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Datums define what input data is seen by your code. It can be all data at once, each directory independently, individual files one by one, or combined data from multiple inputs together.


A datum is the smallest indivisible unit of computation within a job. A job can have one, many or no datums. Each datum is processed independently with a single execution of the user code on one of the pipeline worker pods. The files output by all of the datums are then combined together to create the final output commit.

Data distribution

Think of datums as a way to divide your input data and distribute processing workloads. They are instrumental in optimizing your pipeline performance.

You define how your data is spread among workers by specifying pipeline inputs for your pipeline in its pipeline specification file.

Based on this specification file, the data in the input of your pipeline is turned in datums each of which can contain 1 to many files. Pachyderm provides a wide variety of ways to define the granularity of each datum.

For example, you can configure a whole branch of an input repository to be one datum, each top-level filesystem object of a given branch to be a separate datum, specific paths on a given branch can be datums, etc... You can also create a combination of the above by aggregating multiple input.

Pipeline Inputs

This section details the tools at your disposal to "break down" your data and fit your specific use case.

PFS Input and Glob Pattern

The most primitive input of a pipeline is a PFS input, defined, at a minimum, by:

  • a repo containing the data you want your pipeline to consider
  • a branch to watch for commits
  • and a glob pattern to determine how the input data is partitioned.

A pipeline input can have one or multiple PFS inputs. In the latter case, Pachyderm provides a variety of options to aggregate several PFS inputs together.

Aggregating Datums Further

Your initial PFS inputs can be further combined by using the following aggregation methods, each configurable in the "input" section of the pipeline specification:

A cross input creates a cross-product of multiple repositories. Therefore, each datum from one repository is combined with each datum from the other repository.
A group input can take one or multiple repositories and processes all the data that match a particular file path pattern at once. Pachyderm's group is similar to the database group-by, but it matches on file paths only, not the content of the files.
A union input can take multiple repositories and processes all the data in each input independently. The pipeline processes the datums in no defined order and the output repository includes results from all input sources.
A join input enables you to join files that are stored in different Pachyderm repositories and match a particular file path pattern. Pachyderm supports joins similar to the database’s inner join and outer join operations, although they only match on file paths, not the actual file content.


Pipeline inputs, number of workers, parallelism parameters, and other performance knobs can all be configured through their corresponding fields in the pipeline specification.

To understand how datums affect data processing in Pachyderm, you need to understand the following subconcepts:

Last update: July 23, 2022
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