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update sample repo so that it can be successfully applied for 0.9"
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from datetime import timedelta, datetime | ||
from tecton import StreamFeatureView, FilteredSource | ||
from tecton import StreamFeatureView, FilteredSource, BatchTriggerType | ||
from ads.entities import content_keyword | ||
from ads.data_sources.ad_impressions import keyword_click_source | ||
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@@ -19,5 +19,6 @@ | |
ttl=timedelta(days=30), | ||
tags={'release': 'production'}, | ||
owner='[email protected]', | ||
description='The ad clicks for a content keyword' | ||
description='The ad clicks for a content keyword', | ||
batch_trigger=BatchTriggerType.MANUAL | ||
) |
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# This is the Tecton repo config. It's used to configure how Tecton builds and applies your | ||
# feature definitions during `tecton plan/apply/test`. | ||
# | ||
# By default, the Tecton CLI will use the Repo Config specified at <TECTON_REPO_ROOT>/repo.yaml, but you | ||
# can specify another file by using `tecton plan --config my_config.yaml`. | ||
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# The `defaults` keyword specifies default parameter values for Tecton objects defined in your Feature Repository. | ||
# For example, you can set a default `tecton_materialization_runtime` for all Batch Feature Views. | ||
# Defaults can be overridden on a per-object basis in your Python feature definitions. | ||
# See Tecton's documentation for details on which Tecton objects are currently supported by the `defaults` keyword. | ||
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defaults: | ||
batch_feature_view: | ||
tecton_materialization_runtime: 0.9.0 | ||
environment: tecton-rift-core-0.9.0 # For Rift-based Batch Feature Views | ||
stream_feature_view: | ||
tecton_materialization_runtime: 0.9.0 | ||
environment: tecton-rift-core-0.9.0 # For Rift-based Stream Feature Views | ||
feature_table: | ||
tecton_materialization_runtime: 0.9.0 | ||
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# Below is an example of other defaults that can be set using the `defaults` keyword. | ||
# defaults: | ||
# batch_feature_view: | ||
# tecton_materialization_runtime: 0.9.0 | ||
# 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: 0.9.0 | ||
# 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: 0.9.0 | ||
# 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 |