To prevent overwhelming the client or significantly impacting performance, Cloud Dataprep by TRIFACTA® generates one or more samples of the data for display and manipulation in the client application. Since Cloud Dataprep by TRIFACTA 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
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 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.
If you are wrangling a dataset with parameters, the initial sample that is loaded in the Transformer page is taken from the first matching dataset. Subsequent samples generated from the Transformer page are sampled across all datasets matched by parameter values.
- When a source has been swapped, the previous initial sample becomes invalid, and a new initial sample is automatically generated for you.
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.
NOTE: When a sample is executed from the Samples panel, it is launched based on the steps leading up to current location in the recipe steps. For example, if your recipe includes joining in other datasets, those steps are executed, and the sample is generated with dependencies on these other datasets. As a result, if you change your recipe steps that occur before the step where the sample was generated, you can invalidate your sample. More information is available below.
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:
- on a specified set of rows (firstrows)
- on a quick scan across the dataset
- on a full scan of the entire dataset
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.
For more information on creating samples, see Samples Panel.
Important notes on sampling
- A new sampling job is executed in Cloud Dataflow, which may incur costs.
- If the source file is in Avro format, the Cloud Dataflow job samples from the entire file. As a result, additional processing costs may be incurred. This is a known issue.
- 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, Cloud Dataprep by TRIFACTA automatically switches you back to the most recently collected sample that is currently valid. Details are below.
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:
- How well each sample represents the underlying dataset. Does the current sample reflect the likely statistics and outliers of the entire dataset at scale?
- How well each sample supports your next recipe step. If you're developing steps for managing bad data or outliers, for example, you may need to choose a different sample.
Tip: You can begin work on an outdated yet still valid sample while you generate a new one based on the current recipe.
- Some advanced sampling options are available only with execution across a scan of the full dataset.
- Undo/redo do not change the sample state, even if the sample becomes invalid.
With each step that is added or modified to your recipe, Cloud Dataprep by TRIFACTA 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.
Tip: After you have completed a step that significantly changes the number of rows, columns, or both in your dataset, you may need to generate a new sample, factoring in any costs associated with running the job. Performance costs may be displayed in the Transformer page.
NOTE: If you modify a SQL statement for an imported dataset, any samples based on the old SQL statement are invalidated.
- The Transformer page reverts to displaying the most recently collected sample that is currently valid.
You can generate a new sample of the same type through the Samples panel. If no sample is valid, you must generate a new sample before you can open the dataset.
A sample that is invalidated is listed under the Unavailable tab. It cannot be selected for use. If subsequent steps make it valid again, it re-appears in the Available tab.
Cloud Dataprep by TRIFACTA 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: 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 selection of a subset of rows in the dataset. These samples are comparatively fast to generate.You can apply quick scan or full scan to determine the scope of the sample.
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.
Find mismatched or missing data or both in one or more columns.
You specify one or more columns and whether the anomaly is:
- either of the above
Optionally, you can define an additional filter on other columns.
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.
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.