So I have this data model, based on a Facebook Insights API response, and you can assume is inserted as a document in a mongo collection.
My problem is:
- The representation of the Insights data from FB is not the best, with keys that potentially could contain ".", or other chars not mongo friendly.
- The queries I am trying to achieve, are sorted by some of the insights values:
- Get all documents ranked by "Melbourne, VIC, Australia".
Based on the problem, my question then is:
- Would you recommend transforming this documents into something more mongo friendly, and making the queries simpler? or
- Would you leave this model representation and work around more complex queries
At the moment of editing this question, I have transformed Insights data representation but your opinion is welcomed!
Cheers!
{
"id": "155257214638229/insights/page_fans_city/lifetime",
"name": "page_fans_city",
"period": "lifetime",
"values": [
{
"value": {
"Bogotá, Distrito Especial, Colombia": 206,
"São Paulo, Brazil": 102,
"Melbourne, VIC, Australia": 95,
"Cali, Valle del Cauca, Colombia": 76,
"Medellín, Antioquia, Colombia": 72,
"Bangalore, Karnataka, India": 39,
"Cartagena, Bolivar, Colombia": 38,
"Barranquilla, Atlantico, Colombia": 36,
"New Delhi, Delhi, India": 34,
"Ibagué, Tolima, Colombia": 32,
"Calcutta, West Bengal, India": 32,
"Bucaramanga, Santander, Colombia": 31,
"Sydney, NSW, Australia": 29,
"Mumbai, Maharashtra, India": 27,
"Lisbon, Lisboa, Portugal": 27,
"Mexico City, Distrito Federal, Mexico": 26,
"Neiva, Huila, Colombia": 23,
"Chennai, Tamil Nadu, India": 22,
"Hyderabad, Andhra Pradesh, India": 19,
"Chandigarh, India": 17,
"Pasto, Narino, Colombia": 17,
"Cúcuta, Norte de Santander, Colombia": 16,
"Santa Marta, Colombia": 16,
"Salvador, Bahia, Brazil": 16,
"Valledupar, Cesar, Colombia": 15,
"Palmira, Valle del Cauca, Colombia": 15,
"Montería, Cordoba, Colombia": 15,
"Rio de Janeiro, Brazil": 14,
"Lucknow, Uttar Pradesh, India": 14,
"Villavicencio, Meta, Colombia": 13,
"Belo Horizonte, Minas Gerais, Brazil": 13,
"Surat, Gujarat, India": 13,
"Ahmedabad, Gujarat, India": 13,
"Santiago, Region Metropolitana, Chile": 12,
"Brasília, Distrito Federal, Brazil": 12,
"Pereira, Risaralda, Colombia": 12,
"Jalandhar, Punjab, India": 11,
"Barrancabermeja, Santander, Colombia": 11,
"Tuluá, Valle del Cauca, Colombia": 11,
"Buenaventura, Valle del Cauca, Colombia": 11,
"Guadalajara, Jalisco, Mexico": 10,
"Goiânia, Goias, Brazil": 10,
"Adelaide, SA, Australia": 10,
"London, England, United Kingdom": 9,
"Armenia, Quindio, Colombia": 9
},
"end_time": "2015-02-19T08:00:00+0000"
},
{
"value": {
"Bogotá, Distrito Especial, Colombia": 207,
"São Paulo, Brazil": 103,
"Melbourne, VIC, Australia": 95,
"Cali, Valle del Cauca, Colombia": 75,
"Medellín, Antioquia, Colombia": 72,
"Bangalore, Karnataka, India": 40,
"Cartagena, Bolivar, Colombia": 38,
"Barranquilla, Atlantico, Colombia": 36,
"New Delhi, Delhi, India": 34,
"Ibagué, Tolima, Colombia": 32,
"Calcutta, West Bengal, India": 31,
"Bucaramanga, Santander, Colombia": 30,
"Sydney, NSW, Australia": 29,
"Lisbon, Lisboa, Portugal": 27,
"Mumbai, Maharashtra, India": 27,
"Mexico City, Distrito Federal, Mexico": 26,
"Neiva, Huila, Colombia": 24,
"Chennai, Tamil Nadu, India": 22,
"Hyderabad, Andhra Pradesh, India": 19,
"Chandigarh, India": 17,
"Pasto, Narino, Colombia": 17,
"Cúcuta, Norte de Santander, Colombia": 16,
"Santa Marta, Colombia": 16,
"Salvador, Bahia, Brazil": 16,
"Palmira, Valle del Cauca, Colombia": 15,
"Valledupar, Cesar, Colombia": 15,
"Montería, Cordoba, Colombia": 15,
"Rio de Janeiro, Brazil": 14,
"Lucknow, Uttar Pradesh, India": 14,
"Belo Horizonte, Minas Gerais, Brazil": 13,
"Surat, Gujarat, India": 13,
"Ahmedabad, Gujarat, India": 13,
"Villavicencio, Meta, Colombia": 13,
"Santiago, Region Metropolitana, Chile": 12,
"Brasília, Distrito Federal, Brazil": 12,
"Pereira, Risaralda, Colombia": 12,
"Jalandhar, Punjab, India": 11,
"Barrancabermeja, Santander, Colombia": 11,
"Tuluá, Valle del Cauca, Colombia": 11,
"Buenaventura, Valle del Cauca, Colombia": 11,
"Guadalajara, Jalisco, Mexico": 10,
"Goiânia, Goias, Brazil": 10,
"Adelaide, SA, Australia": 10,
"Dehra Dun, Uttarakhand, India": 9,
"San Luis Potosí, San Luis Potosi, Mexico": 9
},
"end_time": "2015-02-20T08:00:00+0000"
},
{
"value": {
"Bogotá, Distrito Especial, Colombia": 206,
"São Paulo, Brazil": 103,
"Melbourne, VIC, Australia": 95,
"Cali, Valle del Cauca, Colombia": 75,
"Medellín, Antioquia, Colombia": 72,
"Bangalore, Karnataka, India": 40,
"Cartagena, Bolivar, Colombia": 38,
"Barranquilla, Atlantico, Colombia": 36,
"New Delhi, Delhi, India": 34,
"Ibagué, Tolima, Colombia": 32,
"Calcutta, West Bengal, India": 31,
"Bucaramanga, Santander, Colombia": 31,
"Sydney, NSW, Australia": 29,
"Lisbon, Lisboa, Portugal": 27,
"Mumbai, Maharashtra, India": 27,
"Mexico City, Distrito Federal, Mexico": 26,
"Neiva, Huila, Colombia": 23,
"Chennai, Tamil Nadu, India": 22,
"Hyderabad, Andhra Pradesh, India": 19,
"Chandigarh, India": 17,
"Pasto, Narino, Colombia": 17,
"Santa Marta, Colombia": 16,
"Salvador, Bahia, Brazil": 16,
"Palmira, Valle del Cauca, Colombia": 15,
"Cúcuta, Norte de Santander, Colombia": 15,
"Rio de Janeiro, Brazil": 15,
"Valledupar, Cesar, Colombia": 15,
"Montería, Cordoba, Colombia": 15,
"Lucknow, Uttar Pradesh, India": 14,
"Belo Horizonte, Minas Gerais, Brazil": 13,
"Surat, Gujarat, India": 13,
"Villavicencio, Meta, Colombia": 13,
"Ahmedabad, Gujarat, India": 13,
"Santiago, Region Metropolitana, Chile": 12,
"Brasília, Distrito Federal, Brazil": 12,
"Pereira, Risaralda, Colombia": 12,
"Jalandhar, Punjab, India": 11,
"Barrancabermeja, Santander, Colombia": 11,
"Tuluá, Valle del Cauca, Colombia": 11,
"Buenaventura, Valle del Cauca, Colombia": 11,
"Guadalajara, Jalisco, Mexico": 10,
"Adelaide, SA, Australia": 10,
"Goiânia, Goias, Brazil": 10,
"Dehra Dun, Uttarakhand, India": 9,
"San Luis Potosí, San Luis Potosi, Mexico": 9
},
"end_time": "2015-02-21T08:00:00+0000"
}
],
"title": "Lifetime Likes by City",
"description": "Lifetime: Aggregated Facebook location data, sorted by city, about the people who like your Page. (Unique Users)"
}
