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2024年4月16日发(作者：c语言求最大公约数和最小公倍数函数调用)

spark作业运行流程

英文回答：

The workflow of a Spark job involves several steps,

including data input, transformation, and output. Let me

explain the process in detail.

1. Data Input:

The first step in running a Spark job is to provide the

input data. This can be done by reading data from various

sources such as Hadoop Distributed File System (HDFS),

Amazon S3, or local file systems. Spark supports reading

data in various formats like CSV, JSON, Parquet, etc. For

example, you can read a CSV file using the following code

in Scala:

scala.

val data = ("csv").option("header",

"true").load("")。

2. Transformation:

Once the data is loaded into Spark, you can perform

various transformations on it. Transformations are

operations that produce a new dataset from an existing one.

Spark provides a rich set of transformation functions like

map, filter, reduce, join, etc. These transformations are

lazily evaluated, meaning they are not executed immediately

but rather when an action is called. For instance, you can

transform the data by filtering out certain records using

the filter transformation:

scala.

val filteredData = ("age > 18")。

3. Action:

Actions are operations that trigger the execution of

all the transformations and produce a result or write data


本文标签： 最大公约数 语言 运行 流程 作业

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