Debezium SQL Server Source Connector+Kafka+Spark+MySQL 实时数据处理

写在前面

前段时间在实时获取SQLServer数据库变化时候，整个过程可谓是坎坷。然后就想在这里记录一下。

本文的技术栈： Debezium SQL Server Source Connector+Kafka+Spark+MySQL

ps:后面应该会将数据放到Kudu上。

然后主要记录一下，整个组件使用和组件对接过程中一些注意点和坑。

开始吧

在处理实时数据时，需要即时地获得数据库表中数据的变化，然后将数据变化发送到Kafka中。不同的数据库有不同的组件进行处理。

常见的MySQL数据库，就有比较多的支持 canal ，maxwell等，他们都是类似 MySQL binlog 增量订阅&消费组件这种模式。那么关于微软的SQLServer数据库，好像整个开源社区支持就没有那么好了。

1.选择Connector

Debezium的SQL Server连接器是一种源连接器，可以获取SQL Server数据库中现有数据的快照，然后监视和记录对该数据的所有后续行级更改。每个表的所有事件都记录在单独的Kafka Topic中，应用程序和服务可以轻松使用它们。然后本连接器也是基于MSSQL的change data capture实现。

2.安装Connector

我参照官方文档安装是没有问题的。

2.1 Installing Confluent Hub Client

Confluent Hub客户端本地安装为Confluent Platform的一部分，位于/ bin目录中。

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/

总用量 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目录到系统环境变量中


export CONN_HOME=/root/app/conn

export PATH=$CONN_HOME/bin:$PATH

确认是否安装成功


[root@hadoop001 ~]# source /etc/profile

[root@hadoop001 ~]# confluent-hub

usage: confluent-hub  [  ]

Commands are:

    help      Display help information

    install  install a component from either Confluent Hub or from a local file

See 'confluent-hub help ' 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：这个安装目录最好是你刚才的confluent-hub 目录下的 /share/java/confluent-hub-client 这个目录下。其余的基本操作就好。

3.配置Connector

首先需要对Connector进行配置，配置文件位于 $KAFKA_HOME/config/connect-distributed.properties：


# These are defaults. This file just demonstrates how to override some settings.

# kafka集群地址，我这里是单节点多Broker模式

bootstrap.servers=haoop001:9093,hadoop001:9094,hadoop001:9095

# Connector集群的名称，同一集群内的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

# 存储到kafka的数据格式

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

# 内部转换器的格式，针对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.

# 用于保存offsets的topic，应该有多个partitions，并且拥有副本(replication)，主要根据你的集群实际情况来

# Kafka Connect会自动创建这个topic，但是你可以根据需要自行创建

offset.storage.topic=connect-offsets-2

offset.storage.replication.factor=3

offset.storage.partitions=1

# 保存connector和task的配置，应该只有1个partition，并且有3个副本

config.storage.topic=connect-configs-2

config.storage.replication.factor=3

# 用于保存状态，可以拥有多个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端口号

rest.port=18083

# 保存connectors的路径

#plugin.path=/root/app/kafka_2.11-0.10.1.1/connectors

plugin.path=/root/app/conn/share/java/confluent-hub-client

4.创建kafka Topic

我这里是单节点多Broker模式的Kafka,那么创建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

查看状态 <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.开启SqlServer Change Data Capture（CDC）更改数据捕获

变更数据捕获用于捕获应用到 SQL Server 表中的插入、更新和删除活动，并以易于使用的关系格式提供这些变更的详细信息。变更数据捕获所使用的更改表中包含镜像所跟踪源表列结构的列，同时还包含了解所发生的变更所需的元数据。变更数据捕获提供有关对表和数据库所做的 DML 更改的信息。通过使用变更数据捕获，您无需使用费用高昂的方法，如用户触发器、时间戳列和联接查询等。

数据变更历史表会随着业务的持续，变得很大，所以默认情况下，变更数据历史会在本地数据库保留3天（可以通过视图msdb.dbo.cdc_jobs的字段retention来查询，当然也可以更改对应的表来修改保留时间），每天会通过SqlServer后台代理任务，每天晚上2点定时删除。所以推荐定期的将变更数据转移到数据仓库中。

以下命令基本就够用了


--查看数据库是否起用CDC

  GO

  SELECT [name], database_id, is_cdc_enabled

  FROM sys.databases     

  GO

--数据库起用CDC

USE test1

GO

EXEC sys.sp_cdc_enable_db

GO

--关闭数据库CDC

USE test1

go

exec sys.sp_cdc_disable_db

go

--查看表是否启用CDC

USE test1

GO

SELECT [name], is_tracked_by_cdc

FROM sys.tables

GO

--启用表的CDC,前提是数据库启用

USE Demo01

GO

EXEC sys.sp_cdc_enable_table

@source_schema = 'dbo',

@source_name  = 'user',

@capture_instance='user',

@role_name    = NULL

GO

--关闭表上的CDC功能

USE test1

GO

EXEC sys.sp_cdc_disable_table

@source_schema = 'dbo',

@source_name  = 'user',

@capture_instance='user'

GO

--可能不记得或者不知道开启了什么表的捕获,返回所有表的变更捕获配置信息

EXECUTE sys.sp_cdc_help_change_data_capture;

GO

--查看对某个实例(即表)的哪些列做了捕获监控:

EXEC sys.sp_cdc_get_captured_columns

@capture_instance = 'user'

--查找配置信息 -retention 变更数据保留的分钟数

SELECT * FROM test1.dbo.cdc_jobs

--更改数据保留时间为分钟

EXECUTE sys.sp_cdc_change_job

@job_type = N'cleanup',

@retention=1440

GO

--停止捕获作业

exec sys.sp_cdc_stop_job N'capture'

go

--启动捕获作业

exec sys.sp_cdc_start_job N'capture'

go

6.运行Connector

怎么运行呢？参照


[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

... 这里就会有大量日志输出

验证：


[root@hadoop001 ~]# netstat -tanp |grep 18083

tcp6      0      0 :::18083                :::*                    LISTEN      29436/java         

[root@hadoop001 ~]#

6.1 获取Worker的信息

ps:可能你需要安装jq这个软件: yum -y install jq ,当然可以在浏览器上打开


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

{

  "version": "1.1.1",

  "commit": "8e07427ffb493498",

  "kafka_cluster_id": "dmUSlNNLQ9OyJiK-bUc6Tw"

}

[root@hadoop001 ~]#

6.2 获取Worker上已经安装的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 ~]#

可以看见io.debezium.connector.sqlserver.SqlServerConnector 这个是我们自己刚才安装的连接器

6.3 列出当前运行的connector(task)


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

[]

[root@hadoop001 ~]#

6.4 提交Connector用户配置《重点》

当提交用户配置时，就会启动一个Connector Task，

Connector Task执行实际的作业。

用户配置是一个Json文件，同样通过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

马上查看connector当前状态,确保状态是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 ~]#

此时查看Kafka Topic


[root@hadoop001 ~]#  kafka-topics.sh --list --zookeeper hadoop001:2181

__consumer_offsets

connect-configs-2

connect-offsets-2

connect-status-2

#自动生成的Topic， 记录表结构的变化，生成规则：你的connect中自定义的

test1.t201909262.bak

[root@hadoop001 ~]#

再次列出运行的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下运行的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的配置信息继承自connector的配置

6.7 暂停/重启/删除 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.从Kafka中读取变动数据


# 记录表结构的变化，生成规则：你的connect中自定义的

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

# 记录数据的变化，生成规则：test1.dbo.t201909262

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

这里就是：


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. 对表进行 DML语句操作

新增数据：

然后kafka控制台也就会马上打出日志

Debezium SQL Server Source Connector+Kafka+Spark+MySQL 实时数据处理_第1张图片

在这里插入图片描述

spark 对接kafka 10s一个批次

Debezium SQL Server Source Connector+Kafka+Spark+MySQL 实时数据处理_第2张图片

在这里插入图片描述

然后就会将这个新增的数据插入到MySQL中去

具体的处理逻辑后面再花时间来记录一下

修改和删除也是OK的，就不演示了

有任何问题，欢迎留言一起交流~~

更多好文：https://blog.csdn.net/liuge36

*参考文章：

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*