Conceptos básicos. Seminario web 4: Indexación avanzada, índices de texto y geoespaciales

MongoDBEurope2016
Old Billingsgate, London
15th November
Use my code rubenterceno20 for 20% off tickets
mongodb.com/europe

Conceptos Básicos 2016
Indexación Avanzada:
Índices de texto y Geoespaciales
Rubén Terceño
Senior Solutions Architect, EMEA
ruben@mongodb.com
@rubenTerceno

Agenda del Curso
Date Time Webinar
25-Mayo-2016 16:00 CEST Introducción a NoSQL
7-Junio-2016 16:00 CEST Su primera aplicación MongoDB
21-Junio-2016 16:00 CEST Diseño de esquema orientado a documentos
07-Julio-2016 16:00 CEST Indexación avanzada, índices de texto y geoespaciales
19-Julio-2016 16:00 CEST Introducción al Aggregation Framework
28-Julio-2016 16:00 CEST Despliegue en producción

Resumen de lo visto hasta ahora
• ¿Porqué existe NoSQL?
• Tipos de bases de datos NoSQL
• Características clave de MongoDB
• Instalación y creación de bases de datos y colecciones
• Operaciones CRUD
• Índices y explain()
• Diseño de esquema dinámico
• Jerarquía y documentos embebidos
• Polimorfismo

Indexing
• An efficient way to look up data by its value
• Avoids table scans
1 2 3 4 5 6 7

Traditional Databases Use B-trees
• … and so does MongoDB

Creating a Simple Index
db.coll.createIndex( { fieldName : <Direction> } )
Database Name
Collection Name
Command
Field Name to
be indexed
Ascending : 1
Descending : -1

Two Other Kinds of Indexes
• Full Text Index
• Allows searching inside the text of a field or several fields, ordering the
results by relevance.
• Geospatial Index
• Allows geospatial queries
• People around me.
• Countries I’m traversing during my trip.
• Restaurants in a given neighborhood.
• These indexes do not use B-trees

Full Text Indexes
• An “inverted index” on all the words inside text fields (only one text index per collection)
{ “comment” : “I think your blog post is very interesting
and informative. I hope you will post more
info like this in the future” }
>> db.posts.createIndex( { “comments” : “text” } )
MongoDB Enterprise > db.posts.find( { $text: { $search : "info" }} )
{ "_id" : ObjectId(“…"), "comment" : "I think your blog post is very
interesting and informative. I hope you will post more info like this in
the future" }
MongoDB Enterprise >

On The Server
2016-07-07T09:48:48.605+0200 I INDEX [conn4] build index on:
indexes.products properties: { v: 1,
key: { _fts: "text", _ftsx: 1 },
name: "longDescription_text_shortDescription_text_name_text”,
ns: "indexes.products",
weights: { longDescription: 1,
name: 10,
shortDescription: 3 },
default_language: "english”,
language_override: "language”,
textIndexVersion: 3 }

More Detailed Example
>> db.posts.insert( { "comment" : "Red yellow orange green" } )
>> db.posts.insert( { "comment" : "Pink purple blue" } )
>> db.posts.insert( { "comment" : "Red Pink" } )
>> db.posts.find( { "$text" : { "$search" : "Red" }} )
{ "_id" : ObjectId("…"), "comment" : "Red yellow orange green" }
{ "_id" : ObjectId("…"), "comment" : "Red Pink" }
>> db.posts.find( { "$text" : { "$search" : "Pink Green" }} )
>> db.posts.find( { "$text" : { "$search" : "red" }} ) # <- Case Insensitve
>>

Using Weights
• We can assign different weights to different fields in the text index
• E.g. I want to favour name over shortDescription in searching
• So I increase the weight for the the name field
>> db.blog.createIndex( { shortDescription: "text",
longDescription: "text”,
name: "text” },
{ weights: { shortDescription: 3,
longDescription: 1,
name: 10 }} )
• Now searches will favour name over shortDesciption over longDescription

$textscore
• We may want to favor results with higher weights, thus:
>> db.products.find({$text : {$search: "humongous"}}, {score:
{$meta : "textScore"}, name: 1, longDescription: 1,
shortDescription: 1}).sort( { score: { $meta: "textScore" } } )

Other Parameters
• Language : Pick the language you want to search in e.g.
• $language : Spanish
• Support case sensitive searching
• $caseSensitive : True (default false)
• Support accented characters (diacritic sensitive search e.g. café
is distinguished from cafe )
• $diacriticSensitive : True (default false)

Geospatial Indexes
• 2d
• Represents a flat surface. A good fit if:
• You have legacy coordinate pairs (MongoDB 2.2 or earlier).
• You do not plan to use geoJSON objects.
• You don’t worry about the Earth's curvature. (Yup, earth is not flat)
• 2dsphere
• Represents a flat surface on top of an spheroid.
• It should be the default choice for geoData
• Coordinates are (usually) stored in GeoJSON format
• The index is based on a QuadTree representation
• The index is based on WGS 84 standard

Coordinates
• Coordinates are represented as longitude, latitude
• Longitude
• Measured from Greenwich meridian (0 degrees)
• For locations east up to +180 degrees
• For locations west we specify as negative up to -180
• Latitude
• Measured from equator north and south (0 to 90 north, 0 to -90 south)
• Coordinates in MongoDB are stored on Longitude/Latitude order
• Coordinates in Google Maps are stored in Latitude/Longitude order

2dSphere Versions
• Two versions of 2dSphere index in MongoDB
• Version 1 : Up to MongoDB 2.4
• Version 2 : From MongoDB 2.6 onwards
• Version 3 : From MongoDB 3.2 onwards
• We will only be talking about Version 3 in this webinar

Creating a 2dSphere Index
db.collection.createIndex
( { <location field> : "2dsphere" } )
• Location field must be coordinate or GeoJSON data

Example
>> db.wines.createIndex( { geometry: "2dsphere" } )
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}

Testing Geo Queries
• Lets search for wine regions in the world
• Using two collections from my gitHub repo
• https://github.com/terce13/geoData
• Import them into MongoDB
• mongoimport -c wines -d geo wine_regions.json
• mongoimport -c countries -d geo countries.json

Country Document (Vatican)
{
"_id" : ObjectId("577e2ebd1007503076ac8c86"),
"type" : "Feature",
"properties" : {
"featurecla" : "Admin-0 country",
"sovereignt" : "Vatican",
"type" : "Sovereign country",
"admin" : "Vatican",
"adm0_a3" : "VAT",
"name" : "Vatican",
"name_long" : "Vatican",
"abbrev" : "Vat.",
"postal" : "V",
"formal_en" : "State of the Vatican
City",
"name_sort" : "Vatican (Holy Sea)",
"name_alt" : "Holy Sea”,
"pop_est" : 832,
"economy" : "2. Developed region:
nonG7",
"income_grp" : "2. High income:
nonOECD",
"continent" : "Europe",
"region_un" : "Europe",
"subregion" : "Southern Europe",
"region_wb" : "Europe & Central Asia",
},
"geometry" : {
"type" : "Polygon",
"coordinates" : [ [
[12.439160156250011,
41.898388671875],
[12.430566406250023,
41.89755859375],
[12.427539062500017,
41.900732421875],
[12.430566406250023,
41.90546875],
[12.438378906250023,
41.906201171875],
[12.439160156250011,
41.898388671875]]]
}
}

Wine region document
MongoDB Enterprise > db.wines.findOne()
{
"_id" : ObjectId("577e2e7e1007503076ac8769"),
"properties" : {
"name" : "AOC Anjou-Villages",
"description" : null,
"id" : "a629ojjxl15z"
},
"type" : "Feature",
"geometry" : {
"type" : "Point",
"coordinates" : [ -0.618980171610645, 47.2211343496821]
}
}
You can type this into
google maps but
remember to reverse the
coordinate order

Add Indexes
MongoDB Enterprise > db.wines.createIndex({ geometry: "2dsphere" })
{
"ok" : 1
}
MongoDB Enterprise > db.countries.createIndex({ geometry: "2dsphere" })
{
"ok" : 1

$geoIntersects to find our country
• Assume we are at lat: 43.47, lon: -3.81
• What country are we in? Use $geoIntersects
db.countries.findOne({ geometry:
{ $geoIntersects:
{ $geometry:
{ type: "Point",
coordinates:
[ -3.81, 43.47 ]}}}},
{"properties.name": 1})

Results
{
"_id" :
ObjectId("577e2ebd1007503076ac8be5"),
"properties" : {
"name" : "Spain"
}
}

Wine regions around me
• Use $near (ordered results by distance)
db.wines.find({geometry:
{$near:
{$geometry:{type : "Point",
coordinates : [-3.81,43.47]},
$maxDistance: 250000 }
}
}
)

Results (Projected)
{ "properties" : { "name" : "DO Arabako-Txakolina" } }
{ "properties" : { "name" : "DO Chacoli de Vizcaya" } }
{ "properties" : { "name" : "DO Chacoli de Guetaria" } }
{ "properties" : { "name" : "DO Rioja" } }
{ "properties" : { "name" : "DO Navarra" } }
{ "properties" : { "name" : "DO Cigales" } }
{ "properties" : { "name" : "AOC Irouléguy" } }
{ "properties" : { "name" : "DO Ribera de Duero" } }
{ "properties" : { "name" : "DO Rueda" } }
{ "properties" : { "name" : "AOC Béarn-Bellocq" } }

But screens are not circular
db.wines.find({ geometry:
{ $geoWithin: { $geometry:{type : "Polygon",
coordinates : [[[-51,-29],
[-71,-29],
[-71,-33],
[-51,-33],
[-51,-29]]]}
}
}
})

Results – (Projected)
{ "properties" : { "name" : "Pinheiro Machado" } }
{ "properties" : { "name" : "Rio Negro" } }
{ "properties" : { "name" : "Tacuarembó" } }
{ "properties" : { "name" : "Rivera" } }
{ "properties" : { "name" : "Artigas" } }
{ "properties" : { "name" : "Salto" } }
{ "properties" : { "name" : "Paysandú" } }
{ "properties" : { "name" : "Mendoza" } }
{ "properties" : { "name" : "Luján de Cuyo" } }
{ "properties" : { "name" : "Aconcagua" } }

Use geo objects smartly
• Use polygons and/or multipolygons from a collection to query a
second one.
var mex = db.countries.findOne({"properties.name" : "Mexico"})
db.wines.find({geometry: {
$geoWithin: {
$geometry: mex.geometry}}})
{ "_id" : ObjectId("577e2e7e1007503076ac8ab9"), "properties" : { "name" : "Los Romos",
"description" : null, "id" : "a629ojjkguyw" }, "type" : "Feature", "geometry" : { "type" :
"Point", "coordinates" : [ -102.304048304437, 22.0992980768825 ] } }
{ "_id" : ObjectId("577e2e7e1007503076ac8a8d"), "properties" : { "name" : "Hermosillo",
"description" : null, "id" : "a629ojiw0i7f" }, "type" : "Feature", "geometry" : { "type" :
"Point", "coordinates" : [ -111.03600413129, 29.074715739466 ] } }

Let’s do crazy things
var wines = db.wines.find()
while (wines.hasNext()){
var wine = wines.next();
var country = db.countries.findOne({geometry :
{$geoIntersects : {$geometry : wine.geometry}}});
if (country!=null){
db.wines.update({"_id" : wine._id},
{$set : {"properties.country" :
country.properties.name}});
}
}

Summary of Operators
• $geoIntersect: Find areas or points that overlap or are
adjacent
• Points or polygons, doesn’t matter.
• $geoWithin: Find areas on points that lie within a specific area
• Use screen limits smartly
• $near: Returns locations in order from nearest to furthest away
• Find closest objects.

Summary
• Los índices de texto permiten hacer búsquedas tipo Google, SOLR, ElasticSearch
• Pueden tenere en cuenta los pesos de diferentes campos
• Pueden combinarse con otras búsquedas
• Pueden devolver los resultado ordenados por relevancia
• Pueden ser multilenguaje y case/accent insensitive
• Los índices geoespaciales permiten manejar objetos GeoJSON
• Permiten hacer búsquedas por proximidad, inclusión e intersección
• Utilizan el sistema de referencia más habitual, WGS84
• Ojo!!! Latitud y longitud son al revés que Google Maps.
• Pueden combinarse con otras búsquedas
• Existe un índice especial (2d) para superficies planas (un campo de fútbol, un mundo
virtual, etc.)

Próximo Webinar
Introducción a Aggregation Framework
• 19 de Julio 2016 – 16:00 CEST, 11:00 ART, 9:00
• ¡Regístrese si aún no lo ha hecho!
• MongoDB Aggregation Framework concede al desarrollador la capacidad de
desplegar un procesamiento de análisis avanzado dentro de la base de
datos..
• Este procesa los datos en una pipeline tipo Unix y permite a los
desarrolladores:
• Remodelar, transformar y extraer datos.
• Aplicar funciones analíticas estándares que van desde las sumas y las medias hasta la
desviación estándar.
• Regístrese en : https://www.mongodb.com/webinars
• Denos su opinión, por favor: back-to-basics@mongodb.com

Conceptos básicos. Seminario web 4: Indexación avanzada, índices de texto y geoespaciales

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