Getting local json data with reactjs - json

I'm getting json error in my first reactjs work. It is very important for me and work which I am a candidate. How can I fix this? Thank u very much.
related class:
constructor() {
super();
this.state = {
data:[],
}
}
componentDidMount(){
fetch('../JsonFiles/routes.json').
then(response => response.json()).
then(findresponse => this.setState ({ data: findresponse.data }));
}
render() {
return (
<div>
fasfasfa
{
this.state.data.map((dynamicData, key) =>
<div>
<span> {dynamicData.routes.origin.cityName} </span>
<span> {dynamicData.routes.destinations[{}].cityName} </span>
</div>
)
}
</div>
);
}
}
json file:
{"routes": [
{
"origin": {
"name": "Aalborg Airport",
"country": "DK",
"cityName": "Aalborg",
"cityCode": "AAL"
},
"destinations": [
{
"name": "Bornholm Rønne",
"country": "DK",
"cityName": "Bornholm",
"cityCode": "RNN"
}
]
},
{
"origin": {
"name": "Barca",
"country": "ES",
"cityName": "Barcelona",
"cityCode": "BCN"
},
"destinations": [
{
"name": "Borispol",
"country": "UA",
"cityName": "Kyiv",
"cityCode": "KBP"
}
]
},
{
"origin": {
"name": "Billund",
"country": "DK",
"cityName": "Billund",
"cityCode": "BLL"
},
"destinations": [
{
"name": "Bornholm Rønne",
"country": "DK",
"cityName": "Bornholm",
"cityCode": "RNN"
},
{
"name": "København Kastrup",
"country": "DK",
"cityName": "Copenhagen",
"cityCode": "CPH"
},
{
"name": "Stavanger Sola",
"country": "NO",
"cityName": "Stavanger",
"cityCode": "SVG"
}
]
},
{
"origin": {
"name": "Bornholm Rønne",
"country": "DK",
"cityName": "Bornholm",
"cityCode": "RNN"
},
"destinations": [
{
"name": "Aalborg Airport",
"country": "DK",
"cityName": "Aalborg",
"cityCode": "AAL"
},
{
"name": "Billund",
"country": "DK",
"cityName": "Billund",
"cityCode": "BLL"
},
{
"name": "København Kastrup",
"country": "DK",
"cityName": "Copenhagen",
"cityCode": "CPH"
},
{
"name": "Midtjyllands Lufthavn (Karup)",
"country": "DK",
"cityName": "Midtjyllands Lufthavn (Karup)",
"cityCode": "KRP"
},
{
"name": "Stavanger Sola",
"country": "NO",
"cityName": "Stavanger",
"cityCode": "SVG"
}
]
},
{
"origin": {
"name": "Charles de Gaulle",
"country": "FR",
"cityName": "Charles de Gaulle",
"cityCode": "CDG"
},
"destinations": [
{
"name": "Billund",
"country": "DK",
"cityName": "Billund",
"cityCode": "BLL"
}
]
},
{
"origin": {
"name": "København Kastrup",
"country": "DK",
"cityName": "Copenhagen",
"cityCode": "CPH"
},
"destinations": [
{
"name": "Billund",
"country": "DK",
"cityName": "Billund",
"cityCode": "BLL"
},
{
"name": "Bornholm Rønne",
"country": "DK",
"cityName": "Bornholm",
"cityCode": "RNN"
},
{
"name": "Midtjyllands Lufthavn (Karup)",
"country": "DK",
"cityName": "Midtjyllands Lufthavn (Karup)",
"cityCode": "KRP"
}
]
},
{
"origin": {
"name": "Esbjerg",
"country": "DK",
"cityName": "Esbjerg",
"cityCode": "EBJ"
},
"destinations": [
{
"name": "Stavanger Sola",
"country": "NO",
"cityName": "Stavanger",
"cityCode": "SVG"
}
]
},
{
"origin": {
"name": "Borispol",
"country": "UA",
"cityName": "Kyiv",
"cityCode": "KBP"
},
"destinations": [
{
"name": "Barca",
"country": "ES",
"cityName": "Barcelona",
"cityCode": "BCN"
}
]
},
{
"origin": {
"name": "Midtjyllands Lufthavn (Karup)",
"country": "DK",
"cityName": "Midtjyllands Lufthavn (Karup)",
"cityCode": "KRP"
},
"destinations": [
{
"name": "Bornholm Rønne",
"country": "DK",
"cityName": "Bornholm",
"cityCode": "RNN"
},
{
"name": "København Kastrup",
"country": "DK",
"cityName": "Copenhagen",
"cityCode": "CPH"
}
]
},
{
"origin": {
"name": "Oslo Gardermoen",
"country": "NO",
"cityName": "Oslo",
"cityCode": "OSL"
},
"destinations": [
{
"name": "Stord lufthavn, Sørstokken",
"country": "NO",
"cityName": "Stord",
"cityCode": "SRP"
},
{
"name": "Ørland",
"country": "NO",
"cityName": "Ørland",
"cityCode": "OLA"
}
]
},
{
"origin": {
"name": "Rhodos",
"country": "GR",
"cityName": "Rhodos",
"cityCode": "RHO"
},
"destinations": [
{
"name": "Billund",
"country": "DK",
"cityName": "Billund",
"cityCode": "BLL"
}
]
},
{
"origin": {
"name": "Stavanger Sola",
"country": "NO",
"cityName": "Stavanger",
"cityCode": "SVG"
},
"destinations": [
{
"name": "Billund",
"country": "DK",
"cityName": "Billund",
"cityCode": "BLL"
},
{
"name": "Bornholm Rønne",
"country": "DK",
"cityName": "Bornholm",
"cityCode": "RNN"
},
{
"name": "Esbjerg",
"country": "DK",
"cityName": "Esbjerg",
"cityCode": "EBJ"
}
]
},
{
"origin": {
"name": "Stord lufthavn, Sørstokken",
"country": "NO",
"cityName": "Stord",
"cityCode": "SRP"
},
"destinations": [
{
"name": "Oslo Gardermoen",
"country": "NO",
"cityName": "Oslo",
"cityCode": "OSL"
}
]
},
{
"origin": {
"name": "Ørland",
"country": "NO",
"cityName": "Ørland",
"cityCode": "OLA"
},
"destinations": [
{
"name": "Oslo Gardermoen",
"country": "NO",
"cityName": "Oslo",
"cityCode": "OSL"
}
]
}
]}
AND I AM GETTING THIS ERROR:
I AM GETTING THIS ERROR
1
Unhandled Rejection (SyntaxError): Unexpected token < in JSON at position 0
(anonymous function)
D:/development/js-projects/material-tutorial/src/Components/TrialForm/FlightSearch.js:13
10 |
11 | componentDidMount(){
12 | fetch('../JsonFiles/routes.json').
13 | then(response => response.json()).
14 | then(findresponse => this.setState ({ data: findresponse.data }));
15 |
16 |

You can import JSON file.
import * as jsonFile from '../JsonFiles/routes.json';
Define it on state in constructor function:
constructor() {
super();
this.state = {
data: jsonFile
}
}

es2015 version:
import jsonFile from '../JsonFiles/routes.json';
Now you have all json data available in "jsonFile" and you can use it any where.

Related

need Jolt Spec to convent JSON array key name to updated name

Original JSON message:
[
{
"correlationId": "12345",
"payloadFormat": "Money",
"payload": {
"stateName": "TX",
"location": "south",
"name": "Dallas",
"pop": "2M"
}
},
{
"correlationId": "ed1e3",
"payloadFormat": "Cash",
"payload": {
"stateName": "CA",
"location": "west",
"name": "LosAngeles",
"pop": "4M"
}
}
]
Output should be in below format:
[
{
"correlationId": "12345",
"payloadFormat": "Money",
"payload": {
"California": "TX",
"MontGomery": "south",
"City": "Dallas",
"ID": "2M"
}
},
{
"correlationId": "ed1e3",
"payloadFormat": "Cash",
"payload": {
"California": "CA",
"MontGomery": "west",
"City": "LosAngeles",
"ID": "4M"
}
}
]
Check this spec,
[
{
"operation": "shift",
"spec": {
"*": {
"correlationId": "[&1].correlationId",
"payloadFormat": "[&1].payloadFormat",
"payload": {
"stateName": "[&2].payload.California",
"location": "[&2].payload.MontGomery",
"name": "[&2].payload.City",
"pop": "[&2].payload.ID"
}
}
}
}
]

How to group flight segments/legs in Bargain Finder Max?

I am trying to search for flights between two places in Sabre using BargainFindermax API. The API provides me with a list of all flights. However, there is no direct relation to which of the flights are linked.
For example, I searched for flights between BLR > LCY. The response I got included flights for AMS > LCY, BLR > DEL, BLR > DEL, DEL > AMS, AMS > LCY.
From this, I can construct the following route BLR > DEL > AMS > LCY. But there are two flights for BLR > DEL and AMS > LCY.
How can I identify which of these flights are related?
Here is the complete JSON response,
{
"groupedItineraryResponse": {
"version": "5.2.0",
"messages": [
{
"severity": "Info",
"type": "WORKERTHREAD",
"code": "TRANSACTIONID",
"text": "2074398531853061390"
},
{
"severity": "Info",
"type": "SERVER",
"code": "ASE032LPSCIL9UM.IDM.SGDCCERT.SABRE.COM",
"text": "27036"
},
{
"severity": "Info",
"type": "DRE",
"code": "RULEID",
"text": "17203"
},
{
"severity": "Info",
"type": "DEFAULT",
"code": "RULEID",
"text": "25238"
}
],
"statistics": {
"itineraryCount": 2
},
"scheduleDescs": [
{
"id": 1,
"frequency": "*MTWTFS",
"stopCount": 0,
"eTicketable": true,
"totalMilesFlown": 257,
"departure": {
"airport": "AMS",
"city": "AMS",
"country": "NL",
"time": "08:45:00+02:00"
},
"arrival": {
"airport": "LCY",
"city": "LON",
"country": "GB",
"time": "08:50:00+01:00"
},
"carrier": {
"marketing": "KL",
"marketingFlightNumber": 985,
"operating": "KL",
"operatingFlightNumber": 985,
"codeShared": "/KLM CITYHOPPER",
"equipment": {
"code": "E90",
"typeForFirstLeg": "N",
"typeForLastLeg": "N"
}
}
},
{
"id": 2,
"trafficRestriction": "G",
"frequency": "SMTWTFS",
"stopCount": 0,
"eTicketable": true,
"totalMilesFlown": 1058,
"departure": {
"airport": "BLR",
"city": "BLR",
"country": "IN",
"time": "19:50:00+05:30"
},
"arrival": {
"airport": "DEL",
"city": "DEL",
"country": "IN",
"time": "22:50:00+05:30",
"terminal": "3"
},
"carrier": {
"marketing": "KL",
"marketingFlightNumber": 3798,
"operating": "9W",
"operatingFlightNumber": 812,
"disclosure": "9W",
"equipment": {
"code": "73H",
"typeForFirstLeg": "N",
"typeForLastLeg": "N"
}
}
},
{
"id": 3,
"trafficRestriction": "G",
"frequency": "SMTWTFS",
"stopCount": 0,
"eTicketable": true,
"totalMilesFlown": 1058,
"departure": {
"airport": "BLR",
"city": "BLR",
"country": "IN",
"time": "18:55:00+05:30"
},
"arrival": {
"airport": "DEL",
"city": "DEL",
"country": "IN",
"time": "22:00:00+05:30",
"terminal": "3"
},
"carrier": {
"marketing": "KL",
"marketingFlightNumber": 3703,
"operating": "9W",
"operatingFlightNumber": 833,
"disclosure": "9W",
"equipment": {
"code": "738",
"typeForFirstLeg": "N",
"typeForLastLeg": "N"
}
}
},
{
"id": 4,
"frequency": "*M**T**",
"stopCount": 0,
"eTicketable": true,
"totalMilesFlown": 3961,
"departure": {
"airport": "DEL",
"city": "DEL",
"country": "IN",
"time": "02:10:00+05:30",
"terminal": "3"
},
"arrival": {
"airport": "AMS",
"city": "AMS",
"country": "NL",
"time": "07:40:00+02:00"
},
"carrier": {
"marketing": "KL",
"marketingFlightNumber": 3817,
"operating": "9W",
"operatingFlightNumber": 234,
"disclosure": "9W",
"equipment": {
"code": "333",
"typeForFirstLeg": "W",
"typeForLastLeg": "W"
}
}
},
{
"id": 5,
"frequency": "*MTWTFS",
"stopCount": 0,
"eTicketable": true,
"totalMilesFlown": 257,
"departure": {
"airport": "AMS",
"city": "AMS",
"country": "NL",
"time": "09:25:00+02:00"
},
"arrival": {
"airport": "LCY",
"city": "LON",
"country": "GB",
"time": "09:30:00+01:00"
},
"carrier": {
"marketing": "KL",
"marketingFlightNumber": 987,
"operating": "KL",
"operatingFlightNumber": 987,
"codeShared": "/KLM CITYHOPPER",
"equipment": {
"code": "E90",
"typeForFirstLeg": "N",
"typeForLastLeg": "N"
}
}
}
],
"taxDescs": [
{
"id": 1,
"code": "YRI",
"amount": 160,
"currency": "USD",
"description": "SERVICE FEE - CARRIER-IMPOSED MISC",
"publishedAmount": 160,
"publishedCurrency": "USD",
"station": "DEL"
},
{
"id": 2,
"code": "YQI",
"amount": 13.3,
"currency": "USD",
"description": "SERVICE FEE - INSURANCE",
"publishedAmount": 925,
"publishedCurrency": "INR",
"station": "BLR"
},
{
"id": 3,
"code": "WO",
"amount": 3.8,
"currency": "USD",
"description": "PASSENGER SERVICE FEE",
"publishedAmount": 3.8,
"publishedCurrency": "USD",
"station": "BLR",
"country": "IN"
},
{
"id": 4,
"code": "K38",
"amount": 11.5,
"currency": "USD",
"description": "GOODS AND SERVICE TAX INTERIM DOMESTIC AND INTERNATIONAL",
"publishedAmount": 799,
"publishedCurrency": "INR",
"station": "BLR",
"country": "IN"
},
{
"id": 5,
"code": "CJ",
"amount": 7.4,
"currency": "USD",
"description": "SECURITY SERVICE CHARGE",
"publishedAmount": 6.5,
"publishedCurrency": "EUR",
"station": "AMS",
"country": "NL"
},
{
"id": 6,
"code": "RN",
"amount": 7.3,
"currency": "USD",
"description": "PASSENGER SERVICE CHARGE",
"publishedAmount": 6.4,
"publishedCurrency": "EUR",
"station": "AMS",
"country": "NL"
},
{
"id": 7,
"code": "IN",
"amount": 5.2,
"currency": "USD",
"description": "USER DEVELOPMENT FEE DEPARTURES",
"publishedAmount": 362,
"publishedCurrency": "INR",
"station": "BLR",
"country": "IN"
}
],
"taxSummaryDescs": [
{
"id": 1,
"code": "YRI",
"amount": 160,
"currency": "USD",
"description": "SERVICE FEE - CARRIER-IMPOSED MISC",
"publishedAmount": 160,
"publishedCurrency": "USD",
"station": "DEL"
},
{
"id": 2,
"code": "YQI",
"amount": 13.3,
"currency": "USD",
"description": "SERVICE FEE - INSURANCE",
"publishedAmount": 925,
"publishedCurrency": "INR",
"station": "BLR"
},
{
"id": 3,
"code": "WO",
"amount": 3.8,
"currency": "USD",
"description": "PASSENGER SERVICE FEE",
"publishedAmount": 3.8,
"publishedCurrency": "USD",
"station": "BLR",
"country": "IN"
},
{
"id": 4,
"code": "K38",
"amount": 11.5,
"currency": "USD",
"description": "GOODS AND SERVICE TAX INTERIM DOMESTIC AND INTERNATIONAL",
"publishedAmount": 0.05,
"publishedCurrency": "INR",
"station": "BLR",
"country": "IN"
},
{
"id": 5,
"code": "CJ",
"amount": 7.4,
"currency": "USD",
"description": "SECURITY SERVICE CHARGE",
"publishedAmount": 6.5,
"publishedCurrency": "EUR",
"station": "AMS",
"country": "NL"
},
{
"id": 6,
"code": "RN",
"amount": 7.3,
"currency": "USD",
"description": "PASSENGER SERVICE CHARGE",
"publishedAmount": 6.4,
"publishedCurrency": "EUR",
"station": "AMS",
"country": "NL"
},
{
"id": 7,
"code": "IN",
"amount": 5.2,
"currency": "USD",
"description": "USER DEVELOPMENT FEE DEPARTURES",
"publishedAmount": 362,
"publishedCurrency": "INR",
"station": "BLR",
"country": "IN"
}
],
"fareComponentDescs": [
{
"id": 1,
"governingCarrier": "KL",
"fareAmount": 24.65,
"fareCurrency": "INR",
"fareBasisCode": "XPLWIA",
"farePassengerType": "ADT",
"publishedFareAmount": 1717,
"oneWayFare": true,
"directionality": "FROM",
"direction": "EH",
"notValidBefore": "2019-07-21",
"notValidAfter": "2019-07-21",
"applicablePricingCategories": "4 5 6 7 8 10 12 16 18",
"vendorCode": "ATP",
"fareTypeBitmap": "00",
"fareType": "PRU",
"fareTariff": "4",
"fareRule": "INPL",
"segments": [
{
"segment": {
"surcharges": [
{
"amount": 28.71,
"currency": "NUC",
"description": "MISCELLANEOUS/OTHER",
"type": "Q"
}
]
}
},
{
"segment": {}
},
{
"segment": {}
}
]
}
],
"validatingCarrierDescs": [
{
"id": 1,
"settlementMethod": "ARC",
"newVcxProcess": true,
"default": {
"code": "DL"
}
}
],
"baggageAllowanceDescs": [
{
"id": 1,
"pieceCount": 1
}
],
"legDescs": [
{
"id": 1,
"schedules": [
{
"ref": 2
},
{
"ref": 4,
"departureDateAdjustment": 1
},
{
"ref": 5,
"departureDateAdjustment": 1
}
]
},
{
"id": 2,
"schedules": [
{
"ref": 3
},
{
"ref": 4,
"departureDateAdjustment": 1
},
{
"ref": 1,
"departureDateAdjustment": 1
}
]
}
],
"itineraryGroups": [
{
"groupDescription": {
"legDescriptions": [
{
"departureDate": "2019-07-21",
"departureLocation": "BLR",
"arrivalLocation": "LCY"
}
]
},
"itineraries": [
{
"id": 1,
"pricingSource": "ADVJR1",
"legs": [
{
"ref": 2
}
],
"pricingInformation": [
{
"pricingSubsource": "MIP",
"fare": {
"validatingCarrierCode": "DL",
"vita": true,
"eTicketable": true,
"lastTicketDate": "2019-07-05",
"governingCarriers": "KL",
"passengerInfoList": [
{
"passengerInfo": {
"passengerType": "ADT",
"passengerNumber": 1,
"nonRefundable": true,
"fareComponents": [
{
"ref": 1,
"segments": [
{
"segment": {
"bookingCode": "X",
"cabinCode": "Y",
"mealCode": "D",
"seatsAvailable": 9
}
},
{
"segment": {
"bookingCode": "X",
"cabinCode": "Y",
"mealCode": "RB",
"seatsAvailable": 9
}
},
{
"segment": {
"bookingCode": "L",
"cabinCode": "Y",
"mealCode": "M",
"seatsAvailable": 9,
"availabilityBreak": true
}
}
]
}
],
"taxes": [
{
"ref": 2
},
{
"ref": 1
},
{
"ref": 7
},
{
"ref": 4
},
{
"ref": 3
},
{
"ref": 5
},
{
"ref": 6
}
],
"taxSummaries": [
{
"ref": 2
},
{
"ref": 1
},
{
"ref": 3
},
{
"ref": 4
},
{
"ref": 7
},
{
"ref": 5
},
{
"ref": 6
}
],
"currencyConversion": {
"from": "INR",
"to": "USD",
"exchangeRateUsed": 0.01442679
},
"fareMessages": [
{
"type": "N",
"code": "0",
"carrier": "KL",
"info": "FARE RESTRICTIONS APPLY/CARRIER RESTRICTIONS APPLY"
},
{
"type": "W",
"code": "0",
"info": "VALIDATING CARRIER - DL PER GSA AGREEMENT WITH KL"
}
],
"passengerTotalFare": {
"totalFare": 264.5,
"totalTaxAmount": 208.5,
"currency": "USD",
"baseFareAmount": 3875,
"baseFareCurrency": "INR",
"equivalentAmount": 56,
"equivalentCurrency": "USD",
"constructionAmount": 55.59,
"constructionCurrency": "NUC",
"commissionPercentage": 0,
"commissionAmount": 0,
"exchangeRateOne": 69.651352
},
"baggageInformation": [
{
"provisionType": "A",
"airlineCode": "KL",
"segments": [
{
"id": 0
},
{
"id": 1
},
{
"id": 2
}
],
"allowance": {
"ref": 1
}
}
]
}
}
],
"totalFare": {
"totalPrice": 264.5,
"totalTaxAmount": 208.5,
"currency": "USD",
"baseFareAmount": 3875,
"baseFareCurrency": "INR",
"constructionAmount": 55.59,
"constructionCurrency": "NUC",
"equivalentAmount": 56,
"equivalentCurrency": "USD"
},
"validatingCarriers": [
{
"ref": 1
}
]
}
}
],
"diversitySwapper": {
"weighedPrice": 409.855
}
},
{
"id": 2,
"pricingSource": "ADVJR1",
"legs": [
{
"ref": 1
}
],
"pricingInformation": [
{
"pricingSubsource": "MIP",
"fare": {
"validatingCarrierCode": "DL",
"vita": true,
"eTicketable": true,
"lastTicketDate": "2019-07-05",
"governingCarriers": "KL",
"passengerInfoList": [
{
"passengerInfo": {
"passengerType": "ADT",
"passengerNumber": 1,
"nonRefundable": true,
"fareComponents": [
{
"ref": 1,
"segments": [
{
"segment": {
"bookingCode": "X",
"cabinCode": "Y",
"mealCode": "D",
"seatsAvailable": 9
}
},
{
"segment": {
"bookingCode": "X",
"cabinCode": "Y",
"mealCode": "RB",
"seatsAvailable": 9
}
},
{
"segment": {
"bookingCode": "L",
"cabinCode": "Y",
"mealCode": "M",
"seatsAvailable": 9,
"availabilityBreak": true
}
}
]
}
],
"taxes": [
{
"ref": 2
},
{
"ref": 1
},
{
"ref": 7
},
{
"ref": 4
},
{
"ref": 3
},
{
"ref": 5
},
{
"ref": 6
}
],
"taxSummaries": [
{
"ref": 2
},
{
"ref": 1
},
{
"ref": 3
},
{
"ref": 4
},
{
"ref": 7
},
{
"ref": 5
},
{
"ref": 6
}
],
"currencyConversion": {
"from": "INR",
"to": "USD",
"exchangeRateUsed": 0.01442679
},
"fareMessages": [
{
"type": "N",
"code": "0",
"carrier": "KL",
"info": "FARE RESTRICTIONS APPLY/CARRIER RESTRICTIONS APPLY"
},
{
"type": "W",
"code": "0",
"info": "VALIDATING CARRIER - DL PER GSA AGREEMENT WITH KL"
}
],
"passengerTotalFare": {
"totalFare": 264.5,
"totalTaxAmount": 208.5,
"currency": "USD",
"baseFareAmount": 3875,
"baseFareCurrency": "INR",
"equivalentAmount": 56,
"equivalentCurrency": "USD",
"constructionAmount": 55.59,
"constructionCurrency": "NUC",
"commissionPercentage": 0,
"commissionAmount": 0,
"exchangeRateOne": 69.651352
},
"baggageInformation": [
{
"provisionType": "A",
"airlineCode": "KL",
"segments": [
{
"id": 0
},
{
"id": 1
},
{
"id": 2
}
],
"allowance": {
"ref": 1
}
}
]
}
}
],
"totalFare": {
"totalPrice": 264.5,
"totalTaxAmount": 208.5,
"currency": "USD",
"baseFareAmount": 3875,
"baseFareCurrency": "INR",
"constructionAmount": 55.59,
"constructionCurrency": "NUC",
"equivalentAmount": 56,
"equivalentCurrency": "USD"
},
"validatingCarriers": [
{
"ref": 1
}
]
}
}
],
"diversitySwapper": {
"weighedPrice": 367.631
}
}
]
}
]
}
}
You have to use legDescs list to connect your segments, and use those "ref" values.
For example:
{
"id": 2,
"schedules": [
{
"ref": 3
},
{
"departureDateAdjustment": 1,
"ref": 4
},
{
"departureDateAdjustment": 1,
"ref": 1
}
]
}
means, your flight with ID = 2 contains 3 segments, with id 3 - 4 and 1. When you search numbered segments in "scheduleDescs" object, you can see routes are:
BLR - DEL
DEL - AMS
AMS - LCY
and as you can see you got the BLR - LCY flight.

Json Conversion using mule dataweave

I need to convert a json(got after multiple calls and then merged it) into a specific structure.
here is my input json payload
[{
"shops": [{
"shop": {
"code": "AU5",
"streetName": "a",
"city": "a",
"district": "a",
"state": "a",
"postalCode": "a",
"country": "a"
}
}, {
"shop": {
"code": "b",
"streetName": "b",
"city": "b",
"district": "b",
"state": "b",
"postalCode": "b",
"country": "b"
}
}]
},
[
[{
"salesOffice": {
"shop": {
"code": "AU5"
},
"office": "MEL",
"branch": "MEL",
"district": "SPR",
"subRegion": "SPR",
"region": "AP"
}
}],
[{
"salesOffice": {
"shop": {
"code": "b"
},
"office": "999",
"branch": "999",
"district": "999",
"subRegion": "999",
"region": "999"
}
}
]
]]
below is the expected output json
{
"shops": [
{
"shop": {
"code": "AU5",
"streetName": "a",
"city": "a",
"district": "a",
"state": "a",
"postalCode": "a",
"country": "a",
"salesOffice": {
"office": "MEL",
"branch": "MEL",
"district": "SPR",
"subRegion": "SPR",
"region": "AP"
}
}
},
{
"shop": {
"code": "b",
"streetName": "b",
"city": "b",
"district": "b",
"state": "b",
"postalCode": "b",
"country": "b",
"salesOffice": {
"office": "999",
"branch": "999",
"district": "999",
"subRegion": "999",
"region": "999"
}
}
}
]}
while transformation, 'code' inside shop should match with 'code' inside salesOffice>>shop>>'code'
below is Json Schema for the output payload(should validate against the output)
{
"$schema": "http://json-schema.org/draft-04/schema#",
"type": "object",
"properties": {
"shops": {
"type": "array",
"items": {
"type": "object",
"properties": {
"shop": {
"type": "object",
"properties": {
"code": {
"type": "string"
},
"streetName": {
"type": "string"
},
"city": {
"type": "string"
},
"district": {
"type": "string"
},
"state": {
"type": "string"
},
"postalCode": {
"type": "string"
},
"country": {
"type": "string"
}
},
"salesOffice": {
"type": "object",
"properties": {
"office": {
"type": "string"
},
"branch": {
"type": "string"
},
"district": {
"type": "string"
},
"subRegion": {
"type": "string"
},
"region": {
"type": "string"
}
}
},
"required": [
"code",
"streetName",
"city",
"district",
"state",
"postalCode",
"country",
"salesOffice"
]
}
},
"required": [
"shop"
]
}
}
},
"required": [
"shops"
]}
Any solution or any pointer would be a great help
Refer below dataweave:
%dw 1.0
%output application/json
---
{
shops: using (salesOffice = payload[1]..salesOffice)( payload[0].shops map {
shop :
{
code: $.shop.code,
streetName:$.shop.streetName,
city:$.shop.city,
district:$.shop.district,
state:$.shop.state,
postalCode:$.shop.postalCode,
country:$.shop.country,
salesOffice: using (code= $.shop.code) ( salesOffice[?(code == $.shop.code)] map {
office:$.office,
branch:$.branch,
district:$.district,
subRegion:$.subRegion,
region:$.region
})[0]
}
})
}
You can use Validate JSON Schema mule components.

in2csv fail on JSON

I have a small JSON file which fails to convert a data value properly. The value in question is the "id" field and the conversion produces 2016-05-06. I have a much larger file (same format) and it works properly. Any help?
{
"results": [
{
"seq": "10457-g1AAAAPreJy90lFqAjEQBuCgFj2B4EOpUgRfXNyscWef9CbtTMZFZF3Brs96E3uT9ib1IMIaE2XpmynUlwkE8n_8TDIhRGtRZzFg0uvNfMakAloNGbPhR4E544ZloLP1ljEvgnxeZOZFDQW9lGW5XNRJiKfnlblrYiRZqtQj6rcr73CpayZNK3pg6bGOmZE80vwr0-xCv1X0q6XViDGJ_t46vId-v9C7iu5YegIEmIw90vxb5w0zxd4cRj9c-Z7lSUEEIP-1ueM_Hf_l-MbJ8iGmxDE8oP2343-u7fvuy6WaRqQf0P7o-Nvu25ZPATRA6JG4PAMPEEFw",
"id": "20160506175330-3168-01",
"changes": [
{
"rev": "1-8bda66017d962508e51ac5061557635b"
}
],
"doc": {
"_id": "20160506175330-3168-01",
"_rev": "1-8bda66017d962508e51ac5061557635b",
"year": "2016",
"month": "05",
"day": "06",
"hour": "17",
"minute": "53",
"second": "30",
"imagebox": "18x94+428+180",
"alchemy": {
"text": "person",
"score": "0.845535"
},
"visual": {
"image": "20160506175330-3168-01.jpg",
"scores": [
{
"classifier_id": "Mixed_Color",
"name": "Mixed_Color",
"score": 0.964822
},
{
"classifier_id": "Stove",
"name": "Stove",
"score": 0.923713
},
{
"classifier_id": "Archery",
"name": "Archery",
"score": 0.836994
},
{
"classifier_id": "Dish_Washer",
"name": "Dish_Washer",
"score": 0.835787
},
{
"classifier_id": "Barber_Shop",
"name": "Barber_Shop",
"score": 0.825716
},
{
"classifier_id": "Full_Body",
"name": "Full_Body",
"score": 0.824954
},
{
"classifier_id": "Musical_Instrument",
"name": "Musical_Instrument",
"score": 0.817304
},
{
"classifier_id": "Beauty_Salon",
"name": "Beauty_Salon",
"score": 0.808518
},
{
"classifier_id": "Clothing_Store",
"name": "Clothing_Store",
"score": 0.784035
},
{
"classifier_id": "Distillery",
"name": "Distillery",
"score": 0.67477
},
{
"classifier_id": "Sweet_Treat",
"name": "Sweet_Treat",
"score": 0.588916
},
{
"classifier_id": "Beer",
"name": "Beer",
"score": 0.538915
},
{
"classifier_id": "Store",
"name": "Store",
"score": 0.536244
},
{
"classifier_id": "Yellow",
"name": "Yellow",
"score": 0.526932
}
]
}
}
}
],
"last_seq": "10457-g1AAAAPreJy90lFqAjEQBuCgFj2B4EOpUgRfXNyscWef9CbtTMZFZF3Brs96E3uT9ib1IMIaE2XpmynUlwkE8n_8TDIhRGtRZzFg0uvNfMakAloNGbPhR4E544ZloLP1ljEvgnxeZOZFDQW9lGW5XNRJiKfnlblrYiRZqtQj6rcr73CpayZNK3pg6bGOmZE80vwr0-xCv1X0q6XViDGJ_t46vId-v9C7iu5YegIEmIw90vxb5w0zxd4cRj9c-Z7lSUEEIP-1ueM_Hf_l-MbJ8iGmxDE8oP2343-u7fvuy6WaRqQf0P7o-Nvu25ZPATRA6JG4PAMPEEFw",
"pending": 0
}
I needed to use --no-inference.

Extracting variable number of fields using jq

I am new to this website and came here because I am really struggling with a problem of extracting information from a JSON file. The tricky part is that there are variable number of fields, so I can't get away with simple syntax.
Here's a sample code:
{
"addresses": {
"#count": "1",
"address_name": {
"address_spec": {
"#addr_no": "1",
"full_address": "Tel Aviv Univ, Eitan Berglas Sch Econ, IL-69978 Tel Aviv, Israel",
"organizations": {
"#count": "2",
"organization": [
"Tel Aviv Univ",
{
"#pref": "Y",
"#text": "Tel Aviv University"
}
]
},
"suborganizations": {
"#count": "1",
"suborganization": "Eitan Berglas Sch Econ"
},
"city": "Tel Aviv",
"country": "Israel",
"zip": {
"#location": "BC",
"#text": "IL-69978"
}
}
}
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "1",
"address_name": {
"address_spec": {
"#addr_no": "1",
"full_address": "MIT, Cambridge, MA 02139 USA",
"organizations": {
"#count": "2",
"organization": [
"MIT",
{
"#pref": "Y",
"#text": "Massachusetts Institute of Technology (MIT)"
}
]
},
"city": "Cambridge",
"state": "MA",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "02139"
}
}
}
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "2",
"address_name": [
{
"address_spec": {
"#addr_no": "1",
"full_address": "Univ Kentucky, Lexington, KY 40506 USA",
"organizations": {
"#count": "2",
"organization": [
"Univ Kentucky",
{
"#pref": "Y",
"#text": "University of Kentucky"
}
]
},
"city": "Lexington",
"state": "KY",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "40506"
}
}
},
{
"address_spec": {
"#addr_no": "2",
"full_address": "Univ Bonn, ZEI, D-5300 Bonn, Germany",
"organizations": {
"#count": "2",
"organization": [
"Univ Bonn",
{
"#pref": "Y",
"#text": "University of Bonn"
}
]
},
"suborganizations": {
"#count": "1",
"suborganization": "ZEI"
},
"city": "Bonn",
"country": "Germany",
"zip": {
"#location": "BC",
"#text": "D-5300"
}
}
}
]
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "1",
"address_name": {
"address_spec": {
"#addr_no": "1",
"full_address": "Harvard Univ, Cambridge, MA 02138 USA",
"organizations": {
"#count": "2",
"organization": [
"Harvard Univ",
{
"#pref": "Y",
"#text": "Harvard University"
}
]
},
"city": "Cambridge",
"state": "MA",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "02138"
}
}
}
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "3",
"address_name": [
{
"address_spec": {
"#addr_no": "1",
"full_address": "Columbia Univ, New York, NY 10027 USA",
"organizations": {
"#count": "2",
"organization": [
"Columbia Univ",
{
"#pref": "Y",
"#text": "Columbia University"
}
]
},
"city": "New York",
"state": "NY",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "10027"
}
}
},
{
"address_spec": {
"#addr_no": "2",
"full_address": "NYU, New York, NY USA",
"organizations": {
"#count": "2",
"organization": [
"NYU",
{
"#pref": "Y",
"#text": "New York University"
}
]
},
"city": "New York",
"state": "NY",
"country": "USA"
}
},
{
"address_spec": {
"#addr_no": "3",
"full_address": "Univ Pompeu Fabra, Barcelona, Spain",
"organizations": {
"#count": "2",
"organization": [
"Univ Pompeu Fabra",
{
"#pref": "Y",
"#text": "Pompeu Fabra University"
}
]
},
"city": "Barcelona",
"country": "Spain"
}
}
]
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "2",
"address_name": [
{
"address_spec": {
"#addr_no": "1",
"full_address": "Univ Chicago, Chicago, IL 60637 USA",
"organizations": {
"#count": "2",
"organization": [
"Univ Chicago",
{
"#pref": "Y",
"#text": "University of Chicago"
}
]
},
"city": "Chicago",
"state": "IL",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "60637"
}
}
},
{
"address_spec": {
"#addr_no": "2",
"full_address": "Amer Bar Fdn, Chicago, IL 60611 USA",
"organizations": {
"#count": "1",
"organization": "Amer Bar Fdn"
},
"city": "Chicago",
"state": "IL",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "60611"
}
}
}
]
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "2",
"address_name": [
{
"address_spec": {
"#addr_no": "1",
"full_address": "Ohio State Univ, Columbus, OH 43210 USA",
"organizations": {
"#count": "2",
"organization": [
"Ohio State Univ",
{
"#pref": "Y",
"#text": "Ohio State University"
}
]
},
"city": "Columbus",
"state": "OH",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "43210"
}
}
},
{
"address_spec": {
"#addr_no": "2",
"full_address": "Harvard Univ, Cambridge, MA 02138 USA",
"organizations": {
"#count": "2",
"organization": [
"Harvard Univ",
{
"#pref": "Y",
"#text": "Harvard University"
}
]
},
"city": "Cambridge",
"state": "MA",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "02138"
}
}
}
]
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "1",
"address_name": {
"address_spec": {
"#addr_no": "1",
"full_address": "Univ Chicago, Chicago, IL 60637 USA",
"organizations": {
"#count": "2",
"organization": [
"Univ Chicago",
{
"#pref": "Y",
"#text": "University of Chicago"
}
]
},
"city": "Chicago",
"state": "IL",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "60637"
}
}
}
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "2",
"address_name": [
{
"address_spec": {
"#addr_no": "1",
"full_address": "Wissensch Zentrum Berlin Sozialforsch, D-1000 Berlin, Germany",
"organizations": {
"#count": "1",
"organization": "Wissensch Zentrum Berlin Sozialforsch"
},
"city": "Berlin",
"country": "Germany",
"zip": {
"#location": "BC",
"#text": "D-1000"
}
}
},
{
"address_spec": {
"#addr_no": "2",
"full_address": "Harvard Univ, Dept Govt, Cambridge, MA 02138 USA",
"organizations": {
"#count": "2",
"organization": [
"Harvard Univ",
{
"#pref": "Y",
"#text": "Harvard University"
}
]
},
"suborganizations": {
"#count": "1",
"suborganization": "Dept Govt"
},
"city": "Cambridge",
"state": "MA",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "02138"
}
}
}
]
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
{
"addresses": {
"#count": "2",
"address_name": [
{
"address_spec": {
"#addr_no": "1",
"full_address": "NYU, CV Starr Ctr Appl Econ, New York, NY 10003 USA",
"organizations": {
"#count": "2",
"organization": [
"NYU",
{
"#pref": "Y",
"#text": "New York University"
}
]
},
"suborganizations": {
"#count": "1",
"suborganization": "CV Starr Ctr Appl Econ"
},
"city": "New York",
"state": "NY",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "10003"
}
}
},
{
"address_spec": {
"#addr_no": "2",
"full_address": "Princeton Univ, Princeton, NJ 08544 USA",
"organizations": {
"#count": "2",
"organization": [
"Princeton Univ",
{
"#pref": "Y",
"#text": "Princeton University"
}
]
},
"city": "Princeton",
"state": "NJ",
"country": "USA",
"zip": {
"#location": "AP",
"#text": "08544"
}
}
}
]
},
"category_info": {
"headings": {
"#count": "1",
"heading": "Social Sciences"
},
"subjects": {
"#count": "3",
"subject": [
{
"#ascatype": "traditional",
"#text": "Economics"
},
{
"#ascatype": "extended",
"#text": "Business & Economics"
},
{
"#ascatype": "traditional",
"#text": "ECONOMICS"
}
]
}
}
}
What I was hoping to extract is a country for each of the records (some records have more than one country, which seems to be causing the problem). So my naive approach was to say:
.static_data."fullrecord_metadata".addresses.address_name.country
This however gives me several errors (null has no keys, and cannot index array with string). Checking using the keys command:
.static_data."fullrecord_metadata".addresses.address_name | keys
I can see that it's seems there's a problem with the way the data is structured...
So, could you suggest if I can actually extract the list of countries for each entry using jq? Thank you!
For each input top-level JSON entity, the following filter will recursively examine all the objects to see if they have a "country" key, and it will then report the distinct "country" values for that top-level entity:
jq -c '[.. | if type == "object" and has("country")
then .country
else empty end] | unique'
["Israel"]
["USA"]
["Germany","USA"]
["USA"]
["Spain","USA"]
["USA"]
["USA"]
["USA"]
["Germany","USA"]
["USA"]
Here's a filter that will produce the same results in your example, though it is not exactly equivalent:
[.. | .country? // empty] | unique
[Exercise for the interested reader: what is the difference? :-) ]
Here is a solution which uses a function to handle the variation in .address_name
def address_specs:
if type == "array" then .[].address_spec else .address_spec end
;
.addresses | .address_name | [address_specs | .country] | unique