【原创】大数据基础之Logstash（2）应用之mysql-kafka

应用一：mysql数据增量同步到kafka

1 准备mysql测试表

mysql> create table test_sync(id int not null auto_increment, name varchar(32), description varchar(64), create_time timestamp, update_time timestamp, primary key(id));
Query OK, 0 rows affected (0.04 sec)

mysql> insert into test_sync (name, description, create_time, update_time) values('test1', '', now(), now());
Query OK, 1 row affected (0.02 sec)

mysql> select * from test_sync;
+----+-------+-------------+---------------------+---------------------+
| id | name  | description | create_time         | update_time         |
+----+-------+-------------+---------------------+---------------------+
|  1 | test1 | 1           | 2019-03-13 10:45:49 | 2019-03-13 10:45:49 |
+----+-------+-------------+---------------------+---------------------+
1 row in set (0.00 sec)

2 准备kafka topic

# bin/kafka-topics.sh --zookeeper $zk:2181/kafka -create --topic test_sync --partitions 2 --replication-factor 2
WARNING: Due to limitations in metric names, topics with a period ('.') or underscore ('_') could collide. To avoid issues it is best to use either, but not both.
Created topic "test_sync".
# bin/kafka-topics.sh --zookeeper $zk:2181/kafka -describe --topic test_sync
Topic:test_sync PartitionCount:2        ReplicationFactor:2     Configs:
        Topic: test_sync        Partition: 0    Leader: 112     Replicas: 112,111       Isr: 112,111
        Topic: test_sync        Partition: 1    Leader: 110     Replicas: 110,112       Isr: 110,112

3 准备logstash conf

input {
  jdbc {
  jdbc_driver_library => "/path/to/mysql-connector-java-5.1.36-bin.jar"
  jdbc_driver_class => "com.mysql.jdbc.Driver"
  jdbc_connection_string => "jdbc:mysql://localhost:3306/database"
  jdbc_user => "DBUSERNAME"
  jdbc_password => "DBPASSWORD"
  jdbc_paging_enabled => "true"
  jdbc_page_size => "10000"
  jdbc_default_timezone => "UTC"
  statement => "select * from TABLE where id > :sql_last_value"
  tracking_column => id
  tracking_column_type => "numeric"
  use_column_value => true
  record_last_run => true
  clean_run => false
  #I have scheduled this to run in every second
  schedule => "* * * * * * Asia/Shanghai"
  }
}

output {
  kafka {
    bootstrap_servers => "192.168.0.1:9092,192.168.0.2:9092"
    topic_id => "test_sync"
    codec => json
  }
  stdout {codec => json}
}

注意

1）必须要设置时区，注意解析后的时间是UTC（会比Asia/Shanghai的时间晚8小时），如果希望解析后的时间和mysql中的时间保持一致，需要设置jdbc_default_timezone => "UTC"；

2）最好设置分页；

3）增量有两种方式，

• 一种是根据id，适用于数据创建之后不会修改的情形，对应 tracking_column_type => "numeric"；
• 一种是根据update_time，适用于每次创建或修改之后都会修改update_time的情形，对应 tracking_column_type => "timestamp"；

第二种示例

  statement => "select * from table where update_time > :sql_last_value;"
  tracking_column => "update_time"
  tracking_column_type => "timestamp"

增量进度位于last_run_metadata_path，默认路径$HOME/.logstash_jdbc_last_run，如果一台机器上启动多个logstash需要每个单独配置

通过schedule控制多久执行一次增量同步；

4 启动logstash

$ logstash -f $conf

观察日志是否正常，日志中会显示执行的sql以及发送的消息；

[2019-03-13T22:43:00,312][INFO ][logstash.inputs.jdbc ] (0.000253s) SELECT version()
[2019-03-13T22:43:00,314][INFO ][logstash.inputs.jdbc ] (0.000564s) select * from test_sync where update_time > '2019-03-13 14:07:41';
{"update_time":"2019-03-13T06:42:40.000Z","id":1,"create_time":"2019-03-13T06:07:41.000Z","@version":"1","@timestamp":"2019-03-13T14:43:00.507Z","name":"test1","description":"2"}

进一步测试mysql修改和新增的情形，一切正常；

5 kafka确认

# $KAFKA_HOME/bin/kafka-console-consumer.sh --bootstrap-server 192.168.0.1:9092 --topic test_sync --from-beginning

6 可能的问题

报错can't dup Fixnum

[2019-03-13T22:19:46,790][ERROR][logstash.pipeline ] Pipeline aborted due to error {:pipeline_id=>"main", :exception=>#<TypeError: can't dup Fixnum>, :backtrace=>["org/jruby/RubyKernel.java:1882:in `dup'", "uri:classloader:/META-INF/jruby.home/lib/ruby/stdlib/date/format.rb:838:in `_parse'", "uri:classloader:/META-INF/jruby.home/lib/ruby/stdlib/date.rb:1830:in `parse'"...

这有可能是因为tracking_column之前按照numeric运行，后来改成timestamp，检查last_run_metadata_path，默认路径$HOME/.logstash_jdbc_last_run，如果是因为以上原因，删除即可

参考：

https://www.elastic.co/guide/en/logstash/current/plugins-inputs-jdbc.html

https://www.elastic.co/guide/en/logstash/current/plugins-outputs-kafka.html

https://stackoverflow.com/questions/31446405/incremental-mysql-reading-using-logstash