osap一站式分析模型

运营系统分析平台技术设计：

1. 项目定义于运营系统关键指标的数据分析
2. 关键代码描述：
1. HiveWriter 主要用于写hive表抽象，包括加分区，写hive表，写success文件：

   import org.apache.hadoop.fs.{FileSystem, Path}
   import org.apache.spark.sql.SparkSession
   
   class HiveWriter(tableName: String, outputDir: String, currentDate: String) {
   
     val currentYear = currentDate.substring(0, 4)
     val currentMonth = currentDate.substring(4, 6)
     val currentDay = currentDate.substring(6, 8)
   
     //加分区
     def addPartition(ss: SparkSession, hour: String): Unit = {
       var query: String = ""
       if(hour == ""){
         query =
           s"""ALTER TABLE $tableName ADD IF NOT EXISTS
              |PARTITION(year='$currentYear',month='$currentMonth',day='$currentDay')
              |LOCATION '$outputDir/$currentYear/$currentMonth/$currentDay'
             """.stripMargin
       }else{
         query =
           s"""ALTER TABLE $tableName ADD IF NOT EXISTS
              |PARTITION(year='$currentYear',month='$currentMonth',day='$currentDay',hour=$hour)
              |LOCATION '$outputDir/$currentYear/$currentMonth/$currentDay/$hour'
             """.stripMargin
       }
       ss.sql(query)
     }
   
     //写hive表
     def save(ss:SparkSession, name: String, hour: String): Unit = {
       var query: String = ""
       if(hour == ""){
         query =
           s"""
              |INSERT overwrite TABLE ${tableName} partition(YEAR=${currentYear},MONTH=${currentMonth},DAY=${currentDay})
              |SELECT *
              |FROM ${name}
          """.stripMargin
       }else{
         query =
           s"""
              |INSERT overwrite TABLE ${tableName} partition(YEAR=${currentYear},MONTH=${currentMonth},DAY=${currentDay},HOUR=${hour})
              |SELECT *
              |FROM ${name}
          """.stripMargin
   
       }
       ss.sql(query)
     }
   
     //写success文件
     def writeSuccFile(ss: SparkSession, hour: String): Unit = {
       val conf = ss.sparkContext.hadoopConfiguration
       val hdfs = FileSystem.get(conf)
       var path: String = ""
       if(hour == ""){
         path = s"${outputDir}/${currentYear}/${currentMonth}/${currentDay}/_SUCCESS"
       }else{
         path = s"${outputDir}/${currentYear}/${currentMonth}/${currentDay}/${hour}/_SUCCESS"
       }
       val success = new Path(path)
       hdfs.create(success)
     }
   
   }

   HiveConf

2. QueryConf 主要用于根据传递来的表，时间等信息构建各种ETL需要的SQL：

   class QueryConf(tableName: String, currentDate: String, outputDir: String, callTableName: String) {
   
     val oneWeekAgo = CommonUtils.calTime(currentDate, Calendar.DATE, -6)
   
     //查询字段
     val resFields = Seq("pope_act_id","group_id","pid","order_id","is_complete","pay_suc_time")
   
     val payQuery =
       s"""
          |SELECT DISTINCT cast(order_id AS string) order_id,
          |                pay_suc_time
          |FROM table
          |WHERE concat(YEAR,MONTH,DAY) = ${currentDate}
   
          |AND pay_suc_time != ''
          |AND pay_suc_time IS NOT NULL
          |AND order_id IS NOT NULL""".stripMargin
   
     val callQuery =
       s"""
          |SELECT DISTINCT pope_act_id,
          |                group_id,
          |                pid,
          |                order_id,
          |                is_complete
          |FROM ${callTableName}
          |WHERE concat(YEAR,MONTH,DAY) BETWEEN ${oneWeekAgo} AND ${currentDate}
          |AND pope_act_id != ''
          |AND pid != 0""".stripMargin
   
   }

   QueryConf

3. Process 执行器代码主要用于实际执行的封装： 

   import org.apache.spark.sql.SparkSession
   
   class Process(ss: SparkSession, queryConf: QueryConf, writer: HiveWriter) {
   
     def processPay(): Unit = {
   
       val payRawData = ss.sql(queryConf.payQuery)
       val callRawData = ss.sql(queryConf.callQuery)
       val payData = payRawData.join(callRawData,Seq("order_id"),"inner")
         .selectExpr(queryConf.resFields: _*)
   
       val name = "pay_tbl"
       payData.createOrReplaceTempView(name)
   
       writer.addPartition(ss, "")
       writer.save(ss, name, "")
       writer.writeSuccFile(ss, "")
   
     }
   
   }
   object Process {
     def apply(ss: SparkSession, queryConf: QueryConf, writer: HiveWriter): Process = {
       new Process(ss, queryConf, writer)
     }
   }

4. 程序串联 : 

   val Array(appName, tableName, currentDate, outputDir, callTableName) = args
       val ss = SparkSession.builder().appName(appName).enableHiveSupport().getOrCreate()
   
       val queryConf = new QueryConf(tableName, currentDate, outputDir, callTableName)
       val writer = new HiveWriter(tableName, outputDir, currentDate)
   
       val processor = Process(ss,queryConf,writer)
       processor.processPay()
   
       ss.stop()

   串联程序

3. 关键技术细节：

1. 1. SQL字符串表达式建议如下：s"""sql""".stripMargin，更整洁更好维护复杂的SQL
   2. 涉及到时间计算，预先unix_timestamp(observe_end_time) 在SQL中转化成时间戳，在map算子中只做最简单的判断计算
   3. Scala 枚举：

      object ActivityCallTBL extends Enumeration {
          type ActivityCallTBL = Value
          val callStatus = Value("gulfstream_ods.ods_binlog_d_order_status_increment")
          val callBase = Value("gulfstream_ods.ods_binlog_d_order_base_increment")
        }

   4. 定义结构体： {} 里面能写复杂代码定义产出结果，适用于一句话搞不定返回值的情况
   5. ROW_NUMBER()函数，常用案例包括 

1. 1. 1. 　　取出每种rate下score最大的两条记录
      2. 　　SQL实例：以下意思就是取a_birth_time最早的order信息：

         select order_id, passenger_id pid, product_id, a_birth_time ,unix_timestamp(a_birth_time) tim, area, starting_lng, starting_lat, dest_lng, dest_lat,  type order_type, combo_type, require_level, extra_info
          from ( select * , row_number() over (partition by order_id order by a_birth_time ) as tid
          from  gulfstream_ods.ods_binlog_d_order_base_increment
          where concat(year,month,day,hour)='2017112309' ) a where tid = 1

   2. sql cast功能： cast(order_id AS string) order_id， spark.sql实现： df.select(df("colA").cast("int"))

   3. sql nvl功能 ： nvl(t1.a_create_time,'1971-01-01 00:00:00') AS create_time

   4. spark select Seq() : selectExpr(Seq(): _*)
   5. import org.apache.spark.sql.functions  定义spark中用Scala实现的各种SQL常用算子
   6. withColumn("is_complete",functions.lit(0))  使用常数类型构造一个新的column，withColumn("testkey", concat($"apollo_exp_id", lit(":0:"), $"apollo_group_id"))
   7. df("colA") =!= df("colB")   column不等于算子
   8. 添加UDF函数 val genCarpool = spark.udf.register("genCarpool", func), 使用ds.withColumn("ds_carpool", genCarpool(ds("raw_ct")))
   9. import org.apache.spark.sql.functions._ 之后就能使用   from_unixtime(Colunm) 函数了。
   10. import spark.implicits._   牛逼的隐式转换把Scala类型 转化为  DF的类型，包括能使用df.select($"sss")
   11. functions.explode : spark中集合类打平函数，会把一个集合中的多个元素打平成多个Row
   12. df.drop  删除指定的column
   13. functions.when : functions.when(resPre("new_carpool").contains(resPre("ds_carpool")) and ???,1).otherwise(0), 关键技巧先转换成String(1,2,3,4) 再判断contains基本值
   14. coalesce(10) 在结果DF之后加上控制写hive表最合适的part个数
   15. package object activity 包对象更好适合常量场景
   16. <: 继承关系，[T <: Conf]

   17. DF启用别名： activeMarketingActDF.alias("a").join(odsGCouponRuleInfoDF.alias("b"), $"a.info_id" === $"b.id").selectExpr("a.id"）

   18. flatMap函数： 当map中返回的是 LIST 会把结果打平
   19. selectExpr表达式： ds.selectExpr("colA", "colB as newName", "abs(colC)")
   20. 多个列中最大，最小值： greatest(exprs: Column*)  跳过null least(exprs: Column*) 跳过null 取最小的值

   21. 其他有效函数

       1. between
       2. isNaN
       3. isNull

   22. sparkGC太多可以优化点如下

       1. 调大task个数，更快GC，减少长时间GC时间拖慢整个进程。优化参数如下

          1. --conf spark.dynamicAllocation.maxExecutors=1000 \
             --conf spark.executor.cores=4 \

       2. 调大执行器的年轻代大小： --conf "spark.executor.extraJavaOptions=-XX:NewSize=2048m"
   23. YarnScheduler: Lost executor 13 on bigdata-hdp-apache1005.xg01.diditaxi.com: Container killed by YARN for exceeding memory limits. 12.5 GB of 12 GB physical memory used. Consider boosting spark.yarn.executor.memoryOverhead.   解决手段：
   1. --conf spark.executor.memory=12g
   2. repartition 设置大点
   3. --conf  spark.shuffle.spill.numElementsForceSpillThreshold=1500000 ，内存中row的数量超过这个值，不再放到内存直接写磁盘