大資料開發-資料表監控-實現
1.簡介
大資料開發-表資料波動、碼值分佈波動監控 && 報警，是關於理論和設計部分，初步計算已經寫完，管理平臺部分，後續完善，本文主要針對模組設計部分，整體模組實現上是離線資料來源的非同步模組，分為指標跑批模組，監控報警模組，平臺管理模組，指標跑批模組和監控報警模組主要是基於離線資料的表的通訊，即不是耦合的架構，分別是兩個例行的任務,看下面的圖，
2.說明
其中表設計了五張表，分別是指標跑批記錄表，報警跑批記錄表，和mysql表監控配置表，報警配置表，分佈欄位配置表，指標跑批和監控跑批不直接任務依賴，而是透過指標跑批記錄表，直接產生報警跑批記錄
3.程式碼實現
整個專案目錄結構如下圖：
resource: 配置檔案
common: 一些公共的模組，Builder是傳送訊息構建器，Father是Spark專案的公共程式碼
rules: 下面有5個指標的規則，分別是檢查分割槽是否儲存，檢查分割槽數量是否大於某個值，檢查分割槽數量波動，檢查分佈，檢查分佈大向量波動
utils: 裡面放的是一些工具，比如日期處理工具類，表格式處理工具，sql處理工具等
Monitor: 指標跑批的主類
SunRobot: 報警跑批的主類入庫
4.Monitor類說明
rule的一些實現，不細說了，根據原始碼很好看懂，而Monitor是怎麼根據這些規則，生成對應的流水，主要實現程式碼如下：
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("入參:: " + args.mkString(","))
JCommander.newBuilder().addObject(info).build().parse(args.toArray: _*)
//廣播配置表
prepare()
//生成表 * 規則 個 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]]() //所有規則一起跑
//預設值填充
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
)})
//遍歷所有規則
for (elem <- tConfs) {
//規則1
if ("1".equals(elem.if_check_partition)) {
ll +:= Rule1.runRule(elem.db_table_name, elem.db_table_key, elem.where_condition, info.runDay)
}
//規則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)
}
//規則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)
}
//規則4
if ("1".equals(elem.if_check_distribute)) {
ll +:= Rule4.runRule(elem.db_table_name, elem.db_table_key, elem.where_condition, info.runDay)
}
//規則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.基礎配置表快取到叢集 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.配置表快取到叢集 table_monitor_distribute_conf
spark.read.jdbc(mysqlProps.getProperty("url"), "table_monitor_distribute_conf", mysqlProps).cache().createOrReplaceTempView("table_monitor_distribute_conf")
}
}
整理流程就是讀配置表的資訊，包括設定預設引數，最後就是呼叫拿出來的配置表的資訊和規則的資訊
5.SunRobot說明
這個模組就是報警以及生成攔截done檔案的模組，主要功能就是根據前面的指標流水以及配置表的配置的表元資訊進行比對，看看是否滿足check要求，如果滿足了就生成done，如果不滿足就生成undone, 如果配置了報警，就會根據報警規則進行報警，暫且只有傳送到通訊工具一項，主要程式碼如下：
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
/**
* 根據rule跑批結果 和 報警配置進行報警
*/
object SunRobot extends Father {
import spark.implicits._
val mysqlProps = PropertiesUtils.getMysqlProps()
def main(args: Array[String]): Unit = {
val info: ObserverArgs = new ObserverArgs
println("入參:: " + args.mkString(","))
JCommander.newBuilder().addObject(info).build().parse(args.toArray: _*)
//1.基礎配置表快取到叢集 table_monitor_conf
val tableConfs: Dataset[TableMonitorConf] = spark.read.jdbc(mysqlProps.getProperty("url"), "table_monitor_conf", mysqlProps).as[TableMonitorConf].cache()
//2.預設規則提取-->driver
val defaultConf: TableMonitorConf = tableConfs.where("db_table_name='default.default'").as[TableMonitorConf].take(1)(0)
//3.配置表快取到叢集 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._
//從配置表撈取需要的資訊 and 預設值填充
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='$runDay' ) tmp
| WHERE rn = 1
|""".stripMargin)
record
.join(confUnions, Seq("db_table_name","db_table_key"), "left")
.filter("notify_enable='1'")
.createOrReplaceTempView("tmp_records")
val now = DateTime.now().toString("yyyy-MM-dd HH:mm:ss")
val result = spark.sql("")
// done檔案
val notifyRecords = result
.as[NotifyRecord]
.collect()
.map(r => NotifyRecord(
r.db_table_name,
r.view_url,
r.db_table_key,
r.rule_name,
r.if_ok,
if (r.db_table_key != null) s"${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
//寫日誌記錄
spark.sql("set hive.reduce.tasks = 1")
spark.sql(s"insert overwrite table table_monitor.table_monitor_notify_records partition(dt=${runDay}) select * from tmp_notify_records")
//取是否生成done 或者 undone檔案 （針對只監控超時產出的表，不需要做依賴，就不需要生成done依賴檔案）
val ifDoneMap = confUnions
.selectExpr("concat(db_table_key, db_table_name) AS key_name", "if_done")
.rdd
.map(row => row.getAs("key_name").toString -> row.getAs("if_done").toString)
.collectAsMap()
//1.所有的寫done或者非done
for (elem <- notifyRecords) {
if (ifDoneMap.getOrElse(elem.db_table_key + elem.db_table_name, "0").equals("1")) {
if ("0".equals(elem.if_ok)) {
dgs.createNewFile(new Path(s"${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.有問題的才報警
val notifyRecordsTmp = notifyRecords.filter(_.trouble_description != null)
//2.1 取超時時間
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) {
//未生成且大於超時時間才報警
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"""
|【表監控報警-${elem.db_table_key}/${elem.db_table_name}】
|分割槽: ${elem.checked_partition}
|檢查時間：${elem.check_data_time}
|規則：${elem.rule_name}
|問題：${elem.trouble_description}
|詳情：${elem.view_url}""".stripMargin))
}
}
// println("報警：" + 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("寫日誌：" + 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: 程式碼塊太長，放不了一起，所以分開兩個塊來放
吳邪，小三爺，混跡於後臺，大資料，人工智慧領域的小菜鳥。
來自 “ ITPUB部落格 ” ，連結：http://blog.itpub.net/75/viewspace-2797962/，如需轉載，請註明出處，否則將追究法律責任。
相關文章
- GO實現資料夾監控Go
- 大資料技術 - 監控預警大資料
- 資料庫監控資料庫
- 靠監控大資料識別網約車大資料
- 某次資料庫效能監控中發現的問題資料庫
- 資料監控可以監測業務指標的實現情況，發現是否有升高或降低指標
- 全球疫情實時監控——約翰斯·霍普金斯大學資料大屏實現方案
- 資料庫效能監控資料庫
- 大資料的開放式創新：如何才能實現大資料的深發展？大資料
- HP PCS 雲監控大資料解決方案大資料
- 大快DKH大資料基礎資料平臺的監控引數說明大資料
- 【大資料】MapReduce開發小實戰大資料
- zabbix 監控資料庫表記錄數（Discovery方式）資料庫
- 如何監控ORACLE資料庫表的增長量Oracle資料庫
- 多臺ORACLE資料庫表空間監控方案Oracle資料庫
- 資料庫繁忙程度監控資料庫
- 資料庫監控軟體資料庫
- SQL Server資料庫監控SQLServer資料庫
- 資料庫監控指令碼資料庫指令碼
- 前端相關資料監控前端
- SQLserver 監控資料檔案SQLServer
- 監控Oracle資料庫方法Oracle資料庫
- 監控資料庫指令碼資料庫指令碼
- zabbix監控oracle資料庫Oracle資料庫
- PostgreSQL業務資料質量實時監控實踐SQL
- 前端資料監控到底在監控什麼？前端
- MySQL監控-Datadog資料庫監控調研MySql資料庫
- UAVStack的慢SQL資料庫監控功能及其實現SQL資料庫
- zabbix和mysql performance monitor模板實現mysql資料庫的監控MySqlORM資料庫
- 大資料開發實戰：實時資料平臺和流計算大資料
- 教育大資料之資料開發系統大資料
- 觸發器實現表資料自動更新觸發器
- Python呼叫阿里雲資料庫監控資料Python阿里資料庫
- 監控採集上報和儲存監控資料策略
- 系統監控&JVM監控指標資料查詢JVM指標
- 資料庫監控---PIGOSS BSM資料庫Go
- 前端監控(效能/資料/異常)前端
- shell監控mysql 8.0資料庫MySql資料庫