Output Many CSV files, and Combing into one without performance impact with Transform data using mapping data flows via Azure Data Factory - TagMerge
2Output Many CSV files, and Combing into one without performance impact with Transform data using mapping data flows via Azure Data FactoryOutput Many CSV files, and Combing into one without performance impact with Transform data using mapping data flows via Azure Data Factory

Output Many CSV files, and Combing into one without performance impact with Transform data using mapping data flows via Azure Data Factory

Asked 1 years ago
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2 answers

The number of files generated from the process is dependent upon a number of factors. If you've set the default partitioning in the optimize tab on your sink, that will tell ADF to use Spark's current partitioning mode, which will be based on the number of cores available on the worker nodes. So the number of files will vary based upon how your data is distributed across the workers. You can manually set the number of partitions in the sink's optimize tab. Or, if you wish to name a single output file, you can do that, but it will result in Spark coalescing to a single partition, which is why you see that warning. You may find it takes a little longer to write that file because Spark has to coalesce existing partitions. But that is the nature of a big data distributed processing cluster.

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Adding transformations requires three basic steps: adding the core transformation data, rerouting the input stream, and then rerouting the output stream. This can be seen easiest in an example. Let's say we start with a simple source to sink data flow like the following:
source(output(
        movieId as string,
        title as string,
        genres as string
    ),
    allowSchemaDrift: true,
    validateSchema: false) ~> source1
source1 sink(allowSchemaDrift: true,
    validateSchema: false) ~> sink1
If we decide to add a derive transformation, first we need to create the core transformation text, which has a simple expression to add a new uppercase column called upperCaseTitle:
derive(upperCaseTitle = upper(title)) ~> deriveTransformationName
Then, we take the existing DFS and add the transformation:
source(output(
        movieId as string,
        title as string,
        genres as string
    ),
    allowSchemaDrift: true,
    validateSchema: false) ~> source1
derive(upperCaseTitle = upper(title)) ~> deriveTransformationName
source1 sink(allowSchemaDrift: true,
    validateSchema: false) ~> sink1
And now we reroute the incoming stream by identifying which transformation we want the new transformation to come after (in this case, source1) and copying the name of the stream to the new transformation:
source(output(
        movieId as string,
        title as string,
        genres as string
    ),
    allowSchemaDrift: true,
    validateSchema: false) ~> source1
source1 derive(upperCaseTitle = upper(title)) ~> deriveTransformationName
source1 sink(allowSchemaDrift: true,
    validateSchema: false) ~> sink1
Finally we identify the transformation we want to come after this new transformation, and replace its input stream (in this case, sink1) with the output stream name of our new transformation:
source(output(
        movieId as string,
        title as string,
        genres as string
    ),
    allowSchemaDrift: true,
    validateSchema: false) ~> source1
source1 derive(upperCaseTitle = upper(title)) ~> deriveTransformationName
deriveTransformationName sink(allowSchemaDrift: true,
    validateSchema: false) ~> sink1

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