Debezium SQL Server Source Connector+Kafka+Spark+MySQL 實時數據處理

寫在前面

前段時間在實時獲取SQLServer數據庫變化時候，整個過程可謂是坎坷。然后就想在這里記錄一下。

本文的技術棧： Debezium SQL Server Source Connector+Kafka+Spark+MySQL

ps:后面應該會將數據放到Kudu上。

然后主要記錄一下，整個組件使用和組件對接過程中一些注意點和坑。

開始吧

在處理實時數據時，需要即時地獲得數據庫表中數據的變化，然后將數據變化發送到Kafka中。不同的數據庫有不同的組件進行處理。

常見的MySQL數據庫，就有比較多的支持 canal ，maxwell等，他們都是類似 MySQL binlog 增量訂閱&消費組件這種模式 。那么關於微軟的SQLServer數據庫，好像整個開源社區 支持就沒有那么好了。

1.選擇Connector

Debezium的SQL Server連接器是一種源連接器，可以獲取SQL Server數據庫中現有數據的快照，然后監視和記錄對該數據的所有后續行級更改。每個表的所有事件都記錄在單獨的Kafka Topic中，應用程序和服務可以輕松使用它們。然后本連接器也是基於MSSQL的change data capture實現。

2.安裝Connector

我參照官方文檔安裝是沒有問題的。

2.1 Installing Confluent Hub Client

Confluent Hub客戶端本地安裝為Confluent Platform的一部分，位於/ bin目錄中。
Linux
Download and unzip the Confluent Hub tarball.

[root@hadoop001 softs]# ll confluent-hub-client-latest.tar 
-rw-r--r--. 1 root root 6909785 9月  24 10:02 confluent-hub-client-latest.tar
[root@hadoop001 softs]# tar confluent-hub-client-latest.tar -C ../app/conn/
[root@hadoop001 softs]# ll ../app/conn/
總用量 6748
drwxr-xr-x. 2 root root      27 9月  24 10:43 bin
-rw-r--r--. 1 root root 6909785 9月  24 10:02 confluent-hub-client-latest.tar
drwxr-xr-x. 3 root root      34 9月  24 10:05 etc
drwxr-xr-x. 2 root root       6 9月  24 10:08 kafka-mssql
drwxr-xr-x. 4 root root      29 9月  24 10:05 share
[root@hadoop001 softs]#

配置bin目錄到系統環境變量中

export CONN_HOME=/root/app/conn
export PATH=$CONN_HOME/bin:$PATH

確認是否安裝成功

[root@hadoop001 ~]# source /etc/profile
[root@hadoop001 ~]# confluent-hub
usage: confluent-hub <command> [ <args> ]

Commands are:
    help      Display help information
    install   install a component from either Confluent Hub or from a local file

See 'confluent-hub help <command>' for more information on a specific command.
[root@hadoop001 ~]# 

2.2 Install the SQL Server Connector
使用命令confluent-hub

[root@hadoop001 ~]# confluent-hub install debezium/debezium-connector-sqlserver:0.9.4
The component can be installed in any of the following Confluent Platform installations: 
  1. / (installed rpm/deb package) 
  2. /root/app/conn (where this tool is installed) 
Choose one of these to continue the installation (1-2): 2
Do you want to install this into /root/app/conn/share/confluent-hub-components? (yN) n

Specify installation directory: /root/app/conn/share/java/confluent-hub-client
 
Component's license: 
Apache 2.0 
https://github.com/debezium/debezium/blob/master/LICENSE.txt 
I agree to the software license agreement (yN) y

You are about to install 'debezium-connector-sqlserver' from Debezium Community, as published on Confluent Hub. 
Do you want to continue? (yN) y

注意：Specify installation directory：這個安裝目錄最好是你剛才的confluent-hub 目錄下的 /share/java/confluent-hub-client 這個目錄下。其余的基本操作就好。

3.配置Connector

首先需要對Connector進行配置，配置文件位於 $KAFKA_HOME/config/connect-distributed.properties：

# These are defaults. This file just demonstrates how to override some settings.
# kafka集群地址，我這里是單節點多Broker模式
bootstrap.servers=haoop001:9093,hadoop001:9094,hadoop001:9095

# Connector集群的名稱，同一集群內的Connector需要保持此group.id一致
group.id=connect-cluster

# The converters specify the format of data in Kafka and how to translate it into Connect data. Every Connect user will
# need to configure these based on the format they want their data in when loaded from or stored into Kafka
# 存儲到kafka的數據格式
key.converter=org.apache.kafka.connect.json.JsonConverter
value.converter.schemas.enable=false

# The internal converter used for offsets and config data is configurable and must be specified, but most users will
# 內部轉換器的格式，針對offsets、config和status，一般不需要修改
internal.key.converter=org.apache.kafka.connect.json.JsonConverter
internal.value.converter=org.apache.kafka.connect.json.JsonConverter
internal.key.converter.schemas.enable=false
internal.value.converter.schemas.enable=false

# Topic to use for storing offsets. This topic should have many partitions and be replicated.
# 用於保存offsets的topic，應該有多個partitions，並且擁有副本(replication)，主要根據你的集群實際情況來
# Kafka Connect會自動創建這個topic，但是你可以根據需要自行創建
offset.storage.topic=connect-offsets-2
offset.storage.replication.factor=3
offset.storage.partitions=1

# 保存connector和task的配置，應該只有1個partition，並且有3個副本
config.storage.topic=connect-configs-2
config.storage.replication.factor=3


# 用於保存狀態，可以擁有多個partition和replication
# Topic to use for storing statuses. This topic can have multiple partitions and should be replicated.
status.storage.topic=connect-status-2
status.storage.replication.factor=3
status.storage.partitions=1


offset.storage.file.filename=/root/data/kafka-logs/offset-storage-file

# Flush much faster than normal, which is useful for testing/debugging
offset.flush.interval.ms=10000


# REST端口號
rest.port=18083

# 保存connectors的路徑
#plugin.path=/root/app/kafka_2.11-0.10.1.1/connectors
plugin.path=/root/app/conn/share/java/confluent-hub-client

4.創建kafka Topic

我這里是單節點多Broker模式的Kafka,那么創建Topic可以如下：

kafka-topics.sh --zookeeper hadoop001:2181 --create --topic connect-offsets-2 --replication-factor 3 --partitions 1

kafka-topics.sh --zookeeper hadoop001:2181 --create --topic connect-configs-2 --replication-factor 3 --partitions 1

kafka-topics.sh --zookeeper hadoop001:2181 --create --topic connect-status-2 --replication-factor 3 --partitions 1

查看狀態 <很重要>

[root@hadoop001 ~]# kafka-topics.sh --describe --zookeeper hadoop001:2181 --topic connect-offsets-2
Topic:connect-offsets-2	PartitionCount:1	ReplicationFactor:3	Configs:
	Topic: connect-offsets-2	Partition: 0	Leader: 3	Replicas: 3,1,2	Isr: 3,1,2

[root@hadoop001 ~]# kafka-topics.sh --describe --zookeeper hadoop001:2181 --topic connect-configs-2
Topic:connect-configs-2	PartitionCount:1	ReplicationFactor:3	Configs:
	Topic: connect-configs-2	Partition: 0	Leader: 1	Replicas: 1,2,3	Isr: 1,2,3

[root@hadoop001 ~]# kafka-topics.sh --describe --zookeeper hadoop001:2181 --topic connect-status-2
Topic:connect-status-2	PartitionCount:1	ReplicationFactor:3	Configs:
	Topic: connect-status-2	Partition: 0	Leader: 3	Replicas: 3,1,2	Isr: 3,1,2
[root@hadoop001 ~]#

5.開啟SqlServer Change Data Capture（CDC）更改數據捕獲

變更數據捕獲用於捕獲應用到 SQL Server 表中的插入、更新和刪除活動，並以易於使用的關系格式提供這些變更的詳細信息。變更數據捕獲所使用的更改表中包含鏡像所跟蹤源表列結構的列，同時還包含了解所發生的變更所需的元數據。變更數據捕獲提供有關對表和數據庫所做的 DML 更改的信息。通過使用變更數據捕獲，您無需使用費用高昂的方法，如用戶觸發器、時間戳列和聯接查詢等。

數據變更歷史表會隨着業務的持續，變得很大，所以默認情況下，變更數據歷史會在本地數據庫保留3天（可以通過視圖msdb.dbo.cdc_jobs的字段retention來查詢，當然也可以更改對應的表來修改保留時間），每天會通過SqlServer后台代理任務，每天晚上2點定時刪除。所以推薦定期的將變更數據轉移到數據倉庫中。

以下命令基本就夠用了

--查看數據庫是否起用CDC
  GO
  SELECT [name], database_id, is_cdc_enabled
  FROM sys.databases      
  GO
	
--數據庫起用CDC
 USE test1
 GO
 EXEC sys.sp_cdc_enable_db
 GO
 
--關閉數據庫CDC
 USE test1
 go
 exec sys.sp_cdc_disable_db
 go
 
--查看表是否啟用CDC
USE test1
GO
SELECT [name], is_tracked_by_cdc 
FROM sys.tables
GO
--啟用表的CDC,前提是數據庫啟用 
USE Demo01
GO
EXEC sys.sp_cdc_enable_table
@source_schema = 'dbo',
@source_name   = 'user',
@capture_instance='user',
@role_name     = NULL
GO

--關閉表上的CDC功能
USE test1
GO
EXEC sys.sp_cdc_disable_table
@source_schema = 'dbo',
@source_name   = 'user',
@capture_instance='user'
GO
 
--可能不記得或者不知道開啟了什么表的捕獲,返回所有表的變更捕獲配置信息

EXECUTE sys.sp_cdc_help_change_data_capture;
GO

--查看對某個實例(即表)的哪些列做了捕獲監控:

EXEC sys.sp_cdc_get_captured_columns
@capture_instance = 'user'
 
 
 --查找配置信息 -retention 變更數據保留的分鍾數
SELECT * FROM test1.dbo.cdc_jobs

--更改數據保留時間為分鍾
EXECUTE sys.sp_cdc_change_job
@job_type = N'cleanup',
@retention=1440
GO

--停止捕獲作業
exec sys.sp_cdc_stop_job N'capture'
go
--啟動捕獲作業
exec sys.sp_cdc_start_job N'capture'
go
 

6.運行Connector

怎么運行呢？參照

[root@hadoop001 bin]# pwd
/root/app/kafka_2.11-1.1.1/bin
[root@hadoop001 bin]# ./connect-distributed.sh 
USAGE: ./connect-distributed.sh [-daemon] connect-distributed.properties
[root@hadoop001 bin]#
[root@hadoop001 bin]# ./connect-distributed.sh ../config/connect-distributed.properties

... 這里就會有大量日志輸出

驗證：

[root@hadoop001 ~]# netstat -tanp |grep 18083
tcp6       0      0 :::18083                :::*                    LISTEN      29436/java          
[root@hadoop001 ~]#

6.1 獲取Worker的信息

ps:可能你需要安裝jq這個軟件: yum -y install jq ,當然可以在瀏覽器上打開

[root@hadoop001 ~]# curl -s hadoop001:18083 | jq
{
  "version": "1.1.1",
  "commit": "8e07427ffb493498",
  "kafka_cluster_id": "dmUSlNNLQ9OyJiK-bUc6Tw"
}
[root@hadoop001 ~]#

6.2 獲取Worker上已經安裝的Connector

[root@hadoop001 ~]# curl -s hadoop001:18083/connector-plugins | jq
[
  {
    "class": "io.debezium.connector.sqlserver.SqlServerConnector",
    "type": "source",
    "version": "0.9.5.Final"
  },
  {
    "class": "org.apache.kafka.connect.file.FileStreamSinkConnector",
    "type": "sink",
    "version": "1.1.1"
  },
  {
    "class": "org.apache.kafka.connect.file.FileStreamSourceConnector",
    "type": "source",
    "version": "1.1.1"
  }
]
[root@hadoop001 ~]# 

可以看見io.debezium.connector.sqlserver.SqlServerConnector 這個是我們自己剛才安裝的連接器

6.3 列出當前運行的connector(task)

[root@hadoop001 ~]#  curl -s hadoop001:18083/connectors | jq
[]
[root@hadoop001 ~]# 

6.4 提交Connector用戶配置 《重點》

當提交用戶配置時，就會啟動一個Connector Task，
Connector Task執行實際的作業。
用戶配置是一個Json文件，同樣通過REST API提交：

curl -s -X POST -H "Content-Type: application/json" --data '{
 "name": "connector-mssql-online-1",
 "config": {
     "connector.class" : "io.debezium.connector.sqlserver.SqlServerConnector",
     "tasks.max" : "1",
     "database.server.name" : "test1",
     "database.hostname" : "hadoop001",
     "database.port" : "1433",
     "database.user" : "sa",
     "database.password" : "xxx",
     "database.dbname" : "test1",
     "database.history.kafka.bootstrap.servers" : "hadoop001:9093",
     "database.history.kafka.topic": "test1.t201909262.bak"
     }
}' http://hadoop001:18083/connectors

馬上查看connector當前狀態,確保狀態是RUNNING

[root@hadoop001 ~]# curl -s hadoop001:18083/connectors/connector-mssql-online-1/status | jq
{
  "name": "connector-mssql-online-1",
  "connector": {
    "state": "RUNNING",
    "worker_id": "xxx:18083"
  },
  "tasks": [
    {
      "state": "RUNNING",
      "id": 0,
      "worker_id": "xxx:18083"
    }
  ],
  "type": "source"
}
[root@hadoop001 ~]# 

此時查看Kafka Topic

[root@hadoop001 ~]#  kafka-topics.sh --list --zookeeper hadoop001:2181
__consumer_offsets
connect-configs-2
connect-offsets-2
connect-status-2
#自動生成的Topic， 記錄表結構的變化，生成規則：你的connect中自定義的
test1.t201909262.bak

[root@hadoop001 ~]# 

再次列出運行的connector(task)

[root@hadoop001 ~]#  curl -s hadoop001:18083/connectors | jq
[
  "connector-mssql-online-1"
]
[root@hadoop001 ~]# 

6.5 查看connector的信息

[root@hadoop001 ~]# curl -s hadoop001:18083/connectors/connector-mssql-online-1 | jq
{
  "name": "connector-mssql-online-1",
  "config": {
    "connector.class": "io.debezium.connector.sqlserver.SqlServerConnector",
    "database.user": "sa",
    "database.dbname": "test1",
    "tasks.max": "1",
    "database.hostname": "hadoop001",
    "database.password": "xxx",
    "database.history.kafka.bootstrap.servers": "hadoop001:9093",
    "database.history.kafka.topic": "test1.t201909262.bak",
    "name": "connector-mssql-online-1",
    "database.server.name": "test1",
    "database.port": "1433"
  },
  "tasks": [
    {
      "connector": "connector-mssql-online-1",
      "task": 0
    }
  ],
  "type": "source"
}
[root@hadoop001 ~]#

6.6 查看connector下運行的task信息

[root@hadoop001 ~]# curl -s hadoop001:18083/connectors/connector-mssql-online-1/tasks | jq
[
  {
    "id": {
      "connector": "connector-mssql-online-1",
      "task": 0
    },
    "config": {
      "connector.class": "io.debezium.connector.sqlserver.SqlServerConnector",
      "database.user": "sa",
      "database.dbname": "test1",
      "task.class": "io.debezium.connector.sqlserver.SqlServerConnectorTask",
      "tasks.max": "1",
      "database.hostname": "hadoop001",
      "database.password": "xxx",
      "database.history.kafka.bootstrap.servers": "hadoop001:9093",
      "database.history.kafka.topic": "test1.t201909262.bak",
      "name": "connector-mssql-online-1",
      "database.server.name": "test1",
      "database.port": "1433"
    }
  }
]
[root@hadoop001 ~]#

task的配置信息繼承自connector的配置

6.7 暫停/重啟/刪除 Connector

# curl -s -X PUT hadoop001:18083/connectors/connector-mssql-online-1/pause
# curl -s -X PUT hadoop001:18083/connectors/connector-mssql-online-1/resume
# curl -s -X DELETE hadoop001:18083/connectors/connector-mssql-online-1

7.從Kafka中讀取變動數據

# 記錄表結構的變化，生成規則：你的connect中自定義的
kafka-console-consumer.sh --bootstrap-server hadoop001:9093 --topic test1.t201909262.bak --from-beginning


# 記錄數據的變化，生成規則：test1.dbo.t201909262

kafka-console-consumer.sh --bootstrap-server hadoop001:9093 --topic test1.dbo.t201909262 --from-beginning

這里就是：

kafka-console-consumer.sh --bootstrap-server hadoop001:9093 --topic test1.dbo.t201909262 --from-beginning

kafka-console-consumer.sh --bootstrap-server hadoop001:9093 --topic test1.dbo.t201909262

8. 對表進行 DML語句 操作

新增數據：
然后kafka控制台也就會馬上打出日志

spark 對接kafka 10s一個批次

然后就會將這個新增的數據插入到MySQL中去
具體的處理邏輯后面再花時間來記錄一下

修改和刪除也是OK的，就不演示了

有任何問題，歡迎留言一起交流~~

更多好文：https://blog.csdn.net/liuge36

參考文章：
https://docs.confluent.io/current/connect/debezium-connect-sqlserver/index.html#sqlserver-source-connector
https://docs.microsoft.com/en-us/sql/relational-databases/track-changes/track-data-changes-sql-server?view=sql-server-2017
https://blog.csdn.net/qq_19518987/article/details/89329464
http://www.tracefact.net/tech/087.html