
"columnName": "#os",
"comparator": "equal",
"ftv": [
"android"
],
"tableType": "event"
}
],
"intervalType": "def",
"quota": "",
"relation": "and"
}],
"eventView": {
"endTime": "2019-11-26 00:00:00",
"groupBy": [
{
"columnName": "#province",
"tableType": "event"
}
],
"recentDay": "D31",
"startTime": "2019-10-28 00:00:00",
"timeParticleSize": "week"
},
// [10,20)小时
"interval": "10,20",
// 2019-11-18当周
"sliceDate": "2019-11-18",
"sliceGroupVal": [
"北京市"
]
}
- Response body (application/json)
{
"data": {
"datalist": [
{
"#account_id": "h7784497",
"#distinct_id": "h14456917",
"user_level": 13,
"register_time": "2019-11-24 21:52:38",
"diamond_num": 1201,
"latest_login_time": "2019-11-24 23:35:49",
"channel": "百度手机助手",
"#user_id": 3336217
},
{
"#account_id": "h6201359",
"#distinct_id": "h11516759",
"user_level": 68,
"register_time": "2019-06-23 09:25:18",
"diamond_num": 1686,
"first_recharge_time": "2019-06-23 09:25:38",
"latest_login_time": "2019-11-18 23:01:49",
"channel": "华为应用市场",
"#user_id": 2657759
},
{
"#account_id": "g4102426",
"#distinct_id": "g7618786",
"user_level": 47,
"register_time": "2019-07-29 13:58:23",
"diamond_num": 1,
"first_recharge_time": "2019-07-29 15:42:20",
"latest_login_time": "2019-11-24 16:04:03",
"channel": "应用宝",
"#user_id": 1758186
}
],
"columMeta": {
"#account_id": "账户ID",
"#distinct_id": "访客ID",
"user_level": "用户等级",
"register_time": "注册时间",
"diamond_num": "当前拥有钻石数",
"first_recharge_time": "首次充值时间",
"latest_login_time": "最后登录时间",
"channel": "渠道"
}
},
"return_code": 0,
"return_message": "success"
}
# 4.5 路径分析用户列表
[POST /open/path-user-list?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"events": {
"event_names": [
"obtain_item",
"consume_item",
"obtain_coin"
],
"source_event": {
"event_name": "consume_item",
"filter": {
"filts": [
{
"columnName": "#os",
"comparator": "equal",
"ftv": [
"ios"
],
"tableType": "event"
}
],
"relation": "and"
}
},
"source_type": "initial_event",
"user_filter": {
"filts": [
{
"columnName": "user_level",
"comparator": "equal",
"ftv": [
"6"
],
"tableType": "user"
}
],
"relation": "and"
}
},
"eventView": {
"col_limit": 10,
"from_date": "2019-11-20 00:00:00",
"recent_day": "1-7",
"session_interval": 22,
"session_type": "minute",
"to_date": "2019-11-26 00:00:00"
},
"next_slice_event_by_values": [
{
"slice_event_name": "obtain_coin"
}
],
// 当前所处的路径层级（从0开始计数）
"session_level": 2,
// total 合计; with_next 后续有节点; without_next 后续没有节点; with_next_specific：后续有某个事件的节点
"slice_type": "with_next_specific",
// slice_type=with_next_specific时必须传
"slice_event_by_values": [
{
"slice_event_name": "obtain_item"
}
]
}
- Response body (application/json)
{
"data": {
"datalist": [
{
"#account_id": "j77444535",
"#distinct_id": "j143825535",
"user_level": 6,
"register_time": "2019-11-22 17:07:12",
"diamond_num": 1270,
"latest_login_time": "2019-11-22 18:23:19",
"channel": "app store",
"#user_id": 33190535
}
],
"total_num": 1,
"columMeta": {
"#account_id": "账户ID",
"#distinct_id": "访客ID",
"user_level": "用户等级",
"register_time": "注册时间",
"diamond_num": "当前拥有钻石数",
"first_recharge_time": "首次充值时间",
"latest_login_time": "最后登录时间",
"channel": "渠道"
}
},
"return_code": 0,
"return_message": "success"
}
# 4.6 用户属性分析用户列表
[POST /open/user-prop-user-list?token=xxxxxxx]
- Request body (application/json)
{
"projectId": 0,
"events": [{
"analysis": "AVG",
"filts": [
{
"columnName": "latest_login_time",
"comparator": "relativeCurrentBetween",
"ftv": [
"7",
"1"
],
"tableType": "user"
}
],
"quota": "diamond_num",
"relation": "and",
"tableType": "user"
}],
"eventView": {
"groupBy": [
{
"columnName": "user_level",
"tableType": "user"
},
{
"columnName": "channel",
"tableType": "user"
}
]
},
"sliceGroupVal": [
"31",
"app store"
]
}
- Response body (application/json)
{
"data": {
"datalist": [
{
"#account_id": "b6909071",
"#distinct_id": "b12831131",
"user_level": 31,
"register_time": "2019-09-23 09:33:31",
"diamond_num": 1250,
"first_recharge_time": "2019-11-18 08:50:33",
"latest_login_time": "2019-11-26 18:15:51",
"channel": "app store",
"#user_id": 2961031
},
{
"#account_id": "a6013000",
"#distinct_id": "a11167000",
"user_level": 31,
"register_time": "2019-09-02 19:15:08",
"diamond_num": 72,
"first_recharge_time": "2019-09-02 19:18:36",
"latest_login_time": "2019-11-24 10:02:38",
"channel": "app store",
"#user_id": 2577000
},
{
"#account_id": "j2614535",
"#distinct_id": "j4855535",
"user_level": 31,
"register_time": "2019-08-24 20:40:19",
"diamond_num": 820,
"latest_login_time": "2019-11-21 14:16:42",
"channel": "app store",
"#user_id": 1120535
}
],
"columMeta": {
"#account_id": "账户ID",
"#distinct_id": "访客ID",
"user_level": "用户等级",
"register_time": "注册时间",
"diamond_num": "当前拥有钻石数",
"first_recharge_time": "首次充值时间",
"latest_login_time": "最后登录时间",
"channel": "渠道"
}
},
"return_code": 0,
"return_message": "success"
}
# 五、用户事件列表查询
[POST /open/user-event-list?token=xxxxxxx]
- Request body (application/json)
{
"eventNames": [
"ta_app_start",
"ta_app_view",
"use_item",
"recharge",
"participate_quest",
"participate_activity"
],
"pagerHeader": {
"pageNum": 1,
"pageSize": 30
},
"projectId": 0,
"startDateTime": "2019-11-22 00:00:00",
// 查询从开始时间到之后一周以内的，取值 minute/hour/day/week/month/total
"dateFormat": "week",
// "#user_id", 作为查询请求字段省略掉#号
"userId": 33171371
}
- Response body (application/json)
{
"data": {
"userEventSeqList": [
{
"event_name": "use_item",
"time": "2019-11-22 00:03:17",
"properties": {
"#ip": "59.71.165.109",
"#country": "中国",
"#province": "湖北省",
"#city": "武汉市",
"#manufacturer": "iphone",
"#os": "ios",
"#device_id": "200ECB2114238E4ECCACBFE4D7D88343",
"#screen_height": 1920,
"#device_model": "iphone6sp",
"#app_version": "7.12.2",
"#screen_width": 1080,
"#lib": "ios",
"#network_type": "4G",
"#carrier": "中国移动",
"server_id": "43",
"channel": "app store"
}
},
{
"event_name": "use_item",
"time": "2019-11-22 00:03:20",
"properties": {
"#ip": "59.71.165.109",
"#country": "中国",
"#province": "湖北省",
"#city": "武汉市",
"#manufacturer": "iphone",
"#os": "ios",
"#device_id": "200ECB2114238E4ECCACBFE4D7D88343",
"#screen_height": 1920,
"#device_model": "iphone6sp",
"#app_version": "7.12.2",
"#screen_width": 1080,
"#lib": "ios",
"#network_type": "4G",
"#carrier": "中国移动",
"server_id": "43",
"channel": "app store"
}
},
{
"event_name": "use_item",
"time": "2019-11-22 00:03:28",
"properties": {
"#ip": "59.71.165.109",
"#country": "中国",
"#province": "湖北省",
"#city": "武汉市",
"#manufacturer": "iphone",
"#os": "ios",
"#device_id": "200ECB2114238E4ECCACBFE4D7D88343",
"#screen_height": 1920,
"#device_model": "iphone6sp",
"#app_version": "7.12.2",
"#screen_width": 1080,
"#lib": "ios",
"#network_type": "4G",
"#carrier": "中国移动",
"server_id": "43",
"channel": "app store"
}
},
{
"event_name": "use_item",
"time": "2019-11-22 11:35:04",
"properties": {
"#ip": "59.71.165.109",
"#country": "中国",
"#province": "湖北省",
"#city": "武汉市",
"#manufacturer": "iphone",
"#os": "ios",
"#device_id": "200ECB2114238E4ECCACBFE4D7D88343",
"#screen_height": 1920,
"#device_model": "iphone6sp",
"#app_version": "7.12.2",
"#screen_width": 1080,
"#lib": "ios",
"#network_type": "4G",
"#carrier": "中国移动",
"server_id": "43",
"channel": "app store"
}
}
],
"eventNameDescMeta": {
"anyEvent": "任意事件",
"participate_activity": "参与活动",
"participate_quest": "参与任务",
"recharge": "充值",
"ta_app_start": "ta_app_start",
"ta_app_view": "ta_app_view",
"use_item": "使用道具"
},
"columnDescMeta": {
"#ip": "客户端IP",
"#country": "国家",
"#province": "省份",
"#city": "城市",
"#manufacturer": "生产商",
"#os": "操作系统",
"#device_id": "设备号",
"#screen_height": "屏幕高度",
"#device_model": "设备型号",
"#app_version": "app版本",
"#screen_width": "屏幕宽度",
"#lib": "系统库",
"#network_type": "网络类型",
"#carrier": "运营商",
"server_id": "服务器",
"channel": "渠道1",
"diamond_obtain": "充值获得钻石数",
"recharge_level": "充值等级",
"recharge_value": "充值金额",
"activity_type": "活动类型",
"activity_item_operation": "活动项目",
"recharge_first_day": "是否首日充值",
"recharge_first_time": "是否首次充值",
"quest_type": "任务类型",
"quest_name": "任务名称"
}
},
"return_code": 0,
"return_message": "success"
}