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Comment: Published by Scroll Versions from space DEV and version r093

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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

Info

NOTE: Generated samples are created by executing jobs on the applicable running environment. Quick Scan samples are executed in

D s photon
. Full Scan samples are generated in the applicable running environment on the cluster. Each running environment has a proprietary method of calculating the available volume of data in memory which is used for executing the sampling job that is launched in the running environment. As a result, the number of rows returned for the same sample type across different running environments can vary significantly.

Initial Data

When a dataset is first created, a background job begins to generate a sample using the first set of rows of the dataset.

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This initial data

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sample is usually very quick to generate, so that you can get to work right away on your transformations.

  • The default sample is the initial sample.By default, each sample is 10 MB in size or the entire dataset if it's smaller.  
  • If your source of data is a directory containing multiple files, the initial sample for the combined dataset is generated from the first set of rows in the first filename listed in the directory.
    • The maximum number of files in a directory that can be read in the initial sample is limited by parameter for performance reasons. 

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    • If you are wrangling a dataset with parameters, the initial sample loaded in the Transformer page is taken from the first matching dataset.

  • If the matching file is a multi-sheet Excel file, the sample is taken from the first sheet in the file.

  • By default, each initial sample is either: 
    • 10 MB in size
    • Limited by the maximum number of files
    • The entire dataset

  • If you are wrangling a dataset with parameters, the initial sample the source data is larger than 10MB in size, a random sample is automatically generated for you when the recipe is first loaded in the Transformer page is taken from the first matching dataset
    • The initial sample is selected by default. When the automatic random sample has finished generation, it can be manually selected for display.

Generating samples

Additional samples can be generated from the context panel on the right side of the Transformer page. Sample jobs are independent job executions. When a sample job succeeds or fails, a notification is displayed for you.

As you develop your recipe, you might need to take new samples of the data. For example, you might need to focus on the mismatched or invalid values that appear in a single column. Through the Transformer page, you can specify the type of sample that you wish to create and initiate the job to create the sample. This sampling job occurs in the background.

You can create a new sample at any time. When a sample is created, it is stored within your storage directory on the backend datastore.

Info

NOTE: The Initial Data sample contains raw data from the source. Any generated sample is stored in JSONLines format with additional metadata on the sample. These different storage formats can result is differences between initial and generated sample sizes.

For more information on creating samples, see Samples Panel.

Sampling methods

Depending on the type of sample you select, it may be generated based on one of the following methods, in increasing order of time to create:

  1. on a specified set of rows (firstrows)
  2. on a quick scan across the dataset 

    1. By default, Quick Scan samples are executed on the 

      D s photon
       running environment. 

    2. If 
      D s photon
       is not available or is disabled, the 
      D s webapp
       attempts to execute the Quick Scan sample on an available clustered running environment. 
    3. If the clustered running environment is not available or doesn't support Quick Scan sampling, then the Quick Scan sample job fails.
  3. on a full scan of the entire dataset 

    1. Full Scan samples are executed in the cluster running environment.

Sampling mechanics

When a non-initial sample is executed for a single dataset-recipe combination, the following steps occur:

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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

  • Depending on the running environment, 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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Info

NOTE:

D s product
does not delete samples after they have been created. If you are concerned about data accumulation, you should configure periodic purges of the appropriate directories on the base storage layer. For more information, please contact your IT administrator.

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 Job History icon in the left nav bar. Select Sample jobs. For more information, see Sample Jobs Page.

Choosing Samples

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:

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  • When a new sample is generated, any Sort transformations that have been applied previously must be re-applied. Depending on the type of output, sort order may not be preserved.

  • Samples taken from a dataset with parameters are limited to a maximum of 50 files when executed on the
    D s photon
    running environment. You can modify parameters as they apply to sampling jobs. See Samples Panel.

Sample Invalidation

With each step that is added or modified to your recipe,

D s product
 checks to see if the current sample is valid. Samples are valid based on the state of your flow and recipe at the step when the sample was collected. If you add steps before the step where it was created, the currently active sample can be invalidated. For example, if you change the source of data, then the sample in the Transformer page no longer applies, and a new sample must be displayed.

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Tip

Tip: You can annotate your recipe with comments, such as: sample: random and then create a new sample at that location.

Sample Types

D s product
 currently supports the following sampling methods.

First rows samples

This sample is taken from the first set of rows in the transformed dataset based on the current cursor location in the recipe. The first N rows in the dataset are collected based on the recipe steps up to the configured sample size.

  • This sample may span multiple datasets and files, depending on how the recipe is constructed.
  • The first rows sample is different from the initial sample, which is gathered without reference to any recipe steps.  

These samples are fast to generate. These samples may load faster in the application than samples of other types.

Tip

Tip: If you have chained together multiple recipes, all steps in all linked recipes must be run to provide visual updates. If you are experiencing performance problems related to this kind of updating, you can select a recipe in the middle of the chain of recipes and switch it off the initial sample to a different sample. When invoked, the recipes from the preceding datasets do not need to be executed, which can improve performance.

Random samples

Random selection of a subset of rows in the dataset. These samples are comparatively fast to generate.

Filter-based samples

Find specific values in one or more columns. For the matching set of values, a random sample is generated.

You must define your filter in the Filter textbox.

Anomaly-based samples

Find mismatched or missing data or both in one or more columns.

You specify one or more columns and whether the anomaly is:

  1. mismatched
  2. missing
  3. either of the above

Optionally, you can define an additional filter on other columns.

Stratified samples

Find all unique values within a column and create a sample that contains the unique values, up to the sample size limit. The distribution of the column values in the sample reflects the distribution of the column values in the dataset. Sampled values are sorted by frequency, relative to the specified column.

Optionally, you can apply a filter to this one.

Tip

Tip: Collecting samples containing all unique values can be useful if you are performing mapping transformations, such as values to columns. If your mapping contains too many unique values among your key-value pairs, you can try to delete all columns except the one containing key-value pairs in a step, collect the sample, add the mapping step, and then delete the step where all other columns are removed.

Cluster-based samples

Cluster sampling collects contiguous rows in the dataset that correspond to a random selection from the unique values in a column. All rows corresponding to the selected unique values appear in the sample, up to the maximum sample size. This sampling is useful for time-series analysis and advanced aggregations.

Optionally, you can apply an advanced filter to the column.For more information on sample types, see Sample Types.

D s also
inCQLtrue
label((label = "sample") OR (label = "sampling"))