如何kill掉hadoop正在执行的jobs

当MapReduce任务出现异常,无法正常执行时,可通过hadoop命令进行任务的终止操作。首先使用'hadoop job -list'查看当前运行的所有job,找到目标job的编号后,执行'hadoop job -kill [job编号]'即可终止指定的MapReduce任务。

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当MapReduce任务不能正常执行时,我们可以将该任务kill掉。
使用 hadoop job -list 列出当前hadoop正在执行的jobs。
可以查看到job任务的编号,然后使用命令:
hadoop job -kill job编号 来杀死该job任务。

### Hadoop Framework Jobs Explanation and Usage In the context of Hadoop, a job refers to an execution unit that processes data using MapReduce or other processing frameworks like Apache Spark. The configuration files mentioned such as `mapred-site.xml` play crucial roles in setting up how these jobs are executed within the cluster environment[^1]. For configuring mapreduce jobs specifically, one must ensure properties related to framework name point towards YARN which manages resources across distributed applications: ```xml <property> <name>mapreduce.framework.name</name> <value>yarn</value> </property> ``` This snippet ensures all mapreduce tasks will be managed by YARN resource manager. When submitting jobs from client machines configured similarly with correct `/etc/hosts`, it allows seamless interaction between nodes involved during computation phases including master node responsible for coordination alongside worker nodes performing actual computations on chunks of input datasets provided through HDFS (Hadoop Distributed File System)[^2]. The submission process typically involves specifying necessary parameters either via command line arguments when invoking jar files containing user-defined logic written against APIs exposed by chosen programming model over Hadoop ecosystem or programmatically inside scripts automating batch operations regularly scheduled throughout day-to-day analytics pipelines. To illustrate this concept practically consider following Python code utilizing PySpark API interacting directly with underlying infrastructure abstracted away under higher level constructs yet still leveraging core functionalities offered natively out-of-the-box without much hassle once setup correctly according previous sections regarding configurations adjustments made earlier steps described above. ```python from pyspark import SparkContext sc = SparkContext(appName="WordCount") lines = sc.textFile("hdfs://master:9000/user/data/input.txt") counts = lines.flatMap(lambda line: line.split()) \ .map(lambda word: (word, 1)) \ .reduceByKey(lambda a, b: a + b) output = counts.collect() for (word, count) in output: print(f"{word}: {count}") sc.stop() ``` --related questions-- 1. How does modifying `mapred-site.xml` affect job scheduling? 2. What role do workers play in executing Hadoop jobs? 3. Can you explain the significance of adjusting settings in `hadoop-env.sh` concerning Java options? 4. In what ways can Sqoop's installation impact Hadoop job performance? 5. Why is proper keytab file management critical for secure job submissions?
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