我刚开始使用MDX查询,所以我不是专家。我们已经提供了MDX查询,可以通过Web套接字连接从我们的前端应用程序触发。收到的响应是一个多维数据集,而不是看起来像这样的标准JSON。
{
"type": "cellSetData",
"streamId": "cb6fdd98-d528-44fb-8f14-366970e574b5",
"queryId": "cb6fdd98-d528-44fb-8f14-366970e574b5",
"data": {
"axes": [
{
"id":0,
"hierarchies": [
{
"dimension": "Measures",
"hierarchy": "Measures"
}
],
"positions": [
[ { "namePath": [ "5-Day ADV" ] } ],
[ { "namePath": [ "Target Value" ] } ],
[ { "namePath": [ "Performance Vs VWAP (Targ. Val. W.A.)" ] } ]
],
"maxLevelPerHierarchy": [1]
},
{
"id":1,
"hierarchies": [
{
"dimension": "Order",
"hierarchy": "OrderId"
}
],
"positions": [
[ { "namePath": [ "AllMember" ] } ],
[ { "namePath": [ "AllMember", "20180829-142357889-114-29" ] } ],
[ { "namePath": [ "AllMember", "20180829-142357896-775-32" ] } ],
[ { "namePath": [ "AllMember", "20180829-142357897-394-35" ] } ]
],
"maxLevelPerHierarchy": [2]
}
],
"cells": [
{
"ordinal": 0,
"value": 1.8702095375E7
},
{
"ordinal": 1,
"value": 41461.2
},
{
"ordinal": 2,
"value": 0.0
},
{
"ordinal": 3,
"value": 1968021.375
},
{
"ordinal": 4,
"value": 17719.2
},
{
"ordinal": 5,
"value": 0.0
},
{
"ordinal": 6,
"value": 1043997.0
},
{
"ordinal": 7,
"value": 10328.4
},
{
"ordinal": 8,
"value": 0.0
},
{
"ordinal": 9,
"value": 1.5690077E7
},
{
"ordinal": 10,
"value": 13413.6
},
{
"ordinal": 11,
"value": 0.0
}
]
}
}
{“Stock_Percentage”:“1.8702095375E7”,“Stock_Quantity”:“21997538”,“Stock_Price”:“333”} {“Stock_Percentage”:“1968021.375”,“Stock_Quantity”:“17719.2”,“Stock_Price”:“0.0” }
任何信息都非常感谢!
最好的祝福!!
MDX是为多维分析而设计的,结果看起来不像一个简单的表,它无法反映这种多维特征,尤其是层次结构。
据我所知,没有开源解析器将这种响应转换为表格,也没有接近你的格式。但是,您可以尝试将任何MDX查询导出为CSV http://server:port/pivot/rest/v4/cube/export/mdx/download
的REST调用。根据你的JSON cellset,它会提供这样的东西:
[Order].[OrderId].[OrderId];[Measures];VALUE
;"5-Day ADV";1.8702095375E7
;"Target Value";41461.2
;"Performance Vs VWAP (Targ. Val. W.A.)";0.0
20180829-142357889-114-29;"5-Day ADV";1.8702095375E7
20180829-142357889-114-29;"Target Value";41461.2
20180829-142357889-114-29;"Performance Vs VWAP (Targ. Val. W.A.)";0.0
...
最后,您始终可以自己解析结果。我们的想法是,单元序数表示多维数据集中的给定位置。它们通过迭代轴位置来计算,从具有较高id的轴开始到具有最低id的轴。例如,
0
是为"5-Day ADV"|"AllMember"
,1
是为"Target Value"|"AllMember"
3
是为"5-Day ADV"|"AllMember"\20180829-142357889-114-29
干杯
PS:我在ActiveViam工作