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2024年4月14日发(作者：工作在osi七层模型)
kettle job中结果的传递
英文版
Passing Results in Kettle Jobs
Kettle, an open-source ETL (Extract, Transform, Load) tool, is
widely used in data integration tasks. In Kettle, a job is a
collection of transformations and other jobs that are executed
in a sequential or parallel manner to achieve a specific data
processing goal. Passing results between different steps or
transformations within a Kettle job is crucial for ensuring data
integrity and flow control.
Methods of Passing Results:
Variables: Kettle allows you to define and use variables
throughout a job. These variables can be set in one step and
accessed in subsequent steps, enabling the passing of results.
For example, you might calculate a total count in one step and
use that count as a variable in a subsequent step to filter data.
Job Result: Each Kettle transformation or job can have a
result status (success, failure, etc.). This result status can be used
to control the flow of a job. For instance, if a transformation fails,
you might want to skip certain steps and proceed with error
handling.
Row Sets: Row sets are temporary data stores within a Kettle
job. They allow you to pass data between transformations or
steps without writing to a physical database or file. This is useful
when you need to share data between multiple steps but don't
want to persist it.
Database Lookup: If your Kettle job involves database
interactions, you can use database lookups to pass results. For
instance, you might query a table in one step and use the
retrieved data in a subsequent step for further processing.
Best Practices:
Centralized Logging: Implement centralized logging to track
and monitor the flow of data and results within your Kettle jobs.
This helps in debugging and understanding the behavior of
your ETL processes.
Error Handling: Design your jobs to handle errors gracefully.
Use try-catch blocks, failure steps, and conditional logic to
handle unexpected results and ensure data integrity.
Testing: Thoroughly test your Kettle jobs to ensure that
results are passed correctly under various scenarios. Test for
both successful and failed outcomes to validate your error
handling mechanisms.
In summary, passing results effectively in Kettle jobs is
crucial for ensuring smooth data processing. By utilizing
variables, job results, row sets, and database lookups, you can
achieve this goal and build robust ETL processes.
中文版
Kettle作业中结果的传递
Kettle是一个开源的ETL（Extract, Transform, Load）工具，广
泛用于数据集成任务。在Kettle中，一个作业是一系列按顺序或并行
方式执行的转换和其他作业，以实现特定的数据处理目标。在Kettle
作业中，将结果在不同的步骤或转换之间传递至关重要，以确保数据
完整性和流程控制。
传递结果的方法：
**变量：**Kettle允许在整个作业中定义和使用变量。这些变量可
以在一个步骤中设置，并在后续步骤中访问，从而实现结果的传递。
例如，您可能在一个步骤中计算总数，并在后续步骤中使用该总数作
为变量来过滤数据。
**作业结果：**每个Kettle转换或作业都可以有结果状态（成功、
失败等）。此结果状态可用于控制作业的流程。例如，如果转换失
败，您可能希望跳过某些步骤并执行错误处理。
**行集：**行集是作业内部的临时数据存储。它们允许您在不需要
写入物理数据库或文件的情况下，在多个步骤或转换之间传递数据。
当您需要在多个步骤之间共享数据但不想持久化它时，这很有用。
**数据库查找：**如果您的Kettle作业涉及数据库交互，您可以使
用数据库查找来传递结果。例如，您可能在一个步骤中查询表，并在
后续步骤中使用检索到的数据进行进一步处理。
最佳实践：
**集中日志记录：**实施集中日志记录以跟踪和监视您的Kettle作
业中数据和结果的流程。这有助于调试和理解您的ETL过程的行为。
**错误处理：**设计您的作业以优雅地处理错误。使用try-catch
块、失败步骤和条件逻辑来处理意外结果，并确保数据完整性。
**测试：**彻底测试您的Kettle作业，以确保在各种场景下结果都
能正确传递。测试成功和失败的结果，以验证您的错误处理机制。
总之，在Kettle作业中有效地传递结果对于确保顺畅的数据处理
至关重要。通过利用变量、作业结果、行集和数据库查找，您可以实
现这一目标，并构建健壮的ETL过程。
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