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Excerpt

To prevent overwhelming the client or significantly impacting performance, 

D s product
rtrue
 generates one or more samples of the data for display and manipulation in the client application. Since 
D s product
 supports a variety of clients and use cases, you can change the size of samples, the scope of the sample, and the method by which the sample is created. This section provides background information on how the product manages dataset sampling.

How Sampling Works

Initial

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Data

When a dataset is first created, a background job begins to generate a sample using the first set of rows of the dataset. This initial sample is data sample is usually very quick to generate, so that you can get to work right away on your transformations.

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Info

NOTE: When a flow is shared, its samples are shared with other users. However, if those users do not have access to the underlying files that back a sample, they do not have access to the sample and must create their own.

Changing sample sizes

If needed, you can change the size of samples that are loaded into the browser your current recipe. You may need to reduce these sizes if you are experiencing performance problems or memory issues in the browser. For more information, see Change Recipe Sample Size.

Important notes on sampling

  • Sampling jobs may incur costs. These costs may vary between
    D s photon
    and your clustered running environments, depending on type of sample and cost of job execution.

  • When sampling from compressed data, the data is uncompressed and then expanded. As a result, the sample size reflects the uncompressed data.
  • Changes to preceding steps that alter the number of rows or columns in your dataset can invalidate the current sample, which means that the sample is no longer a valid representation of the state of the dataset in the recipe. In this case, 
    D s product
     automatically switches you back to the most recently collected sample that is currently valid. Details are below.

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