amazon-web-services - S3A hadoop aws jar 总是返回 AccessDeniedException

Question

谁能帮我弄清楚为什么我会得到以下异常？我正在尝试从我的 spark 程序中的本地文件中读取一些数据并写入 S3。 我有这样指定的正确 key 和访问 key -

您认为这与某些库的版本不匹配有关吗？

    SparkConf conf = new SparkConf();
    // add more spark related properties

    AWSCredentials credentials = DefaultAWSCredentialsProviderChain.getInstance().getCredentials();

    conf.set("spark.hadoop.fs.s3a.access.key", credentials.getAWSAccessKeyId());
    conf.set("spark.hadoop.fs.s3a.secret.key", credentials.getAWSSecretKey());

java代码是普通的 -

protected void process() throws JobException {
    JavaRDD<String> linesRDD = _sparkContext.textFile(_jArgs.getFileLocation());

    linesRDD.saveAsTextFile("s3a://my.bucket/" + Math.random() + "final.txt");

这是我的代码和 gradle。

Gradle

ext.libs = [
    aws:    [
        lambda: 'com.amazonaws:aws-lambda-java-core:1.2.0',
        // The AWS SDK will dynamically import the X-Ray SDK to emit subsegments for downstream calls made by your
        // function
        //recorderCore: 'com.amazonaws:aws-xray-recorder-sdk-core:1.1.2',
        //recorderCoreAwsSdk: 'com.amazonaws:aws-xray-recorder-sdk-aws-sdk:1.1.2',
        //recorderCoreAwsSdkInstrumentor: 'com.amazonaws:aws-xray-recorder-sdk-aws-sdk-instrumentor:1.1.2',
        // https://mvnrepository.com/artifact/com.amazonaws/aws-java-sdk
        javaSDK: 'com.amazonaws:aws-java-sdk:1.11.311',

        recorderSDK: 'com.amazonaws:aws-java-sdk-dynamodb:1.11.311',
        // https://mvnrepository.com/artifact/com.amazonaws/aws-lambda-java-events
        lambdaEvents: 'com.amazonaws:aws-lambda-java-events:2.0.2',
        snsSDK: 'com.amazonaws:aws-java-sdk-sns:1.11.311',
        // https://mvnrepository.com/artifact/com.amazonaws/aws-java-sdk-emr
        emr :'com.amazonaws:aws-java-sdk-emr:1.11.311'

    ],
    //jodaTime: 'joda-time:joda-time:2.7',
    //guava   : 'com.google.guava:guava:18.0',
    jCommander : 'com.beust:jcommander:1.71',
    //jackson: 'com.fasterxml.jackson.module:jackson-module-scala_2.11:2.8.8',

    jackson: 'com.fasterxml.jackson.core:jackson-databind:2.8.0',
    apacheCommons: [
            lang3: "org.apache.commons:commons-lang3:3.3.2",
    ],
    spark: [
            core: 'org.apache.spark:spark-core_2.11:2.3.0',
            hadoopAws: 'org.apache.hadoop:hadoop-aws:2.8.1',
            //hadoopClient:'org.apache.hadoop:hadoop-client:2.8.1',
            //hadoopCommon:'org.apache.hadoop:hadoop-common:2.8.1',
            jackson: 'com.fasterxml.jackson.module:jackson-module-scala_2.11:2.8.8'
    ],

异常(exception)

2018-04-10 22:14:22.270 | ERROR |  |  |  |c.f.d.p.s.SparkJobEntry-46 
Exception found in job for file type : EMAIL
java.nio.file.AccessDeniedException: s3a://my.bucket/0.253592564392344final.txt: getFileStatus on 
s3a://my.bucket/0.253592564392344final.txt: 
com.amazonaws.services.s3.model.AmazonS3Exception: Forbidden (Service: 
Amazon S3; Status Code: 403; Error Code: 403 Forbidden; Request ID: 
62622F7F27793DBA; S3 Extended Request ID: BHCZT6BSUP39CdFOLz0uxkJGPH1tPsChYl40a32bYglLImC6PQo+LFtBClnWLWbtArV/z1SOt68=), S3 Extended Request ID: BHCZT6BSUP39CdFOLz0uxkJGPH1tPsChYl40a32bYglLImC6PQo+LFtBClnWLWbtArV/z1SOt68=
at org.apache.hadoop.fs.s3a.S3AUtils.translateException(S3AUtils.java:158) ~[hadoop-aws-2.8.1.jar:na]
at org.apache.hadoop.fs.s3a.S3AUtils.translateException(S3AUtils.java:101) ~[hadoop-aws-2.8.1.jar:na]
at org.apache.hadoop.fs.s3a.S3AFileSystem.getFileStatus(S3AFileSystem.java:1568) ~[hadoop-aws-2.8.1.jar:na]
at org.apache.hadoop.fs.s3a.S3AFileSystem.getFileStatus(S3AFileSystem.java:117) ~[hadoop-aws-2.8.1.jar:na]
at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1436) ~[hadoop-common-2.8.1.jar:na]
at org.apache.hadoop.fs.s3a.S3AFileSystem.exists(S3AFileSystem.java:2040) ~[hadoop-aws-2.8.1.jar:na]
at org.apache.hadoop.mapred.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:131) ~[hadoop-mapreduce-client-core-2.6.5.jar:na]
at org.apache.spark.internal.io.HadoopMapRedWriteConfigUtil.assertConf(SparkHadoopWriter.scala:283) ~[spark-core_2.11-2.3.0.jar:2.3.0]
at org.apache.spark.internal.io.SparkHadoopWriter$.write(SparkHadoopWriter.scala:71) ~[spark-core_2.11-2.3.0.jar:2.3.0]
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply$mcV$sp(PairRDDFunctions.scala:1096) ~[spark-core_2.11-2.3.0.jar:2.3.0]
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1094) ~[spark-core_2.11-2.3.0.jar:2.3.0]
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsHadoopDataset$1.apply(PairRDDFunctions.scala:1094) ~[spark-core_2.11-2.3.0.jar:2.3.0]
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) ~[spark-core_2.11-2.3.0.jar:2.3.0]
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) ~[spark-core_2.11-2.3.0.jar:2.3.0]
at org.apache.spark.rdd.RDD.withScope(RDD.scala:363) ~[spark-core_2.11-2.3.0.jar:2.3.0]

Answer 1

一旦你在玩 Hadoop Configuration类，你需要去掉 spark.hadoop前缀，所以只需使用 fs.s3a.access.key ， ETC。

所有选项都在类org.apache.hadoop.fs.s3a.Constants中定义: 如果你引用它们，你也会避免拼写错误。

要考虑的一件事是 spark 和 hadoop 的所有来源都是公开的:没有什么可以阻止您获取堆栈跟踪、设置一些断点并尝试在您的 IDE 中运行它。当事情变得糟糕时，这是我们通常自己做的事情。