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Starting with pypiper v0.13.0 pipestat is the recommended way of reporting pipeline statistics. You can browse the pipestat documentation to learn more about it, but briefly pipestat is a tool that standardizes reporting of pipeline results. It provides 1) a standard specification for how pipeline outputs should be stored; and 2) an implementation to easily write results to that format from within Python or from the command line.


There are a multiple advantages of using pipestat instead of the current pipeline results reporting system:

  1. Database results storage: the results can be stored either in a database or a YAML-formatted results file. This way a pypiper pipeline running in an emphemeral compute environment can report the results to the database and exit. No need to sync the results with a central results storage.
  2. Strict and clear results definition: all the results that can be reported by a pipeline run must be pre-defined in a pipestat results schema that in a simplest case just indicates the result's type. This presents pipestat clients with the possibility to reliably gather all the possible results and related metadata.
  3. On-the-fly results validation: the schema is used to validate and/or convert the reported result to a strictly determined type, which makes the connection of pypiper with downstream pipeline results processing software seamless.
  4. Unified, pipeline-agnostic results interface: other pipelines, possibly created with different pipeline frameworks, can read and write results via Python API or command line interface. This feature significantly incerases your pipeline interoperability.


In order to start reporting results with pipestat in your pipeline all you need to do is define a pipestat results schema:

  type: integer
  description: "This is my first result"
  type: string

And in the simplest case... that's it! Now you can use pipestat property of the PipelineManager object to report/retrieve results.

Pypiper by default will use a YAML-formatted file to store the reported results in the selected outfolder and will look for pipestat_results_schema.yaml file in the pipeline Python script directory.

Advanced features

Pypiper-pipestat integration really shines when more advanced features are used. Here's how to set them up.

Configure custom pipestat options

You can configure pipestat by passing arguments with custom values to pypiper.PipelineManager constructor:

pm = pypiper.PipelineManager(

Use a database to store reported results

In order to establish a database connection pipestat requires few pieces of information, which must be provided in a pipestat configuration file passed to the PipelineManager constructor.

This is an example of such a file:

  name: pypiper # database name
  user: pypiper # database user name
  password: pypiper # database password
  host: localhost # database host address
  port: 5433 # port the database is running on
  dialect: postgresql # type of the database 
  driver: psycopg2 # driver to use to communicate

For reference, here is a Docker command that would run a PostgreSQL instance that could be used to store the pipeline results when configured with with the configuration file above:

docker volume create postgres-data

docker run -d --name pypiper-postgres \
-p 5432:5433 -e POSTGRES_PASSWORD=pypiper \
-e POSTGRES_USER=pypiper -e POSTGRES_DB=pypiper \
-v postgres-data:/var/lib/postgresql/data postgres

Highlight results

The pipestat results schema can include any number of additional attributes for results. An example of that is results highlighting.

When a highlight: true attribute is included attribute under result identifier in the schema file the highlighted results can be later retrieved by pipestat clients via PipelineManager.pipestat.highlighted_results property, which simply returns a list of result identifiers. to be presented in a special way.


Since a pipeline run-specific PipestatManager instance is attached to the PipelineManager object all the public pipestat API can be used. Please refer to the pipestat API documentation to read about all the currently available features.

Here we present the most commonly used features:

  • results reporting

report a result, convert to schema-defined type and overwrite previously reported result

results = {
  "my_int_result": 10,
  "my_str_result": "test"
  • results retrieval
  • results schema exploration
  • exploration of canonical jsonschema representation of result schemas