一.文件类型
1.Text文件
读写
读取
scala> val hdfsFile = sc.textFile("hdfs://hadoop102:9000/fruit.txt")
hdfsFile: org.apache.spark.rdd.RDD[String] = hdfs://hadoop102:9000/fruit.txt MapPartitionsRDD[21] at textFile at <console>:24
保存
scala> hdfsFile.saveAsTextFile("/fruitOut")
2.Json文件
使用RDD读取JSON文件处理很复杂，同时SparkSQL集成了很好的处理JSON文件的方式，所以应用中多是采用SparkSQL处理JSON文件。
（1）导入解析json所需的包
scala> import scala.util.parsing.json.JSON
（2）上传json文件到HDFS
[atguigu@hadoop102 spark]$ hadoop fs -put ./examples/src/main/resources/people.json /
（3）读取文件
scala> val json = sc.textFile("/people.json")
json: org.apache.spark.rdd.RDD[String] = /people.json MapPartitionsRDD[8] at textFile at <console>:24
（4）解析json数据
scala> val result = json.map(JSON.parseFull)
result: org.apache.spark.rdd.RDD[Option[Any]] = MapPartitionsRDD[10] at map at <console>:27
3.对象文件
对象文件是将对象序列化后保存的文件，采用Java的序列化机制。可以通过objectFilek,v 函数接收一个路径，读取对象文件，返回对应的 RDD，也可以通过调用saveAsObjectFile() 实现对对象文件的输出。因为是序列化所以要指定类型
读写
（1）创建一个RDD
scala> val rdd = sc.parallelize(Array(1,2,3,4))
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[19] at parallelize at <console>:24
（2）将RDD保存为Object文件
scala> rdd.saveAsObjectFile("file:///opt/module/spark/objectFile")
（3）查看该文件
[hadoop@hadoop102 objectFile]$ pwd
/opt/module/spark/objectFile
[hadoop@hadoop102 objectFile]$ ll
总用量 8
-rw-r--r-- 1 atguigu atguigu 142 10月 9 10:37 part-00000
-rw-r--r-- 1 atguigu atguigu 142 10月 9 10:37 part-00001
-rw-r--r-- 1 atguigu atguigu 0 10月 9 10:37 _SUCCESS
[hadoop@hadoop102 objectFile]$ cat part-00000
SEQ!org.apache.hadoop.io.NullWritable"org.apache.hadoop.io.BytesWritableW@`l
（4）读取Object文件
scala> val objFile = sc.objectFile[Int]("file:///opt/module/spark/objectFile")
objFile: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[31] at objectFile at <console>:24
（5）打印读取后的Sequence文件
scala> objFile.collect
res19: Array[Int] = Array(1, 2, 3, 4)
4.Sequence文件
很少用了。。
注意：SequenceFile文件只针对PairRDD
（1）创建一个RDD
scala> val rdd = sc.parallelize(Array((1,2),(3,4),(5,6)))
rdd: org.apache.spark.rdd.RDD[(Int, Int)] = ParallelCollectionRDD[13] at parallelize at <console>:24
（2）将RDD保存为Sequence文件
scala> rdd.saveAsSequenceFile("file:///opt/module/spark/seqFile")
（3）查看该文件
[hadoop@hadoop102 seqFile]$ pwd
/opt/module/spark/seqFile
[hadoop@hadoop102 seqFile]$ ll
总用量 8
-rw-r--r-- 1 atguigu atguigu 108 10月 9 10:29 part-00000
-rw-r--r-- 1 atguigu atguigu 124 10月 9 10:29 part-00001
-rw-r--r-- 1 atguigu atguigu 0 10月 9 10:29 _SUCCESS
[hadoop@hadoop102 seqFile]$ cat part-00000
SEQ org.apache.hadoop.io.IntWritable org.apache.hadoop.io.IntWritableط
（4）读取Sequence文件
scala> val seq = sc.sequenceFile[Int,Int]("file:///opt/module/spark/seqFile")
seq: org.apache.spark.rdd.RDD[(Int, Int)] = MapPartitionsRDD[18] at sequenceFile at <console>:24
（5）打印读取后的Sequence文件
scala> seq.collect
res14: Array[(Int, Int)] = Array((1,2), (3,4), (5,6))
二.文件系统
1. MySQL
依赖
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.27</version>
</dependency>
读取
import java.sql.DriverManager
import org.apache.spark.rdd.JdbcRDD
import org.apache.spark.{SparkConf, SparkContext}
/**
* @description: 从Mysql读取数据
* @author: HaoWu
* @create: 2020年08月05日
*/
object MySqlReadWriteTest {
def main(args: Array[String]): Unit = {
val sparkConf: SparkConf = new SparkConf().setMaster("local[2]").setAppName("JdbcApp")
val sc: SparkContext = new SparkContext(sparkConf)
val rdd: JdbcRDD[(Int, String)] = new JdbcRDD(
sc,
() => {
Class.forName("com.mysql.jdbc.Driver").newInstance()
DriverManager.getConnection("jdbc:mysql://hadoop102:3306/azkaban", "root", "root")
},
"select * from project_files where project_id >= ? and project_id <= ?;",
1,
4,
1,
//返回值是个数组，已经将JDBC返回的结果处理过。
r => (r.getInt(1), r.getString(2)))
println(rdd.count())
rdd.foreach(println(_))
sc.stop()
}
}
保存
import java.sql.{Connection, DriverManager, PreparedStatement}
import org.apache.spark.rdd.{JdbcRDD, RDD}
import org.apache.spark.{SparkConf, SparkContext}
/**
* @description: 向Mysql中插入数据
* @author: HaoWu
* @create: 2020年08月05日
*/
object MySqlReadWriteTest {
def main(args: Array[String]): Unit = {
val sparkConf: SparkConf = new SparkConf().setMaster("local[2]").setAppName("JdbcApp")
val sc: SparkContext = new SparkContext(sparkConf)
val list = List((1, 9), (1, 10))
val rdd: RDD[(Int, Int)] = sc.makeRDD(list)
//使用foreachPartition效率更高，批量，不用频繁创建mysql连接
rdd.foreachPartition(iter => {
// 创建Connection
val con: Connection = DriverManager.getConnection("jdbc:mysql://hadoop102:3306/azkaban", "root", "root")
//准备sql
val sql="insert into project_files(project_id,version) values(?,?)"
//PreapredStatement
val ps: PreparedStatement = con.prepareStatement(sql)
//将批量数据依次插入
iter.foreach{
case(project_id,version) => {
//插入int类型
ps.setInt(1,project_id)
ps.setInt(2,version)
//执行sql
ps.executeUpdate()
}
}
ps.close()
con.close()
})
sc.stop()
}
}
2. Hbase
依赖
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>2.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>2.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-mapreduce</artifactId>
<version>2.0.0</version>
</dependency>
将hbase的配置文件hbase-site.xml，放到resource目录，保留连接zookeeper
<property>
<name>hbase.zookeeper.quorum</name>
<value>hadoop102,hadoop103,hadoop104</value>
</property>
读取
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.hbase.{Cell, CellUtil, HBaseConfiguration}
import org.apache.hadoop.hbase.client.Result
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableInputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.spark.rdd.NewHadoopRDD
import org.apache.spark.{SparkConf, SparkContext}
/**
* @description: Hbase的读取
* @author: HaoWu
* @create: 2020年08月05日
*/
object HbaseReadWriterTest {
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setAppName("RDDTest").setMaster("local[*]")
val sc = new SparkContext(sparkConf)
// 创建连接 默认读取hadoop中的配置文件，和hbase中的配置文件 默认使用的还是TextInputFormat
val conf: Configuration = HBaseConfiguration.create()
// 设置当前要读取哪个表
conf.set(TableInputFormat.INPUT_TABLE, "bigdata:user")
//核心创建RDD
val rdd = new NewHadoopRDD[ImmutableBytesWritable, Result](sc,
classOf[TableInputFormat],
classOf[ImmutableBytesWritable],
classOf[Result],
conf)
rdd.foreach {
case (rowKey, result) => {
// CellUtil : 取出Cell某个属性 Bytes: 将Java中的数据类型 和byte[]互转
// 获取一条数据的所有cell
val cells: Array[Cell] = result.rawCells()
for (cell <- cells) {
println(Bytes.toString(CellUtil.cloneRow(cell)) + " " +
Bytes.toString(CellUtil.cloneFamily(cell)) + ":" + Bytes.toString(CellUtil.cloneQualifier(cell)) + " " +
Bytes.toString(CellUtil.cloneValue(cell)))
}
}
}
}
}
写入
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.hbase.HBaseConfiguration
import org.apache.hadoop.hbase.client.Put
import org.apache.hadoop.hbase.io.ImmutableBytesWritable
import org.apache.hadoop.hbase.mapreduce.TableOutputFormat
import org.apache.hadoop.hbase.util.Bytes
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.rdd. RDD
import org.apache.spark.{SparkConf, SparkContext}
/**
* @description: Hbase的保存
* @author: HaoWu
* @create: 2020年08月05日
*/
object HbaseReadWriterTest {
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setAppName("RDDTest").setMaster("local[*]")
val sc = new SparkContext(sparkConf)
// 创建连接 默认读取hadoop中的配置文件，和hbase中的配置文件 默认使用的还是TextInputFormat
val conf: Configuration = HBaseConfiguration.create()
// 设置当前要写出到哪个表
conf.set(TableOutputFormat.OUTPUT_TABLE, "bigdata:user")
//在Conf中设置各种参数
val job: Job = Job.getInstance(conf)
//设置输出格式
job.setOutputFormatClass(classOf[TableOutputFormat[ImmutableBytesWritable]])
// 设置输出的key,value的类型
job.setOutputKeyClass(classOf[ImmutableBytesWritable])
job.setOutputValueClass(classOf[Put])
// 用list封装数据(rowkey,(列簇，列，值))
val list = List(("1005", ("info2", "age", "20")), ("1005",( "info2", "name", "marry")), ("1006", ("info2", "age", "21")))
val rdd: RDD[(String, (String, String, String))] = sc.makeRDD(list, 2)
// 使用spark将数据封装为输出的key-value类型
val rdd2: RDD[(ImmutableBytesWritable, Put)] = rdd.map {
case (rowkey, (cf, cq, v)) => {
//封装rowkey
val key = new ImmutableBytesWritable()
key.set(Bytes.toBytes(rowkey))
//封装put
val value = new Put(Bytes.toBytes(rowkey))
value.addColumn(Bytes.toBytes(cf), Bytes.toBytes(cq), Bytes.toBytes(v))
(key, value)
}
}
//之前设置的配置传入
rdd2.saveAsNewAPIHadoopDataset(job.getConfiguration)
}
}
踩的坑
在跑读取hbase数据的时候发现程序报错：
原因：pom的hbase依赖包必须放置spark-core包后面，不然就报这个错误。
java.lang.ExceptionInInitializerError
at org.apache.spark.SparkContext.withScope(SparkContext.scala:751)
at org.apache.spark.SparkContext.textFile(SparkContext.scala:882)
at com.spark.rdd.RDDTest.testMap(RDDTest.scala:62)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:50)
at org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:12)
at org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:47)
at org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:17)
at org.junit.internal.runners.statements.RunBefores.evaluate(RunBefores.java:26)
at org.junit.internal.runners.statements.RunAfters.evaluate(RunAfters.java:27)
at org.junit.runners.ParentRunner.runLeaf(ParentRunner.java:325)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:78)
at org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:57)
at org.junit.runners.ParentRunner$3.run(ParentRunner.java:290)
at org.junit.runners.ParentRunner$1.schedule(ParentRunner.java:71)
at org.junit.runners.ParentRunner.runChildren(ParentRunner.java:288)
at org.junit.runners.ParentRunner.access$000(ParentRunner.java:58)
at org.junit.runners.ParentRunner$2.evaluate(ParentRunner.java:268)
at org.junit.runners.ParentRunner.run(ParentRunner.java:363)
at org.junit.runner.JUnitCore.run(JUnitCore.java:137)
at com.intellij.junit4.JUnit4IdeaTestRunner.startRunnerWithArgs(JUnit4IdeaTestRunner.java:68)
at com.intellij.rt.execution.junit.IdeaTestRunner$Repeater.startRunnerWithArgs(IdeaTestRunner.java:47)
at com.intellij.rt.execution.junit.JUnitStarter.prepareStreamsAndStart(JUnitStarter.java:242)
at com.intellij.rt.execution.junit.JUnitStarter.main(JUnitStarter.java:70)
Caused by: com.fasterxml.jackson.databind.JsonMappingException: Scala module 2.10.0 requires Jackson Databind version >= 2.10.0 and < 2.11.0
at com.fasterxml.jackson.module.scala.JacksonModule.setupModule(JacksonModule.scala:61)
at com.fasterxml.jackson.module.scala.JacksonModule.setupModule$(JacksonModule.scala:46)
at com.fasterxml.jackson.module.scala.DefaultScalaModule.setupModule(DefaultScalaModule.scala:17)
at com.fasterxml.jackson.databind.ObjectMapper.registerModule(ObjectMapper.java:751)
at org.apache.spark.rdd.RDDOperationScope$.<init>(RDDOperationScope.scala:82)
at org.apache.spark.rdd.RDDOperationScope$.<clinit>(RDDOperationScope.scala)
... 27 more