Oozie

In Oozie site it say 'Shell action's stdout and stderr output are redirected to the Oozie Launcher map-reduce job task STDOUT that runs the shell command'. Can any one tell me where to exactly look at? Oozie runs the Shell action in a "launcher" (i.e. dummy Mapper) YARN countainer (#00002) under control of the mandatory AppMaster container (#00001) check your Oozie logs for "external ID job_xxxxx_xxxx " connect to the dreadful YARN console search for application_xxxxx_xxxx (yeah, not "job"...) ignore the AM logs link, go straight to the history link; if you are lucky it will redirect you

I need a solution for the following problem: My project has two jars in which one jar contains all bean classes like Employee etc, and the other jar contains MR jobs which uses the first jar bean class so when iam trying to run the MR job as a simple java program i am facing the issue of class not found (com.abc.Employee class not found as it is in another jar) so can any one provide me the solution how to solve the issue .... as in real time there may be many jars not 1 or 2 how to specify all those jars can any one please reply as soon as possible. You should have a lib folder in the HDFS

Let us say we have a case where an Oozie workflow is started with 3 Java action nodes. Each Java action is going to make an async HTTP call to an external web services (such as some web service exposed by google.com, yahoo.com, etc.) outside the Oozie/Hadoop cluster. I assume this is doable since Oozie support custom action node. Now, I don't want to have Oozie poll the external web services from time to time to check if the work is done in external web service. I want to have the external web service (let us assume we can modify that freely) call back Oozie to nofiy Oozie the work by external

I am facing issue while executing oozie sqoop action. In logs I can see that sqoop is able to import data to temp directory then sqoop creates hive scripts to import data. It fails while importing temp data to hive. In logs I am not getting any exception. Below is a sqoop action I am using. <workflow-app name="testSqoopLoadWorkflow" xmlns="uri:oozie:workflow:0.4"> <credentials> <credential name='hive_credentials' type='hcat'> <property> <name>hcat.metastore.uri</name> <value>${HIVE_THRIFT_URL}</value> </property> <property> <name>hcat.metastore.principal</name> <value>${KERBEROS_PRINCIPAL}<

How can I solve this problem when I submit a mapreduce job in Oozie Editor in Hue? : JA017: Could not lookup launched hadoop Job ID [job_local152843681_0009] which was associated with action [0000009-150711083342968-oozie-root-W@mapreduce-f660]. Failing this action! UPDATE: Here are log file: 2015-07-15 04:54:40,304 INFO ActionStartXCommand:520 - SERVER[myserver] USER[root] GROUP[-] TOKEN[] APP[My_Workflow] JOB[0000010-150711083342968-oozie-root-W] ACTION[0000010-150711083342968-oozie-root-W@:start:] Start action [0000010-150711083342968-oozie-root-W@:start:] with user-retry state :

I have a shell script in HDFS. I have scheduled this script in oozie with the following workflow. Workflow: <workflow-app name="Shell_test" xmlns="uri:oozie:workflow:0.5"> <start to="shell-8f63"/> <kill name="Kill"> <message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message> </kill> <action name="shell-8f63"> <shell xmlns="uri:oozie:shell-action:0.1"> <job-tracker>${jobTracker}</job-tracker> <name-node>${nameNode}</name-node> <exec>shell.sh</exec> <argument>${input_file}</argument> <env-var>HADOOP_USER_NAME=${wf:user()}</env-var> <file>/user/xxxx/shell_script/lib

最近遇到了很多正在研究ETL及其工具的同学向我们抱怨：同样都在用 Kettle ，起点明明没差异，但为什么别人ETL做的那么快那么好，自己却不断掉坑？ 其实，类似于像 Kettle 这样开源的工具，已经覆盖了大部分日常工作所需的功能了，直接部署一套就能够解决企业基本的需求。但在实际使用的过程中我们也会发现，kettle 如同是一个出场自带电话短信功能的智能手机，少了功能各异的智能 App 的配合，和只能接打电话的老年机也没什么不同。 今天我们就先对其中一个比较火热的“App”——调度工具，做一个简单的评测对比，帮助大家快速解锁用开源工具做 ETL 的新姿势。 一、为什么需要调度系统？ 开局我们先扫盲。 我们都知道大数据的计算、分析和处理，一般由多个任务单元组成（Hive、Sparksql、Spark、Shell等），每个任务单元完成特定的数据处理逻辑。 多个任务单元之间往往有着强依赖关系，上游任务执行并成功，下游任务才可以执行。比如上游任务结束后拿到 A 结果，下游任务需结合 A 结果才能产出 B 结果，因此下游任务的开始一定是在上游任务成功运行拿到结果之后才可以开始。 而为了保证数据处理结果的准确性，就必须要求这些任务按照上下游依赖关系有序、高效的执行。一个较为基础的处理方式是，预估出每个任务处理所需时间，根据先后顺序，计算出每个任务的执行的起止时间，通过定时跑任务的方式