Provenance capture

Specification scope and status

This specification describes the current implementation.

The ability to capture process provenance—the information what activity initiated by which entity yielded which outputs, given a set of parameters, a computational environment, and potential input data—is a core feature of DataLad.

Provenance capture is supported for any computational process that can be expressed as a command line call. The simplest form of provenance tracking can be implemented by prefixing any such a command line call with datalad run .... When executed in the content of a dataset (with the current working directory typically being in the root of a dataset), DataLad will then:

  1. check the dataset for any unsaved modifications

  2. execute the given command, when no modifications were found

  3. save any changes to the dataset that exist after the command has exited without error

The saved changes are annotated with a structured record that, at minimum, contains the executed command.

This kind of usage is sufficient for building up an annotated history of a dataset, where all relevant modifications are clearly associated with the commands that caused them. By providing more, optional, information to the run command, such as a declaration of inputs and outputs, provenance records can be further enriched. This enables additional functionality, such as the automated re-execution of captured processes.

The provenance record

A DataLad provenance record is a key-value mapping comprising the following main items:

  • cmd: executed command, which may contain placeholders

  • dsid: DataLad ID of dataset in whose context the command execution took place

  • exit: numeric exit code of the command

  • inputs: a list of (relative) file paths for all declared inputs

  • outputs: a list of (relative) file paths for all declared outputs

  • pwd: relative path of the working directory for the command execution

A provenance record is stored in a JSON-serialized form in one of two locations:

  1. In the body of the commit message created when saving caused the dataset modifications

  2. In a sidecar file underneath .datalad/runinfo in the root dataset

Sidecar files have a filename (record_id) that is based on checksum of the provenance record content, and are stored as LZMA-compressed binary files. When a sidecar file is used, its record_id is added to the commit message, instead of the complete record.

Declaration of inputs and outputs

While not strictly required, it is possible and recommended to declare all paths for process inputs and outputs of a command execution via the respective options of run.

For all declared inputs, run will ensure that their file content is present locally at the required version before executing the command.

For all declared outputs, run will ensure that the respective locations are writeable.

It is recommended to declare inputs and outputs both exhaustively and precise, in order to enable the provenance-based automated re-execution of a command. In case of a future re-execution the dataset content may have changed substantially, and a needlessly broad specification of inputs/outputs may lead to undesirable data transfers.

Placeholders in commands and IO specifications

Both command and input/output specification can employ placeholders that will be expanded before command execution. Placeholders use the syntax of the Python format() specification. A number of standard placeholders are supported (see the run documentation for a complete list):

  • {pwd} will be replaced with the full path of the current working directory

  • {dspath} will be replaced with the full path of the dataset that run is invoked on

  • {inputs} and {outputs} expand a space-separated list of the declared input and output paths

Additionally, custom placeholders can be defined as configuration variables under the prefix For example, a configuration setting will cause the placeholder {myfile} to expand to data.txt.

Selection of individual items for placeholders that expand to multiple values is possible via the standard Python format() syntax, for example {inputs[0]}.

Result records emitted by run

When performing a command execution run will emit results for:

  1. Input preparation (i.e. downloads)

  2. Output preparation (i.e. unlocks and removals)

  3. Command execution

  4. Dataset modification saving (i.e. additions, deletions, modifications)

By default, run will stop on the first error. This means that, for example, any failure to download content will prevent command execution. A failing command will prevent saving a potential dataset modification. This behavior can be altered using the standard on_failure switch of the run command.

The emitted result for the command execution contains the provenance record under the run_info key.

Implementation details

Most of the described functionality is implemented by the function It is interfaced by the run command, but also rerun, a utility for automated re-execution based on provenance records, and containers-run (provided by the container extension package) for command execution in DataLad-tracked containerized environments. This function has a more complex interface, and supports a wider range of use cases than described here.