【自动化】基于Spark streaming的SQL服务实时自动化运维

设计背景

spark thriftserver目前线上有10个实例，以往通过监控端口存活的方式很不准确，当出故障时进程不退出情况很多，而手动去查看日志再重启处理服务这个过程很低效，故设计利用Spark streaming去实时获取spark thriftserver的log，通过log判断服务是否停止服务，从而进行对应的自动重启处理，该方案能达到秒级 7 * 24h不间断监控及维护服务。

设计架构

• 在需要检测的spark thriftserver服务节点上部署flume agent来监控日志流 （flume使用interceptor给日志加host信息）
• flume收集的日志流打入kafka
• spark streaming接收kafka的日志流，根据自定义关键词检测日志内容，如果命中关键字则认为服务不可用，把该日志对应的host信息打入mysql
• 写一个shell脚本从mysql读取host信息，执行重启服务操作

软件版本及配置

spark 2.0.1， kafka 0.10， flume 1.7

1）flume配置及命令：

修改flume-conf.properties

agent.sources = sparkTS070
agent.channels = c
agent.sinks = kafkaSink
# For each one of the sources, the type is defined
agent.sources.sparkTS070.type = TAILDIR

agent.sources.sparkTS070.interceptors = i1
agent.sources.sparkTS070.interceptors.i1.type = host
agent.sources.sparkTS070.interceptors.i1.useIP = false
agent.sources.sparkTS070.interceptors.i1.hostHeader = agentHost

# The channel can be defined as follows.
agent.sources.sparkTS070.channels = c
agent.sources.sparkTS070.positionFile = /home/hadoop/xu.wenchun/apache-flume-1.7.0-bin/taildir_position.json
agent.sources.sparkTS070.filegroups = f1
agent.sources.sparkTS070.filegroups.f1 = /data1/spark/logs/spark-hadoop-org.apache.spark.sql.hive.thriftserver.HiveThriftServer2-1-hadoop070.dx.com.out

# Each sink‘s type must be defined
agent.sinks.kafkaSink.type = org.apache.flume.sink.kafka.KafkaSink
agent.sinks.kafkaSink.kafka.topic = mytest-topic1
agent.sinks.kafkaSink.kafka.bootstrap.servers = 10.87.202.51:9092
agent.sinks.kafkaSink.useFlumeEventFormat = true

#Specify the channel the sink should use
agent.sinks.kafkaSink.channel = c

# Each channel‘s type is defined.
agent.channels.c.type = memory

运行命令：

nohup bin/flume-ng agent -n agent -c conf -f conf/flume-conf.properties -Dflume.root.logger=INFO,LOGFILE &

2）kafka配置及执行命令：

修改config/server.properties

broker.id=1
listeners=PLAINTEXT://10.87.202.51:9092
log.dirs=/home/hadoop/xu.wenchun/kafka_2.11-0.10.0.1/kafka.log
zookeeper.connect=10.87.202.44:2181,10.87.202.51:2181,10.87.202.52:2181
1
2
3
4

运行命令

nohup bin/kafka-server-start.sh config/server.properties &

spark streaming执行命令 ：

/opt/spark-2.0.1-bin-2.6.0/bin/spark-submit --master yarn-cluster --num-executors 3 --class SparkTSLogMonito

3）shell脚本

写一个shell脚本从mysql读取host信息，执行重启服务操作

spark streaming监控job的核心代码

这类分享spark streaming代码，以下代码经过一些坑摸索出来验证可用。

stream.foreachRDD { rdd =>
        rdd.foreachPartition { rddOfPartition =>
          val conn = ConnectPool.getConnection
          println(" conn:" + conn)
          conn.setAutoCommit(false)  //设为手动提交
          val  stmt = conn.createStatement()
          rddOfPartition.foreach { event =>
            val body = event.value().get()
            val decoder = DecoderFactory.get().binaryDecoder(body, null)
            val result = new SpecificDatumReader[AvroFlumeEvent](classOf[AvroFlumeEvent]).read(null, decoder)
            val hostname = result.getHeaders.get(new Utf8("agentHost"))
            val text = new String(result.getBody.array())

            if (text.contains("Broken pipe") || text.contains("No active SparkContext")) {
              val dateFormat:SimpleDateFormat = new SimpleDateFormat("yyyyMMddhhmmssSSS")
              val id = dateFormat.format(new Date()) + "_" + (new util.Random).nextInt(999)
              stmt.addBatch("insert into monitor(id,hostname) values (‘" + id + "‘,‘" + hostname + "‘)")
              println("insert into monitor(id,hostname) values (‘" + id + "‘,‘" + hostname + "‘)")
            }
          }
          stmt.executeBatch()
          conn.commit()
          conn.close()
        }
      }

以上是一个实时处理的典型入门应用，刚好遇到这类监控运维问题，于是采用该方案进行处理，效果不错。

转自：http://blog.csdn.net/xwc35047/article/details/75309350