Mongo3.2.10
La requête géographique de clustering dense renvoie les 30 derniers enregistrements, ce qui nécessite l'analyse de plus de 20 000 enregistrements.
Pour le même ensemble de points, essayez de créer une requête d'index 2D et d'index 2dsphere, et l'efficacité d'exécution n'est pas très différente.
Indice 2D
{
"v" : 1,
"key" : {
"lag" : "2d"
},
"name" : "lag",
"min" : -180,
"ns" : "mobike_default.mBKBikeInfo",
"bits" : 26,
"max" : 180
},db.coll.find({lag:{'$nearSphere':[120.993965,31.449034 ], '$maxDistance':7.83927971443699e-05*2}}).limit(30).explain(true)
Version du shell MongoDB : 3.2.8
connexion à : 10.108.93.135:7004/admin
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au revoir
Indice 2dsphère
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"ns" : "mobike_default.kdytbl",
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}db.kdytbl.find({loc:{ $nearSphere: { $geometry: { type : "Point",coordinates : [120.993965,31.449034]},$maxDistance:1000}}}).limit(30).explain(true)Version du shell MongoDB : 3.2.8
connexion à : 10.108.93.135:7004/admin
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![[Web front-end] Démarrage rapide de Node.js](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
Étant donné que l'index 2D prend en charge la requête sphérique elle-même, qui pose de gros problèmes de performances, j'examine principalement le deuxième résultat en utilisant l'index 2dsphere.
À en juger par les résultats de la requête d'explication, cela fonctionne normalement. Dans la figure ci-dessous, le cercle correspond à la maxDistance de la requête et le carré correspond à l'endroit où les données sont lues. Les coordonnées sur l'image sont des coordonnées GPS. Si les données sont des coordonnées de Mars, l'emplacement affiché sera biaisé.
Il se peut que dans cette plage de 1 kilomètre, la distribution des données soit très concentrée, mais elle est loin du point initial. Lorsque $nearSphere estime initialement la densité de points et ne trouve pas de données autour, elle s'étendra jusqu'à maxDistance. Il n'y a pas de solution particulièrement bonne pour cela, les solutions possibles dégraderont également les performances des requêtes ordinaires. Il est recommandé de télécharger MongoDB Compass et de jeter un œil à la distribution des données de loc pour le confirmer. Ou si les données de localisation dans un rayon de 5 kilomètres peuvent être publiées, je peux les reproduire localement et comprendre où se situe le problème. De manière générale, si la distribution des points de départ de la requête est similaire à la distribution des données, les résultats ne seront en moyenne pas aussi extrêmes.
Enfin, la requête d'explication n'est pas la limite 30, mais la limite 1, mais la nature du problème devrait être la même. La version que vous avez utilisée a été compilée par vous-même et aucune modification ne devrait être apportée au code associé à 2dshpere.
Nous avons toujours compilé le code nous-mêmes. Cette version n'a pas modifié la logique liée à l'index 2d ou 2dsphere.
Vous avez raison. Notre logique de code de suivi a également révélé qu'il existe un point dense légèrement éloigné du point de requête. L'algorithme d'augmentation de la taille du pas est trop sauvage et passe directement à la taille maximale du pas.
Je dois postuler pour voir si les données extraites peuvent être fournies, mais ce n'est pas un gros problème.
mongo compass Je ne l'ai jamais utilisé auparavant. C'est un outil tellement magique que je vais l'essayer.
Le problème avec la limite 1 et la limite 30 peut être dû à un oubli dans les déclarations que j'ai postées lors de la collecte des données. Cependant, j'ai testé les deux et les résultats sont similaires.
En fait, vous pouvez envisager d'ouvrir une interface pour l'algorithme d'approximation floue, permettant aux utilisateurs de choisir d'abandonner l'exactitude stricte, ce qui entraînera des améliorations exponentielles des performances. C'est l'idée d'optimisation que nous avons ici