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教程:将变更流与 Amazon DocumentDB 结合使用
Amazon DocumentDB(与 MongoDB 兼容)中的变更流功能提供按时间顺序排列的更改事件,这些事件在您的集群集合内发生。您可以从更改流中读取事件,以实现许多不同的使用案例,包括以下情况:
-
更改通知
-
使用 Amazon OpenSearch Service(OpenSearch 服务)进行全文搜索
-
使用 Amazon Redshift 分析
应用程序可以使用变更流在各个集合中订阅数据变更。变更流事件在集群上发生时按顺序排列,并在记录事件之后存储 3 个小时(默认情况下)。使用 change_stream_log_retention_duration
参数可以将保留期延长至 7 天。要修改更改流保留期,请参阅修改更改流日志保留期限。
支持的操作
Amazon DocumentDB 支持以下更改流操作:
-
MongoDB
db.collection.watch()
、db.watch()
和client.watch()
API 中支持的所有变更事件。 -
查找完整文档以获取更新。
-
聚合阶段:
$match
、$project
、$redact
、$addFields
和$replaceRoot
。 -
从简历令牌恢复更改流
-
使用
startAtOperation
从时间戳恢复更改流(适用于 Amazon DocumentDB v4.0+)
计费
默认情况下,Amazon DocumentDB 变更流功能处于禁用状态,并且在启用该功能之前不会产生任何额外费用。在集群中使用变更流时,会产生额外的读取和写入 IOs 和存储成本。可以使用 modifyChangeStreams
API 为集群启用此功能。有关定价的更多信息,请参阅 Amazon DocumentDB 定价
限制
更改流在 Amazon DocumentDB 中存在以下限制:
-
变更流只能通过与 Amazon DocumentDB 集群主实例的连接打开。当前不支持从副本实例上的变更流中进行读取。在调用
watch()
API 操作时,您必须指定primary
读取首选项,以确保所有读取都定向到主实例(请参阅示例部分)。 -
写入集合的变更流的事件最多可在 7 天(默认为 3 小时)内使用。变更流数据将在日志保留时段过后删除,即使没有发生新更改也是如此。
-
对
updateMany
或deleteMany
之类的集合执行长时间运行的写入操作时,会暂时延迟变更流事件的写入,直至长时间运行的写入操作完成为止。 -
Amazon DocumentDB 不支持 MongoDB 操作日志 (
oplog
)。 -
使用 Amazon DocumentDB,您必须明确在给定集合上启用变更流。
-
如果变更流事件的总大小(包括变更数据,在请求的情况下还包括完整文档)大于
16 MB
,客户端将在变更流上遇到读取失败情况。 -
当使用
db.watch()
和client.watch()
搭配 Amazon DocumentDB v3.6 时,目前不支持 Ruby 驱动程序。
启用变更流
您可以为给定数据库中的所有集合启用 Amazon DocumentDB 变更流,也可以只针对选定集合启用。下面是如何使用 mongo shell 为不同使用案例启用变更流的示例。在指定数据库和集合名称时,将空字符串视为通配符。
//Enable change streams for the collection "foo" in database "bar" db.adminCommand({modifyChangeStreams: 1, database: "bar", collection: "foo", enable: true});
//Disable change streams on collection "foo" in database "bar" db.adminCommand({modifyChangeStreams: 1, database: "bar", collection: "foo", enable: false});
//Enable change streams for all collections in database "bar" db.adminCommand({modifyChangeStreams: 1, database: "bar", collection: "", enable: true});
//Enable change streams for all collections in all databases in a cluster db.adminCommand({modifyChangeStreams: 1, database: "", collection: "", enable: true});
如果满足以下任意条件,则将为集合启用变更流:
-
数据库和集合均已明确启用。
-
包含该集合的数据库已启用。
-
所有数据库均已启用。
如果父数据库也启用了更改流,或者集群中的所有数据库都已启用,则从数据库中删除集合不会禁用该集合的更改流。如果创建了与已删除收藏同名的新收藏集,则将为该集合启用更改流。
您可以使用 $listChangeStreams
聚合管道阶段列出集群所有已启用的更改流。Amazon DocumentDB 支持的所有聚合阶段都可以在管道中用于额外处理。如果以前启用的某个集合被禁用,则该集合将不会显示在 $listChangeStreams
输出中。
//List all databases and collections with change streams enabled cursor = new DBCommandCursor(db, db.runCommand( {aggregate: 1, pipeline: [{$listChangeStreams: 1}], cursor:{}}));
//List of all databases and collections with change streams enabled { "database" : "test", "collection" : "foo" } { "database" : "bar", "collection" : "" } { "database" : "", "collection" : "" }
//Determine if the database “bar” or collection “bar.foo” have change streams enabled cursor = new DBCommandCursor(db, db.runCommand( {aggregate: 1, pipeline: [{$listChangeStreams: 1}, {$match: {$or: [{database: "bar", collection: "foo"}, {database: "bar", collection: ""}, {database: "", collection: ""}]}} ], cursor:{}}));
示例:在 Python 中使用变更流
以下是在集合级别使用带有 Python 的 Amazon DocumentDB 变更流的示例。
import os import sys from pymongo import MongoClient, ReadPreference username = "DocumentDBusername" password = <Insert your password> clusterendpoint = "DocumentDBClusterEndpoint” client = MongoClient(clusterendpoint, username=username, password=password, tls='true', tlsCAFile='rds-combined-ca-cn-bundle.pem') db = client['bar'] #While ‘Primary’ is the default read preference, here we give an example of #how to specify the required read preference when reading the change streams coll = db.get_collection('foo', read_preference=ReadPreference.PRIMARY) #Create a stream object stream = coll.watch() #Write a new document to the collection to generate a change event coll.insert_one({'x': 1}) #Read the next change event from the stream (if any) print(stream.try_next()) """ Expected Output: {'_id': {'_data': '015daf94f600000002010000000200009025'}, 'clusterTime': Timestamp(1571788022, 2), 'documentKey': {'_id': ObjectId('5daf94f6ea258751778163d6')}, 'fullDocument': {'_id': ObjectId('5daf94f6ea258751778163d6'), 'x': 1}, 'ns': {'coll': 'foo', 'db': 'bar'}, 'operationType': 'insert'} """ #A subsequent attempt to read the next change event returns nothing, as there are no new changes print(stream.try_next()) """ Expected Output: None """ #Generate a new change event by updating a document result = coll.update_one({'x': 1}, {'$set': {'x': 2}}) print(stream.try_next()) """ Expected Output: {'_id': {'_data': '015daf99d400000001010000000100009025'}, 'clusterTime': Timestamp(1571789268, 1), 'documentKey': {'_id': ObjectId('5daf9502ea258751778163d7')}, 'ns': {'coll': 'foo', 'db': 'bar'}, 'operationType': 'update', 'updateDescription': {'removedFields': [], 'updatedFields': {'x': 2}}} """
以下是在数据库级别使用带有 Python 的 Amazon DocumentDB 变更流的示例。
import os import sys from pymongo import MongoClient username = "DocumentDBusername" password = <Insert your password> clusterendpoint = "DocumentDBClusterEndpoint” client = MongoClient(clusterendpoint, username=username, password=password, tls='true', tlsCAFile='rds-combined-ca-cn-bundle.pem') db = client['bar'] #Create a stream object stream = db.watch() coll = db.get_collection('foo') #Write a new document to the collection foo to generate a change event coll.insert_one({'x': 1}) #Read the next change event from the stream (if any) print(stream.try_next()) """ Expected Output: {'_id': {'_data': '015daf94f600000002010000000200009025'}, 'clusterTime': Timestamp(1571788022, 2), 'documentKey': {'_id': ObjectId('5daf94f6ea258751778163d6')}, 'fullDocument': {'_id': ObjectId('5daf94f6ea258751778163d6'), 'x': 1}, 'ns': {'coll': 'foo', 'db': 'bar'}, 'operationType': 'insert'} """ #A subsequent attempt to read the next change event returns nothing, as there are no new changes print(stream.try_next()) """ Expected Output: None """ coll = db.get_collection('foo1') #Write a new document to another collection to generate a change event coll.insert_one({'x': 1}) print(stream.try_next()) """ Expected Output: Since the change stream cursor was the database level you can see change events from different collections in the same database {'_id': {'_data': '015daf94f600000002010000000200009025'}, 'clusterTime': Timestamp(1571788022, 2), 'documentKey': {'_id': ObjectId('5daf94f6ea258751778163d6')}, 'fullDocument': {'_id': ObjectId('5daf94f6ea258751778163d6'), 'x': 1}, 'ns': {'coll': 'foo1', 'db': 'bar'}, 'operationType': 'insert'} """
完整文档查找
更新变更事件不包括完整文档;只包括已执行的变更。如果您的使用案例需要用到受更新影响的完整文档,则可以在打开流时启用完整文档查找。
更新变更流事件的 fullDocument
文档会指明文档查找时已更新文档的最新版本。如果在更新操作与 fullDocument
查找之间发生了变更,则 fullDocument
文档可能无法指明更新时的文档状态。
#Create a stream object with update lookup enabled stream = coll.watch(full_document='updateLookup') #Generate a new change event by updating a document result = coll.update_one({'x': 2}, {'$set': {'x': 3}}) stream.try_next() #Output: {'_id': {'_data': '015daf9b7c00000001010000000100009025'}, 'clusterTime': Timestamp(1571789692, 1), 'documentKey': {'_id': ObjectId('5daf9502ea258751778163d7')}, 'fullDocument': {'_id': ObjectId('5daf9502ea258751778163d7'), 'x': 3}, 'ns': {'coll': 'foo', 'db': 'bar'}, 'operationType': 'update', 'updateDescription': {'removedFields': [], 'updatedFields': {'x': 3}}}
恢复变更流
您可以在以后通过使用恢复令牌来恢复变更流,该令牌相当于上次检索的变更事件文档的 _id
字段。
import os import sys from pymongo import MongoClient username = "DocumentDBusername" password = <Insert your password> clusterendpoint = "DocumentDBClusterEndpoint” client = MongoClient(clusterendpoint, username=username, password=password, tls='true', tlsCAFile='rds-combined-ca-cn-bundle.pem', retryWrites='false') db = client['bar'] coll = db.get_collection('foo') #Create a stream object stream = db.watch() coll.update_one({'x': 1}, {'$set': {'x': 4}}) event = stream.try_next() token = event['_id'] print(token) """ Output: This is the resume token that we will later us to resume the change stream {'_data': '015daf9c5b00000001010000000100009025'} """ #Python provides a nice shortcut for getting a stream’s resume token print(stream.resume_token) """ Output {'_data': '015daf9c5b00000001010000000100009025'} """ #Generate a new change event by updating a document result = coll.update_one({'x': 4}, {'$set': {'x': 5}}) #Generate another change event by inserting a document result = coll.insert_one({'y': 5}) #Open a stream starting after the selected resume token stream = db.watch(full_document='updateLookup', resume_after=token) #Our first change event is the update with the specified _id print(stream.try_next()) """ #Output: Since we are resuming the change stream from the resume token, we will see all events after the first update operation. In our case, the change stream will resume from the update operation {x:5} {'_id': {'_data': '015f7e8f0c000000060100000006000fe038'}, 'operationType': 'update', 'clusterTime': Timestamp(1602129676, 6), 'ns': {'db': 'bar', 'coll': 'foo'}, 'documentKey': {'_id': ObjectId('5f7e8f0ac423bafbfd9adba2')}, 'fullDocument': {'_id': ObjectId('5f7e8f0ac423bafbfd9adba2'), 'x': 5}, 'updateDescription': {'updatedFields': {'x': 5}, 'removedFields': []}} """ #Followed by the insert print(stream.try_next()) """ #Output: {'_id': {'_data': '015f7e8f0c000000070100000007000fe038'}, 'operationType': 'insert', 'clusterTime': Timestamp(1602129676, 7), 'ns': {'db': 'bar', 'coll': 'foo'}, 'documentKey': {'_id': ObjectId('5f7e8f0cbf8c233ed577eb94')}, 'fullDocument': {'_id': ObjectId('5f7e8f0cbf8c233ed577eb94'), 'y': 5}} """
使用 startAtOperationTime
恢复变更流
您可以稍后使用 startAtOperationTime
从特定时间戳恢复更改流。
注意
Amazon DocumentDB 4.0+ 提供了使用 startAtOperationTime
的功能。使用 startAtOperationTime
时,更改流光标将仅返回在指定时间戳或之后发生的更改。startAtOperationTime
和 resumeAfter
命令是互斥的,因此不能一起使用。
import os import sys from pymongo import MongoClient username = "DocumentDBusername" password = <Insert your password> clusterendpoint = "DocumentDBClusterEndpoint” client = MongoClient(clusterendpoint, username=username, password=password, tls='true', tlsCAFile='rds-root-ca-2020.pem',retryWrites='false') db = client['bar'] coll = db.get_collection('foo') #Create a stream object stream = db.watch() coll.update_one({'x': 1}, {'$set': {'x': 4}}) event = stream.try_next() timestamp = event['clusterTime'] print(timestamp) """ Output Timestamp(1602129114, 4) """ #Generate a new change event by updating a document result = coll.update_one({'x': 4}, {'$set': {'x': 5}}) result = coll.insert_one({'y': 5}) #Generate another change event by inserting a document #Open a stream starting after specified time stamp stream = db.watch(start_at_operation_time=timestamp) print(stream.try_next()) """ #Output: Since we are resuming the change stream at the time stamp of our first update operation (x:4), the change stream cursor will point to that event {'_id': {'_data': '015f7e941a000000030100000003000fe038'}, 'operationType': 'update', 'clusterTime': Timestamp(1602130970, 3), 'ns': {'db': 'bar', 'coll': 'foo'}, 'documentKey': {'_id': ObjectId('5f7e9417c423bafbfd9adbb1')}, 'updateDescription': {'updatedFields': {'x': 4}, 'removedFields': []}} """ print(stream.try_next()) """ #Output: The second event will be the subsequent update operation (x:5) {'_id': {'_data': '015f7e9502000000050100000005000fe038'}, 'operationType': 'update', 'clusterTime': Timestamp(1602131202, 5), 'ns': {'db': 'bar', 'coll': 'foo'}, 'documentKey': {'_id': ObjectId('5f7e94ffc423bafbfd9adbb2')}, 'updateDescription': {'updatedFields': {'x': 5}, 'removedFields': []}} """ print(stream.try_next()) """ #Output: And finally the last event will be the insert operation (y:5) {'_id': {'_data': '015f7e9502000000060100000006000fe038'}, 'operationType': 'insert', 'clusterTime': Timestamp(1602131202, 6), 'ns': {'db': 'bar', 'coll': 'foo'}, 'documentKey': {'_id': ObjectId('5f7e95025c4a569e0f6dde92')}, 'fullDocument': {'_id': ObjectId('5f7e95025c4a569e0f6dde92'), 'y': 5}} """
更改流中的事务
更改流事件将不包含来自未提交和/或已中止事务事件。例如,如果您以一个 INSERT
操作和一个 UPDATE
操作启动事务,然后。如果您的 INSERT
操作成功但 UPDATE
操作失败,则事务将回滚。由于此事务已回滚,您的更改流将不包含此事务的任何事件。
修改变更流日志保留期限
可以使用 Amazon Web Services Management Console 或 Amazon CLI 将变更流日志的保留时间修改为 1 小时到 7 天之间的任何时间。
注意
在日志大小大于 (>) 51,200MB 之前,更改流日志保留期不会删除早于配置 change_stream_log_retention_duration
值的日志。