Mongo3.2.10
Pertanyaan geo pengelompokan padat mengembalikan 30 rekod terkini, yang memerlukan pengimbasan 20,000+ rekod.
Untuk set titik yang sama, cuba buat pertanyaan indeks 2d dan indeks 2dsphere, dan kecekapan pelaksanaan tidak jauh berbeza.
indeks 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)
Versi cangkerang MongoDB: 3.2.8
menyambung ke: 10.108.93.135:7004/admin
{
"queryPlanner" : {
"plannerVersion" : 1,
"namespace" : "{db}.{coll}",
"indexFilterSet" : false,
"parsedQuery" : {
"lag" : {
"$nearSphere" : [
120.993965,
31.449034
],
"$maxDistance" : 0.0000783927971443699
}
},
"winningPlan" : {
"stage" : "LIMIT",
"limitAmount" : 30,
"inputStage" : {
"stage" : "GEO_NEAR_2D",
"keyPattern" : {
"lag" : "2d"
},
"indexName" : "lag",
"inputStages" : [
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"isMultiKey" : false,
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"direction" : "forward",
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"[BinData(128, E33C11D900000000), BinData(128, E33C11D9FFFFFFFF)]",
"[BinData(128, E33C11DA10000000), BinData(128, E33C11DA1FFFFFFF)]",
"[BinData(128, E33C11DA40000000), BinData(128, E33C11DA7FFFFFFF)]",
"[BinData(128, E33C11DB00000000), BinData(128, E33C11DB3FFFFFFF)]",
"[BinData(128, E33C11DB40000000), BinData(128, E33C11DB7FFFFFFF)]",
"[BinData(128, E33C11DC00000000), BinData(128, E33C11DC3FFFFFFF)]",
"[BinData(128, E33C11DC80000000), BinData(128, E33C11DCBFFFFFFF)]"
]
}
}
},
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},
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}
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}
}
},
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},
"rejectedPlans" : [ ]
},
"executionStats" : {
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selamat tinggal
2dsfera indeks
{
"v" : 1,
"key" : {
"loc" : "2dsphere"
},
"name" : "loc_2dsphere",
"ns" : "mobike_default.kdytbl",
"2dsphereIndexVersion" : 3
}db.kdytbl.find({loc:{ $nearSphere: { $geometry: { type : "Point",coordinates : [120.993965,31.449034]},$maxDistance:1000}}}).limit(30).explain(true)Versi cangkerang MongoDB: 3.2.8
menyambung ke: 10.108.93.135:7004/admin
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"namespace" : "{db}.{coll}",
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"parsedQuery" : {
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"$geometry" : {
"type" : "Point",
"coordinates" : [
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31.449034
]
},
"$maxDistance" : 1000
}
}
},
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![[Web front-end] Permulaan pantas Node.js](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
Oleh kerana indeks 2d menyokong pertanyaan sfera itu sendiri, yang mempunyai masalah prestasi besar, saya terutamanya melihat hasil kedua menggunakan indeks 2dsphere.
Berdasarkan pertanyaan, terangkan hasil, kerja itu biasa. Dalam rajah di bawah, bulatan ialah maxDistance bagi pertanyaan dan segi empat sama ialah tempat data dibaca. Koordinat dalam gambar adalah koordinat GPS Jika data adalah koordinat Marikh, lokasi yang dipaparkan akan menjadi berat sebelah.
Mungkin dalam julat 1 kilometer ini, pengedaran data sangat tertumpu, tetapi ia jauh dari titik awal Apabila $nearSphere pada mulanya menganggarkan ketumpatan titik dan tidak dapat mencari data di sekelilingnya, ia akan berkembang ke maxDistance Untuk perkara ini, tiada pendekatan yang sangat baik, penyelesaian yang mungkin juga akan merendahkan prestasi pertanyaan biasa. Adalah disyorkan untuk memuat turun Kompas MongoDB dan lihat pengedaran data loc untuk mengesahkan ini. Atau jika data lokasi dalam radius 5 kilometer boleh diterbitkan, saya boleh mengeluarkannya secara tempatan dan memahami di mana masalahnya. Secara umumnya, jika pengedaran titik permulaan pertanyaan adalah serupa dengan pengedaran data, hasilnya tidak akan terlalu melampau secara purata.
Akhirnya, pertanyaan explain bukan had 30, tapi had 1, tapi sifat masalahnya sepatutnya sama. Versi yang anda gunakan telah disusun oleh anda sendiri, dan tidak sepatutnya ada perubahan pada kod berkaitan 2dshpere.
Kami sentiasa menyusun kod itu sendiri. Versi ini tidak mengubah suai logik berkaitan indeks 2d atau 2dsfera.
Anda betul, logik kod penjejakan kami juga mendapati terdapat titik tumpat jauh sedikit dari titik pertanyaan. Algoritma meningkatkan saiz langkah adalah terlalu liar dan melompat terus ke saiz langkah maksimum.
Sama ada data abstrak boleh diberikan, saya perlu memohon, tetapi ia bukan masalah besar.
Saya tidak pernah menggunakan kompas mongo sebelum ini. Saya akan mencubanya.
Masalah dengan had 1 dan had 30 mungkin disebabkan oleh kesilapan dalam kenyataan yang saya siarkan semasa mengumpul data. Walau bagaimanapun, saya telah menguji kedua-duanya dan hasilnya adalah serupa.
Malah, anda boleh mempertimbangkan untuk membuka antara muka untuk algoritma penghampiran kabur, membenarkan pengguna memilih untuk melepaskan ketepatan yang ketat, yang akan membawa kepada peningkatan eksponen dalam prestasi. Inilah idea pengoptimuman yang kami ada di sini