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

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。

注意如果使用confluent端口9092,8083

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 <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：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 - 图1
spark 对接kafka 10s一个批次
Debezium SQL Server - 图2
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
分类: 大数据学习

Debezium SQL Server