[SPARK-12321][SQL] JSON format for TreeNode (use reflection)
An alternative solution for https://github.com/apache/spark/pull/10295 , instead of implementing json format for all logical/physical plans and expressions, use reflection to implement it in `TreeNode`.
Here I use pre-order traversal to flattern a plan tree to a plan list, and add an extra field `num-children` to each plan node, so that we can reconstruct the tree from the list.
example json:
logical plan tree:
```
[ {
"class" : "org.apache.spark.sql.catalyst.plans.logical.Sort",
"num-children" : 1,
"order" : [ [ {
"class" : "org.apache.spark.sql.catalyst.expressions.SortOrder",
"num-children" : 1,
"child" : 0,
"direction" : "Ascending"
}, {
"class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference",
"num-children" : 0,
"name" : "i",
"dataType" : "integer",
"nullable" : true,
"metadata" : { },
"exprId" : {
"id" : 10,
"jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6"
},
"qualifiers" : [ ]
} ] ],
"global" : false,
"child" : 0
}, {
"class" : "org.apache.spark.sql.catalyst.plans.logical.Project",
"num-children" : 1,
"projectList" : [ [ {
"class" : "org.apache.spark.sql.catalyst.expressions.Alias",
"num-children" : 1,
"child" : 0,
"name" : "i",
"exprId" : {
"id" : 10,
"jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6"
},
"qualifiers" : [ ]
}, {
"class" : "org.apache.spark.sql.catalyst.expressions.Add",
"num-children" : 2,
"left" : 0,
"right" : 1
}, {
"class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference",
"num-children" : 0,
"name" : "a",
"dataType" : "integer",
"nullable" : true,
"metadata" : { },
"exprId" : {
"id" : 0,
"jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6"
},
"qualifiers" : [ ]
}, {
"class" : "org.apache.spark.sql.catalyst.expressions.Literal",
"num-children" : 0,
"value" : "1",
"dataType" : "integer"
} ], [ {
"class" : "org.apache.spark.sql.catalyst.expressions.Alias",
"num-children" : 1,
"child" : 0,
"name" : "j",
"exprId" : {
"id" : 11,
"jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6"
},
"qualifiers" : [ ]
}, {
"class" : "org.apache.spark.sql.catalyst.expressions.Multiply",
"num-children" : 2,
"left" : 0,
"right" : 1
}, {
"class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference",
"num-children" : 0,
"name" : "a",
"dataType" : "integer",
"nullable" : true,
"metadata" : { },
"exprId" : {
"id" : 0,
"jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6"
},
"qualifiers" : [ ]
}, {
"class" : "org.apache.spark.sql.catalyst.expressions.Literal",
"num-children" : 0,
"value" : "2",
"dataType" : "integer"
} ] ],
"child" : 0
}, {
"class" : "org.apache.spark.sql.catalyst.plans.logical.LocalRelation",
"num-children" : 0,
"output" : [ [ {
"class" : "org.apache.spark.sql.catalyst.expressions.AttributeReference",
"num-children" : 0,
"name" : "a",
"dataType" : "integer",
"nullable" : true,
"metadata" : { },
"exprId" : {
"id" : 0,
"jvmId" : "cd1313c7-3f66-4ed7-a320-7d91e4633ac6"
},
"qualifiers" : [ ]
} ] ],
"data" : [ ]
} ]
```
Author: Wenchen Fan <wenchen@databricks.com>
Closes #10311 from cloud-fan/toJson-reflection.
7634fe9511