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时间：2018/1/18 10:55:56

参考：

  1. MongoDB TutorialsMongoDB Tutorials

环境：

  1. win 10
  2. MongoDB 3.6.2 Community
  3. python

查询总结#

插入数据#

  1. 插入一条数据，集合不存在自动创建 
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    db.get_collection("inventory").insert_one(
    {
        "item": "canvas",
        "qty": 100,
        "tags": ["cotton"],
        "size": {"h": 28, "w": 35.5, "uom": "cm"}
    })

    1. 批量插入，集合不存在自动创建

      //数据1 db.get_collection("inventory").insert_many([ # MongoDB adds the _id field with an ObjectId if _id is not present { "item": "journal", "qty": 25, "status": "A", "size": { "h": 14, "w": 21, "uom": "cm" }, "tags": [ "blank", "red" ] }, { "item": "notebook", "qty": 50, "status": "A", "size": { "h": 8.5, "w": 11, "uom": "in" }, "tags": [ "red", "blank" ] }, { "item": "paper", "qty": 100, "status": "D", "size": { "h": 8.5, "w": 11, "uom": "in" }, "tags": [ "red", "blank", "plain" ] }, { "item": "planner", "qty": 75, "status": "D", "size": { "h": 22.85, "w": 30, "uom": "cm" }, "tags": [ "blank", "red" ] }, { "item": "postcard", "qty": 45, "status": "A", "size": { "h": 10, "w": 15.25, "uom": "cm" }, "tags": [ "blue" ] } ]) //数据2 db.inventory.insert_many([ {"item": "journal", "qty": 25, "tags": ["blank", "red"], "dim_cm": [14, 21]}, {"item": "notebook", "qty": 50, "tags": ["red", "blank"], "dim_cm": [14, 21]}, {"item": "paper", "qty": 100, "tags": ["red", "blank", "plain"], "dim_cm": [14, 21]}, {"item": "planner", "qty": 75, "tags": ["blank", "red"], "dim_cm": [22.85, 30]}, {"item": "postcard", "qty": 45, "tags": ["blue"], "dim_cm": [10, 15.25]} ]) //数据3 db.inventory.insert([{"item": None}, {}])

查询数据#

  1. 使用 Python

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    import gridfs
import pymongo

  2. 普通集合查询 

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    # 查询全部
db.get_collection("inventory").find()
# 查询指定值
db.get_collection("inventory").find({"status": "D"})
# or
db.get_collection("inventory").find({"$or": [{"status": "D"}, {"qty": 75}]})
# and
db.get_collection("inventory").find({"$and": [{"status": "D"}, {"qty": 75}]})
db.get_collection("inventory").find({"status": "D", "qty": 75})
# 嵌套查询，匹配一个属性
db.get_collection("inventory").find({"size.h": 14})
# 嵌套查询，size的所有属性必须匹配，顺序也要和原始文档相同
db.get_collection("inventory").find({"size": {"h": 14, "w": 21, "uom": "cm"}})
# 数组查询，匹配数组里面的一个元素
db.get_collection("inventory").find({"tags": "red"})
# 数组查询，匹配数组里面的多个元素，顺序可变
db.get_collection("inventory").find({"tags": ["red", "blank"]})
# 数组查询，匹配只包含 red 和 blank 的文档
db.get_collection("inventory").find({"tags": {"$all": ["red", "blank"]}})
# 数组查询，查询 dim_ct 包含大于25的元素的文档
db.get_collection("inventory").find({"dim_cm": {"$gt": 25}})
# 数组查询，查询 dim_cm 包含大于15小于小于20的元素的文档
db.get_collection("inventory").find({"dim_cm": {"$gt": 15, "$lt": 20}})
# 数组查询，匹配 dim_cm 里面，一个元素同时满足两个条件的文档 
db.get_collection("inventory").find({"dim_cm": {"$elemMatch": {"$gt": 22, "$lt": 30}}})
# 数组查询，匹配数组里面第一个元素，满足条件的文档
db.get_collection("inventory").find({"dim_cm.1": {"$gt": 25}})
# 数组查询，匹配数组大小为3的文档
db.get_collection("inventory").find({"tags": {"$size": 3}})
# in查询 
db.get_collection("inventory").find({"status": {"$in": ["A", "D"]}})    
# and和or查询结合
db.get_collection("inventory").find({"status": "A", "$or": [{"qty": {"$lt": 30}}, {"item": {"$regex": "^p"}}]})
# 限制返回字段，只返回 status 和 item 字段
db.get_collection("inventory").find({"status": "D", "qty": 75}, {"item": 1, "status": 1})
# 限制返回字段，返回item、size.h以及数组的前两个元素
db.get_collection("inventory").find({"status": "D"}, {"item": 1, "size.h": 1, "tags": {"$slice": 2}})
# 空查询，返回字段值是空和不包含该字段的文档 
db.get_collection("inventory").find({"item": None})
# 空查询，只返回包含该字段且字段值是空的文档， 10对应的是 Null
db.get_collection("inventory").find({"item": {"$type": 10}})
# 字段存在与否查询，返回不包含item字段的文档 
db.get_collection("inventory").find({"item": {"$exists": False}})

  3. 固定大小集合查询 

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    # 以插入顺序的逆序返回结果
db.cappedCollection.find().sort( { $natural: -1 } )

更新数据#

  1. 更新一条数据。更新第一条匹配的数据，$set 设置新的值，$currentDate 设置为当前日期。
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    db.get_collection("inventory").update_one(
    {"item": "paper"},
    {"$set": {"size.uom": "cm", "status": "P"}, "$currentDate": {"lastModified": True}})

    1. 更新多条数据，更新所有匹配的数据。

      db.get_collection("inventory").update_many( {"item": "paper"}, {"$set": {"size.uom": "cm", "status": "P"}, "$currentDate": {"lastModified": True}}) 3. 替换整个文档。

      db.inventory.replace_one( {"item": "paper"}, {"item": "paper", "instock": [ {"warehouse": "A", "qty": 60}, {"warehouse": "B", "qty": 40} ] } )

注： 指定upsert选项为True，当文档不存在的时候会插入文档。默认为False。

删除数据#

  1. 删除所有(直接删除集合效率更高)。 
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    db.get_collection("inventory").delete_many({})

    1. 根据条件删除。

      db.get_collection("inventory").delete_many({"status": "A"}) 3. 删除第一个匹配的。

      db.get_collection("inventory").delete_one({"status": "D"})

批量操作#

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requests = list();
requests.append(InsertOne({"name": "x", "age": 23}))
requests.append(InsertOne({"name": "x", "age": 23}))
requests.append(InsertOne({"name": "x", "age": 25}))
requests.append(UpdateOne({"name": "x"}, {"$set": {"age": 25}}))
requests.append(UpdateMany({"name": "x"}, {"$set": {"age": 24}}))
requests.append(ReplaceOne({"name": "x"}, {"name": "x", "age": 23, "sex": "man"}))
requests.append(DeleteOne({"name": "x"}))
requests.append(DeleteMany({"name": "x"}))
result = db.get_collection("test").bulk_write(requests)
print("bulk API", result.bulk_api_result, sep=" : ")

文本搜索 (需要创建索引)#

  1. 插入数据
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    db.get_collection("stores").insert_many(
        [
            {"name": "Java Hut", "description": "Coffee and cakes"},
            {"name": "Burger Buns", "description": "Gourmet hamburgers"},
            {"name": "Coffee Shop", "description": "Just coffee"},
            {"name": "Clothes Clothes Clothes", "description": "Discount clothing"},
            {"name": "Java Shopping", "description": "Indonesian goods"}
        ]
    )

    1. 创建索引，一个集合只允许创建一个文本索引，可以是多个字段。

      index = IndexModel([("name", pymongo.TEXT), ("description", pymongo.TEXT)], name="name_description") db.get_collection("stores").create_indexes([index]) 3. 文本搜索

      搜索 name 和 description 包含 java coffee shop三个单词的文本#

      db.get_collection("stores").find({"$text": {"$search": "java coffee shop"}})

      搜索 name 和 description 包含 java 以及 "coffee shop" 短语的文本#

      db.get_collection("stores").find({"$text": {"$search": "java coffee shop"}})

      排除单词#

      result = db.get_collection("stores").find({"$text": {"$search": "java -coffee shop"}})

      相关性评分#

      result = db.get_collection("stores").find( {"$text": {"$search": "java coffee shop"}}, {"score": {"$meta": "textScore"}} ).sort([("score", {"$meta": "textScore"})])

地理位置搜索#

  1. 初始化数据。 

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    db.get_collection("places").insert({
        "name": "Central Park",
        "location": {
            "type": "Point",
            "coordinates": [-73.97, 40.77]
        },
        "category": "Parks"
    })
    db.get_collection("places").insert({
        "name": "Sara D. Roosevelt Park",
        "location": {
            "type": "Point",
            "coordinates": [-73.9928, 40.7193]
        },
        "category": "Parks"
    })
    db.get_collection("places").insert({
        "name": "Polo Grounds",
        "location": {
            "type": "Point",
            "coordinates": [-73.9375, 40.8303]
        },
        "category": "Stadiums"
    })

  2. 创建 GEOSPHERE 索引

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    index = IndexModel([("location", pymongo.GEOSPHERE)], name="location_2ds")
db.get_collection("places").create_indexes([index])

    1. 查询，距离在指定范围之间。

      result = db.get_collection("places").find({ "location": { "$near": { "$geometry": {"type": "Point", "coordinates": [-73.9667, 40.78]}, "$minDistance": 1000, "$maxDistance": 5000 } } })