Custom Configuration Mode

The primary configuration sections for Indexing Engine pipelines are described below. Each configuration section can be expressed in Python (for use in Python API mode) as well as YAML, but YAML is show here for brevity.

Using custom configuration is an advanced use-case. Most users will want to use the Default Configuration instead.

Indexing Engine Examples

The examples directory contains several examples of how to use the indexing engine with custom configuration.

Most examples include two different forms of running the pipeline, both are contained in the examples run.py

Using mostly the Python API
Using mostly the a pipeline configuration file

To run an example:

Run poetry shell to activate a virtual environment with the required dependencies.
Run PYTHONPATH="$(pwd)" python examples/path_to_example/run.py from the root directory.

For example to run the single_verb example, you would run the following commands:

poetry shell

PYTHONPATH="$(pwd)" python examples/single_verb/run.py

Configuration Sections

> extends

This configuration allows you to extend a base configuration file or files.

# single base
extends: ../base_config.yml

# multiple bases
extends:
  - ../base_config.yml
  - ../base_config2.yml

> root_dir

This configuration allows you to set the root directory for the pipeline. All data inputs and outputs are assumed to be relative to this path.

root_dir: /workspace/data_project

> storage

This configuration allows you define the output strategy for the pipeline.

type: The type of storage to use. Options are file, memory, and blob
base_dir (type: file only): The base directory to store the data in. This is relative to the config root.
connection_string (type: blob only): The connection string to use for blob storage.
container_name (type: blob only): The container to use for blob storage.

> cache

This configuration allows you define the cache strategy for the pipeline.

type: The type of cache to use. Options are file and memory, and blob.
base_dir (type: file only): The base directory to store the cache in. This is relative to the config root.
connection_string (type: blob only): The connection string to use for blob storage.
container_name (type: blob only): The container to use for blob storage.

> reporting

This configuration allows you define the reporting strategy for the pipeline. Report files are generated artifacts that summarize the performance metrics of the pipeline and emit any error messages.

type: The type of reporting to use. Options are file, memory, and blob
base_dir (type: file only): The base directory to store the reports in. This is relative to the config root.
connection_string (type: blob only): The connection string to use for blob storage.
container_name (type: blob only): The container to use for blob storage.

> workflows

This configuration section defines the workflow DAG for the pipeline. Here we define an array of workflows and express their inter-dependencies in steps:

name: The name of the workflow. This is used to reference the workflow in other parts of the config.
steps: The DataShaper steps that this workflow comprises. If a step defines an input in the form of workflow:<workflow_name>, then it is assumed to have a dependency on the output of that workflow.

workflows:
  - name: workflow1
    steps:
      - verb: derive
        args:
          column1: "col1"
          column2: "col2"
  - name: workflow2
    steps:
      - verb: derive
        args:
          column1: "col1"
          column2: "col2"
        input:
          # dependency established here
          source: workflow:workflow1

> input

type: The type of input to use. Options are file or blob.
file_type: The file type field discriminates between the different input types. Options are csv and text.
base_dir: The base directory to read the input files from. This is relative to the config file.
file_pattern: A regex to match the input files. The regex must have named groups for each of the fields in the file_filter.
post_process: A DataShaper workflow definition to apply to the input before executing the primary workflow.
source_column (type: csv only): The column containing the source/author of the data
text_column (type: csv only): The column containing the text of the data
timestamp_column (type: csv only): The column containing the timestamp of the data
timestamp_format (type: csv only): The format of the timestamp

input:
  type: file
  file_type: csv
  base_dir: ../data/csv # the directory containing the CSV files, this is relative to the config file
  file_pattern: '.*[\/](?P<source>[^\/]+)[\/](?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})_(?P<author>[^_]+)_\d+\.csv$' # a regex to match the CSV files
  # An additional file filter which uses the named groups from the file_pattern to further filter the files
  # file_filter:
  #   # source: (source_filter)
  #   year: (2023)
  #   month: (06)
  #   # day: (22)
  source_column: "author" # the column containing the source/author of the data
  text_column: "message" # the column containing the text of the data
  timestamp_column: "date(yyyyMMddHHmmss)" # optional, the column containing the timestamp of the data
  timestamp_format: "%Y%m%d%H%M%S" # optional,  the format of the timestamp
  post_process: # Optional, set of steps to process the data before going into the workflow
    - verb: filter
      args:
        column: "title",
        value: "My document"

input:
  type: file
  file_type: csv
  base_dir: ../data/csv # the directory containing the CSV files, this is relative to the config file
  file_pattern: '.*[\/](?P<source>[^\/]+)[\/](?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})_(?P<author>[^_]+)_\d+\.csv$' # a regex to match the CSV files
  # An additional file filter which uses the named groups from the file_pattern to further filter the files
  # file_filter:
  #   # source: (source_filter)
  #   year: (2023)
  #   month: (06)
  #   # day: (22)
  post_process: # Optional, set of steps to process the data before going into the workflow
    - verb: filter
      args:
        column: "title",
        value: "My document"