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Version: Beta 🚧

0.9 to 1.0 Upgrade Guide

Overview​

With Tecton 1.0, we’re excited to announce new capabilities along two major fronts:

GenAI. We are applying our 5+ years experience of helping enterprises run their AI applications in production to the GenAI world. With Tecton 1.0, you can now manage, enrich and serve your prompts in production, cost efficiently generate, store and serve embeddings, and provide your LLM with additional context in the form of features as tools and knowledge.

Core platform. We are continuing to evolve our core platform, improving performance and cost efficiency at scale. New capabilities include Remote Dataset Generation, Compaction for Streaming Time Window Aggregation features, and improved controls for realtime compute and feature serving infrastructure. 1.0 also includes capabilities such as Plan Integration tests to further simplify our developer experience; or Model Generated Features to allow our customers to make the most of all of their data. And there’s much more!

Upgrade to 1.0​

To begin, read the general Upgrade Process for background on migrating between SDK versions and view the changelog for a comprehensive list of improvements in 1.0.

The majority of code changes you'll make while upgrading from 0.9 are driven by the a pair of framework improvements in 1.0 which are covered in depth below.

caution

Before you begin, make sure your local repository is in sync with the version currently applied to Tecton. You can also run tecton restore to sync from Tecton to your working directory.

Migrating to the features Parameter​

In Tecton 1.0, all feature views will declare their features with a new features parameter. The streamlined feature definition includes feature-level descriptions and tagging for improved discoverability.

# Before
user = Entity(name="user", join_keys=["user_id"])


@batch_feature_view(
    # ...
    entities=[user],
    schema=[
        Field("user_id", String),
        Field("signup_timestamp", Timestamp),
        Field("credit_card_issuer", String),
    ],
)
def my_bfv():
    pass


# After
user = Entity(name="user", join_keys=[Field("user_id", String)])


@batch_feature_view(
    # ...
    entities=[user],
    timestamp_field="signup_timestamp",
    features=[Attribute("credit_card_issuer", String)],
)
def my_bfv():
    pass

The features parameter consolidates and replaces the old parameters schema and aggregations previously defined on feature views.

New Filtered Source Default​

Previous versions of Tecton have offered FilteredSource as a wrapper for DataSources. Filtering data at the source ensures that only the necessary data is processed, reducing computational overhead and improving the efficiency of your feature engineering pipeline. This was recommended for most use cases, and has become the default behavior as of 1.0.

To improve the flexibility and UX for data source filtering, Tecton 1.0 offers a new set of DataSource APIs and support for start/end_time as opposed to just start_time_offset.

# Before:
@batch_feature_view(
    # ...
    sources=[
        ds_one,  # Unfiltered by default
        FilteredSource(ds_two),
        FilteredSource(ds_three, start_time_offset=timedelta(days=-35)),
    ],
)
def bfv():
    pass


# Equivalent in 1.0.0
from tecton import TectonTimeConstant


@batch_feature_view(
    # ...
    sources=[
        ds_one.unfiltered(),
        ds_two,  # Filtered by default
        ds_three.select_range(
            start_time=TectonTimeConstant.MATERIALIZATION_START_TIME - timedelta(days=35),
            end_time=TectonTimeConstant.MATERIALIZATION_END_TIME,
        ),
    ]
)
def bfv():
    pass

Upgrade Utilities​

We've provided a set of tools to ease the transition between SDK versions. Notably:

  • The v09_compat module contains objects compatible with Tecton 0.9. Note that these are meant as a temporary bridge between versions, and prolonged use is not recommended.
  • tecton upgrade is a new CLI command which can inspect your workspace and provide an interactive upgrade guide with code suggestions.
  • tecton plan / apply will block on changes which include both a tecton.v09_compat to tecton upgrade and a substantive change to feature views including destruction or creation. This guard rail is meant to ensure that SDK upgrade work does not affect production feature serving or materialization.

Tecton recommends relying on tecton upgrade for upgrading feature definitions. The tool is capable of generating sample code specific to your workspace such as type suggestions or imports. This guide covers the same material as the upgrade tool.

Prepare your Repository​

Upgrade repo.yaml​

  1. In the repo.yaml file at the base of your repository:
    1. Spark Based Feature Views: Update the tecton_materialization_runtime parameter in repo.yaml to 1.0.0
    2. Rift Based Feature Views: Update the environment field in your repo.yaml to tecton-rift-core-1.0.0
  2. Install Tecton 1.0 locally with pip install tecton==1.0.0

Modify Import Paths​

We will begin the upgrade process by first converting all the objects to the backwards-compatible 0.9 versions using the v09_compat library. We will then go through each Tecton Object and make the necessary changes to use the 1.0 version of the object.

Globally replace all tecton imports with tecton.v09_compat imports. Note that this does not apply to tecton.types or tecton.aggregation_functions, which can remain unchanged. This will will allow you to migrate all Data Sources, Feature Views, Entities, Feature Services, and Transformations piece by piece.

# Before
from tecton import Entity, batch_feature_view
from tecton.types import Field, Int64

# After
from tecton.v09_compat import Entity, batch_feature_view
from tecton.types import Field, Int64
tip

Consider a command line incantation such as find . -type f -exec perl -i -pe 's/from tecton import/from tecton.v09_compat import/g' {} +

Run tecton plan to validate these changes. You may see a list of yellow warnings along with an Update SDK Version confirmation on top. You should not see destructive or creation changes for your feature views.

  ~ Update SDK Version: 0.9.14 -> 1.0.0

  ~ Update Stream Data Source
    name:           ad_impressions_stream

  ~ Update Batch Feature View
    name:           user_approx_distinct_merchant_transaction_count_30d
    description:    How many transactions the user has made to distinct merchants in the last 30 days.
    tecton_materialization_runtime: 0.9.0 -> 1.0

  ~ Update Stream Feature View
    name:           user_continuous_transaction_count
    owner:          demo-user@tecton.ai
    description:    Number of transactions a user has made recently
    tecton_materialization_runtime: 0.9.0 -> 1.0
    aggregation_leading_edge: AGGREGATION_MODE_UNSPECIFIED -> AGGREGATION_MODE_LATEST_EVENT_TIME
    warning:        `LATEST_EVENT_TIME` will be deprecated in Tecton sdk 1.1.

Upgrade your Feature Definitions​

This section describes step by step instructions for upgrading your feature definitions.

tip

The tecton upgrade CLI tool accompanies this guide. It provides updated code snippets and guidance specific to your workspace.

❯ tecton upgrade
✅ Imported 29 Python modules from the feature repository

Migration Progress:
  Step 1: Migrate Data Sources - ✅ (4/4 Data Sources migrated).
  Step 2: Migrate Entities - ✅ (4/4 Entities migrated).
  Step 3: Migrate Batch Feature Views - ✅ (8/8 Batch Feature Views migrated).
  Step 4: Migrate Stream Feature Views - 🚫 (0/5 Stream Feature Views migrated).
  Step 5: Migrate On-Demand Feature Views - 🚫 (0/5 On Demand Feature Views migrated).
  Step 6: Migrate Feature Tables - 🚫 (0/3 Feature Tables migrated).
  Step 7: Migrate all other imports from `tecton.v09_compat` to `tecton`.

Step 4: Migrate Stream Feature Views.

Detected 5 Stream Feature Views in need of an upgrade:

# ...

PushSource​

PushSource was deprecated in 0.9 and has been removed in 1.0. It should be replaced by a StreamSource using a PushConfig as shown below:

# Before
from tecton.v09_compat import PushSource

user_click_push_source = PushSource(
    name="user_event_source",
    schema=user_schema,
)

# After
from tecton import StreamSource, PushConfig

user_click_push_source = StreamSource(
    name="user_event_source",
    stream_config=PushConfig(),
    schema=user_schema,
)

Entity​

Previously, Entity objects accepted untyped join key names. In 1.0 and beyond, Tecton requires that join_keys be typed Field objects. tecton upgrade can infer join_key types for you.

# Before
from tecton.v09_compat import Entity

user_entity = Entity(name="user", join_keys=["user_id"])

# After
from tecton import Entity

user_entity = Entity(name="user", join_keys=[Field("user_id", String)])

BatchFeatureView​

In 1.0 Batch Feature Views accept a features parameter instead of schema. The upgrade command can help generate properly typed Features arguments. You will also need to set timestamp_field explicitly. tecton upgrade can infer Feature schema for you.

You will also be required to handle the new FilteredSource default. For more information on this change, see the accompanying documentation.

Migrate non aggregate batch feature views:

# Before
@batch_feature_view(
    # ...
    entities=[FilteredSource(user)],
    schema=[
        Field("user_id", String),
        Field("value", Int64),
        Field("timestamp", Timestamp),
    ],
)
def my_feature_view(input):
    return f"""
        SELECT user_id, value, timestamp FROM {input}
    """


# After
@batch_feature_view(
    # ...
    entities=[user],  # Source is filtered by default
    features=[
        Attribute(name="value", dtype=Int64),
    ],
    timestamp_field="timestamp",
)
def feature_view(input):
    return f"""
        SELECT user_id, value, timestamp FROM {input}
    """

Migrate aggregation batch feature views:

# Before
@batch_feature_view(
    # ...
    entities=[FilteredSource(user)],
    aggregations=[
        Aggregation(
            column="value",
            function="count",
            time_window=timedelta(days=7),
        ),
    ],
)
def feature_view(input):
    return f"""
        SELECT user_id, value, timestamp FROM {input}
    """


# After
@batch_feature_view(
    # ...
    entities=[user],  # Source is filtered by default
    features=[
        Aggregate(
            input_column=Field("value", Int64),
            function="count",
            time_window=timedelta(days=7),
        ),
    ],
    timestamp_field="timestamp",
)
def feature_view(input):
    return f"""
        SELECT user_id, value, timestamp FROM {input}
    """

StreamFeatureView​

All the updates required for BatchFeatureViews apply to StreamFeatureViews as well. These include the features parameter, a required timestamp_field, and a new FilteredSource default. There is one additional change:

  • 1.0 introduces a new aggregation_leading_edge parameter, allowing users to set the aggregation strategy for processing stream events. To upgrade safely, this value should be set to aggregation_leading_edge=AggregationLeadingEdge.LATEST_EVENT_TIME.

Migrate non aggregate stream feature views:

# Before
@stream_feature_view(
    # ...
    source=FilteredSource(transactions_stream),
)
def last_transaction_amount_sql(transactions):
    return f"""
        SELECT
            timestamp,
            user_id,
            value
        FROM
            {transactions}
        """


# After
@stream_feature_view(
    # ...
    source=transactions_stream,  # Source is filtered by default
    features=[
        Attribute("value", Int64),
    ],
    timestamp_field="timestamp",
    aggregation_leading_edge=AggregationLeadingEdge.LATEST_EVENT_TIME,
)
def last_transaction_amount_sql(transactions):
    return f"""
        SELECT
            timestamp,
            user_id,
            value
        FROM
            {transactions}
        """

Migrate aggregate stream feature views:

# Before
@stream_feature_view(
    # ...
    source=FilteredSource(transactions_stream),
    aggregations=[
        Aggregation(column="amt", function="sum", time_window=timedelta(hours=1)),
    ],
)
def last_transaction_amount_sql(transactions):
    return f"""
        SELECT
            timestamp,
            user_id,
            value
        FROM
            {transactions}
        """


# After
@stream_feature_view(
    # ...
    source=transactions_stream,  # Source is filtered by default
    features=[
        Aggregate(input_column=Field("amt", Int64), function="sum", time_window=timedelta(hours=1)),
    ],
    timestamp_field="timestamp",
    aggregation_leading_edge=AggregationLeadingEdge.LATEST_EVENT_TIME,
)
def last_transaction_amount_sql(transactions):
    return f"""
        SELECT
            timestamp,
            user_id,
            value
        FROM
            {transactions}
        """

OnDemandFeatureView -> RealtimeFeatureView​

on_demand_feature_view has been renamed to realtime_feature_view. Upgrading these feature views requires the same features parameter change as Batch and Stream feature views.

# Before
@on_demand_feature_view(
    mode="pandas",
    sources=[transaction_request],
    schema=[Field("amount", Int64)],
)
def my_feature_view(request_df: pandas.DataFrame):
    pass


# After
@realtime_feature_view(
    mode="pandas",
    sources=[transaction_request],
    features=[Attribute("amount", Int64)],
)
def my_feature_view(request_df: pandas.DataFrame):
    pass

FeatureTable​

Feature Tables now require a timestamp_field and the new features parameter

# Before
ft = FeatureTable(
    entities=[user_entity],
    schema=[
        Field("user_id", String),
        Field("clicked", Int64),
        Field("timestamp", Timestamp),
    ],
)

# After
ft = FeatureTable(
    entities=[user_entity],
    features=[
        Attribute("clicked", Int64),
    ],
    timestamp_field="timestamp",
)

Unit Tests and Timestamps​

In 1.0, TectonDataFrame#to_pandas returns Timezone (TZ) aware datetime64[us, UTC] objects for Timestamp columns. This return type aligns with the rest of Tecton's framework, but diverges from previous SDKs, which returned TZ-naive objects from this call pattern.

This change has the potential to break Timestamp comparisons in unit tests or similar code paths. If this affects your repo, you have two options:

  1. Localize the timezone to UTC with df['timestamp'] = df['timestamp'].dt.tz_localize('UTC')
  2. Opt into the old behavior by setting conf.set("TECTON_STRIP_TIMEZONE_FROM_FEATURE_VALUES", "true")

Option 1 is recommended, as it aligns online / offline return values exactly and will be the preferred behavior moving forward.

Final Step​

Congratulations! You've successfully migrated all objects which include a breaking change in Tecton 1.0. It is now safe to migrate existing tecton.v09_compat imports to the top level tecton module. You should apply this final change and return to feature development with the newfound power of Tecton 1.0.

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