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1. 简介
大数据开发 - 表数据稳定、码值散布稳定监控 && 报警，是对于实践和设计局部，初步计算曾经写完，治理平台局部，后续欠缺，本文次要针对模块设计局部，整体模块实现上是离线数据源的异步模块，分为指标跑批模块，监控报警模块，平台治理模块，指标跑批模块和监控报警模块次要是基于离线数据的表的通信，即不是耦合的架构，别离是两个例行的工作, 看上面的图，
2. 阐明
其中表设计了五张表，别离是指标跑批记录表，报警跑批记录表，和 mysql 表监控配置表，报警配置表，散布字段配置表，指标跑批和监控跑批不间接工作依赖，而是通过指标跑批记录表，间接产生报警跑批记录
3. 代码实现
整个我的项目目录构造如下图：
resource: 配置文件
common: 一些公共的模块，Builder 是发送音讯构建器，Father 是 Spark 我的项目的公共代码
rules: 上面有 5 个指标的规定，别离是查看分区是否存储，查看分区数量是否大于某个值，查看分区数量稳定，查看散布，查看散布大向量稳定
utils: 外面放的是一些工具，比方日期解决工具类，表格局解决工具，sql 解决工具等
Monitor: 指标跑批的主类
SunRobot: 报警跑批的主类入库
4.Monitor 类阐明
rule 的一些实现，不细说了，依据源代码很难看懂，而 Monitor 是怎么依据这些规定，生成对应的流水，次要实现代码如下：
package com.hoult
import com.beust.jcommander.JCommander
import com.hoult.common.Father
import com.hoult.rules.{Rule1, Rule2, Rule3, Rule4, Rule5, TableMonitorConf, TableMonitorRecord}
import com.hoult.utils.{DateTool, PropertiesUtils}
import org.apache.spark.sql.Dataset
import scala.collection.mutable.ListBuffer
object Monitor extends Father {val mysqlProps = PropertiesUtils.getMysqlProps()
var broadTableConfs: Dataset[TableMonitorConf] = null
def main(args: Array[String]): Unit = {
val info: ObserverArgs = new ObserverArgs
println("入参::" + args.mkString(","))
JCommander.newBuilder().addObject(info).build().parse(args.toArray: _*)
// 播送配置表
prepare()
// 生成表 * 规定 个 dataframe
import spark.implicits._
val tableConfArray: Array[TableMonitorConf] = spark.sql("select * from table_monitor_conf where db_table_name !='default.default'").as[TableMonitorConf].collect()
val defaultTableConf = spark.sql("select * from table_monitor_conf where db_table_name ='default.default'").as[TableMonitorConf].collect().take(1)(0)
var ll: ListBuffer[Dataset[TableMonitorRecord]] = ListBuffer[Dataset[TableMonitorRecord]]() // 所有规定一起跑
// 默认值填充
val tConfs = tableConfArray.map( conf => {
TableMonitorConf(if(conf.db_table_key == null) defaultTableConf.db_table_key else conf.db_table_key,
conf.db_table_name,
if (conf.table_charge_people == null) defaultTableConf.table_charge_people else conf.table_charge_people,
if (conf.done_path == null) defaultTableConf.done_path else conf.done_path,
if (conf.where_condition == null) defaultTableConf.where_condition else conf.where_condition,
if (conf.if_done == null) defaultTableConf.if_done else conf.if_done,
if (conf.if_check_partition == null) defaultTableConf.if_check_partition else conf.if_check_partition,
if (conf.if_check_partition_count == null) defaultTableConf.if_check_partition_count else conf.if_check_partition_count,
if (conf.if_check_partition_count_fluctuates == null) defaultTableConf.if_check_partition_count_fluctuates else conf.if_check_partition_count_fluctuates,
if (conf.if_check_distribute == null) defaultTableConf.if_check_distribute else conf.if_check_distribute,
if (conf.if_check_distribute_fluctuates == null) defaultTableConf.if_check_distribute_fluctuates else conf.if_check_distribute_fluctuates
)})
// 遍历所有规定
for (elem <- tConfs) {
// 规定 1
if ("1".equals(elem.if_check_partition)) {ll +:= Rule1.runRule(elem.db_table_name, elem.db_table_key, elem.where_condition, info.runDay)
}
// 规定 2
if ("1".equals(elem.if_check_partition_count)) {ll +:= Rule2.runRule(elem.db_table_name, elem.db_table_key, elem.where_condition, info.runDay)
}
// 规定 3
if ("1".equals(elem.if_check_partition_count_fluctuates)) {ll +:= Rule3.runRule(elem.db_table_name, elem.db_table_key, elem.where_condition, info.runDay)
}
// 规定 4
if ("1".equals(elem.if_check_distribute)) {ll +:= Rule4.runRule(elem.db_table_name, elem.db_table_key, elem.where_condition, info.runDay)
}
// 规定 5
if ("1".equals(elem.if_check_distribute_fluctuates)) {ll +:= Rule5.runRule(elem.db_table_name, elem.db_table_key, elem.where_condition, info.runDay)
}
}
if (ll.size == 0)
return
ll.reduce(_.union(_)).select(
"db_table_key",
"db_table_name",
"check_data_time",
"rule_name",
"rule_result",
"rule_error",
"checked_partition"
).createOrReplaceTempView("temp_table_rule_records")
val partition = DateTool.getLatest30minutePatition
spark.sql("set hive.reduce.tasks=1")
spark.sql(s"insert overwrite table table_monitor.table_rule_records partition(dt=${info.runDay},hm=$partition) select * from temp_table_rule_records")
}
def prepare(): Unit = {
import spark.implicits._
//1. 根底配置表缓存到集群 table_monitor_conf
val tableConfs: Dataset[TableMonitorConf] = spark.read.jdbc(mysqlProps.getProperty("url"), "table_monitor_conf", mysqlProps).as[TableMonitorConf].cache()
tableConfs.createOrReplaceTempView("table_monitor_conf")
//2. 配置表缓存到集群 table_monitor_distribute_conf
spark.read.jdbc(mysqlProps.getProperty("url"), "table_monitor_distribute_conf", mysqlProps).cache().createOrReplaceTempView("table_monitor_distribute_conf")
}
}
整顿流程就是读配置表的信息，包含设置默认参数，最初就是调用拿进去的配置表的信息和规定的信息
5.SunRobot 阐明
这个模块就是报警以及生成拦挡 done 文件的模块，次要性能就是依据后面的指标流水以及配置表的配置的表元信息进行比对，看看是否满足 check 要求，如果满足了就生成 done，如果不满足就生成 undone, 如果配置了报警，就会依据报警规定进行报警，暂且只有发送到通信工具一项，次要代码如下：
import com.beust.jcommander.JCommander
import com.hoult.common.{Father, Message, RobotConf}
import com.hoult.rules.TableMonitorConf
import com.hoult.utils.{DgsFileSystem, PropertiesUtils}
import org.apache.hadoop.fs.Path
import org.apache.spark.sql.Dataset
import org.joda.time.DateTime
import scalaj.http.Http
/**
* 依据 rule 跑批后果 和 报警配置进行报警
*/
object SunRobot extends Father {
import spark.implicits._
val mysqlProps = PropertiesUtils.getMysqlProps()
def main(args: Array[String]): Unit = {
val info: ObserverArgs = new ObserverArgs
println("入参::" + args.mkString(","))
JCommander.newBuilder().addObject(info).build().parse(args.toArray: _*)
//1. 根底配置表缓存到集群 table_monitor_conf
val tableConfs: Dataset[TableMonitorConf] = spark.read.jdbc(mysqlProps.getProperty("url"), "table_monitor_conf", mysqlProps).as[TableMonitorConf].cache()
//2. 默认规定提取 -->driver
val defaultConf: TableMonitorConf = tableConfs.where("db_table_name='default.default'").as[TableMonitorConf].take(1)(0)
//3. 配置表缓存到集群 table_monitor_notify_conf
import org.apache.spark.sql.functions.broadcast
val tableNotifyConf = spark.read.jdbc(mysqlProps.getProperty("url"), "table_monitor_notify_conf", mysqlProps).as[TableNotifyConf].cache()
broadcast(tableNotifyConf).createOrReplaceTempView("table_monitor_notify_conf")
import spark.implicits._
// 从配置表捞取须要的信息 and 默认值填充
val tConfs = tableConfs.map( conf => {
TableMonitorConf(if (conf.db_table_key == null) defaultConf.db_table_key else conf.db_table_key,
conf.db_table_name,
if (conf.table_charge_people == null) defaultConf.table_charge_people else conf.table_charge_people,
if (conf.done_path == null) defaultConf.done_path else conf.done_path,
if (conf.where_condition == null) defaultConf.where_condition else conf.where_condition,
if (conf.if_done == null) defaultConf.if_done else conf.if_done,
if (conf.if_check_partition == null) defaultConf.if_check_partition else conf.if_check_partition,
if (conf.if_check_partition_count == null) defaultConf.if_check_partition_count else conf.if_check_partition_count,
if (conf.if_check_partition_count_fluctuates == null) defaultConf.if_check_partition_count_fluctuates else conf.if_check_partition_count_fluctuates,
if (conf.if_check_distribute == null) defaultConf.if_check_distribute else conf.if_check_distribute,
if (conf.if_check_distribute_fluctuates == null) defaultConf.if_check_distribute_fluctuates else conf.if_check_distribute_fluctuates
)})
broadcast(tConfs).createOrReplaceTempView("table_monitor_conf")
val confUnions: Dataset[ConfUnion] = spark.sql(
"""
|SELECT
| a.db_table_key,
| a.db_table_name,
| notify_enable,
| check_count_threshold,
| normal_produce_datetime,
| check_distribute_json_threshold,
| check_count_fluctuates_threshold,
| b.if_done,
| b.done_path,
| b.table_charge_people
|FROM table_monitor_notify_conf a
|LEFT JOIN table_monitor_conf b on a.db_table_name = b.db_table_name and a.db_table_key = b.db_table_key
|where notify_enable='1'
|""".stripMargin).as[ConfUnion]
excute(confUnions, info.runDay)
}
def excute(confUnions: Dataset[ConfUnion], runDay: String): Unit = {
val record = spark.sql(s"""
|SELECT db_table_key,
| db_table_name,
| check_data_time,
| rule_name,
| rule_result,
| checked_partition,
| rule_error
|FROM
| (SELECT db_table_key,
| db_table_name,
| check_data_time,
| rule_name,
| rule_result,
| rule_error,
| checked_partition,
| row_number() over(partition by db_table_key, db_table_name, rule_name
| order by check_data_time desc) rn
| FROM table_monitor.table_rule_records
| WHERE dt='$runDay' ) tmp
| WHERE rn = 1
|""".stripMargin)
record
.join(confUnions, Seq("db_table_name","db_table_key"), "left")
.filter("notify_enable='1'")
.createOrReplaceTempView("tmp_records")
val now = DateTime.now().toString("yyyy-MM-dd HH:mm:ss")
val result = spark.sql("")
// done 文件
val notifyRecords = result
.as[NotifyRecord]
.collect()
.map(r => NotifyRecord(
r.db_table_name,
r.view_url,
r.db_table_key,
r.rule_name,
r.if_ok,
if (r.db_table_key != null) s"${r.done_path}/${r.db_table_name}/${r.db_table_key}/" else s"${r.done_path}/${r.db_table_name}/default/",
r.table_charge_people,
r.trouble_description,
r.check_data_time,
r.checked_partition
))
sc.makeRDD(notifyRecords).toDS().createOrReplaceTempView("tmp_notify_records")
val dgs = DgsFileSystem.getFileSystem
// 写日志记录
spark.sql("set hive.reduce.tasks = 1")
spark.sql(s"insert overwrite table table_monitor.table_monitor_notify_records partition(dt=${runDay}) select * from tmp_notify_records")
// 取是否生成 done 或者 undone 文件（针对只监控超时产出的表，不须要做依赖，就不须要生成 done 依赖文件）val ifDoneMap = confUnions
.selectExpr("concat(db_table_key, db_table_name) AS key_name", "if_done")
.rdd
.map(row => row.getAs("key_name").toString -> row.getAs("if_done").toString)
.collectAsMap()
//1. 所有的写 done 或者非 done
for (elem <- notifyRecords) {if (ifDoneMap.getOrElse(elem.db_table_key + elem.db_table_name, "0").equals("1")) {if ("0".equals(elem.if_ok)) {dgs.createNewFile(new Path(s"${elem.done_path}$runDay/${elem.rule_name}.undone"))
} else {dgs.deleteOnExit(new Path(s"${elem.done_path}$runDay/${elem.rule_name}.undone"))
dgs.createNewFile(new Path(s"${elem.done_path}$runDay/${elem.rule_name}.done"))
}
}
}
//2. 有问题的才报警
val notifyRecordsTmp = notifyRecords.filter(_.trouble_description != null)
//2.1 取超时工夫
val normalTimeMap = spark.sql(
s"""
|SELECT concat(db_table_key, db_table_name) AS key_name,
| normal_produce_datetime
|FROM table_monitor_notify_conf
|where normal_produce_datetime is not null
|GROUP BY db_table_key,
| db_table_name,
| normal_produce_datetime
|""".stripMargin)
.rdd
.map(row => row.getAs("key_name").toString -> row.getAs("normal_produce_datetime").toString)
.collectAsMap()
for (elem <- notifyRecordsTmp) {
// 未生成且大于超时工夫才报警
if ("0".equals(elem.if_ok) && normalTimeMap.getOrElse(elem.db_table_key + elem.db_table_name, "08:00").compareTo(elem.check_data_time.substring(11, 16)) < 0) {
resp(Message.build(RobotConf.GROUP_ID, elem.table_charge_people,
s"""
|【表监控报警 -${elem.db_table_key}/${elem.db_table_name}】| 分区: ${elem.checked_partition}
| 查看工夫：${elem.check_data_time}
| 规定：${elem.rule_name}
| 问题：${elem.trouble_description}
| 详情：${elem.view_url}""".stripMargin))
}
}
// println("报警：" + Message.build(RobotConf.GROUP_ID, elem.table_charge_people, elem.db_table_name + "\n" + elem.trouble_description + "\n" + elem.view_url))
// println("done：" + elem)
// println("写日志：" + elem)
}
def resp(msg: String) = {Http(RobotConf.API_URL)
.header("Content-Type", "application/json")
.postData(msg).asBytes
}
case class NotifyRecord(
db_table_name: String,
view_url: String,
db_table_key: String,
rule_name: String,
if_ok: String,
done_path: String,
table_charge_people: String,
trouble_description: String,
check_data_time: String,
checked_partition: String
)
case class TableNotifyConf(
var db_table_key: String,
var db_table_name: String,
var notify_enable: String,
var check_count_threshold: String,
var normal_produce_datetime: String,
var check_distribute_json_threshold: String,
var check_count_fluctuates_threshold: String
)
case class ConfUnion(
var db_table_key: String,
var db_table_name: String,
var notify_enable: String,
var check_count_threshold: String,
var normal_produce_datetime: String,
var check_distribute_json_threshold: String,
var check_count_fluctuates_threshold: String,
var if_done: String,
var done_path: String,
var table_charge_people: String
)
}
PS: 代码块太长，放不了一起，所以离开两个块来放
吴邪，小三爷，混迹于后盾，大数据，人工智能畛域的小菜鸟。
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