SparkSQL学习——SparkSQL配置与文件的读取与保存

目录

一、添加依赖

二、配置log4j

三、spark提交jar包

四、读取文件

(一)加载数据

(二)保存数据

1.Parquet

2.json

3.CSV

4.MySql

5.hive on spark

6.IDEA的Spark中操作Hive

---

一、添加依赖

    UTF-8
    1.8
    1.8
    3.1.2
    8.0.29
  

        
            org.apache.spark
            spark-core_2.12
            ${spark.version}
        

        
        
            org.apache.spark
            spark-sql_2.12
            ${spark.version}
        

        
        
            org.apache.spark
            spark-hive_2.12
            ${spark.version}
        

        
        
            org.apache.spark
            spark-graphx_2.12
            ${spark.version}
        

        
        
            mysql
            mysql-connector-java
            ${mysql.version}
        

        
        
            com.alibaba
            fastjson
            1.2.62
        

二、配置log4j

src/main/resources/log4j.properties 

# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

# Set everything to be logged to the console
log4j.rootCategory=ERROR, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Set the default spark-shell log level to WARN. When running the spark-shell, the
# log level for this class is used to overwrite the root logger's log level, so that
# the user can have different defaults for the shell and regular Spark apps.
log4j.logger.org.apache.spark.repl.Main=WARN

# Settings to quiet third party logs that are too verbose
log4j.logger.org.spark_project.jetty=WARN
log4j.logger.org.spark_project.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO

# SPARK-9183: Settings to avoid annoying messages when looking up nonexistent UDFs in SparkSQL with Hive support
log4j.logger.org.apache.hadoop.hive.metastore.RetryingHMSHandler=FATAL
log4j.logger.org.apache.hadoop.hive.ql.exec.FunctionRegistry=ERROR

将log4j-defaults.properties复制到resources下，并重命名为log4j.properties，第19行：log4j.rootCategory=INFO, console 修改为log4j.rootCategory=ERROR, console

import org.apache.spark.rdd.RDD
import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}

object SparkDemo {
  def main(args: Array[String]): Unit = {
    val conf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("sparkDemo")
    val sc :SparkContext= SparkContext.getOrCreate(conf)
//    println(sc) // org.apache.spark.SparkContext@63998bf4

    val spark: SparkSession = SparkSession.builder().master("local[*]").appName("sparkSessionDemo").getOrCreate()
//    println(spark) // org.apache.spark.sql.SparkSession@682c1e93

    val rdd: RDD[Int] = sc.parallelize(1 to 10)
    rdd.collect().foreach(println)

    val pNum: Int = rdd.getNumPartitions
    println("分区数量",pNum)// (分区数量,20)
  }
}

三、spark提交jar包

[root@lxm147 opt]# spark-submit --class nj.zb.kb21.WordCount --master local[*] ./sparkstu-1.0-SNAPSHOT.jar 

(cjdison,1)
(spark,1)
(cdio,1)
(cjiodscn,1)
(hcuediun,1)
(hello,3)
(java,1)
(nodsn,1)
(jcido,1)
(jcndio,1)
(cjidsovn,1)
(world,1)

四、读取文件

(一)加载数据

SparkSQL 默认读取和保存的文件格式为parquet

scala> spark.read.
csv   format   jdbc   json   load   option  
options   orc   parquet   schema   table   text   textFile

如果读取不同格式的数据，可以对不同的数据格式进行设定：

scala> spark.read.format("…")[.option("…")].load("…") 

➢ format("…")：指定加载的数据类型，包括"csv"、"jdbc"、"json"、"orc"、"parquet"和

"textFile"。

➢ load("…")：在"csv"、"jdbc"、"json"、"orc"、"parquet"和"textFile"格式下需要传入加载

数据的路径。

➢ option("…")：在"jdbc"格式下需要传入 JDBC 相应参数，url、user、password 和 dbtable

前面都是使用 read API 先把文件加载到 DataFrame 然后再查询，也可以直接在文件上进行查询： 文件格式.`文件路径`

# 读取本地文件
# 方式一：
spark.sql("select * from json.`file:///opt/soft/spark312/data/user.json`").show

# 方式二：
scala> val df = spark.read.json("file:///opt/soft/spark312/data/user.json")
df: org.apache.spark.sql.DataFrame = [age: bigint, username: string]

scala> df.show
+---+--------+
|age|username|
+---+--------+
| 30|zhangsan|
| 20|    lisi|
| 40|  wangwu|
+---+--------+


# 读取HDFS上的文件
# 方式一：
scala> spark.sql("select * from json.`hdfs://lxm147:9000/data/user.json`").show
2023-03-30 16:25:09,132 WARN metastore.ObjectStore: Failed to get database json, returning NoSuchObjectException
+---+--------+
|age|username|
+---+--------+
| 30|zhangsan|
| 20|    lisi|
| 40|  wangwu|
+---+--------+

# 方式二：
la> spark.read.json("hdfs://lxm147:9000/data/user.json").show
+---+--------+
|age|username|
+---+--------+
| 30|zhangsan|
| 20|    lisi|
| 40|  wangwu|
+---+--------+

(二)保存数据

df.write.save 是保存数据的通用方法

scala> df.write.
bucketBy   formart   jdbc   mode     options   parquet     save          
sortBy     csv       insertInto      json      option      orc       
partitionBy saveAsTable     text 

如果保存不同格式的数据，可以对不同的数据格式进行设定

scala>df.write.format("…")[.option("…")].save("…") 

➢ format("…")：指定保存的数据类型，包括"csv"、"jdbc"、"json"、"orc"、"parquet"和

"textFile"。

➢ save ("…")：在"csv"、"orc"、"parquet"和"textFile"格式下需要传入保存数据的路径。

➢ option("…")：在"jdbc"格式下需要传入 JDBC 相应参数，url、user、password 和 dbtable

保存操作可以使用 SaveMode, 用来指明如何处理数据，使用 mode()方法来设置。

有一点很重要: 这些 SaveMode 都是没有加锁的, 也不是原子操作。

SaveMode 是一个枚举类，其中的常量包括：

示例：

1.保存为指定格式文件：

scala> df.write.format("json").save("file:///opt/soft/spark312/data/output1")

如果同一个文件存储两次，会报错文件已存在

scala> df.write.format("json").save("file:///opt/soft/spark312/data/output")

scala> df.write.format("json").save("file:///opt/soft/spark312/data/output")
org.apache.spark.sql.AnalysisException: path file:/opt/soft/spark312/data/output already exists.
  at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:122)

2.追加存储文件：append

scala> df.write.format("json").mode("append").save("file:///opt/soft/spark312/data/output")

3.覆盖存储文件：overwrite

scala> df.write.format("json").mode("overwrite").save("file:///opt/soft/spark312/data/output")

4.忽略原文件：ignore

scala> df.write.format("json").mode("ignore").save("file:///opt/soft/spark312/data/output")

1.Parquet

# 读取json文件​
scala> spark.read.load("file:///opt/soft/spark312/examples/src/main/resources/users.parquet")
​

# 将json文件保存为parquet文件
scala> df1.write.mode("append").save("file:///opt/soft/spark312/data/output1")

2.json

# 加载json文件
scala> val df2 = spark.read.json("file:///opt/soft/spark312/data/user.json")
df2: org.apache.spark.sql.DataFrame = [age: bigint, username: string]

# 创建临时表
scala> df2.createOrReplaceTempView("user")

# 数据查询
scala> spark.sql("select * from user where age > 30").show
+---+--------+
|age|username|
+---+--------+
| 40|  wangwu|
+---+--------+

3.CSV

# 读取CSV文件
scala> val df = spark.read.format("csv").option("seq",";").option("inferSchema","true").option("header","true").load("file:///opt/soft/spark312/examples/src/main/resources/people.csv")
df: org.apache.spark.sql.DataFrame = [name;age;job: string]

scala> df.show
+------------------+
|      name;age;job|
+------------------+
|Jorge;30;Developer|
|  Bob;32;Developer|
+------------------+

4.MySql

添加依赖

      mysql
      mysql-connector-java
      8.0.29

package com.atguigu.bigdata.spark.sql

import org.apache.spark.SparkConf
import org.apache.spark.sql._

import java.util.Properties

object Spark04_SparkSQL_JDBC {
  def main(args: Array[String]): Unit = {
    // TODO 创建SparkSQL的运行环境
    val sparkConf: SparkConf = new SparkConf().setMaster("local[*]").setAppName("sparkSQL")
    val spark: SparkSession = SparkSession.builder().config(sparkConf).getOrCreate()
    import spark.implicits._

    // TODO 读取MySql数据
    //方式 1：通用的 load 方法读取
    spark.read
      .format("jdbc")
      .option("url", "jdbc:mysql://192.168.180.141:3306/exam")
      .option("driver", "com.mysql.cj.jdbc.Driver")
      .option("user", "root")
      .option("password", "root")
      .option("dbtable", "student")
      .load()
      .show()
    
    //方式 2:通用的 load 方法读取 参数另一种形式
    spark.read
      .format("jdbc")
      .options(Map("url" -> "jdbc:mysql://192.168.180.141:3306/exam?user=root&password=root", "dbtable" -> "student", "driver" -> "com.mysql.cj.jdbc.Driver"))
      .load()
      .show()

    //方式 3:使用 jdbc 方法读取
    val props: Properties = new Properties()
    props.setProperty("user", "root")
    props.setProperty("password", "root")
    val df: DataFrame = spark.read.jdbc("jdbc:mysql://192.168.180.141:3306/exam", "users", props)
    df.show()

    // TODO 保存MySql数据
    df.write
      .format("jdbc")
      .option("url", "jdbc:mysql://192.168.180.141:3306/exam")
      .option("driver", "com.mysql.cj.jdbc.Driver")
      .option("user", "root")
      .option("password", "root")
      .option("dbtable", "student1")
      .mode(SaveMode.Append)
      .save()

    // TODO 关闭环境
    spark.stop()
  }
}

5.hive on spark

如果想连接外部已经部署好的 Hive，需要通过以下几个步骤：

➢ Spark 要接管 Hive 需要把 hive-site.xml 拷贝到 conf/目录下

cp /opt/soft/hive312/conf/hive-site.xml /opt/soft/spark-local/conf

➢ 把 Mysql 的驱动 copy 到 jars/目录下

cp /opt/soft/hive312/lib/mysql-connector-java-8.0.29.jar /opt/soft/spark-local/jars

➢ 如果访问不到 hdfs，则需要把 core-site.xml 和 hdfs-site.xml 拷贝到 conf/目录下

➢ 重启 spark-shell

scala>  spark.sql("show tables").show
returning NoSuchObjectException
+--------+---------+-----------+
|database|tableName|isTemporary|
+--------+---------+-----------+
+--------+---------+-----------+

# 读取本地文件
scala> val df = spark.read.json("file:///opt/soft/spark-local/data/user.json")
df: org.apache.spark.sql.DataFrame = [age: bigint, username: string]  

# 创建临时表
scala> df.createOrReplaceTempView("user")

# 读取临时表
scala> spark.sql("select * from user").show
+---+--------+
|age|username|
+---+--------+
| 30|zhangsan|
| 20|    lisi|
| 40|  wangwu|
+---+--------+

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

# 创建表并设定字段
scala> spark.sql("create table atguigu(id int)")

# 读取hdfs上的文件中的数据加载到
scala> spark.sql("load data inpath '/opt/soft/hive312/warehouse/atguigu/id.txt' into table atguigu")
res5: org.apache.spark.sql.DataFrame = []

# 查询默认数据库
scala> spark.sql("show tables").show
+--------+---------+-----------+
|database|tableName|isTemporary|
+--------+---------+-----------+
| default|  atguigu|      false|
|        |     user|       true|
+--------+---------+-----------+

# 查询表中的数据
scala> spark.sql("select * from atguigu").show
+---+
| id|
+---+
|  1|
|  2|
|  3|
|  4|
+---+

6.IDEA的Spark中操作Hive

        
            org.apache.spark
            spark-core_2.12
            3.1.2
        
        
            org.apache.spark
            spark-sql_2.12
            3.1.2
        
        
            mysql
            mysql-connector-java
            8.0.29
        
        
            org.apache.spark
            spark-hive_2.12
            3.1.2
        
        
            org.apache.hive
            hive-exec
            3.1.2