一、原理

  spark-submit --files通常用来加载外部资源文件，在driver和executor进程中进行访问

  –files和–jars基本相同

二、使用步骤

2.1 添加文件

  spark-submit --files file_paths

  其中file_paths可为多种方式：file: | hdfs:// | http:// | ftp:// | local:（多个路径用逗号隔开）

spark-submit \
--master yarn \
--deploy-mode cluster \
--principal xxx.com \
--keytab /xxx/keytabs/xxx.keytab \
--driver-java-options "-Dspring.profiles.active=prod -Dorg.springframework.boot.logging.LoggingSystem=none -Djava.ext.dirs=/xxx/CDH-x.x.x-1.cdhx.x.x.p0.xxx/lib/spark/jars/gson-2.8.1.jar,/xxx/CDH-x.x.x-1.cdhx.1.1.p0.xxx/lib/hive/lib/* -Dspark.yarn.dist.files=/xxx/CDH-x.1.1-1.cdhx.1.1.p0.xxx/etc/hadoop/conf.dist/yarn-site.xml" \
--driver-class-path "/xxx/CDH-x.x.x-x.cdhx.x.x.p1000.xxx/jars/gson-2.8.1.jar:$JAVA_HOME/jre/lib/ext/*:/xxx/CDH-x.x.x-1.cdhx.x.x.p1000.xxx/jars/commons-cli-1.4.jar" \
--driver-cores $dc \
--driver-memory $dm \
--num-executors $ne \
--executor-cores $ec \
--executor-memory $em \
--conf spark.sql.crossJoin.enabled=true \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.yarn.maxAppAttempts=1 \
--conf spark.driver.maxResultSize=20G \
--conf spark.sql.shuffle.partitions=$partitions \
--conf spark.default.parallelism=$partitions \
--conf spark.sql.adaptive.enabled=true \
--conf spark.sql.adaptive.shuffle.targetPostShuffleInputSize=128000000 \
--conf spark.yarn.queue=xxx \
--conf spark.shuffle.io.maxRetries=200 \
--conf spark.shuffle.io.retryWait=30 \
--conf spark.port.maxRetries=120 \
--conf spark.core.connection.ack.wait.timeout=6000 \
--conf spark.shuffle.sort.bypassMergeThreshold=300 \
--conf spark.hadoop.hive.exec.dynamic.partition=$dp \
--conf spark.hadoop.hive.exec.dynamic.partition.mode=nonstrict \
--conf spark.hadoop.hive.exec.max.dynamic.partitions=2000 \
--name $name \
--files "$files" \ #/path/服务器本地文件
--class xxxApplication /xxx/xxx-1.0-SNAPSHOT.jar -jn $obj -sq "$sql" -ptby $ptby

2.2 获取文件

2.2.1 方案一

//If you add your external files using "spark-submit --files" your files will be uploaded to this HDFS folder: hdfs://your-cluster/user/your-user/.sparkStaging/application_1449220589084_0508//application_1449220589084_0508 is an example of yarn application ID!//1. find the spark staging directory by below code: (but you need to have the hdfs uri and your username)System.getenv("SPARK_YARN_STAGING_DIR"); --> .sparkStaging/application_1449220589084_0508//2. find the complete comma separated file paths by using:System.getenv("SPARK_YARN_CACHE_FILES"); -->
hdfs://yourcluster/user/hdfs/.sparkStaging/application_1449220589084_0508/spark-assembly-1.4.1.2.3.2.0-2950-hadoop2.7.1.2.3.2.0-2950.jar#__spark__.jar,
hdfs://yourcluster/user/hdfs/.sparkStaging/application_1449220589084_0508/your-spark-job.jar#__app__.jar,
hdfs://yourcluster/user/hdfs/.sparkStaging/application_1449220589084_0508/test_file.txt#test_file.txt

   --files会把文件上传到hdfs的.sparkStagin/applicationId目录下。

  spark.read().textFile(System.getenv("SPARK_YARN_STAGING_DIR") + "/xxx.xxx")

  textFile不指定hdfs、file或者其他前缀的话默认是hdfs://yourcluster/user/your_username下的相对路径。

2.2.2 方案二 SparkFiles.get(fileName)

  
  SparkFiles.get(fileName)    适用于local模式

JavaRDD<String> stringJavaRDD = sparkcontext.textFile(SparkFiles.get(fileName));List<String> collect = stringJavaRDD.collect();

  [注意事项]

  在cluster模式下(-- deploy-mode cluster )，-- files必须使用全局可视的地址（比如hdfs），否则driver将无法找到文件，出现FileNotFoundException。这是因为driver会在集群中任意一台worker节点上运行，使用本地地址无法找到文件。FileNotFoundException异常出现在SparkSession的getOrCreate()初始化方法中，因为此方法会调用addFile()，但是确找不到文件，导致SparkSession初始化失败。注意：–jars原理相同，但是getOrCreate()中调用addJars出现异常，但是并不会导SparkSession初始化失败，程序会继续运行。

  值得一提的是，在cluster模式下，spark-submit   --deploy-mode cluster   path-to-jar，其中path-to-jar也必须是全局可视路径，否则会发生找不到jar的异常。

2.2.3 方案三 new FileInputStream(fileName)

FileInputStream sqlstream = new FileInputStream(fileName);StringBuilder sqlContent = new StringBuilder();Scanner scanner = new Scanner(sqlstream, "UTF-8");while (scanner.hasNextLine()) {String line = scanner.nextLine();sqlContent.append(line).append("\n");
}

  适用于local、yarn client、yarn cluster模式,

2.2.4 方案四

Properties properties = new Properties();
properties.load(Thread.currentThread().getContextClassLoader().getResourceAsStream("test.properties"));

  适应于yarn、cluster模式