MySql教程
sparksql结果快速到mysql（scala代码、airflow调度）
本文主要是介绍sparksql结果快速到mysql（scala代码、airflow调度），对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！
经常会有这样的需求：在现有数仓表的基础上，写一些sql，然后生成hive表并同步到mysql。
次数多了，就像写一个工具完成这个工作
一：背景、功能、流程介绍
1.背景：
1.数仓使用hive存储，datax导数据、airflow调度 2.不知道怎么利用hive解析sql，拿到对应的schema，但是spark知道 spark.sql(sql).schema.toList所以就用了scala
2.功能
就是通过配置完成hive,mysql的建表，airflow调度任务的生成
3.流程
1.配置mysql链接 2.根据输入sparksql，生成对应的hive，mysql表结构，建表 3.生成airflow调度任务（插入hive数据，调用datax同步数据到mysql）
二：代码
1.配置文件介绍：
MysqlToHive.properties
jdbcalias:ptx_read #mysql别名要和同步的数据库的别名保持一致 table:be_product #要同步的表名 owner=owner ##airflow任务的owner lifecycle=180 ##hive表的生命周期，数据数据产品删除数据 airflowpath=/airflow/dags/ods/ ##生成airflow任务文件的路径 jdbc1alias : hive ##可以写多个mysql链接，不用一个来回改 jdbc1host : 127.0.0.1 jdbc1port : 3306 jdbc1user : root jdbc1passwd : ** jdbc1db_name : test jdbc2alias:read jdbc2host : 127.0.0.1 jdbc2port : 3306 jdbc2user : root jdbc2passwd :** jdbc2db_name :test
2.基本代码：
MysqlToHive.java
object HiveToMysql {
//mysql配置内部类
case class Database(host: String,port: Int,user: String,passwd: String,db_name: String){}
//读取配置文件
def readDbPropertiesFile(fileName: String,spark:SparkSession,sql: String): Unit = {
val pp = new Properties
val fps = new FileInputStream("HiveToMysql.properties")
// val fps = Thread.currentThread.getContextClassLoader.getResourceAsStream(fileName)
pp.load(fps)
parseProperties(pp,spark,sql)
fps.close()
}
//解析配置文件对应配置
def parseProperties(pp: Properties,spark:SparkSession,sql: String): Unit = {
val table = pp.getProperty("table")
val owner = pp.getProperty("owner")
val lifecycle = pp.getProperty("lifecycle")
val jdbcalias = pp.getProperty("jdbcalias")
val airflowpath = pp.getProperty("airflowpath")
import scala.collection.mutable.ArrayBuffer
var tableColumn: ArrayBuffer[String] = new ArrayBuffer[String]();
var dbindex = 1
while (pp.getProperty("jdbc" + dbindex + "alias") != null && !pp.getProperty("jdbc" + dbindex + "alias").equals(jdbcalias)) {
dbindex += 1
}
var database = new Database(pp.getProperty("jdbc" + dbindex + "host"),pp.getProperty("jdbc" + dbindex + "port").toInt,
pp.getProperty("jdbc" + dbindex + "user"),pp.getProperty("jdbc" + dbindex + "passwd"),pp.getProperty("jdbc" + dbindex + "db_name"))
val mysqlSelectBuilder = new StringBuilder
val schemaList = spark.sql(sql).schema.toList
//sparksql 利用schema生成hive建表语句和mysql建表语句
for ( i <- 0 until schemaList.length ) {
println(schemaList.apply(i).name+"|"+schemaList.apply(i).dataType.typeName)
tableColumn += (schemaList.apply(i).name+"|"+schemaList.apply(i).dataType.typeName)
mysqlSelectBuilder.append(schemaList.apply(i).name+",")
}
mysqlSelectBuilder.deleteCharAt(mysqlSelectBuilder.length - 1)
buildExecuteHiveSql(table,tableColumn,lifecycle,owner)
buildExecuteMysql(table,tableColumn,database);
printAirflowJob(airflowpath,table,owner,jdbcalias,mysqlSelectBuilder.toString(),sql: String)
}
//airflow封装太多了，就不写了
def printAirflowJob(airflowpath:String,table:String,owner:String,jdbcalias:String,mysqlSelect:String,sql:String){
val db = table.substring(0, table.indexOf("."));
val tableNoDatabase = table.substring(table.indexOf(".") + 1);
System.out.println(airflowpath +db+"/"+ tableNoDatabase)
if (new File(airflowpath +db+"/"+ tableNoDatabase).exists())
System.out.println("folder exist,please delete the folder " + airflowpath +db+"/"+ tableNoDatabase)
else {
val dir = new File(airflowpath +db+"/"+ tableNoDatabase);
dir.mkdirs();
val pw = new PrintWriter(airflowpath +db+"/"+ tableNoDatabase + "/" + tableNoDatabase + "_dag.py")
pw.println("import airflow");
pw.println("from airflow import DAG");
pw.println(")");
pw.println("");
pw.println("");
pw.flush()
pw.close()
}
}
@throws[IOException]
def buildExecuteMysql(table:String,tableColumn:ArrayBuffer[String],database:Database): Unit = {
val mysqlSqlBuilder = new StringBuilder
mysqlSqlBuilder.append("CREATE TABLE " + table.substring(table.indexOf(".")+1)+ " ( \n")
mysqlSqlBuilder.append("dt varchar(10) DEFAULT NULL,"+"\n")
println("tableColumnCopy"+tableColumn.size)
val tableColumnCopy = tableColumn.toArray[String];
for (i <- 0 until tableColumnCopy.size) {
val fieldAndType = tableColumnCopy.apply(i).split("\\|")
mysqlSqlBuilder.append(fieldAndType(0)+ " ")
if (fieldAndType(1).contains("integer") || fieldAndType(1).contains("long"))
mysqlSqlBuilder.append(" bigint(10)")
else if (fieldAndType(1).contains("float") || fieldAndType(1).contains("double") || fieldAndType(1).contains("decimal"))
mysqlSqlBuilder.append(" decimal(36,6)")
else if (fieldAndType(1).contains("string") )
mysqlSqlBuilder.append(" varchar(36)")
else if (fieldAndType(1).contains("boolean") )
mysqlSqlBuilder.append(" boolean")
else if (fieldAndType(1).contains("date") || fieldAndType(1).contains("timestamp") )
mysqlSqlBuilder.append(" varchar(36)")
mysqlSqlBuilder.append(" DEFAULT NULL," +"\n")
}
mysqlSqlBuilder.deleteCharAt(mysqlSqlBuilder.length - 2) //去除最后的回车和，
mysqlSqlBuilder.append(") ENGINE=InnoDB DEFAULT CHARSET=utf8mb4")
System.out.println(mysqlSqlBuilder.toString)
Class.forName("com.mysql.cj.jdbc.Driver")
var con = DriverManager.getConnection("jdbc:mysql://" + database.host + ":" + database.port + "/" + database.db_name + "?serverTimezone=UTC", database.user, database.passwd)
var st = con.createStatement
st.execute(mysqlSqlBuilder.toString)
st.close();con.close();
}
@throws[IOException]
@throws[InterruptedException]
def buildExecuteHiveSql(table:String,tableColumn:ArrayBuffer[String],lifecycle:String,owner:String): Unit = {
val mysqlSqlBuilder = new StringBuilder
mysqlSqlBuilder.append("CREATE TABLE " + table+ " ( \n")
println("tableColumnCopy"+tableColumn.size)
val tableColumnCopy = tableColumn.toArray[String];
for (i <- 0 until tableColumnCopy.size) {
val fieldAndType = tableColumnCopy.apply(i).split("\\|")
if(fieldAndType.apply(1).contains("integer") || fieldAndType.apply(1).contains("long"))
mysqlSqlBuilder.append(fieldAndType.apply(0)+" bigint,")
else if(fieldAndType.apply(1).contains("float") || fieldAndType.apply(1).contains("double") || fieldAndType.apply(1).contains("decimal"))
mysqlSqlBuilder.append(fieldAndType.apply(0)+" "+"double ,")
else if(fieldAndType.apply(1).contains("string"))
mysqlSqlBuilder.append(fieldAndType.apply(0)+" string,")
else if(fieldAndType.apply(1).contains("boolean"))
mysqlSqlBuilder.append(fieldAndType.apply(0)+" boolean,")
else if(fieldAndType.apply(1).contains("date")||fieldAndType.apply(1).contains("timestamp"))
mysqlSqlBuilder.append(fieldAndType.apply(0)+" string,")
mysqlSqlBuilder.append("\n")
}
mysqlSqlBuilder.deleteCharAt(mysqlSqlBuilder.length - 2) //去除最后的回车和，
mysqlSqlBuilder.append(") PARTITIONED BY ( dt string COMMENT '(一级分区)' ) \n")
mysqlSqlBuilder.append("ROW FORMAT DELIMITED STORED AS PARQUET \n")
mysqlSqlBuilder.append("TBLPROPERTIES ('lifecycle'='" + lifecycle + "','owner'='" + owner + "','parquet.compression'='snappy');")
System.out.println(mysqlSqlBuilder.toString)
val process = new ProcessBuilder("hive", "-e", "\"" + mysqlSqlBuilder.toString + "\"").redirectErrorStream(true).start
val br = new BufferedReader(new InputStreamReader(process.getInputStream))
var line = ""
do {
line = br.readLine()
Thread.sleep(1000)
println(line)
}while(line!=null)
process.waitFor
}
def main(args: Array[String]): Unit = {
// val sparkconf = new SparkConf().setAppName("test_Spark_sql").setMaster("local[2]")
// val spark = SparkSession.builder().config(sparkconf).config("spark.driver.host", "localhost").getOrCreate()
val spark= SparkSession.builder.appName("HiveToMysql").enableHiveSupport().getOrCreate()
readDbPropertiesFile("HiveToMysql.properties",spark,args(0))
}
}
3.脚本文件：HiveToMysql.sh
#!/bin/bash mv bigData.jar . mv HiveToMysql.properties . sql=`cat /sql` spark-submit \ --class HiveToMysql \ --master yarn \ --deploy-mode client \ --num-executors 1 \ --executor-memory 4g \ --executor-cores 1 \ --driver-memory 1g \ --name "HiveToMysql" \ --conf spark.speculation=true \ --conf spark.speculation.interval=30000 \ --conf spark.speculation.quantile=0.8 \ --conf spark.speculation.multiplier=1.5 \ --conf spark.dynamicAllocation.enabled=false \ --files HiveToMysql.properties \ --jars fastjson-1.2.62.jar,mysql-connector-java-8.0.18.jar \ bigData.jar "$sql"
4.可能的问题
1.scala比较烂，代码比较难阅读 2.调度的时间一样（可做需改） 3.数据类型的处理，根据业务需求
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