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feast-postgres-0.2.5
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مشاهده بیشترتوضیحات
PostgreSQL registry, and online and offline store for Feast
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | feast-postgres-0.2.5 |
| نام | feast-postgres |
| نسخه کتابخانه | 0.2.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Gunnar Sv Sigurbjörnsson |
| ایمیل نویسنده | gunnar.sigurbjornsson@gmail.com |
| آدرس صفحه اصلی | https://github.com/nossrannug/feast-postgres |
| آدرس اینترنتی | https://pypi.org/project/feast-postgres/ |
| مجوز | Apache License, Version 2.0 |
# Feast PostgreSQL Support
This repo adds PostgreSQL offline and online stores to [Feast](https://github.com/feast-dev/feast)
## Get started
### Install feast:
```shell
pip install feast
```
### Install feast-postgres:
```shell
pip install feast-postgres
```
### Create a feature repository:
```shell
feast init feature_repo
cd feature_repo
```
### Online store:
To configure the online store edit `feature_store.yaml`
```yaml
project: feature_repo
registry: data/registry.db
provider: local
online_store:
type: feast_postgres.PostgreSQLOnlineStore # MUST be this value
host: localhost
port: 5432 # Optional, default is 5432
database: postgres
db_schema: feature_store # Optional, default is None
user: username
password: password
offline_store:
...
```
When running `feast apply`, if `db_schema` is set then that value will be used when creating the schema, else the name of the schema will be the value in `user`. If the schema already exists then no schema is created, but the user must have privileges to create tables and indexes as well as dropping tables and indexes.
### Offline store:
To configure the offline store edit `feature_store.yaml`
```yaml
project: feature_repo
registry: data/registry.db
provider: local
online_store:
...
offline_store:
type: feast_postgres.PostgreSQLOfflineStore # MUST be this value
host: localhost
port: 5432 # Optional, default it 5432
database: postgres
db_schema: my_schema
user: username
password: password
```
The user will need to have privileges to create and drop tables in `db_schema` since temp tables will be created when querying for historical values.
### Registry store:
To configure the registry edit `feature_store.yaml`
```yaml
registry:
registry_store_type: feast_postgres.PostgreSQLRegistryStore
path: feast_registry # This will become the table name for the registry
host: localhost
port: 5432 # Optional, default is 5432
database: postgres
db_schema: my_schema
user: username
password: password
```
If the schema does not exists, the user will need to have privileges to create it. If the schema exists, the user will only need privileges to create the table.
### Example
Start by setting the values in `feature_store.yaml`. Then use `copy_from_parquet_to_postgres.py` to create a table and populate it with data from the parquet file that comes with Feast.
Then `example.py` can be used for the feature_store.
```python
# This is an example feature definition file
from google.protobuf.duration_pb2 import Duration
from feast import Entity, Feature, FeatureView, ValueType
from feast_postgres import PostgreSQLSource
# Read data from parquet files. Parquet is convenient for local development mode. For
# production, you can use your favorite DWH, such as BigQuery. See Feast documentation
# for more info.
driver_hourly_stats = PostgreSQLSource(
query="SELECT * FROM driver_stats",
event_timestamp_column="event_timestamp",
created_timestamp_column="created",
)
# Define an entity for the driver. You can think of entity as a primary key used to
# fetch features.
driver = Entity(name="driver_id", value_type=ValueType.INT64, description="driver id",)
# Our parquet files contain sample data that includes a driver_id column, timestamps and
# three feature column. Here we define a Feature View that will allow us to serve this
# data to our model online.
driver_hourly_stats_view = FeatureView(
name="driver_hourly_stats",
entities=["driver_id"],
ttl=Duration(seconds=86400 * 1),
features=[
Feature(name="conv_rate", dtype=ValueType.FLOAT),
Feature(name="acc_rate", dtype=ValueType.FLOAT),
Feature(name="avg_daily_trips", dtype=ValueType.INT64),
],
online=True,
batch_source=driver_hourly_stats,
tags={},
)
```
Then run:
```shell
feast apply
feast materialize-incremental $(date -u +"%Y-%m-%dT%H:%M:%S")
```
This will create the feature view table and populate if with data from the `driver_stats` table that we created in Postgres.
نیازمندی
| مقدار | نام |
|---|---|
| ==0.19.* | feast |
| >=2.8.3 | psycopg2-binary |
| >=2.0.0 | pyarrow |
| - | flake8 |
| ==21.10b0 | black |
| >=5 | isort |
| ==0.790 | mypy |
| ==0.7.0 | build |
| ==3.4.2 | twine |
| >=6.0.0 | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7.0 | Python |
نحوه نصب
نصب پکیج whl feast-postgres-0.2.5:
pip install feast-postgres-0.2.5.whl
نصب پکیج tar.gz feast-postgres-0.2.5:
pip install feast-postgres-0.2.5.tar.gz