Spark Row Sampling

This node extracts a sample from the input data. The dialog enables you to specify the sample size.

Options

Absolute: Specify the absolute number of rows in the sample. If there are less rows than specified here, all rows are used.
Relative: The percentage of the number of rows in the sample. Must be between 0 and 100, inclusively.
Take from top: This mode selects the top most rows of the input data.
Draw randomly: Random sampling of all rows, you may optionally specify a fixed seed and adapt the sample with replacement setting (see below).
Stratified sampling: Check this button if you want stratified sampling, i.e. the distribution of values in the selected column is (approximately) retained in the output table. You may optionally specify a fixed seed and adapt the exact sampling and sample with replacement setting (see below).
Exact sampling: Exact sampling requires significant more resources than the per-stratum simple random sampling used in by default, but will provide the exact sampling size with 99.99% confidence.
Use random seed: If either random or stratified sampling is selected, you may enter a fixed seed here in order to get reproducible results upon re-execution. If you do not specify a seed, a new random seed is taken for each execution.
Sample with replacement: If selected a row from the input data can be chosen more than once.

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To use this node in KNIME, install the extension KNIME Extension for Apache Spark (legacy) from the below update site following our NodePit Product and Node Installation Guide:

v5.2

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.2.0.v202311021430

On NodePit since: 2023-12-06

Last update: 2024-04-19

KNIME versions: Since v3.6

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