在阿里云Serverless K8S集群上部署Spark任务并连接OSS（详细步骤）
在阿里云ASK集群上部署Spark任务并连接OSS
简介
ASK是阿里云的一个产品，属于Serverless Kubernetes 集群，这次实验是要在ASK集群上运行Spark计算任务（以WordCount为例），另外为了能让计算和存储分离，我使用了阿里云OSS来存放数据。
（连接OSS这块找了好多资料都不全，在本地可以运行的代码一放在集群就报错，遇到很多bug才终于弄好了，记录下来希望对以后的小伙伴有帮助）
环境准备
本机需要安装：
JAVA jdk1.8
IDEA
Maven
Docker（安装在Linux或者Windows）
需要在阿里云开通的服务有：
ASK集群：https://www.aliyun.com/product/cs/ask?spm=5176.166170.J_8058803260.27.586451643ru45z
OSS对象存储： https://www.aliyun.com/product/oss?spm=5176.166170.J_8058803260.32.58645164XpoJle
ACR镜像服务：https://www.aliyun.com/product/acr?spm=5176.19720258.J_8058803260.31.281e2c4astzVxy
一、在OSS中准备数据
- 在OSS对象存储中上传好txt文件（我的是hp1.txt）
则访问该文件的url为，
oss://spark-on-k8s-1/hp1.txt
（按照这种【oss://桶名/路径/文件名】格式改成你自己的，后面代码要用到）
二、编写代码
1.使用IDEA新建一个maven项目
目录结构如下：
需要写的就只有pom.xml文件和java下的osstest.java文件。下面会给出代码：
（1）osstest.java
这是一份词频统计（wordcount）的代码。步骤是：
- 连接OSS，获取到实现准备好的hp1.txt文件
- 对hp1.txt进行词频统计
- 把最终结果传回到OSS上
具体实现如下：
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.stream.Collectors;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import scala.Tuple2;
public class osstest {
public static void main(String[] args) {
// 这些都是OSS的依赖包，不写的话在本地能跑，放上集群会报错
List<String> jarList = new ArrayList<>();
jarList.add("emr-core-1.4.1.jar");
jarList.add("aliyun-sdk-oss-3.4.1.jar");
jarList.add("commons-codec-1.9.jar");
jarList.add("jdom-1.1.jar");
jarList.add("commons-logging-1.2.jar");
jarList.add("httpclient-4.5.1.jar");
jarList.add("httpcore-4.4.1.jar");
String ossDepPath = jarList.stream()
.map(s -> "/opt/spark/jars/" + s)
.collect(Collectors.joining(","));
SparkConf conf = new SparkConf().setAppName("JavaWordCount");
// 如果在本地IDEA执行，需要打开下面一行代码
// .setMaster("local");
conf.set("spark.hadoop.fs.oss.impl", "com.aliyun.fs.oss.nat.NativeOssFileSystem");
// 如果在本地IDEA执行，需要打开下面一行代码
// conf.set("spark.hadoop.mapreduce.job.run-local", "true");
conf.set("spark.hadoop.fs.oss.endpoint", "oss-cn-shenzhen.aliyuncs.com");// 改成你存放文本的OSS桶的地区
conf.set("spark.hadoop.fs.oss.accessKeyId", "*****"); // 改成你自己的accessKeyId
conf.set("spark.hadoop.fs.oss.accessKeySecret", "******");// 改成你自己的accessKeySecret
// 需要指定oss依赖的路径，否则会报错
conf.set("spark.hadoop.fs.oss.core.dependency.path", ossDepPath);
System.out.println("----------开始-----------");
//创建sparkContext
JavaSparkContext sc = new JavaSparkContext(conf);
JavaRDD<String> lines = sc.textFile("oss://spark-on-k8s-1/hp1.txt", 10); // 改成你自己的读取文件路径
System.out.println("-----------读取数据"+lines.count()+"行。----------------");
JavaRDD<String> words = lines.flatMap(line -> Arrays.asList(line.split(" ")).iterator());
System.out.println("-----------3:"+words);
//将单词和一组合
JavaPairRDD<String, Integer> wordAndOne = words.mapToPair(w -> new Tuple2<>(w, 1));
System.out.println("-----------4:"+wordAndOne);
//聚合
JavaPairRDD<String, Integer> reduced = wordAndOne.reduceByKey((m, n) -> m + n);
System.out.println("-----------5:"+reduced);
//调整顺序
JavaPairRDD<Integer, String> swaped = reduced.mapToPair(tp -> tp.swap());
System.out.println("-----------6"+swaped);
//排序
JavaPairRDD<Integer, String> sorted = swaped.sortByKey(false);
System.out.println("-----------7"+sorted);
//调整顺序
JavaPairRDD<String, Integer> result = sorted.mapToPair(tp -> tp.swap());
System.out.println("-----------8"+result);
//将结果保存到oss
result.saveAsTextFile("oss://spark-on-k8s-1/hp1-result-1");// 改成你自己的输出文件路径
System.out.println("-----------结束------------------------");
//释放资源
sc.stop();
}
}
因此以上代码需要修改的地方有：
- 存储桶的endpoint
- accessKeyId
- accessKeySecret
- 输入输出的桶地址
（2）pom.xml
pom.xml声明了Spark和OSS的一些依赖。
注意EMR虽然是阿里云的另一项服务，在这里我们不需要开通它。但少了com.aliyun.emr这个依赖就不能访问到oss://开头的地址，所以要加进pom.xml里。
maven-assembly-plugin是用来自定义打包的。
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0modelVersion>
<groupId>org.examplegroupId>
<artifactId>osstestartifactId>
<version>2.0-SNAPSHOTversion>
<dependencies>
<dependency>
<groupId>com.aliyun.ossgroupId>
<artifactId>aliyun-sdk-ossartifactId>
<version>2.2.0version>
dependency>
<dependency>
<groupId>com.aliyun.dfsgroupId>
<artifactId>aliyun-sdk-dfsartifactId>
<version>1.0.3version>
dependency>
<dependency>
<groupId>com.aliyun.emrgroupId>
<artifactId>emr-coreartifactId>
<version>1.4.1version>
dependency>
<dependency>
<groupId>org.apache.sparkgroupId>
<artifactId>spark-sql_2.12artifactId>
<version>2.4.3version>
dependency>
dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.pluginsgroupId>
<artifactId>maven-assembly-pluginartifactId>
<version>2.6version>
<configuration>
<appendAssemblyId>falseappendAssemblyId>
<descriptorRefs>
<descriptorRef>jar-with-dependenciesdescriptorRef>
descriptorRefs>
<archive>
<manifest>
<mainClass>osstestmainClass>
manifest>
archive>
configuration>
<executions>
<execution>
<id>make-assemblyid>
<phase>packagephase>
<goals>
<goal>assemblygoal>
goals>
execution>
executions>
plugin>
plugins>
build>
<properties>
<maven.compiler.source>8maven.compiler.source>
<maven.compiler.target>8maven.compiler.target>
properties>
project>
- 使用maven打包，先点clean，再点assembly:assembly
打包成功的结果为：
jar包就准备好了。
然后需要一台安装了Docker的机器（Linux或Windows都行）
创建一个test文件夹。
把打包好的osstest-2.0-SNAPSHOT.jar 和 其他要用到的第三方jar全部放到test文件夹下。
第三方jar就是代码中写到的那些，网上都可以下载到：
aliyun-sdk-oss-3.4.1.jar
hadoop-aliyun-2.7.3.2.6.1.0-129.jar
jdom-1.1.jar
httpclient-4.5.1.jar
httpcore-4.4.1.jar
commons-logging-1.2.jar
commons-codec-1.9.jar
emr-core-1.4.1.jar
三、准备镜像
- 在test文件夹中编写Dockerfile
# spark base image
FROM registry.cn-beijing.aliyuncs.com/eci_open/spark:2.4.4
RUN rm $SPARK_HOME/jars/kubernetes-client-*.jar
ADD https://repo1.maven.org/maven2/io/fabric8/kubernetes-client/4.4.2/kubernetes-client-4.4.2.jar $SPARK_HOME/jars
RUN mkdir -p /opt/spark/jars
COPY osstest-2.0-SNAPSHOT.jar /opt/spark/jars
COPY aliyun-sdk-oss-3.4.1.jar /opt/spark/jars
COPY hadoop-aliyun-2.7.3.2.6.1.0-129.jar /opt/spark/jars
COPY jdom-1.1.jar /opt/spark/jars
COPY httpclient-4.5.1.jar /opt/spark/jars
COPY httpcore-4.4.1.jar /opt/spark/jars
COPY commons-logging-1.2.jar /opt/spark/jars
COPY commons-codec-1.9.jar /opt/spark/jars
COPY emr-core-1.4.1.jar /opt/spark/jars
Dokerfile里做的事情是：
把阿里云提供的spark2.4.4作为基础镜像，然后创建了一个 /opt/spark/jars文件夹（注意这个路径和java代码中是一致的），然后把我们写的java代码打的jar包，和其他的第三方包都放进去。
- 构建镜像
接下来使用docker build命令，把我们的Dockerfile制作成一个镜像。
sudo docker build -t registry.cn-shenzhen.aliyuncs.com/sicilly/spark:0.9 -f Dockerfile .
注意在上述命令中：
registry.cn-shenzhen.aliyuncs.com/sicilly 需要改成你自己的镜像仓库地址
spark 是仓库名称，你可以自己起
0.9 是镜像版本，你可以自己起
再注意命令最后有一个英文的句号
- 上传到镜像仓库
使用docker push命令，把镜像推送的阿里云的镜像仓库。
sudo docker push registry.cn-shenzhen.aliyuncs.com/sicilly/spark:0.9
同上，需要改成你自己的镜像仓库地址
三、创建集群
- 创建一个ASK集群
1、自定义集群名。
2、选择地域、以及可用区。
3、专有网络可以用已有的也可以由容器服务自动创建的。开启SNAT。
4、是否公网暴露API server，如有需求建议开启。
5、开启privatezone，必须开启。
6、日志收集，建议开启。
注意开通这些服务是要付费的:
-
安装ack-spark-operator
在容器服务管理控制台的导航栏中选择市场 > 应用目录，通过选择ack-spark-operator来进行部署。
四、提交到集群
上述东西都准备好了以后，就可以编写yaml文件，将任务提交到ASK执行了。
- 编写wordcount-operator-example.yaml
apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
name: wordcount
namespace: default
spec:
type: Java
mode: cluster
image: "registry.cn-shenzhen.aliyuncs.com/sicilly/spark:0.9" # 改成你的镜像地址
imagePullPolicy: IfNotPresent
mainClass: osstest # 代码的主类名
mainApplicationFile: "local:///opt/spark/jars/osstest-2.0-SNAPSHOT.jar" # 代码所在位置
sparkVersion: "2.4.4"
restartPolicy:
type: OnFailure
onFailureRetries: 2
onFailureRetryInterval: 5
onSubmissionFailureRetries: 2
onSubmissionFailureRetryInterval: 10
timeToLiveSeconds: 36000
sparkConf:
"spark.kubernetes.allocation.batch.size": "10"
driver:
cores: 2
memory: "512m"
labels:
version: 2.4.4
spark-app: spark-wordcount
role: driver
annotations:
k8s.aliyun.com/eci-image-cache: "false"
serviceAccount: spark
executor:
cores: 1
instances: 2
memory: "512m"
labels:
version: 2.4.4
role: executor
annotations:
k8s.aliyun.com/eci-image-cache: "false"
如果你用的是我上面的代码，需要改的就只有镜像地址。
- 提交到ASK集群
方法一：使用kubectl。需要在windows上安装kubectl工具（安装方法），连接到你的ASK集群后输入下列命令即完成创建。
kubectl create -f wordcount-operator-example.yaml
方法二：如果不想安装kubectl，也可以在容器服务管理控制台上点击应用-无状态-使用YAML创建资源
-
执行完毕
第一次因为要拉取spark镜像会比较久，耐心等待几分钟，显示Completed就表示已经完成执行。 -
查看日志
使用kubectl查看日志，也可以在容器组右边点击日志。
kubectl get pods
kubectl logs -f wordcount-driver
没有报错说明成功了，有报错的话根据日志排查问题。
下面是一次成功执行的日志。
21/12/21 06:33:33 INFO SparkContext: Created broadcast 5 from broadcast at DAGScheduler.scala:1161
21/12/21 06:33:33 INFO DAGScheduler: Submitting 10 missing tasks from ResultStage 5 (MapPartitionsRDD[10] at saveAsTextFile at osstest.java:63) (first 15 tasks are for partitions Vector(0, 1, 2, 3, 4, 5, 6, 7, 8, 9))
21/12/21 06:33:33 INFO TaskSchedulerImpl: Adding task set 5.0 with 10 tasks
21/12/21 06:33:33 INFO TaskSetManager: Starting task 0.0 in stage 5.0 (TID 40, 192.168.59.99, executor 1, partition 0, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:33 INFO BlockManagerInfo: Added broadcast_5_piece0 in memory on 192.168.59.99:41645 (size: 27.2 KB, free: 116.9 MB)
21/12/21 06:33:33 INFO MapOutputTrackerMasterEndpoint: Asked to send map output locations for shuffle 1 to 192.168.59.99:57144
21/12/21 06:33:34 INFO TaskSetManager: Starting task 1.0 in stage 5.0 (TID 41, 192.168.59.99, executor 1, partition 1, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:34 INFO TaskSetManager: Finished task 0.0 in stage 5.0 (TID 40) in 945 ms on 192.168.59.99 (executor 1) (1/10)
21/12/21 06:33:34 INFO TaskSetManager: Starting task 2.0 in stage 5.0 (TID 42, 192.168.59.99, executor 1, partition 2, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:34 INFO TaskSetManager: Finished task 1.0 in stage 5.0 (TID 41) in 316 ms on 192.168.59.99 (executor 1) (2/10)
21/12/21 06:33:34 INFO TaskSetManager: Starting task 3.0 in stage 5.0 (TID 43, 192.168.59.99, executor 1, partition 3, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:34 INFO TaskSetManager: Finished task 2.0 in stage 5.0 (TID 42) in 316 ms on 192.168.59.99 (executor 1) (3/10)
21/12/21 06:33:35 INFO TaskSetManager: Starting task 4.0 in stage 5.0 (TID 44, 192.168.59.99, executor 1, partition 4, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:35 INFO TaskSetManager: Finished task 3.0 in stage 5.0 (TID 43) in 313 ms on 192.168.59.99 (executor 1) (4/10)
21/12/21 06:33:35 INFO TaskSetManager: Starting task 5.0 in stage 5.0 (TID 45, 192.168.59.99, executor 1, partition 5, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:35 INFO TaskSetManager: Finished task 4.0 in stage 5.0 (TID 44) in 312 ms on 192.168.59.99 (executor 1) (5/10)
21/12/21 06:33:35 INFO TaskSetManager: Starting task 6.0 in stage 5.0 (TID 46, 192.168.59.99, executor 1, partition 6, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:35 INFO TaskSetManager: Finished task 5.0 in stage 5.0 (TID 45) in 350 ms on 192.168.59.99 (executor 1) (6/10)
21/12/21 06:33:36 INFO TaskSetManager: Starting task 7.0 in stage 5.0 (TID 47, 192.168.59.99, executor 1, partition 7, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:36 INFO TaskSetManager: Finished task 6.0 in stage 5.0 (TID 46) in 324 ms on 192.168.59.99 (executor 1) (7/10)
21/12/21 06:33:36 INFO TaskSetManager: Starting task 8.0 in stage 5.0 (TID 48, 192.168.59.99, executor 1, partition 8, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:36 INFO TaskSetManager: Finished task 7.0 in stage 5.0 (TID 47) in 429 ms on 192.168.59.99 (executor 1) (8/10)
21/12/21 06:33:36 INFO TaskSetManager: Starting task 9.0 in stage 5.0 (TID 49, 192.168.59.99, executor 1, partition 9, NODE_LOCAL, 7681 bytes)
21/12/21 06:33:36 INFO TaskSetManager: Finished task 8.0 in stage 5.0 (TID 48) in 335 ms on 192.168.59.99 (executor 1) (9/10)
21/12/21 06:33:37 INFO TaskSetManager: Finished task 9.0 in stage 5.0 (TID 49) in 376 ms on 192.168.59.99 (executor 1) (10/10)
21/12/21 06:33:37 INFO TaskSchedulerImpl: Removed TaskSet 5.0, whose tasks have all completed, from pool
21/12/21 06:33:37 INFO DAGScheduler: ResultStage 5 (runJob at SparkHadoopWriter.scala:78) finished in 4.022 s
21/12/21 06:33:37 INFO DAGScheduler: Job 2 finished: runJob at SparkHadoopWriter.scala:78, took 4.741556 s
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 84
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 94
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 120
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 100
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 97
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 119
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 81
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 118
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 77
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 82
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 99
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 121
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 107
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 102
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 105
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 101
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 110
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 80
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 85
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 75
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 83
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 76
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 96
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 91
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 98
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 124
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 122
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 112
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 95
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 93
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 79
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 116
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 106
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 109
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 88
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 113
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 123
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 104
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 78
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 117
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 89
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 108
21/12/21 06:33:38 INFO BlockManagerInfo: Removed broadcast_5_piece0 on wordcount-1640068323479-driver-svc.default.svc:7079 in memory (size: 27.2 KB, free: 116.9 MB)
21/12/21 06:33:38 INFO BlockManagerInfo: Removed broadcast_5_piece0 on 192.168.59.99:41645 in memory (size: 27.2 KB, free: 116.9 MB)
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 92
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 103
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 90
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 111
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 86
21/12/21 06:33:38 INFO BlockManagerInfo: Removed broadcast_4_piece0 on wordcount-1640068323479-driver-svc.default.svc:7079 in memory (size: 3.0 KB, free: 116.9 MB)
21/12/21 06:33:38 INFO BlockManagerInfo: Removed broadcast_4_piece0 on 192.168.59.99:41645 in memory (size: 3.0 KB, free: 116.9 MB)
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 87
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 114
21/12/21 06:33:38 INFO ContextCleaner: Cleaned accumulator 115
21/12/21 06:33:39 INFO NativeOssFileSystem: OutputStream for key 'hp1-result-1/_SUCCESS' writing to tempfile '/tmp/hadoop-root/dfs/data/data/root/oss/output-4047609689034382569.data' for block 0
21/12/21 06:33:39 INFO NativeOssFileSystem: OutputStream for key 'hp1-result-1/_SUCCESS' closed. Now beginning upload
21/12/21 06:33:39 INFO NativeOssFileSystem: OutputStream for key 'hp1-result-1/_SUCCESS' upload complete
21/12/21 06:33:39 INFO SparkHadoopWriter: Job job_20211221063332_0010 committed.
-----------over------
21/12/21 06:33:39 INFO SparkUI: Stopped Spark web UI at http://wordcount-1640068323479-driver-svc.default.svc:4040
21/12/21 06:33:39 INFO KubernetesClusterSchedulerBackend: Shutting down all executors
21/12/21 06:33:39 INFO KubernetesClusterSchedulerBackend$KubernetesDriverEndpoint: Asking each executor to shut down
21/12/21 06:33:39 WARN ExecutorPodsWatchSnapshotSource: Kubernetes client has been closed (this is expected if the application is shutting down.)
21/12/21 06:33:39 INFO MapOutputTrackerMasterEndpoint: MapOutputTrackerMasterEndpoint stopped!
21/12/21 06:33:39 INFO MemoryStore: MemoryStore cleared
21/12/21 06:33:39 INFO BlockManager: BlockManager stopped
21/12/21 06:33:39 INFO BlockManagerMaster: BlockManagerMaster stopped
21/12/21 06:33:39 INFO OutputCommitCoordinator$OutputCommitCoordinatorEndpoint: OutputCommitCoordinator stopped!
21/12/21 06:33:39 INFO SparkContext: Successfully stopped SparkContext
21/12/21 06:33:39 INFO ShutdownHookManager: Shutdown hook called
21/12/21 06:33:39 INFO ShutdownHookManager: Deleting directory /tmp/spark-fe376e0d-8552-41fa-9620-685390a8ccbb
21/12/21 06:33:39 INFO ShutdownHookManager: Deleting directory /var/data/spark-528397fc-176a-4897-9129-9f4f14327b16/spark-21a04125-581f-48a0-8b84-36704c279704
五、查看结果
- 到OSS上查看结果并下载
部分结果为：
(the,3306)
(,3056)
(to,1827)
(and,1787)
(a,1577)
(of,1235)
(was,1148)
(he,1018)
(Harry,903)
(in,898)
(his,893)
(had,691)
(--,688)
(said,659)
(at,580)
(you,578)
(it,547)
(on,544)
完成！
注意做完实验以后要删掉ASK集群，否则会一直扣费的！
参考资料
ECI SPARK https://github.com/aliyuneci/BestPractice-Serverless-Kubernetes/tree/master/eci-spark
在ECI中访问HDFS的数据 https://help.aliyun.com/document_detail/146235.html
在ECI中访问OSS数据 https://help.aliyun.com/document_detail/146237.html
云上大数据分析最佳实践 https://developer.aliyun.com/live/2196
ECI最佳实践-SPARK应用 https://help.aliyun.com/document_detail/146249.html
通过ASK创建Spark计算任务 https://help.aliyun.com/document_detail/165079.htm?spm=a2c4g.11186623.0.0.427a3edeER2KDl#task-2495864