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Learn about data kinship JSON format design from sqlflow JSON format
2022-06-30 14:23:00 【mizuhokaga】
0.
Data kinship visualization is an excellent tool at present SQLFlow（ Salmon data kinship Analyzer ） 了 . We can SQLFlow Of json Format to learn about data kinship json Format design .
- Are you hungry , Are you hungry? Metadata management data kinship json Format design
1. example
How to get SQLFlow Of json？ Right click on the visualization canvas –》Download as json
With SQLFlow With Hive sample sql For example .
SELECT pageid, adid
FROM pageAds LATERAL VIEW explode(adid_list) adTable AS adid;
In order to see what we are saying paragraph by paragraph , At the end of the article there is a complete json.
1.1 Core overview
json Blood relationship is defined in sqlflow In this object , There is one field and two arrays
- dbvendor : What kind of database , Here is hive
- dbobjs： Nodes in the array , Every node is sqlflow In the canvas, there are pieces of tables
- relations： Relational arrays , Every relationship is sqlflow The lines in the canvas
"sqlflow": {
"dbvendor": "dbvhive",
"dbobjs": [],
"relations": []
}
1.2 dbobjs Array
dbobjs Array holds multiple node objects , The result of the node object is as follows . Each node object contains all the columns and coordinates of the table .
{
"id": "2",
"name": "pageAds",
"type": "table",
"columns": [
{
"id": "9",
"name": "adid_list",
"coordinates": [
{
"x": 3,
"y": 35,
"hashCode": "0"
},
{
"x": 3,
"y": 44,
"hashCode": "0"
}
]
},
{
"id": "13",
"name": "pageid",
"coordinates": [
{
"x": 2,
"y": 8,
"hashCode": "0"
},
{
"x": 2,
"y": 14,
"hashCode": "0"
}
]
}
],
"coordinates": [
{
"x": 3,
"y": 6,
"hashCode": "0"
},
{
"x": 3,
"y": 61,
"hashCode": "0"
}
]
}
json The binary coordinate groups of tables and columns in the are so convenient for the front-end to render the diagram . Tables and fields have their own numbers to facilitate the following relationships to locate their parent nodes
relations
relations Each relationship object in the has the most important target and sources Field . from relations We can also find sqlflow Of json Format and hive utilize hook The log format of field level blood relationship collected passively is basically similar .
{
"id": "101",
//sqlflow Several relationships defined by oneself
"type": "fdd",
"effectType": "select",
"target": {
"id": "14",
"column": "adid",
"parentId": "11",
"parentName": "RS-1",
"coordinates": [
{
"x": 2,
"y": 16,
"hashCode": "0"
},
{
"x": 2,
"y": 20,
"hashCode": "0"
}
]
},
"sources": [
{
"id": "8",
"column": "adid",
"parentId": "7",
"parentName": "adTable",
"coordinates": [
{
"x": 3,
"y": 57,
"hashCode": "0"
},
{
"x": 3,
"y": 61,
"hashCode": "0"
}
]
}
]
}
complete json
{
"code": 200,
"data": {
"mode": "global",
"summary": {
"schema": 0,
"process": 0,
"database": 0,
"view": 0,
"mostRelationTables": [
{
"table": "PAGEADS"
},
{
"table": "ADTABLE"
}
],
"column": 3,
"table": 2,
"relation": 3
},
"sqlflow": {
"dbvendor": "dbvhive",
"dbobjs": [
{
"id": "2",
"name": "pageAds",
"type": "table",
"columns": [
{
"id": "9",
"name": "adid_list",
"coordinates": [
{
"x": 3,
"y": 35,
"hashCode": "0"
},
{
"x": 3,
"y": 44,
"hashCode": "0"
}
]
},
{
"id": "13",
"name": "pageid",
"coordinates": [
{
"x": 2,
"y": 8,
"hashCode": "0"
},
{
"x": 2,
"y": 14,
"hashCode": "0"
}
]
}
],
"coordinates": [
{
"x": 3,
"y": 6,
"hashCode": "0"
},
{
"x": 3,
"y": 61,
"hashCode": "0"
}
]
},
{
"id": "7",
"name": "adTable",
"type": "table",
"columns": [
{
"id": "8",
"name": "adid",
"coordinates": [
{
"x": 3,
"y": 57,
"hashCode": "0"
},
{
"x": 3,
"y": 61,
"hashCode": "0"
}
]
}
],
"coordinates": [
{
"x": 3,
"y": 46,
"hashCode": "0"
},
{
"x": 3,
"y": 53,
"hashCode": "0"
}
]
},
{
"id": "11",
"name": "RS-1",
"type": "select_list",
"columns": [
{
"id": "12",
"name": "pageid",
"coordinates": [
{
"x": 2,
"y": 8,
"hashCode": "0"
},
{
"x": 2,
"y": 14,
"hashCode": "0"
}
]
},
{
"id": "14",
"name": "adid",
"coordinates": [
{
"x": 2,
"y": 16,
"hashCode": "0"
},
{
"x": 2,
"y": 20,
"hashCode": "0"
}
]
}
],
"coordinates": [
{
"x": 2,
"y": 8,
"hashCode": "0"
},
{
"x": 2,
"y": 20,
"hashCode": "0"
}
]
}
],
"relations": [
{
"id": "101",
"type": "fdd",
"effectType": "select",
"target": {
"id": "14",
"column": "adid",
"parentId": "11",
"parentName": "RS-1",
"coordinates": [
{
"x": 2,
"y": 16,
"hashCode": "0"
},
{
"x": 2,
"y": 20,
"hashCode": "0"
}
]
},
"sources": [
{
"id": "8",
"column": "adid",
"parentId": "7",
"parentName": "adTable",
"coordinates": [
{
"x": 3,
"y": 57,
"hashCode": "0"
},
{
"x": 3,
"y": 61,
"hashCode": "0"
}
]
}
]
},
{
"id": "102",
"type": "fdd",
"effectType": "select",
"target": {
"id": "12",
"column": "pageid",
"parentId": "11",
"parentName": "RS-1",
"coordinates": [
{
"x": 2,
"y": 8,
"hashCode": "0"
},
{
"x": 2,
"y": 14,
"hashCode": "0"
}
]
},
"sources": [
{
"id": "13",
"column": "pageid",
"parentId": "2",
"parentName": "pageAds",
"coordinates": [
{
"x": 2,
"y": 8,
"hashCode": "0"
},
{
"x": 2,
"y": 14,
"hashCode": "0"
}
]
}
]
},
{
"id": "103",
"type": "fdd",
"effectType": "select",
"target": {
"id": "8",
"column": "adid",
"parentId": "7",
"parentName": "adTable",
"coordinates": [
{
"x": 3,
"y": 57,
"hashCode": "0"
},
{
"x": 3,
"y": 61,
"hashCode": "0"
}
]
},
"sources": [
{
"id": "9",
"column": "adid_list",
"parentId": "2",
"parentName": "pageAds",
"coordinates": [
{
"x": 3,
"y": 35,
"hashCode": "0"
},
{
"x": 3,
"y": 44,
"hashCode": "0"
}
]
}
]
}
]
},
"graph": {
"elements": {
"tables": [
{
"columns": [
{
"height": 16,
"id": "n0::n0",
"label": {
"content": "pageid",
"fontFamily": "Segoe UI Symbol",
"fontSize": "12",
"height": 13.96875,
"width": 59,
"x": 0,
"y": 0
},
"width": 160,
"x": 445,
"y": -59.49261
},
{
"height": 16,
"id": "n0::n1",
"label": {
"content": "adid",
"fontFamily": "Segoe UI Symbol",
"fontSize": "12",
"height": 13.96875,
"width": 43,
"x": 0,
"y": 0
},
"width": 160,
"x": 445,
"y": -43.49261
}
],
"height": 57.96875,
"id": "n0",
"label": {
"content": "RS-1",
"fontFamily": "Segoe UI Symbol",
"fontSize": "12",
"height": 17.96875,
"width": 162,
"x": 0,
"y": 0
},
"width": 162,
"x": 444,
"y": -81.46136
},
{
"columns": [
{
"height": 16,
"id": "n1::n0",
"label": {
"content": "adid_list",
"fontFamily": "Segoe UI Symbol",
"fontSize": "12",
"height": 13.96875,
"width": 67,
"x": 0,
"y": 0
},
"width": 160,
"x": 1,
"y": -59.49261
},
{
"height": 16,
"id": "n1::n1",
"label": {
"content": "pageid",
"fontFamily": "Segoe UI Symbol",
"fontSize": "12",
"height": 13.96875,
"width": 59,
"x": 0,
"y": 0
},
"width": 160,
"x": 1,
"y": -43.49261
}
],
"height": 57.96875,
"id": "n1",
"label": {
"content": "pageAds",
"fontFamily": "Segoe UI Symbol",
"fontSize": "12",
"height": 17.96875,
"width": 162,
"x": 0,
"y": 0
},
"width": 162,
"x": 0,
"y": -81.46136
},
{
"columns": [
{
"height": 16,
"id": "n2::n0",
"label": {
"content": "adid",
"fontFamily": "Segoe UI Symbol",
"fontSize": "12",
"height": 13.96875,
"width": 43,
"x": 0,
"y": 0
},
"width": 160,
"x": 223,
"y": -30.000149
}
],
"height": 41.96875,
"id": "n2",
"label": {
"content": "adTable",
"fontFamily": "Segoe UI Symbol",
"fontSize": "12",
"height": 17.96875,
"width": 162,
"x": 0,
"y": 0
},
"width": 162,
"x": 222,
"y": -51.9689
}
],
"edges": [
{
"id": "e0",
"sourceId": "n1::n0",
"targetId": "n2::n0"
},
{
"id": "e1",
"sourceId": "n2::n0",
"targetId": "n0::n1"
},
{
"id": "e2",
"sourceId": "n1::n1",
"targetId": "n0::n0"
}
]
},
"tooltip": {
},
"relationIdMap": {
"e0": "fdd",
"e1": "fdd",
"e2": "fdd"
},
"listIdMap": {
"n0": [
"11"
],
"n0::n0": [
"12"
],
"n0::n1": [
"14"
],
"n1": [
"2"
],
"n1::n0": [
"9"
],
"n1::n1": [
"13"
],
"n2": [
"7"
],
"n2::n0": [
"8"
]
}
}
},
"sessionId": "a91895aadcb9d674e7d3edd5ddd12d4fefcda4ff797881d3b4823fe1ec9ff367_1633745113743"
}
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