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# 整合官方和常用示例
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# https://api.mongodb.com/python/current/py-modindex.html
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from pymongo import MongoClient
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from pymongo import InsertOne, DeleteMany, ReplaceOne, UpdateOne
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from bson.objectid import ObjectId
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from bson.son import SON
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from bson import json_util, CodecOptions
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import datetime
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from pprint import pprint
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import pymongo
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from bson.code import Code
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import urllib.parse
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import ssl
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from pymongo import errors
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from pymongo import WriteConcern
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import pytz
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import gridfs
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import multiprocessing
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client = MongoClient(host="192.168.2.15", port=27017)
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all_databases = client.list_database_names()
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pprint(all_databases)
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# using dictionary style access
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db = client["AdminConfigDB"]
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all_collections = db.collection_names()
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pprint(all_collections)
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# using nomal style
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collection = db.arc_AdminConf
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# pprint(collection.find_one({}))
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# for collection in collection.find({"flush":False}).sort("productId"):
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# pprint(collection)
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# 以product_id升序创建索引
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# create_index = collection.create_index([('product_id', pymongo.ASCENDING)], unique=True)
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# 打印集合索引信息
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# pprint(sorted(list(collection.index_information())))
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db2 = client.TestData
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collection2 = db2.things
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# result = collection2.insert_many([{"x": 1, "tags": ["dog", "cat"]},
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# {"x": 2, "tags": ["cat"]},
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# {"x": 2, "tags": ["mouse", "cat", "dog"]},
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# {"x": 3, "tags": []}])
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# pprint(result.inserted_ids)
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# Aggregation Framework示例
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# pipeline = [
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# {"$unwind": "$tags"},
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# {"$group": {"_id": "$tags", "count": {"$sum": 1}}},
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# {"$sort": SON([("count", -1), ("_id", -1)])}]
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# pprint(list(collection2.aggregate(pipeline)))
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# Map/Reduce示例
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# mapper = Code(
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# """
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# function () {
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# this.tags.forEach(function(z) {
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# emit(z, 1);
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# });
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# }
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# """
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# )
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# reducer = Code(
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# """
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# function (key, values) {
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# var total = 0;
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# for (var i = 0; i < values.length; i++) {
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# total += values[i];
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# }
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# return total;
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# }
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# """
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# )
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# result = collection2.map_reduce(mapper, reducer, "map_reduce_result")
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# for doc in result.find():
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# pprint(doc)
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# results = collection2.map_reduce(
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# mapper, reducer, "myresults", query={"x": {"$lt": 2}})
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# for doc in results.find():
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# pprint(doc)
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# 认证示例
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# username = urllib.parse.quote_plus('user')
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# password = urllib.parse.quote_plus('pass/word')
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# client = MongoClient('mongodb://%s:%s@127.0.0.1' % (username, password))
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# version3.7支持SCRAM-SHA-256
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# client = MongoClient('example.com',
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# username='user',
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# password='password',
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# authSource='the_database',
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# authMechanism='SCRAM-SHA-256')
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# mongodb uri连接方式
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# uri = "mongodb://user:password@example.com/?authSource=the_database&authMechanism=SCRAM-SHA-256"
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# client = MongoClient(uri)
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# mongodb-x509认证
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# client = MongoClient('example.com',
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# username="<X.509 derived username>",
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# authMechanism="MONGODB-X509",
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# ssl=True,
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# ssl_certfile='/path/to/client.pem',
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# ssl_cert_reqs=ssl.CERT_REQUIRED,
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# ssl_ca_certs='/path/to/ca.pem')
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# 复制一个数据库
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# client.admin.command('copydb', fromdb='src_db_name', todb='dst_db_name', fromhost='src_host_ip')
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# 批量插入
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# _id对于大多数高写入量的应用程序而言，对于插入的文档本身没有_id字段时，在插入时自动创建代价较高。inserted_ids表示按提供_id的顺序插入文档
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# collection2.insert_many([{'x': i} for i in range(10000)]).inserted_ids
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# print(collection2.count_documents({}))
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# 批量删除
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# collection2.delete_many({'x':{"$gte": 3}})
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# bulk write，混合批量写入
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# 添加write_concern 写关注
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# collection2 = db2.get_collection('things', write_concern=WriteConcern(w=2, wtimeout=10))
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# try:
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# result = collection2.bulk_write([
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# DeleteMany({}), # Remove all documents from the previous example.
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# InsertOne({'_id': 1}),
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# InsertOne({'_id': 2}),
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# InsertOne({'_id': 3}),
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# UpdateOne({'_id': 1}, {'$set': {'foo': 'bar'}}),
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# UpdateOne({'_id': 4}, {'$inc': {'j': 1}}, upsert=True),
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# ReplaceOne({'j': 1}, {'j': 2})])
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# except errors.BulkWriteError as bwe:
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# pprint(bwe.details)
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# pprint(result.bulk_api_result)
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# 日期时间和时区(mongodb默认假定时间以UTC)
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# result = db2.objects.insert_one({"last_modified": datetime.datetime.utcnow()})
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# tz_aware选项，该选项启用“感知” datetime.datetime对象.即知道其所在时区的日期时间
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# result = db2.demo.insert_one( {'date': datetime.datetime(2019, 11, 28, 14, 0, 0)})
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# db2.demo.find_one()['date']
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# datetime.datetime(2019, 11, 28, 14, 0)
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# options = CodecOptions(tz_aware=True)
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# db2.get_collection('demo', codec_options=options).find_one()['date']
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# 使用时区保存日期时间
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# 存储datetime.datetime指定时区的对象，即tzinfo属性不是None时，PyMongo会将这些日期时间自动转换为UTC
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# pacific = pytz.timezone('Asia/Shanghai')
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# aware_datetime = pacific.localize( datetime.datetime(2019, 11, 28, 14, 0, 0))
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# result = db2.demo.insert_one({"date_tz": aware_datetime})
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# datetime.datetime(2019, 11, 28, 14, 0)
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# 地理空间索引示例
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# https://api.mongodb.com/python/current/examples/geo.html
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# GridFS示例
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# 每个GridFS实例都是使用特定Database实例创建的，并将在特定实例上运行
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# db = MongoClient().gridfs_example
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# fs = gridfs.GridFS(db)
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# 将数据写入gridfs,put()在GridFS中创建一个新文件，并返回文件文档"_id"密钥的值
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# data = fs.put(b"hello world")
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# get()方法取回文件内容,get()返回类似文件对象，调用read()方法获取文件内容
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# content = fs.get(data).read()
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# 除了将str作为GridFS文件放置外，还可以放置任何类似文件的对象（带有read() 方法的对象）。GridFS将自动处理按块大小的段读取文件。还可以将其他属性作为关键字参数添加到文件中
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# b = fs.put(fs.get(a), filename="foo", bar="baz")
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# out = fs.get(b)
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# out.read()
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# out.filename
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# out.bar
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# out.upload_date
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# 可拖尾游标，客户端用尽游标中所有结果后自动关闭游标，但对于上限集合(copped集合)可以使用可拖尾的游标
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# https://api.mongodb.com/python/current/examples/tailable.html
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# 自定义类型
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"""
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https://api.mongodb.com/python/current/examples/custom_type.html
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为了编码自定义类型，必须首先为该类型定义类型编解码器
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用户在定义类型编解码器时必须从以下基类中进行选择：
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* TypeEncoder：将其子类化以定义将自定义Python类型编码为已知BSON类型的编解码器。用户必须实现 python_type属性/属性和transform_python方法。
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* TypeDecoder：将其子类化以定义将特定BSON类型解码为自定义Python类型的编解码器。用户必须实现bson_type属性/属性和transform_bson方法。
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* TypeCodec：此方法的子类以定义可以对自定义类型进行编码和解码的编解码器。用户必须实现 python_type和bson_type属性/属性以及 transform_python和transform_bson方法。
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自定义类型的类型编解码器仅需要定义如何将 Decimal实例转换为 Decimal128实例，反之亦然
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from bson.decimal128 import Decimal128
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from bson.codec_options import TypeCodec
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class DecimalCodec(TypeCodec):
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python_type = Decimal # the Python type acted upon by this type codec
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bson_type = Decimal128 # the BSON type acted upon by this type codec
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def transform_python(self, value):
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# Function that transforms a custom type value into a type that BSON can encode
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return Decimal128(value)
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def transform_bson(self, value):
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# Function that transforms a vanilla BSON type value into our custom type
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return value.to_decimal()
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decimal_codec = DecimalCodec()
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# 开始对自定义类型对象进行编码和解码之前，我们必须首先将相应的编解码器告知PyMongo。这是通过创建一个TypeRegistry实例来完成
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# 以使用任意数量的类型编解码器实例化类型注册表。一旦实例化，注册表是不可变的，将编解码器添加到注册表的唯一方法是创建一个新的注册表
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from bson.codec_options import TypeRegistry
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type_registry = TypeRegistry([decimal_codec])
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# 使用CodecOptions实例定义一个实例，type_registry并使用它来获取一个Collection理解Decimal数据类型的对象
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未完待续......
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"""
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# mongodb跨数据库查询、跨表(集合)、跨服务器查询都可根据以下方式修改
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# 查询data下product集合以条件gaId为1不重复的paId
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# 使用此paId作为查询pa下pa_info集合以条件pa_id等于paId且v为1的文档
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# data = client.data
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# product = data.product
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# pa = client.pa
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# pa_info = pa.pa_info
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# pipeline = [
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# {"$match": {"gaId": 1}},
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# {"$sort": {"paId": -1}},
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# {"$group": {"_id": "$paId"}},
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# {"$project": {"paId": 1.0}},
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# ]
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# cursor = pa_info.aggregate(pipeline, allowDiskUse=False)
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# try:
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# for doc in cursor:
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# doc_value = doc['_id']
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# pa_result = pa_info.find({"pa_id": doc_value, "v":1})
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# for pa_doc in pa_result:
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# # 查询到的结果写入到其他集合
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# result_insert = collection2.insert_many([pa_doc])
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# # pass
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# finally:
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# client.close()
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# 父进程和每个子进程必须创建自己的MongoClient实例
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# Each process creates its own instance of MongoClient.
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# def func():
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# db = pymongo.MongoClient().mydb
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# # Do something with db.
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# proc = multiprocessing.Process(target=func)
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# proc.start()
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root
5 years ago