To prevent overwhelming the client or significantly impacting performance,
How Sampling Works
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.
- on a specified set of rows (firstrows)
on a quick scan across the dataset
Tip: Quick scan Tip
By default, Quick Scan samples are executed
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is not available or is disabled, the
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attempts to execute the Quick Scan sample on an available clustered running environment.
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- If the clustered running environment is not available or doesn't support Quick Scan sampling, then the Quick Scan sample job fails.
on a full scan of the entire datasettipTip:
Scan samples are executed in the cluster running environment.
When a non-initial sample is executed for a single dataset-recipe combination, the following steps occur:
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
and your clustered running environments, depending on type of sample and cost of job execution.
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- 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,
automatically switches you back to the most recently collected sample that is currently valid. Details are below.
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- Parameters: Subsequent samples generated from the Transformer page are sampled across all datasets matched by parameter values.
Variables: You can apply override values to the defaults for your dataset's variables at sample execution time. In this manner, you can draw your samples from specific sources files within your dataset with parameters.
After you have created a sample, you cannot delete it through the application.
For more information, see Sample Jobs Page.
Cancel Sample Jobs
Generating a sample can consume significant time, system resources, and in some deployments cost. As needed, you can cancel a sample job that is in progress in either of the following ways:
- Locate the in-progress sampling job in the Samples panel. Click X.
- Click the Jobs icon in the left nav bar. Select Sample jobs. For more information, see Sample Jobs Page.
After you have collected multiple samples of multiple types on your dataset, you can choose the proper sample to use for your current task, based on: