Skip to main content
Version: Beta 🚧

Configure Feature Repositories

Introduction​

The Repo Config is a required file (named repo.yaml by default) used to configure how Tecton builds and applies feature definitions during tecton plan, tecton apply, and tecton test.

It can be used to set defaults for Tecton objects in your Feature Repository, resulting in simpler and more easy-to-understand feature definitions. Defaults defined in the Repo Config can be overridden on a per-object basis in your Feature Repository. This helps teams using Tecton focus on developing powerful features while abstracting away parameters shared across feature definitions.

The Repo Config is supported in Version 0.8 and above.

Defining a Repo Config​

By default, the plan/apply/test commands will look for Repo Config file named repo.yaml in the root of your Feature Repository. To specify another file, use the --config flag (e.g. tecton plan --config my_config.yaml).

Tecton will automatically generate a starter Repo Config during tecton init. To generate a starter Repo Config for an existing Feature Repository, run tecton repo-config init. This command will generate a repo.yaml in your Feature Repository root containing some pre-filled defaults. For additional help with defining a Repo Config, run tecton repo-config show to display your Repo Config (e.g. to validate that it’s defined as expected).

Note that tecton restore will always restore your Repo Config to repo.yaml at the root of your Feature Repository, even after running tecton apply with the --config option. This is to ensure that the Repo Config file is easy to find and can be restored even when defined outside of your Feature Repository.

Supported Defaults​

The Repo Config currently supports setting the following defaults for your Feature Repository:

Tecton ObjectRepo Config KeySupported Parameters
Batch Feature Viewbatch_feature_viewbatch_compute
offline_store & online_store
tecton_materialization_runtime
environment
Stream Feature Viewstream_feature_viewbatch_compute & stream_compute
offline_store & online_store
tecton_materialization_runtime
environment
Feature Tablefeature_tablebatch_compute
offline_store & online_store
tecton_materialization_runtime
Feature Servicefeature_serviceon_demand_environment

The Repo Config will add support for other Tecton objects and parameters in the future.

If a default is specified in both the Repo Config and directly in the Feature Repository, the value defined in the Feature Repository's code takes precedence. This is especially relevant to object-type parameters (offline_store, online_store, batch_compute, and stream_compute); an object-level setting in the repository code will wholly override its counterpart in the Repo Config. In the future, the Repo Config will allow for key-specific configurations within these objects.

Example Repo Config​

Below is an example Repo Config with some defaults configured.

# repo.yaml

defaults:
batch_feature_view:
tecton_materialization_runtime: 0.8.0
online_store:
kind: RedisConfig
offline_store:
kind: OfflineStoreConfig
staging_table_format:
kind: ParquetConfig
batch_compute:
kind: DatabricksClusterConfig
instance_type: m5.xlarge
number_of_workers: 2
extra_pip_dependencies:
- haversine==2.8.0
stream_feature_view:
tecton_materialization_runtime: 0.8.0
stream_compute:
kind: DatabricksClusterConfig
instance_availability: on_demand
instance_type: m5.2xlarge
number_of_workers: 4
offline_store:
kind: OfflineStoreConfig
staging_table_format:
kind: ParquetConfig
feature_table:
tecton_materialization_runtime: 0.8.0
batch_compute:
kind: DatabricksClusterConfig
instance_type: m5.xlarge
number_of_workers: 2
online_store:
kind: RedisConfig
feature_service:
on_demand_environment: tecton-python-extended:0.4

Was this page helpful?

🧠 Hi! Ask me anything about Tecton!

Floating button icon