Submission of Spark tasks

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After preparing the job.properties file, log in to the Oozie client, run the Oozie job command in the corresponding working directory to execute the workflow file, and submit the Oozie tasks. For details, see the service operation guide of the Oozie component in the HD Product Documentation.

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Submission of Oozie tasks
Log in to the Oozie client and use Hue to submit Oozie tasks. For details, see the service operation guide of the Oozie component in the HD Product Documentation.

Advantages of Spark
1. Spark improves the data processing capability by as much as 10 to 100 times over the capabilities of MapReduce. It does this by using distributed memory computing and a Directed Acyclic Graph (DAG) engine. 2. Spark supports multiple development languages including Scala, Java, and Python. It supports dozens of highly abstract operators. This flexibility facilitates the construction of distributed data processing applications. 3. Spark provides one-stop data processing capability by working with SQL, Streaming, MLlib, and GraphX to form data processing stacks. 4. Spark can run in standalone, Mesos, or Yarn mode. It can access HDFS, HBase, and Hive data sources. It supports smooth swift from MapReduce. All of these functions allow Spark to easily fit into the Hadoop ecosystem.

Functions of submission on the USG6000
Functions of submission on the USG6000 are as follows: Configurations can take effect only after they are submitted.

Definition of Spark
Spark is a memory-based distributed computing framework. In iterative computing scenarios, data is stored in the memory during processing. This provides a computing capability that is 10 to 100 times greater than that provided by MapReduce. Spark can use HDFS as the underlying storage system, enabling users to quickly switch to Spark from MapReduce. In addition, Spark provides one-stop data analysis capabilities, including small-batch stream processing, off-line batch processing, SQL query, and data mining. Users can use all these capabilities seamlessly within an application.

What is Spark used for?
Spark is a memory-based distributed computing framework. In iterative computing scenarios, data is stored in the memory during processing. This provides a computing capability that is 10 to 100 times greater than that provided by MapReduce. Spark can use HDFS as the underlying storage system, enabling users to quickly switch to Spark from MapReduce. In addition, Spark provides one-stop data analysis capabilities, including small-batch stream processing, off-line batch processing, SQL query, and data mining. Users can use all these capabilities seamlessly within an application.

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