Environments in Rift

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Environments for Rift-based Feature Views are isolated compute environments where materialization jobs are executed.

Tecton Environments

Tecton publishes a set of Python Environments that include common feature transformation packages.

Environments are identified by a name and a version number. Pinned environments ensure that Feature Views and transformation logic run reliably and predictably.

The table below lists all available versions for these environments.

Environment	Date published
tecton-rift-core-0.9.0	2024-04-01

To view this list from the Tecton CLI, run tecton environment list.

Custom Python Environments

Data pipelines commonly rely on a variety of third-party libraries to help facilitate transformations. With Tecton, users can create custom environments that include dependencies used in Rift-based Feature Views.

The process for creating custom environments is as follows:

1. Create a requirements.txt file
2. Create a custom environment via the Tecton CLI.
3. Set the Feature View environment field to your custom environment name.

Create a requirements.txt file

Here's an example requirements.txt file that will be referenced in this tutorial:

# PyPI packages
fuzzywuzzy==0.18.0
pydantic<2
tecton[rift-materialization,snowflake]==0.9.0
tecton-runtime==0.0.3
urllib3<1.27

info

To support batch materialization for Batch & Stream Feature Views, the requirements.txt must include the tecton[rift-materialization] package pinned to a specific version 0.9.0+.

To support On-Demand Feature Views and Ingest API-based Feature Views, the requirements.txt must include the tecton-runtime package pinned to a specific version 0.0.3+.

We recommend using the latest available versions of these packages.

Create an environment via the Tecton CLI

To create an environment in Tecton, you can use the tecton environment create command.

$ tecton environment create --name "my-custom-env-0.1" --description "My Custom Env 0.1" --requirements /path/to/requirements.txt

💡 Creating environment 'my-custom-env-0.1' for job types:
✅ On Demand
✅ Rift Batch
✅ Rift Stream Ingest


⏳ Resolving dependencies for Python 3.8.17 and architecture x86_64. This may take a few seconds.....
✅ Successfully resolved dependencies

⏳ Downloading wheels. This may take a few seconds.....
⏳ Uploading compressed wheels in parts to S3. This may take a few seconds.....
Upload progress: 100%|████████████████████████████████████████████████| 9/9 [01:44<00:00, 11.62s/it]
✅ Successfully uploaded dependencies

$ tecton environment get --name "my-custom-env-0.1"


               Id               Name               Description      Type     Status    Materialization Version   Tecton Transform Version           Created At
===================================================================================================================================================================
19ba8091843146dc93e3480cd  my-custom-env-0.1   My Custom Env 0.1   CUSTOM    READY           0.9.0                       0.0.3              2024-01-01 00:51:43 UTC

Use an Environment in a Feature View

You can specify the environment name in your Feature definition by setting the environment field.

Below is an example Batch Feature View that uses the fuzzywuzzy package available in the my-custom-env-0.1 environment.

from data_sources import product_source
from entities import product
from tecton import batch_feature_view
from tecton.types import Field, Float64, String, Timestamp


fv_schema = [
    Field("product_id", String),
    Field("timestamp", Timestamp),
    Field("product_name", String),
    Field("similarity_score", Float64),
    Field("partial_similarity_score", Float64),
]


@batch_feature_view(
    sources=[product_source],
    entities=[product],
    mode="pandas",
    schema=fv_schema,
    environment="my-custom-env-0.1",
)
def moch_cheesecake_similarity(product_df):
    from fuzzywuzzy import fuzz

    baseline = "Mocha Cheesecake Fudge Brownie Bars"
    product_df["similarity_score"] = product_df["product_name"].apply(lambda name: fuzz.ratio(name, baseline))
    product_df["partial_similarity_score"] = product_df["product_name"].apply(
        lambda name: fuzz.partial_ratio(name, baseline)
    )
    return product_df

Delete an Environment

A custom environment can be deleted via the CLI using the tecton environment delete command. Deletion will fail if the environment is actively being used in a Feature View.

Usage Notes & FAQs

Usage Notes

• The Admin role is required to create and delete a custom environment via the Tecton CLI.
• Custom environments should be unique across all workspaces and can only contain letters, numbers, hyphens, and underscores.
• Environment creation takes place asynchronously, typically in 2-10 minutes. An environment should be in READY status before it can be used -- check its status with tecton environment get).

FAQ: How do I resolve dependency resolution errors during environment creation?

• These can be caused by conflicting version requirements in the requirements.txt file. Run the resolve-dependencies command to view the fully resolved set of requirements and identify incompatible version specifications.
• Verify that each dependency in the requirements.txt file has a .whl file available for download in PyPI (or any custom Artifactory) for the relevant Python version and x86 architecture.
• Tecton uses the pex utility to generate the fully resolved dependency set. To inspect the underlying commands, run the tecton environment resolve-dependencies --verbose.

FAQ: How do I check which dependencies are available in my custom environment?

• Run tecton environment describe to see the input requirements as well as the fully resolved set of dependencies for a custom environment.