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GeezerVison is showing github.com — feat: Warn when the dbt import timestamp field is not a timestamp type by luc-pimentel · Pull Request #7038 · feast-dev/feast

feat: Warn when the dbt import timestamp field is not a timestamp type #7038

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feat: Warn when the dbt import timestamp field is not a timestamp type #7038

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5b9737d
test: Cover the dbt import timestamp field type warning
luc-pimentel Oct 15, 2026
46b60e0
feat: Warn when the dbt import timestamp field is not a timestamp type
luc-pimentel Oct 15, 2026
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feat: Warn when the dbt import timestamp field is not a timestamp type
The import command only checked that the timestamp column exists, so a column declared STRING or INT64 produced a FeatureView whose timestamp_field cannot anchor point-in-time retrieval. Check the column's dbt type with the existing dbt-to-Feast mapping and print a warning when it does not map to a timestamp type. The model is still imported.

Signed-off-by: luc-pimentel <[email protected]>
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@luc-pimentel
luc-pimentel committed Nov 10, 2026
commit 46b60e0dad31856ba821742aa491cc54663fd81e

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4 changes: 4 additions & 0 deletions 4 docs/how-to-guides/dbt-integration.md
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Expand Up @@ -434,6 +434,10 @@ Ensure your dbt model includes the entity column specified with `--entity-column

By default, Feast looks for `event_timestamp`. Use `--timestamp-field` to specify a different column name.

### "Timestamp field ... does not map to a timestamp type"

The timestamp column exists but its `data_type` in schema.yml is not a timestamp type (for example `STRING` or `INT64`). The model is still imported, but point-in-time retrieval needs a real timestamp column. Declare the column as `TIMESTAMP`, `DATETIME` or `DATE` in schema.yml. Columns with no `data_type` are treated as `STRING` and also trigger this warning.

### "Entity column ... may not be suitable as an entity key"

The entity column's `data_type` in schema.yml maps to a Feast type other than a string or integer type. Floating point values are unreliable join keys, and boolean, binary and timestamp columns rarely identify an entity. The model is still imported. Use a `STRING` or `INT64` column as the entity, or declare the column's type in schema.yml if it is wrong. `NUMBER` types with a precision above 18, such as Snowflake's `NUMBER(38,0)`, map to `Float64`; declare integer IDs as `INTEGER` or `NUMBER(18,0)` instead.
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23 changes: 22 additions & 1 deletion 23 sdk/python/feast/cli/dbt_import.py
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Expand Up @@ -123,7 +123,14 @@ def import_command(
"""
from feast.dbt.mapper import DbtToFeastMapper, map_dbt_type_to_feast_type
from feast.dbt.parser import DbtManifestParser, DbtModel
from feast.types import Array, Int32, Int64, String
from feast.types import (
Array,
Int32,
Int64,
String,
UnixTimestamp,
ZonedTimestamp,
)

# Parse manifest
click.echo(f"{Fore.CYAN}Parsing dbt manifest: {manifest_path}{Style.RESET_ALL}")
Expand Down Expand Up @@ -211,6 +218,20 @@ def import_command(
)
continue

# The parser reports STRING for columns with no declared data_type.
timestamp_column = next(c for c in model.columns if c.name == timestamp_field)
if map_dbt_type_to_feast_type(timestamp_column.data_type) not in (
UnixTimestamp,
ZonedTimestamp,
):
click.echo(
f"{Fore.YELLOW}Warning: Model '{model.name}' timestamp field "
f"'{timestamp_field}' has type '{timestamp_column.data_type}', "
f"which does not map to a timestamp type. Importing anyway. "
f"Declare it as a timestamp type in schema.yml if this is wrong."
f"{Style.RESET_ALL}"
)

# Validate ALL entity columns exist
missing_entities = [e for e in entity_cols if e not in column_names]
if missing_entities:
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