Hi I would like to draw from graphql only those records whose date is equal to the month - August
If I want to pull another month, it is enough to replace it only in the query. At the moment, my query takes all the months instead of the ones it gives inside the filter
schema.json
{
"kind": "collectionType",
"collectionName": "product_popularities",
"info": {
"singularName": "product-popularity",
"pluralName": "product-popularities",
"displayName": "Popularity",
"description": ""
},
"options": {
"draftAndPublish": true
},
"pluginOptions": {},
"attributes": {
"podcast": {
"type": "relation",
"relation": "manyToOne",
"target": "api::product.products",
"inversedBy": "products"
},
"value": {
"type": "integer"
},
"date": {
"type": "date"
}
}
}
My query
query {
Popularities(filters: {date: {contains: [2022-08]}}) {
data {
attributes {
date
value
}
}
}
}
Response
{
"data": {
"Popularities": {
"data": [
{
"attributes": {
"date": "2022-08-03",
"value": 50
}
},
{
"attributes": {
"date": "2022-08-04",
"value": 1
}
},
{
"attributes": {
"date": "2022-08-10",
"value": 100
}
},
{
"attributes": {
"date": "2022-07-06",
"value": 20
}
}
]
}
}
}
Related
I am new to the mongoDB aggregation and I have this situation. I have this Json and I need to convert by "select" this object:
{
"type": "PF",
"code": 12345
"Name": Darth Vader,
"currency": "BRL",
"status": "SINGLE",
"adress": [
{
"localization": "DEATH STAR",
"createDate": 1627990848665
},
{
"localization": "TATOOINE",
"createDate": 1627990555665
},
]
}
this way:
{
"type": "PF",
"code": 12345
"Name": Darth Vader,
"currency": "BRL",
"status": "SINGLE",
"localization": "DEATH STAR",
"createDate": 1627990848665
},
{
"type": "PF",
"code": 12345
"Name": Darth Vader,
"currency": "BRL",
"status": "SINGLE",
"localization": "TATOOINE",
"createDate": 1627990555665
}
So, after my query is complete, I will have 02 objects instead of 01. How can I do this?
I would like to do this via select because after converting I will sort by createDate and limit the number of records to return to the API. I'm using Criteria em my project.
The way to do this is $unwind, this will make 1 copy of the document, for each member of the array.
Test code here
db.collection.aggregate([
{
"$unwind": {
"path": "$user.adress"
}
},
{
"$set": {
"user": {
"$mergeObjects": [
"$user",
"$user.adress"
]
}
}
},
{
"$unset": [
"user.adress"
]
},
{
"$sort": {
"createDate": 1
}
},
{
"$limit": 10
}
])
Edit1 (the above is if user is a field, if it was the name of the collection)
$$ROOT is a system variable that has as value all the document
Test code here
Query
db.collection.aggregate([
{
"$unwind": {
"path": "$adress"
}
},
{
"$replaceRoot": {
"newRoot": {
"$mergeObjects": [
"$$ROOT",
"$adress"
]
}
}
},
{
"$unset": [
"adress"
]
},
{
"$sort": {
"createDate": 1
}
},
{
"$limit": 10
}
])
I am trying to convert a JSON File into CSV but I don't seem to have any luck in doing so. My JSON looks something like that:
...
{
{"meta": {
"contentType": "Response"
},
"content": {
"data": {
"_type": "ObjectList",
"erpDataObjects": [
{
"meta": {
"lastModified": "2020-08-10T08:37:21.000+0000",
},
"head": {
"fields": {
"number": {
"value": "1",
},
"id": {
"value": "10000"
},
}
}
{
"meta": {
"lastModified": "2020-08-10T08:37:21.000+0000",
},
"head": {
"fields": {
"number": {
"value": "2",
},
"id": {
"value": "10001"
},
}
}
{
"meta": {
"lastModified": "2020-08-10T08:37:21.000+0000",
},
"head": {
.. much more data
I basically want my csv to look like this:
number,id
1,10000
2,10001
My flow looks like this:
GetFile -> Set the output-file name -> ConvertRecord -> UpdateAttribute -> PutFile
ConvertRecord uses the JsonTreeReader and a CSVRecordSetWriter
JsonTreeReader
CsvRecordSetWriter.
They both call on an AvroSchemaRegistry which looks like this:
AvroSchemaRegistry
The AvroSchema itself looks like this:
{
"type": "record",
"name": "head",
"fields":
[
{"name": "number", "type": ["string"]},
{"name": "id", "type": ["string"]},
]
}
But I only get this output:
number,id
,
Which makes sense because I'm not specifically indicating where those values are located. I used the JsonPathReader instead before but it only looked like this:
JsonPathReader
Which obvioulsy only gave me one record. I'm not really sure how I can configure either of the two to output exactly what I want. Help would be much appreciated!
Using ConvertRecord for JSON -> CSV is mostly intended for "flat" JSON files where each field in the object becomes a column in the outgoing CSV file. For nested/complex structures, consider JoltConvertRecord, it allows you to do more complex transformations. Your example doesn't appear to be valid JSON as-is, but assuming you have something like this as input:
{
"meta": {
"contentType": "Response"
},
"content": {
"data": {
"_type": "ObjectList",
"erpDataObjects": [
{
"meta": {
"lastModified": "2020-08-10T08:37:21.000+0000"
},
"head": {
"fields": {
"number": {
"value": "1"
},
"id": {
"value": "10000"
}
}
}
},
{
"meta": {
"lastModified": "2020-08-10T08:37:21.000+0000"
},
"head": {
"fields": {
"number": {
"value": "2"
},
"id": {
"value": "10001"
}
}
}
}
]
}
}
}
The following JOLT spec should give you what you want for output:
[
{
"operation": "shift",
"spec": {
"content": {
"data": {
"erpDataObjects": {
"*": {
"head": {
"fields": {
"number": {
"value": "[&4].number"
},
"id": {
"value": "[&4].id"
}
}
}
}
}
}
}
}
}
]
Good day
I am creating custom visualization on d3js and pbiviz for powerbi
Here is the code in capabilities.js:
{
"dataRoles":[
{
"displayName": "HoleDepth",
"name": "depth",
"kind": "Grouping"
},
{
"displayName": "Days",
"name": "days",
"kind": "Measure"
},
{
"displayName": "Diametrs",
"name": "diametrs",
"kind": "Measure"
},
{
"displayName": "Sensor1",
"name": "sensor_1",
"kind": "Measure"
},
{
"displayName": "Sensor2",
"name": "sensor_2",
"kind": "Measure"
},
{
"displayName": "Sensor3",
"name": "sensor_3",
"kind": "Measure"
},
{
"displayName": "Sensor4",
"name": "sensor_4",
"kind": "Measure"
}
],
"dataViewMappings": [
{
"categorical": {
"categories": {
"for": { "in": "depth" }
},
"values": {
"select":[
{ "bind": { "to": "days" } },
{ "bind": { "to": "diametrs" } },
{ "bind": { "to": "sensor_1" } },
{ "bind": { "to": "sensor_2" } },
{ "bind": { "to": "sensor_3" } },
{ "bind": { "to": "sensor_4" } }
]
}
}
}
]
}
But in visualization it is inconvenient to use categorical -> values array
Is it possible to categorical -> values
was like an object with keys?
I do not think that this is possible directly through data mapping. What I usually do if I want to have data prepared in the specific format, convenient for visualization with d3.js, is the custom function that transforms the data from VisualUpdateOptions.
Then I call this function inside public update(options: VisualUpdateOptions)
I have following records of car sales of different brands in different cities.
Document -1
{
"city": "Delhi",
"cars":[{
"name":"Toyota",
"purchase":100,
"sold":80
},{
"name":"Honda",
"purchase":200,
"sold":150
}]
}
Document -2
{
"city": "Delhi",
"cars":[{
"name":"Toyota",
"purchase":50,
"sold":40
},{
"name":"Honda",
"purchase":150,
"sold":120
}]
}
I am trying to come up with query to aggregate car statistics for a given city but not getting the right query.
Required result:
{
"city": "Delhi",
"cars":[{
"name":"Toyota",
"purchase":150,
"sold":120
},{
"name":"Honda",
"purchase":350,
"sold":270
}]
}
First you need to map your array as a nested field (script would be complicated and not performant). Nested field are indexed, aggregation will be pretty fast.
remove your index / or create a new one. Please note i use test as type.
{
"mappings": {
"test": {
"properties": {
"city": {
"type": "keyword"
},
"cars": {
"type": "nested",
"properties": {
"name": {
"type": "keyword"
},
"purchase": {
"type": "integer"
},
"sold": {
"type": "integer"
}
}
}
}
}
}
}
Index your document (same way you did)
For the aggregation:
{
"size": 0,
"aggs": {
"avg_grade": {
"terms": {
"field": "city"
},
"aggs": {
"resellers": {
"nested": {
"path": "cars"
},
"aggs": {
"agg_name": {
"terms": {
"field": "cars.name"
},
"aggs": {
"avg_pur": {
"sum": {
"field": "cars.purchase"
}
},
"avg_sold": {
"sum": {
"field": "cars.sold"
}
}
}
}
}
}
}
}
}
}
result:
buckets": [
{
"key": "Honda",
"doc_count": 2,
"avg_pur": {
"value": 350
},
"avg_sold": {
"value": 270
}
}
,
{
"key": "Toyota",
"doc_count": 2,
"avg_pur": {
"value": 150
},
"avg_sold": {
"value": 120
}
}
]
if you have index the name / city field as a text (you have to ask first if this is necessary), use .keyword in the term aggregation ("cars.name.keyword").
I have an object which looks something like this:
{
"id": 123,
"language_id": 1,
"label": "Pablo de la Pena",
"office": {
"count": 2,
"data": [
{
"id": 1234,
"is_office_lead": false,
"office": {
"id": 1,
"address_line_1": "123 Main Street",
"address_line_2": "London",
"address_line_3": "",
"address_line_4": "UK",
"address_postcode": "E1 2BC",
"city_id": 1
}
},
{
"id": 5678,
"is_office_lead": false,
"office": {
"id": 2,
"address_line_1": "77 High Road",
"address_line_2": "Edinburgh",
"address_line_3": "",
"address_line_4": "UK",
"address_postcode": "EH1 2DE",
"city_id": 2
}
}
]
},
"primary_office": {
"id": 1,
"address_line_1": "123 Main Street",
"address_line_2": "London",
"address_line_3": "",
"address_line_4": "UK",
"address_postcode": "E1 2BC",
"city_id": 1
}
}
My Elasticsearch mapping looks like this:
"mappings": {
"item": {
"properties": {
"office": {
"properties": {
"data": {
"type": "nested",
}
}
}
}
}
}
My Elasticsearch query looks something like this:
GET consultant/item/_search
{
"from": 0,
"size": 24,
"query": {
"bool": {
"must": [
{
"term": {
"language_id": 1
}
},
{
"term": {
"office.data.office.city_id": 1
}
}
]
}
}
}
This returns zero results, however, if I remove the second term and leave it only with the language_id clause, then it works as expected.
I'm sure this is down to a misunderstading on my part of how the nested object is flattened, but I'm out of ideas - I've tried all kinds of permutations of the query and mappings.
Any guidance hugely appreciated. I am using Elasticsearch 6.1.1.
I'm not sure if you need the entire record or not, this solution gives every record that has language_id: 1 and has an office.data.office.id: 1 value.
GET consultant/item/_search
{
"from": 0,
"size": 100,
"query": {
"bool":{
"must": [
{
"term": {
"language_id": {
"value": 1
}
}
},
{
"nested": {
"path": "office.data",
"query": {
"match": {
"office.data.office.city_id": 1
}
}
}
}
]
}
}
}
I put 3 different records in my test index for proofing against false hits, one with different language_id and one with different office ids and only the matching one returned.
If you only need the office data, then that's a bit different but still solvable.