用logstash 作数据的聚合统计
以spark-streaming 处理消费数据,统计日志经spark sql存储在mysql中
日志写入方式为append
val wordsDataFrame = rdd.toDF("supplier", "type", "domain", "pdate", "count", "idate")
wordsDataFrame
.write
.format("jdbc")
.mode("append").options(mysql_conf)
.save()
因为spark-streaming是批处理服务，这些日志只是中间结果，会有大量小批次的统计信息
表名statistics_temp
id(自增id) domain idate count
1 www.baidu.com 2018-06-13 1
2 www.baidu.com 2018-06-13 2
3 www.taobao.com 2018-06-13 3
求每日每domain的数据还需再一次作聚合
原本的构想是通过logstash将日志导入es，elk方案，kibana直接划分时间间隔聚合出结果展示
现在有个需求是要存入每日的量级入一张表，直接在mysql展示
不考虑从kibana聚合再写入es,statistics_temp数据量较大，每次查询都从statistics_temp用聚合显然不可行
因此需要由这张临时表，导出完真正的有效表statistics，这种需求很常见，通常是记录时间点，定时执行，读取时间点后的数据，统计再upsert
导出表名statistics（domain+idate 配置联合唯一索引，DUPLICATE才生效）
id(自增id) domain idate count
1 www.baidu.com 2018-06-13 3
3 www.taobao.com 2018-06-13 3
最近作数据流程设计搭建，不想写这种代码，因为对logstash比较熟悉，因此便想试试logstash的方案，最终实验可行
环境
logstash 5.5
mysql 5.7
聚合 需要官方插件
https://www.elastic.co/guide/en/logstash/current/plugins-filters-aggregate.html#plugins-filters-aggregate-example2
写入 mysql需要第三方插件
https://github.com/theangryangel/logstash-output-jdbc
1安装插件
logstash5.5 默认不含logstash-filters-aggregate 需单独安装，其他版本未知
logstash-plugins install logstash-filters-aggregate
第三方插件，默认不含，需单独安装
logstash-plugins install logstash-output-jdbc
2执行
由于jdbc_fetch_size参数不生效，因此会拿出大量数据（mysql-connector-java-5.1.44-bin.jar，默认的数据量）需要调整堆大小，避免oom
export LS_JAVA_OPTS="-Xms4g -Xmx8g"
logstash -f /logstash/statistic.conf
重点是/logstash/statistic.conf的内容
input {
jdbc {
jdbc_driver_library => "/logstash/mysql-connector-java-5.1.44-bin.jar"
jdbc_driver_class => "com.mysql.jdbc.Driver"
jdbc_connection_string => "jdbc:mysql://host:3306/db1"
jdbc_user => "root"
jdbc_password => "root"
schedule => "* * * * *"
use_column_value => true
tracking_column => "id"
jdbc_fetch_size => 2000
tracking_column_type => "numeric"
statement => "SELECT * from statistics WHERE id > :sql_last_value"
clean_run => false
record_last_run => true
last_run_metadata_path => "/home/logstash/.statistic"
}
}
filter {
aggregate {
task_id => "%{domain}_%{idate}"
code => "
map['domain'] = event.get('domain')
map['idate'] = event.get('idate')
map['count'] ||= 0
map['count'] += event.get('count')
event.cancel()
"
push_previous_map_as_event => true
}
}
output {
stdout{
codec=>rubydebug{}
}
jdbc {
driver_jar_path => "/logstash/mysql-connector-java-5.1.44-bin.jar"
driver_class => "com.mysql.jdbc.Driver"
connection_string => "jdbc:mysql://host:3306/db1?user=root&password=root"
statement => [ "INSERT INTO `bbs`.`statistics2` (`domain`,`idate`,`count`) VALUES(?,?,?) ON DUPLICATE KEY UPDATE `count` = `count` + ?;", "domain","idate","count","count" ]
}
}
tracking_column => "id"
tracking_column_type => "numeric"
statement => "SELECT * from statistics WHERE id > :sql_last_value"
last_run_metadata_path => "/home/logstash/.statistic"
以上四个参数，配置记录点的信息
该例子为自增id numeric类型
记录文件示例
bash-4.3# cat /home/logstash/.statistic
--- 127986843
若是时间类型
tracking_column => "updated_on"
tracking_column_type => "timestamp"
statement => "SELECT * from user WHERE updated_on > :sql_last_value"
last_run_metadata_path => "/home/logstash/.statistic"
记录文件示例
bash-4.3# cat /home/logstash/.statistic
--- 2018-06-13 10:57:59.000000000 +00:00
可以通过，停logstash服务，改记录文件，再启动logstash服务，来重新聚合统计某记录点后的数据。
示例
count 对比
statistics_temp:
mysql> select count(count),idate from statistics_temp where domain='www.abc.com' and idate>'2018-06-15' group by idate;
+--------------+------------+
| count(count) | idate |
+--------------+------------+
| 15290 | 2018-06-16 |
| 27176 | 2018-06-17 |
| 18997 | 2018-06-18 |
| 21785 | 2018-06-19 |
+--------------+------------+
statistics:
mysql> select count(count),idate from statistics where domain='www.abc.com' and idate>'2018-06-15' group by idate;
+--------------+------------+
| count(count) | idate |
+--------------+------------+
| 532 | 2018-06-16 |
| 533 | 2018-06-17 |
| 534 | 2018-06-18 |
| 535 | 2018-06-19 |
+--------------+------------+
sum 对比
statistics_temp
mysql> select sum(count),idate from statistics_temp where domain='www.abc.com' and idate>'2018-06-15' group by idate;
+------------+------------+
| sum(count) | idate |
+------------+------------+
| 390499 | 2018-06-16 |
| 705807 | 2018-06-17 |
| 462147 | 2018-06-18 |
| 600657 | 2018-06-19 |
+------------+------------+
statistics
mysql> select sum(count),idate from statistics where domain='www.abc.com' and idate>'2018-06-15' group by idate;
+------------+------------+
| sum(count) | idate |
+------------+------------+
| 390499 | 2018-06-16 |
| 705807 | 2018-06-17 |
| 462147 | 2018-06-18 |
| 600657 | 2018-06-19 |
+------------+------------+
相比statistics_temp，statistics count减少，sum一致，聚合成功