徐州企业网站建设公司,响应式购物网站设计,雄安做网站的公司,wordpress活动报名一、原理 spark-submit --files通常用来加载外部资源文件#xff0c;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#xff1a;file: | hdfs:// | http:// | ftp:// |…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.activeprod -Dorg.springframework.boot.logging.LoggingSystemnone -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 $em \
--conf spark.sql.crossJoin.enabledtrue \
--conf spark.dynamicAllocation.enabledfalse \
--conf spark.yarn.maxAppAttempts1 \
--conf spark.driver.maxResultSize20G \
--conf spark.sql.shuffle.partitions$partitions \
--conf spark.default.parallelism$partitions \
--conf spark.sql.adaptive.enabledtrue \
--conf spark.sql.adaptive.shuffle.targetPostShuffleInputSize128000000 \
--conf spark.yarn.queuexxx \
--conf spark.shuffle.io.maxRetries200 \
--conf spark.shuffle.io.retryWait30 \
--conf spark.port.maxRetries120 \
--conf spark.core.connection.ack.wait.timeout6000 \
--conf spark.shuffle.sort.bypassMergeThreshold300 \
--conf spark.hadoop.hive.exec.dynamic.partition$dp \
--conf spark.hadoop.hive.exec.dynamic.partition.modenonstrict \
--conf spark.hadoop.hive.exec.max.dynamic.partitions2000 \
--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 $ptby2.2 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
JavaRDDString stringJavaRDD sparkcontext.textFile(SparkFiles.get(fileName));ListString 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
