moonstream-to
Json query language for event Filtering.
select
label_data -> 'args' ->> 'to' as purchaser,
label_data -> 'args' ->> 'tokenId' as tokenId,
to_timestamp(block_timestamp) as time_of_sale
from
polygon_labels
where
label = 'moonworm-alpha'
and address = "<address>"
and label_data ->> 'type' = 'event'
and label_data ->> 'name' = 'Transfer'
and label_data -> 'args' ->> 'from' = '0x0000000000000000000000000000000000000000'
and (label_data -> 'args' ->> 'tokenId') :: int in (
11000,
12000,
13000,
14000,
15000,
16000,
17000,
18000,
19000,
20000
)
order by
tokenId;
Expected Json.
{
"output_statment": [
{ "block_timestamp": { "type": "timestamp" } },
{ "args": { "name": "to" } },
{ "args": { "name": "from" } }
],
"filters": [
{
"address": "<address>",
"label_filters": [
{
"name": "Transfer",
"type": "metod",
"args_combinator": "AND",
"args": [
{
"name": "from",
"value": "0x0000000000000000000000000000000000000000",
"operation": "="
},
{
"name": "tokenId",
"type": "int",
"value": [
11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000,
20000
],
"operation": "in"
}
]
}
]
}
],
"group_condition": [],
"order_by": ["tokenId"]
}
@Andrei-Dolgolev I think is must to add blockchain selector as well. And I assume for some contracts it might be useful to actually query for polygon AND/OR ethereum simultaneously
I think is must to add blockchain selector as well. And I assume for some contracts it might be useful to actually query for polygon AND/OR ethereum simultaneously
@peersky Yes good idea, but not sure if i ready do it in first iteration for now it can apply to different blockchains via sub: query perameter. But later we need support 2 different query to different blockchain it's probably help with watching any bridge operation. But its meen json query must support same logic as we do with query parametrs rifht now or even simple just fully replace it.
Extended version:
{
"output_statment": [
{ "block_timestamp": { "type": "timestamp" } },
{ "args": { "name": "to" } },
{ "args": { "name": "from" } }
],
"filter_block":{
"condition": {
"AND":{
"address": "<address>",
"name": "Transfer",
"type": "metod",
"condition": {
"OR": {
"args": [
{
"name": "from",
"value": "0x0000000000000000000000000000000000000000",
"operation": "="
},
{
"name": "tokenId",
"type": "int",
"value": [
11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000,
20000
],
"operation": "in"
}
]
}
}
}
}
},
"group_condition": [],
"order_by": ["tokenId"]
}
cant it be just part of args[] with
name: OR
operation: boolean_algebra
And if we args can be recursive than we could have smth like
[
{
"name": "from",
"value": "0x0000000000000000000000000000000000000000",
"operation": "="
},
{
"name": "OR",
"operation": "boolean_algebra"
},
{
"name": "from",
"value": "0x0000000000000000000000000000000000000001",
"operation": "="
},
{
"name": "AND",
"operation": "boolean_algebra"
},
{
"name": "nested_args",
"operation": [
{
"name": "to",
"value": "0x000000000000000000000000000000000000d3ad",
"operation": "="
},
{
"name": "OR",
"operation": "boolean_algebra"
},
{
"name": "to",
"value": "vitalik.eth",
"operation": "="
}
]
}
]
My proposal:
{
"outputs": [
{
"selector": "block_timestamp",
"as_type": "timestamp",
"display_name": "time_of_sale"
},
{
"selector": "data.args.to",
"display_name": "purchaser"
},
{
"selector": "data.args.tokenId",
"display_name": "tokenId"
}
],
"order": {
"display_names": ["tokenId"],
"sort": ["asc"]
},
"filters": {
"clause_type": "and",
"subclauses": [
{
"clause_type": "or",
"subclauses": [
{
"clause_type": "simple",
"data.args.from": {
"comparator": "=",
"value": "0x0000000000000000000000000000000000000000"
}
},
{
"clause_type": "simple",
"data.args.tokenId": {
"cast": "int",
"comparator": "in",
"value": [
11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000,
20000
]
}
}
]
}
]
}
}
Alternative - use MongoDB aggregation pipelines syntax: https://docs.mongodb.com/manual/core/aggregation-pipeline/
First draft implementation: @Andrei-Dolgolev and I agreed to start with MongoDB syntax.
This plays well with the react query builder. For example, this example generates this query:
{"$and":[{"firstName":{"$regex":"^Stev"}},{"lastName":{"$in":["Vai","Vaughan"]}},{"age":{"$gt":"28"}},{"$or":[{"isMusician":{"$eq":true}},{"instrument":{"$eq":"Guitar"}}]}]}
MongoDB style syntax for our query:
[
{
"$match": {
"data.args.from": "0x0000000000000000000000000000000000000000",
"data.type": "event",
"data.name": "Transfer",
"data.address": "<contract address>",
"data.args.tokenId": {
"$cast": "int",
"$in": [
11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000
]
}
}
},
{ "$sort": "data.args.tokenId" },
{
"$map": {
"data.args.to": "purchaser",
"data.args.tokenId": "tokenId",
"block_timestamp": "time_of_sale"
}
}
]
Our MongoDB style syntax introduced two extra keys: "$cast" and "$map". Need to think about how to handle with react query builder.
Extend are little logic of map because it require more be the same keys
[
{
"$match": {
"data.args.from": "0x0000000000000000000000000000000000000000",
"data.type": "event",
"data.name": "Transfer",
"data.address": "<contract address>",
"data.args.tokenId": {
"$cast": "int",
"$in": [
11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000
]
}
}
},
{
"$sort": { "data.args.tokenId": "desc" }
},
{
"$map": [
{
"data.args.to": "purchaser"
},
{
"data.args.tokenId": "tokenId"
},
{
"block_timestamp": "time_of_sale"
}
]
}
]