Metadata Services

While building Enterprise extensions for the Elyra project we identified that there is very often a requirement to integrate with external runtimes, data sources, and other components hosted in remote locations or that need additional metadata in order to connect to these external components.

The Metadata Service provides a generic service that can be used to store metadata that can be easily integrated with Elyra backend and/or frontend components.

Metadata Services

Metadata Services structure using the default file system store

The default implementation is for the metadata services to store metadata files in the file system, grouped by directories based on the type of metadata.

The root directory for metadata is relative to the ‘Jupyter Data directory’ (e.g. jupyter –data-dir)


Each type of metadata is then stored in a child directory, which internally is referred to as the namespace.

As an example runtimes is the namespace for runtime metadata instances that reside in the following directory:


The contents of this folder would then include multiple metadata files, each associated with a type or schema corresponding to the desired runtime platform.

For example, the following contains runtime metadata for two runtime platforms, airflow and kfp, where each runtime type has 1 or 2 runtimes defined, respectively.


And each metadata file looks like:

  "display_name": "Kubeflow Pipeline - Fyre",
  "schema_name": "kfp",
  "metadata": {
    "api_endpoint": "",
    "api_username": "",
    "api_password": "mypassword",
    "cos_endpoint": "",
    "cos_username": "minio",
    "cos_password": "minio123",
    "cos_bucket": "lresende"

Because the runtime platform schemas are considered “factory data”, the schema files are provided as part of the distribution and are located in the Elyra distribution under elyra/metadata/schemas:

[path to python distributions]/elyra/metadata/schemas/kfp.json
[path to python distributions]/elyra/metadata/schemas/airflow.json

Metadata Client API

Users can easily manipulate metadata via the Client API

elyra-metadata list runtimes
Available metadata instances for runtimes (includes invalid):

Schema   Instance       Resource  
------   --------       -------- 
kfp      kfp-fyre       /Users/lresende/Library/Jupyter/metadata/runtimes/kfp-fyre.json
kfp      kfp-qa         /Users/lresende/Library/Jupyter/metadata/runtimes/kfp-qa.json
airflow  airflow-cloud  /Users/lresende/Library/Jupyter/metadata/runtimes/airflow-cloud.json

Metadata Service REST API

A REST API is available for easy integration with frontend components:

Retrieve all metadata for a given namespace:

GET /elyra/metadata/<namespace>

Retrieve a given metadata resource from a given namespace:

GET /elyra/metadata/<namespace>/<resource>

Metadata APIs

A Python API is also available for accessing and manipulating metadata. This is accomplished using the MetadataManager along with a corresponding storage class. The default storage class is FileMetadataStore.

from elyra.metadata import MetadataManager, FileMetadataStore

metadata_manager = MetadataManager(namespace="runtimes",

runtime_configuration = metadata_manager.get('kfp')

if not runtime_configuration:
    raise RuntimeError("Runtime metadata not available.")

api_endpoint = runtime_configuration.metadata['api_endpoint']
api_username = runtime_configuration.metadata['api_username']
api_password = runtime_configuration.metadata['api_password']
cos_endpoint = runtime_configuration.metadata['cos_endpoint']
cos_username = runtime_configuration.metadata['cos_username']
cos_password = runtime_configuration.metadata['cos_password']
bucket_name = runtime_configuration.metadata['cos_bucket']