mongodb-18.聚合查询练习1

文章目录

bulk write
addFields
- 增加field
- 嵌套增加field
- 覆盖显示
- 用变量替换
- 向数组中增加元素
分组 bucket
并行执行多个bucket
- $bucketAuto
- $count
- $document
- $facet
- - 1.使用Aggregation对象实现
  - 2.使用Aggregates实现
$graphLookup 文档递归查询
- 跨多文档递归$graphLookup

bulk write

db.pizzas.insertMany( [{ _id: 0, type: "pepperoni", size: "small", price: 4 },{ _id: 1, type: "cheese", size: "medium", price: 7 },{ _id: 2, type: "vegan", size: "large", price: 8 }
] )

try {db.pizzas.bulkWrite( [{ insertOne: { document: { _id: 3, type: "beef", size: "medium", price: 6 } } },{ insertOne: { document: { _id: 4, type: "sausage", size: "large", price: 10 } } },{ updateOne: {filter: { type: "cheese" },update: { $set: { price: 8 } }} },{ deleteOne: { filter: { type: "pepperoni"} } },{ replaceOne: {filter: { type: "vegan" },replacement: { type: "tofu", size: "small", price: 4 }} }] )
} catch( error ) {print( error )
}

输出

{acknowledged: true,insertedCount: 2,insertedIds: { '0': 3, '1': 4 },matchedCount: 2,modifiedCount: 2,deletedCount: 1,upsertedCount: 0,upsertedIds: {}
}

addFields

增加field

db.scores.insertMany([{_id: 1,student: "Maya",homework: [ 10, 5, 10 ],quiz: [ 10, 8 ],extraCredit: 0
},
{_id: 2,student: "Ryan",homework: [ 5, 6, 5 ],quiz: [ 8, 8 ],extraCredit: 8
}])

db.scores.aggregate([{$addFields:{totalHomework:{$sum:"$homework"},totalQuiz:{$sum:"$quiz"}}},{$addFields:{totalSocre:{$add:["$totalHomework","$totalQuiz","$extractCredit"]}}}])

结果

{ _id: 1,student: 'Maya',homework: [ 10, 5, 10 ],quiz: [ 10, 8 ],extraCredit: 0,totalHomework: 25,totalQuiz: 18,totalSocre: null }
{ _id: 2,student: 'Ryan',homework: [ 5, 6, 5 ],quiz: [ 8, 8 ],extraCredit: 8,totalHomework: 16,totalQuiz: 16,totalSocre: null }

嵌套增加field

db.vehicles.insertMany([{ _id: 1, type: "car", specs: { doors: 4, wheels: 4 } },{ _id: 2, type: "motorcycle", specs: { doors: 0, wheels: 2 } },{ _id: 3, type: "jet ski" }]
)

db.vehicles.aggregate( [{$addFields: {"specs.fuel_type": "unleaded"}}] )

{ _id: 1, type: "car",specs: { doors: 4, wheels: 4, fuel_type: "unleaded" } }
{ _id: 2, type: "motorcycle",specs: { doors: 0, wheels: 2, fuel_type: "unleaded" } }
{ _id: 3, type: "jet ski",specs: { fuel_type: "unleaded" } }

覆盖显示

db.scores.aggregate({$addFields:{extraCredit:0}
}){_id: 1,student: 'Maya',homework: [ 10, 5, 10 ],quiz: [ 10, 8 ],extraCredit: 0 }
{ _id: 2,student: 'Ryan',homework: [ 5, 6, 5 ],quiz: [ 8, 8 ],extraCredit: 0 }

用变量替换

db.scores.aggregate({$addFields:{_id:"$item",item:"test"}})
{ student: 'Maya',homework: [ 10, 5, 10 ],quiz: [ 10, 8 ],extraCredit: 0,item: 'test' }
{ student: 'Ryan',homework: [ 5, 6, 5 ],quiz: [ 8, 8 ],extraCredit: 8,item: 'test' }

向数组中增加元素

db.scores.insertMany([{ _id: 1, student: "Maya", homework: [ 10, 5, 10 ], quiz: [ 10, 8 ], extraCredit: 0 },{ _id: 2, student: "Ryan", homework: [ 5, 6, 5 ], quiz: [ 8, 8 ], extraCredit: 8 }
])

db.scores.aggregate([{ $match: { _id: 1 } },{ $addFields: { homework: { $concatArrays: [ "$homework", [ 7 ] ] } } }
])

{ "_id" : 1, "student" : "Maya", "homework" : [ 10, 5, 10, 7 ], "quiz" : [ 10, 8 ], "extraCredit" : 0 }

分组 bucket

$bucket and Memory Restrictions The $bucket stage has a limit of 100 megabytes of RAM. By default, if the stage exceeds this limit, $bucket returns an error. To allow more space for stage processing, use the allowDiskUse option to enable aggregation pipeline stages to write data to temporary files.

{$bucket: {groupBy: <expression>,boundaries: [ <lowerbound1>, <lowerbound2>, ... ],default: <literal>,output: {<output1>: { <$accumulator expression> },...<outputN>: { <$accumulator expression> }}}
}

db.artists.insertMany([{ "_id" : 1, "last_name" : "Bernard", "first_name" : "Emil", "year_born" : 1868, "year_died" : 1941, "nationality" : "France" },{ "_id" : 2, "last_name" : "Rippl-Ronai", "first_name" : "Joszef", "year_born" : 1861, "year_died" : 1927, "nationality" : "Hungary" },{ "_id" : 3, "last_name" : "Ostroumova", "first_name" : "Anna", "year_born" : 1871, "year_died" : 1955, "nationality" : "Russia" },{ "_id" : 4, "last_name" : "Van Gogh", "first_name" : "Vincent", "year_born" : 1853, "year_died" : 1890, "nationality" : "Holland" },{ "_id" : 5, "last_name" : "Maurer", "first_name" : "Alfred", "year_born" : 1868, "year_died" : 1932, "nationality" : "USA" },{ "_id" : 6, "last_name" : "Munch", "first_name" : "Edvard", "year_born" : 1863, "year_died" : 1944, "nationality" : "Norway" },{ "_id" : 7, "last_name" : "Redon", "first_name" : "Odilon", "year_born" : 1840, "year_died" : 1916, "nationality" : "France" },{ "_id" : 8, "last_name" : "Diriks", "first_name" : "Edvard", "year_born" : 1855, "year_died" : 1930, "nationality" : "Norway" }
])

db.artists.aggregate( [// First Stage{$bucket: {groupBy: "$year_born",                        // Field to group byboundaries: [ 1840, 1850, 1860, 1870, 1880 ], // Boundaries for the bucketsdefault: "Other",                             // Bucket ID for documents which do not fall into a bucketoutput: {                                     // Output for each bucket"count": { $sum: 1 },"artists" :{$push: {"name": { $concat: [ "$first_name", " ", "$last_name"] },"year_born": "$year_born"}}}}},// Second Stage{$match: { count: {$gt: 3} }}
] )

备注

The
$bucketstage groups the documents into buckets by the year_born field. The buckets have the following
boundaries:[1840, 1850)[1850, 1860)[1860, 1870)[1870, 1880)

If a document did not contain the year_born field or its year_born field was outside the ranges above, it would be placed in the default bucket with the _id value "Other".

所以第一个stage产出的数据是

{ _id: 1840,count: 1,artists: [ { name: 'OdilonRedon', year_born: 1840 } ] }
{ _id: 1850,count: 2,artists: [ { name: 'VincentVan Gogh', year_born: 1853 },{ name: 'EdvardDiriks', year_born: 1855 } ] }
{ _id: 1860,count: 4,artists: [ { name: 'EmilBernard', year_born: 1868 },{ name: 'JoszefRippl-Ronai', year_born: 1861 },{ name: 'AlfredMaurer', year_born: 1868 },{ name: 'EdvardMunch', year_born: 1863 } ] }
{ _id: 1870,count: 1,artists: [ { name: 'AnnaOstroumova', year_born: 1871 } ] }

然后根据第二个stage 的$match进行过滤，得到

{ _id: 1860,count: 4,artists: [ { name: 'EmilBernard', year_born: 1868 },{ name: 'JoszefRippl-Ronai', year_born: 1861 },{ name: 'AlfredMaurer', year_born: 1868 },{ name: 'EdvardMunch', year_born: 1863 } ] }

并行执行多个bucket

db.artwork.insertMany([{ "_id" : 1, "title" : "The Pillars of Society", "artist" : "Grosz", "year" : 1926,"price" : NumberDecimal("199.99") },{ "_id" : 2, "title" : "Melancholy III", "artist" : "Munch", "year" : 1902,"price" : NumberDecimal("280.00") },{ "_id" : 3, "title" : "Dancer", "artist" : "Miro", "year" : 1925,"price" : NumberDecimal("76.04") },{ "_id" : 4, "title" : "The Great Wave off Kanagawa", "artist" : "Hokusai","price" : NumberDecimal("167.30") },{ "_id" : 5, "title" : "The Persistence of Memory", "artist" : "Dali", "year" : 1931,"price" : NumberDecimal("483.00") },{ "_id" : 6, "title" : "Composition VII", "artist" : "Kandinsky", "year" : 1913,"price" : NumberDecimal("385.00") },{ "_id" : 7, "title" : "The Scream", "artist" : "Munch", "year" : 1893/* No price*/ },{ "_id" : 8, "title" : "Blue Flower", "artist" : "O'Keefe", "year" : 1918,"price" : NumberDecimal("118.42") }
])

db.artwork.aggregate( [{$facet: {                               // Top-level $facet stage"price": [                            // Output field 1{$bucket: {groupBy: "$price",            // Field to group byboundaries: [ 0, 200, 400 ],  // Boundaries for the bucketsdefault: "Other",             // Bucket ID for documents which do not fall into a bucketoutput: {                     // Output for each bucket"count": { $sum: 1 },"artwork" : { $push: { "title": "$title", "price": "$price" } },"averagePrice": { $avg: "$price" }}}}],"year": [                                      // Output field 2{$bucket: {groupBy: "$year",                        // Field to group byboundaries: [ 1890, 1910, 1920, 1940 ],  // Boundaries for the bucketsdefault: "Unknown",                      // Bucket ID for documents which do not fall into a bucketoutput: {                                // Output for each bucket"count": { $sum: 1 },"artwork": { $push: { "title": "$title", "year": "$year" } }}}}]}}
] )

产出

{ price: [ { _id: 0,count: 4,artwork: [ { title: 'The Pillars of Society', price: Decimal128("199.99") },{ title: 'Dancer', price: Decimal128("76.04") },{ title: 'The Great Wave off Kanagawa',price: Decimal128("167.30") },{ title: 'Blue Flower', price: Decimal128("118.42") } ],averagePrice: Decimal128("140.4375") },{ _id: 200,count: 2,artwork: [ { title: 'Melancholy III', price: Decimal128("280.00") },{ title: 'Composition VII', price: Decimal128("385.00") } ],averagePrice: Decimal128("332.50") },{ _id: 'Other',count: 2,artwork: [ { title: 'The Persistence of Memory',price: Decimal128("483.00") },{ title: 'The Scream' } ],averagePrice: Decimal128("483.00") } ],year: [ { _id: 1890,count: 2,artwork: [ { title: 'Melancholy III', year: 1902 },{ title: 'The Scream', year: 1893 } ] },{ _id: 1910,count: 2,artwork: [ { title: 'Composition VII', year: 1913 },{ title: 'Blue Flower', year: 1918 } ] },{ _id: 1920,count: 3,artwork: [ { title: 'The Pillars of Society', year: 1926 },{ title: 'Dancer', year: 1925 },{ title: 'The Persistence of Memory', year: 1931 } ] },{ _id: 'Unknown',count: 1,artwork: [ { title: 'The Great Wave off Kanagawa' } ] } ] }

$bucketAuto

db.artwork.insertMany([{ "_id" : 1, "title" : "The Pillars of Society", "artist" : "Grosz", "year" : 1926,"price" : NumberDecimal("199.99"),"dimensions" : { "height" : 39, "width" : 21, "units" : "in" } },
{ "_id" : 2, "title" : "Melancholy III", "artist" : "Munch", "year" : 1902,"price" : NumberDecimal("280.00"),"dimensions" : { "height" : 49, "width" : 32, "units" : "in" } },
{ "_id" : 3, "title" : "Dancer", "artist" : "Miro", "year" : 1925,"price" : NumberDecimal("76.04"),"dimensions" : { "height" : 25, "width" : 20, "units" : "in" } },
{ "_id" : 4, "title" : "The Great Wave off Kanagawa", "artist" : "Hokusai","price" : NumberDecimal("167.30"),"dimensions" : { "height" : 24, "width" : 36, "units" : "in" } },
{ "_id" : 5, "title" : "The Persistence of Memory", "artist" : "Dali", "year" : 1931,"price" : NumberDecimal("483.00"),"dimensions" : { "height" : 20, "width" : 24, "units" : "in" } },
{ "_id" : 6, "title" : "Composition VII", "artist" : "Kandinsky", "year" : 1913,"price" : NumberDecimal("385.00"),"dimensions" : { "height" : 30, "width" : 46, "units" : "in" } },
{ "_id" : 7, "title" : "The Scream", "artist" : "Munch","price" : NumberDecimal("159.00"),"dimensions" : { "height" : 24, "width" : 18, "units" : "in" } },
{ "_id" : 8, "title" : "Blue Flower", "artist" : "O'Keefe", "year" : 1918,"price" : NumberDecimal("118.42"),"dimensions" : { "height" : 24, "width" : 20, "units" : "in" } }])
{ acknowledged: true,insertedIds: { '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, '5': 6, '6': 7, '7': 8 } }

db.artwork.aggregate([{$bucketAuto:{groupBy:"$price",buckets:4}}])
{ _id: { min: Decimal128("76.04"), max: Decimal128("159.00") },count: 2 }
{ _id: { min: Decimal128("159.00"), max: Decimal128("199.99") },count: 2 }
{ _id: { min: Decimal128("199.99"), max: Decimal128("385.00") },count: 2 }
{ _id: { min: Decimal128("385.00"), max: Decimal128("483.00") },count: 2 }

db.artwork.aggregate( [{$facet: {"price": [{$bucketAuto: {groupBy: "$price",buckets: 4}}],"year": [{$bucketAuto: {groupBy: "$year",buckets: 3,output: {"count": { $sum: 1 },"years": { $push: "$year" }}}}],"area": [{$bucketAuto: {groupBy: {$multiply: [ "$dimensions.height", "$dimensions.width" ]},buckets: 4,output: {"count": { $sum: 1 },"titles": { $push: "$title" }}}}]}}
] )

输出

{"area" : [{"_id" : { "min" : 432, "max" : 500 },"count" : 3,"titles" : ["The Scream","The Persistence of Memory","Blue Flower"]},{"_id" : { "min" : 500, "max" : 864 },"count" : 2,"titles" : ["Dancer","The Pillars of Society"]},{"_id" : { "min" : 864, "max" : 1568 },"count" : 2,"titles" : ["The Great Wave off Kanagawa","Composition VII"]},{"_id" : { "min" : 1568, "max" : 1568 },"count" : 1,"titles" : ["Melancholy III"]}],"price" : [{"_id" : { "min" : NumberDecimal("76.04"), "max" : NumberDecimal("159.00") },"count" : 2},{"_id" : { "min" : NumberDecimal("159.00"), "max" : NumberDecimal("199.99") },"count" : 2},{"_id" : { "min" : NumberDecimal("199.99"), "max" : NumberDecimal("385.00") },"count" : 2 },{"_id" : { "min" : NumberDecimal("385.00"), "max" : NumberDecimal("483.00") },"count" : 2}],"year" : [{ "_id" : { "min" : null, "max" : 1913 }, "count" : 3, "years" : [ 1902 ] },{ "_id" : { "min" : 1913, "max" : 1926 }, "count" : 3, "years" : [ 1913, 1918, 1925 ] },{ "_id" : { "min" : 1926, "max" : 1931 }, "count" : 2, "years" : [ 1926, 1931 ] }]
}

$count

db.scores.insertMany([{ "_id" : 1, "subject" : "History", "score" : 88 },
{ "_id" : 2, "subject" : "History", "score" : 92 },
{ "_id" : 3, "subject" : "History", "score" : 97 },
{ "_id" : 4, "subject" : "History", "score" : 71 },
{ "_id" : 5, "subject" : "History", "score" : 79 },
{ "_id" : 6, "subject" : "History", "score" : 83 }])
{ acknowledged: true,insertedIds: { '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, '5': 6 } }

db.scores.aggregate([{$match:{score:{$gt:80}}}])
{ _id: 1, subject: 'History', score: 88 }
{ _id: 2, subject: 'History', score: 92 }
{ _id: 3, subject: 'History', score: 97 }
{ _id: 6, subject: 'History', score: 83 }

db.scores.aggregate([{$match:{score:{$gt:80}}},{$count:"passing_scores"}])
{ passing_scores: 4 }

$document

db.aggregate([{ $documents: [ { x: 10 }, { x: 2 }, { x: 5 } ] },{ $bucketAuto: { groupBy: "$x", buckets: 4 } }]
)
{ _id: { min: 2, max: 5 }, count: 1 }
{ _id: { min: 5, max: 10 }, count: 1 }
{ _id: { min: 10, max: 10 }, count: 1 }

$facet

facet可以实现在facet管道操作完成多个stage管道操作。减少获取输入文档的次数。

分页查询文档数据的同时，把符合查询条件的总数也查询出来的场景下，如果使用$facet，同时获取分页数据和总数，不用做两次数据库查询（分别查询分页数据和总数）。

$unwind 学习

db.user.insertMany([{user_id:"A_id" ,bonus:[{ type:"a" ,amount:1000 },{ type:"b" ,amount:2000 },{ type:"b" ,amount:3000 }]
}])
{ acknowledged: true,insertedIds: { '0': ObjectId("63b675e5e13faeb56183402e") } }

使用$unwind进行拆分

db.user.aggregate([{$unwind:"$bonus"}
])
{ _id: ObjectId("63b675e5e13faeb56183402e"),user_id: 'A_id',bonus: { type: 'a', amount: 1000 } }
{ _id: ObjectId("63b675e5e13faeb56183402e"),user_id: 'A_id',bonus: { type: 'b', amount: 2000 } }
{ _id: ObjectId("63b675e5e13faeb56183402e"),user_id: 'A_id',bonus: { type: 'b', amount: 3000 } }

结合统计一起查询

db.user.aggregate([{$match:{user_id:"A_id"}},{$unwind:"$bonus"},{$match:{"bonus.type":"b"}},{$group:{_id:"$user_id",amount:{$sum:"$bonus.amount"}}}])
{ _id: 'A_id', amount: 5000 }

db.artwork.insertMany([{ "_id" : 1, "title" : "The Pillars of Society", "artist" : "Grosz", "year" : 1926,"price" : NumberDecimal("199.99"),"tags" : [ "painting", "satire", "Expressionism", "caricature" ] },
{ "_id" : 2, "title" : "Melancholy III", "artist" : "Munch", "year" : 1902,"price" : NumberDecimal("280.00"),"tags" : [ "woodcut", "Expressionism" ] },
{ "_id" : 3, "title" : "Dancer", "artist" : "Miro", "year" : 1925,"price" : NumberDecimal("76.04"),"tags" : [ "oil", "Surrealism", "painting" ] },
{ "_id" : 4, "title" : "The Great Wave off Kanagawa", "artist" : "Hokusai","price" : NumberDecimal("167.30"),"tags" : [ "woodblock", "ukiyo-e" ] },
{ "_id" : 5, "title" : "The Persistence of Memory", "artist" : "Dali", "year" : 1931,"price" : NumberDecimal("483.00"),"tags" : [ "Surrealism", "painting", "oil" ] },
{ "_id" : 6, "title" : "Composition VII", "artist" : "Kandinsky", "year" : 1913,"price" : NumberDecimal("385.00"),"tags" : [ "oil", "painting", "abstract" ] },
{ "_id" : 7, "title" : "The Scream", "artist" : "Munch", "year" : 1893,"tags" : [ "Expressionism", "painting", "oil" ] },
{ "_id" : 8, "title" : "Blue Flower", "artist" : "O'Keefe", "year" : 1918,"price" : NumberDecimal("118.42"),"tags" : [ "abstract", "painting" ] }])
{ acknowledged: true,insertedIds: { '0': 1, '1': 2, '2': 3, '3': 4, '4': 5, '5': 6, '6': 7, '7': 8 } }

按照tag分类：先用unwind拆分tags字段的数组值，交给下一个聚合 $sortByCount, 按照tags的个数排序

db.artwork.aggregate([{$unwind:"$tags"},{$sortByCount:"$tags"}])
{ _id: 'painting', count: 6 }
{ _id: 'oil', count: 4 }
{ _id: 'Expressionism', count: 3 }
{ _id: 'Surrealism', count: 2 }
{ _id: 'abstract', count: 2 }
{ _id: 'ukiyo-e', count: 1 }
{ _id: 'woodblock', count: 1 }
{ _id: 'woodcut', count: 1 }
{ _id: 'satire', count: 1 }
{ _id: 'caricature', count: 1 }

按照price分类：先过滤数据（只处理存在price数据的文档），然后执行$bucket按照价格区间分组 0_150，151200， 201~300， 301~400这样。

db.artwork.aggregate([{$match:{price:{$exists:1}}},{$bucket:{groupBy:"$price",boundaries:[0,150,200,300,400],default:"Other"}}])
{ _id: 0, count: 2 }
{ _id: 150, count: 2 }
{ _id: 200, count: 1 }
{ _id: 300, count: 1 }
{ _id: 'Other', count: 1 }

进一步设置输出字段

db.artwork.aggregate([{$match:{price:{$exists:1}}},{$bucket:{groupBy:"$price",boundaries:[0,150,200,300,400],default:"Other"}}])
{ _id: 0, count: 2 }
{ _id: 150, count: 2 }
{ _id: 200, count: 1 }
{ _id: 300, count: 1 }
{ _id: 'Other', count: 1 }

按照years分类。分成4个区间。

db.artwork.aggregate([{$bucketAuto:{groupBy:"$year",buckets:4}}])
{ _id: { min: null, max: 1902 }, count: 2 }
{ _id: { min: 1902, max: 1918 }, count: 2 }
{ _id: { min: 1918, max: 1926 }, count: 2 }
{ _id: { min: 1926, max: 1931 }, count: 2 }

整合到一起就是

db.artwork.aggregate([{$facet:{"categorizedByTags":[{$unwind:"$tags"},{$sortByCount:"$tags"}],"categorizedByPrice":[{$match:{price:{$exists:1}}},{$bucket:{groupBy:"$price",boundaries:[0,150,200,300,400],default:"Other",output:{"count":{$sum:1},"titles":{$push:"$title"}}}}]  ,"categorizedByYears(Auto)":[{$bucketAuto:{groupBy:"$year",buckets:4}}]}}])
{ categorizedByTags: [ { _id: 'painting', count: 6 },{ _id: 'oil', count: 4 },{ _id: 'Expressionism', count: 3 },{ _id: 'Surrealism', count: 2 },{ _id: 'abstract', count: 2 },{ _id: 'woodcut', count: 1 },{ _id: 'woodblock', count: 1 },{ _id: 'ukiyo-e', count: 1 },{ _id: 'caricature', count: 1 },{ _id: 'satire', count: 1 } ],categorizedByPrice: [ { _id: 0, count: 2, titles: [ 'Dancer', 'Blue Flower' ] },{ _id: 150,count: 2,titles: [ 'The Pillars of Society', 'The Great Wave off Kanagawa' ] },{ _id: 200, count: 1, titles: [ 'Melancholy III' ] },{ _id: 300, count: 1, titles: [ 'Composition VII' ] },{ _id: 'Other',count: 1,titles: [ 'The Persistence of Memory' ] } ],'categorizedByYears(Auto)': [ { _id: { min: null, max: 1902 }, count: 2 },{ _id: { min: 1902, max: 1918 }, count: 2 },{ _id: { min: 1918, max: 1926 }, count: 2 },{ _id: { min: 1926, max: 1931 }, count: 2 } ] }

1.使用Aggregation对象实现

@Testpublic void testFacetAggregations(){String ARTWORK_COLLECTION = "artwork";// Facet中第一组分类（categorizedByTags）的两个聚合操作unwind 和 sortByCountUnwindOperation unwindForByTags = Aggregation.unwind("$tags");SortByCountOperation sortByCountForByTags = Aggregation.sortByCount("$tags");// Facet中第二组分类（categorizedByPrice）的聚合操作match 和 matchMatchOperation matchForByPrice = Aggregation.match(Criteria.where("price").exists(true));// 分别传入bucket分组的字段price，设置区间值，并设置桶内条数统计和值（这里用titles接收title的值）BucketOperation bucketForByPrice = Aggregation.bucket("$price").withBoundaries(0, 150, 200, 300, 400).withDefaultBucket("Other").andOutput("count").sum(1).as("count").andOutput("$title").push().as("titles");// Facet中第三组分类 （categorizedByYears(Auto)）的聚合操作，按年自动分成4个区间。BucketAutoOperation bucketForByYears = Aggregation.bucketAuto("$year", 4);// Aggregation调用facet方法，按照组别分类顺序，把每一组的聚合操作和输出的名称传进去。FacetOperation facetOperation = Aggregation.facet(unwindForByTags, sortByCountForByTags).as("categorizedByTags").and(matchForByPrice, bucketForByPrice).as("categorizedByPrice").and(bucketForByYears).as("categorizedByYears(Auto)");// 把facetOperation传入newAggregation得到Aggregation对象，调用mongoTemplate的Aggregate方法执行得到结果Aggregation aggregation = Aggregation.newAggregation(facetOperation);AggregationResults<Document> resultList = mongoTemplate.aggregate(aggregation, ARTWORK_COLLECTION, Document.class);for (Document document : resultList) {System.out.println("result is :" + document);}}

2.使用Aggregates实现

@Testpublic void testFacetAggregates() {String ARTWORK_COLLECTION = "artwork";// Facet中第一组分类（categorizedByTags）的两个聚合操作unwind 和 sortByCountBson unwindBsonForByTags = Aggregates.unwind("$tags");Bson sortByCountBsonForByTags = Aggregates.sortByCount("$tags");// 新建Facet对象，传入第一组分类的接收名称，以及在第一组分类中要做的聚合操作。Facet categorizedByTags = new Facet("categorizedByTags", unwindBsonForByTags, sortByCountBsonForByTags);// Facet中第二组分类（categorizedByPrice）的聚合操作match 和 matchBson matchBsonForPrice = Aggregates.match(Filters.exists("price"));// 这里面要新建BsonField构建 {"count": { $sum: 1 }  和 "titles": { $push: "$title" }} 作为第二组分类中$Bucket聚合操作中output值BsonField countOutput = new BsonField("count", new Document("$sum", 1));BsonField titleOutput = new BsonField("titles", new Document("$push", "$price"));// 上面2个操作传入到BucketOption对象，最后传到bucket操作BucketOptions bucketOptions = new BucketOptions().defaultBucket("Other").output(countOutput).output(titleOutput);Bson bucketBsonForByPrice = Aggregates.bucket("$price", Arrays.asList(0, 150, 200, 300, 400), bucketOptions);Facet categorizedByPrice = new Facet("categorizedByPrice", matchBsonForPrice, bucketBsonForByPrice);// Facet中第三组分类 （categorizedByYears(Auto)）的聚合操作，按年自动分成4个区间。Bson bucketAutoBsonForByYears = Aggregates.bucketAuto("$year", 4);Facet categorizedByYears = new Facet("categorizedByYears", bucketAutoBsonForByYears);// 新建一个List<Facet>把每组分类的Facet对象传进去。List<Facet> facetList = new ArrayList<>();facetList.add(categorizedByTags);facetList.add(categorizedByPrice);facetList.add(categorizedByYears);// 调用Aggregates的facet方法，传入List<Facet>得到最终Bson对象，并添加到Bson集合中。Bson facetBson = Aggregates.facet(facetList);List<Bson> bsonList = new ArrayList<>();bsonList.add(facetBson);// 调用方法执行得到结果MongoCollection<Document> collection = mongoTemplate.getCollection(ARTWORK_COLLECTION);AggregateIterable<Document> resultList = collection.aggregate(bsonList);for (Document document : resultList) {System.out.println("result is :" + document);}}

$graphLookup 文档递归查询

格式

{$graphLookup: {from: <collection>,startWith: <expression>,connectFromField: <string>,connectToField: <string>,as: <string>,maxDepth: <number>,depthField: <string>,restrictSearchWithMatch: <document>}
}

注意事项
$graphLookup递归查询会用到内存占用

db.collection.aggregate([{ $graphLookup: { from: "fromCollection", ... } }
])

The collection can be sharded.
The fromCollection cannot be sharded.

db.employees.insertMany([{ "_id" : 1, "name" : "Dev" },
{ "_id" : 2, "name" : "Eliot", "reportsTo" : "Dev" },
{ "_id" : 3, "name" : "Ron", "reportsTo" : "Eliot" },
{ "_id" : 4, "name" : "Andrew", "reportsTo" : "Eliot" },
{ "_id" : 5, "name" : "Asya", "reportsTo" : "Ron" },
{ "_id" : 6, "name" : "Dan", "reportsTo" : "Andrew" }])

db.employees.aggregate( [{$graphLookup: {from: "employees",startWith: "$reportsTo",connectFromField: "reportsTo",connectToField: "name",as: "reportingHierarchy"}}
] )
{ _id: 1, name: 'Dev', reportingHierarchy: [] }
{ _id: 2,name: 'Eliot',reportsTo: 'Dev',reportingHierarchy: [ { _id: 1, name: 'Dev' } ] }
{ _id: 3,name: 'Ron',reportsTo: 'Eliot',reportingHierarchy: [ { _id: 2, name: 'Eliot', reportsTo: 'Dev' },{ _id: 1, name: 'Dev' } ] }
{ _id: 4,name: 'Andrew',reportsTo: 'Eliot',reportingHierarchy: [ { _id: 2, name: 'Eliot', reportsTo: 'Dev' },{ _id: 1, name: 'Dev' } ] }
{ _id: 5,name: 'Asya',reportsTo: 'Ron',reportingHierarchy: [ { _id: 3, name: 'Ron', reportsTo: 'Eliot' },{ _id: 2, name: 'Eliot', reportsTo: 'Dev' },{ _id: 1, name: 'Dev' } ] }
{ _id: 6,name: 'Dan',reportsTo: 'Andrew',reportingHierarchy: [ { _id: 2, name: 'Eliot', reportsTo: 'Dev' },{ _id: 4, name: 'Andrew', reportsTo: 'Eliot' },{ _id: 1, name: 'Dev' } ] }

以下以Ron为例

跨多文档递归$graphLookup

db.airports.insertMany( [{ "_id" : 0, "airport" : "JFK", "connects" : [ "BOS", "ORD" ] },{ "_id" : 1, "airport" : "BOS", "connects" : [ "JFK", "PWM" ] },{ "_id" : 2, "airport" : "ORD", "connects" : [ "JFK" ] },{ "_id" : 3, "airport" : "PWM", "connects" : [ "BOS", "LHR" ] },{ "_id" : 4, "airport" : "LHR", "connects" : [ "PWM" ] }
] )

db.travelers.insertMany( [{ "_id" : 1, "name" : "Dev", "nearestAirport" : "JFK" },{ "_id" : 2, "name" : "Eliot", "nearestAirport" : "JFK" },{ "_id" : 3, "name" : "Jeff", "nearestAirport" : "BOS" }
] )

db.travelers.aggregate( [{$graphLookup: {from: "airports",startWith: "$nearestAirport",connectFromField: "connects",connectToField: "airport",maxDepth: 2,depthField: "numConnections",as: "destinations"}}
] )

结果为

{"_id" : 1,"name" : "Dev","nearestAirport" : "JFK","destinations" : [{ "_id" : 3,"airport" : "PWM","connects" : [ "BOS", "LHR" ],"numConnections" : NumberLong(2) },{ "_id" : 2,"airport" : "ORD","connects" : [ "JFK" ],"numConnections" : NumberLong(1) },{ "_id" : 1,"airport" : "BOS","connects" : [ "JFK", "PWM" ],"numConnections" : NumberLong(1) },{ "_id" : 0,"airport" : "JFK","connects" : [ "BOS", "ORD" ],"numConnections" : NumberLong(0) }]
}
{"_id" : 2,"name" : "Eliot","nearestAirport" : "JFK","destinations" : [{ "_id" : 3,"airport" : "PWM","connects" : [ "BOS", "LHR" ],"numConnections" : NumberLong(2) },{ "_id" : 2,"airport" : "ORD","connects" : [ "JFK" ],"numConnections" : NumberLong(1) },{ "_id" : 1,"airport" : "BOS","connects" : [ "JFK", "PWM" ],"numConnections" : NumberLong(1) },{ "_id" : 0,"airport" : "JFK","connects" : [ "BOS", "ORD" ],"numConnections" : NumberLong(0) } ]
}
{"_id" : 3,"name" : "Jeff","nearestAirport" : "BOS","destinations" : [{ "_id" : 2,"airport" : "ORD","connects" : [ "JFK" ],"numConnections" : NumberLong(2) },{ "_id" : 3,"airport" : "PWM","connects" : [ "BOS", "LHR" ],"numConnections" : NumberLong(1) },{ "_id" : 4,"airport" : "LHR","connects" : [ "PWM" ],"numConnections" : NumberLong(2) },{ "_id" : 0,"airport" : "JFK","connects" : [ "BOS", "ORD" ],"numConnections" : NumberLong(1) },{ "_id" : 1,"airport" : "BOS","connects" : [ "JFK", "PWM" ],"numConnections" : NumberLong(0) }]
}

递归匹配时候，增加过滤

  {"_id" : 1,"name" : "Tanya Jordan","friends" : [ "Shirley Soto", "Terry Hawkins", "Carole Hale" ],"hobbies" : [ "tennis", "unicycling", "golf" ]
},
{"_id" : 2,"name" : "Carole Hale","friends" : [ "Joseph Dennis", "Tanya Jordan", "Terry Hawkins" ],"hobbies" : [ "archery", "golf", "woodworking" ]
},
{"_id" : 3,"name" : "Terry Hawkins","friends" : [ "Tanya Jordan", "Carole Hale", "Angelo Ward" ],"hobbies" : [ "knitting", "frisbee" ]
},
{"_id" : 4,"name" : "Joseph Dennis","friends" : [ "Angelo Ward", "Carole Hale" ],"hobbies" : [ "tennis", "golf", "topiary" ]
},
{"_id" : 5,"name" : "Angelo Ward","friends" : [ "Terry Hawkins", "Shirley Soto", "Joseph Dennis" ],"hobbies" : [ "travel", "ceramics", "golf" ]
},
{"_id" : 6,"name" : "Shirley Soto","friends" : [ "Angelo Ward", "Tanya Jordan", "Carole Hale" ],"hobbies" : [ "frisbee", "set theory" ]}])

1.查询

db.people.aggregate([{$match:{name:"Tanya Jordan"}},{$graphLookup:{from:"people",startWith:"$friends",connectFromField:"friend",connectToField:"name",as:"golfers"}}])
{ _id: 1,name: 'Tanya Jordan',friends: [ 'Shirley Soto', 'Terry Hawkins', 'Carole Hale' ],hobbies: [ 'tennis', 'unicycling', 'golf' ],golfers: [ { _id: 2,name: 'Carole Hale',friends: [ 'Joseph Dennis', 'Tanya Jordan', 'Terry Hawkins' ],hobbies: [ 'archery', 'golf', 'woodworking' ] },{ _id: 3,name: 'Terry Hawkins',friends: [ 'Tanya Jordan', 'Carole Hale', 'Angelo Ward' ],hobbies: [ 'knitting', 'frisbee' ] },{ _id: 6,name: 'Shirley Soto',friends: [ 'Angelo Ward', 'Tanya Jordan', 'Carole Hale' ],hobbies: [ 'frisbee', 'set theory' ] } ] }

2.增加匹配限制

db.people.aggregate([{$match:{name:"Tanya Jordan"}},{$graphLookup:{from:"people",startWith:"$friends",connectFromField:"friends",connectToField:"name",as:"golfers",restrictSearchWithMatch: {"hobbies":"golf"},depthField:"length"}}])
{ _id: 1,name: 'Tanya Jordan',friends: [ 'Shirley Soto', 'Terry Hawkins', 'Carole Hale' ],hobbies: [ 'tennis', 'unicycling', 'golf' ],golfers: [ { _id: 4,name: 'Joseph Dennis',friends: [ 'Angelo Ward', 'Carole Hale' ],hobbies: [ 'tennis', 'golf', 'topiary' ],length: 1 },{ _id: 1,name: 'Tanya Jordan',friends: [ 'Shirley Soto', 'Terry Hawkins', 'Carole Hale' ],hobbies: [ 'tennis', 'unicycling', 'golf' ],length: 1 },{ _id: 5,name: 'Angelo Ward',friends: [ 'Terry Hawkins', 'Shirley Soto', 'Joseph Dennis' ],hobbies: [ 'travel', 'ceramics', 'golf' ],length: 2 },{ _id: 2,name: 'Carole Hale',friends: [ 'Joseph Dennis', 'Tanya Jordan', 'Terry Hawkins' ],hobbies: [ 'archery', 'golf', 'woodworking' ],length: 0 } ] }

增加显示输出字段设置

db.people.aggregate([{$match:{name:"Tanya Jordan"}},{$graphLookup:{from:"people",startWith:"$friends",connectFromField:"friends",connectToField:"name",as:"golfers",restrictSearchWithMatch: {"hobbies":"golf"},depthField:"length"}},{$project:{name:1,friends:1,"connections who play golf":"$golfers.name"}}])
{ _id: 1,name: 'Tanya Jordan',friends: [ 'Shirley Soto', 'Terry Hawkins', 'Carole Hale' ],'connections who play golf': [ 'Joseph Dennis', 'Tanya Jordan', 'Angelo Ward', 'Carole Hale' ] }