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diff --git a/docs/user_guides/fs/feature_view/query.md b/docs/user_guides/fs/feature_view/query.md
index 3f7522e3e..05a5242a4 100644
--- a/docs/user_guides/fs/feature_view/query.md
+++ b/docs/user_guides/fs/feature_view/query.md
@@ -8,36 +8,49 @@ The joining functionality is heavily inspired by the APIs used by Pandas to merg
=== "Python"
```python
+ fs = ...
+ credit_card_transactions_fg = fs.get_feature_group(name="credit_card_transactions", version=1)
+ account_details_fg = fs.get_feature_group(name="account_details", version=1)
+ merchant_details_fg = fs.get_feature_group(name="merchant_details", version=1)
+
# create a query
- feature_join = rain_fg.select_all() \
- .join(temperature_fg.select_all(), on=["date", "location_id"]) \
- .join(location_fg.select_all())
+ selected_features = credit_card_transactions_fg.select_all() \
+ .join(account_details_fg.select_all(), on=["cc_num"]) \
+ .join(merchant_details_fg.select_all())
# save the query to feature view
feature_view = fs.create_feature_view(
- name='rain_dataset',
- query=feature_join
+ version=1,
+ name='credit_card_fraud',
+ labels=["is_fraud"],
+ query=selected_features
)
# retrieve the query back from the feature view
- feature_view = fs.get_feature_view(“rain_dataset”, version=1)
+ feature_view = fs.get_feature_view(“credit_card_fraud”, version=1)
query = feature_view.query
```
=== "Scala"
```scala
+
+ val fs = ...
+ val creditCardTransactionsFg = fs.getFeatureGroup("credit_card_transactions", 1)
+ val accountDetailsFg = fs.getFeatureGroup(name="account_details", version=1)
+ val merchantDetailsFg = fs.getFeatureGroup("merchant_details", 1)
+
// create a query
- val featureJoin = (rainFg.selectAll()
- .join(temperatureFg.selectAll(), on=Seq("date", "location_id"))
- .join(locationFg.selectAll()))
+ val selectedFeatures = (creditCardTransactionsFg.selectAll()
+ .join(accountDetailsFg.selectAll(), on=Seq("cc_num"))
+ .join(merchantDetailsFg.selectAll()))
val featureView = featureStore.createFeatureView()
- .name("rain_dataset")
- .query(featureJoin)
+ .name("credit_card_fraud")
+ .query(selectedFeatures)
.build();
// retrieve the query back from the feature view
- val featureView = fs.getFeatureView(“rain_dataset”, 1)
+ val featureView = fs.getFeatureView(“credit_card_fraud”, 1)
val query = featureView.getQuery()
```
@@ -53,18 +66,18 @@ Selecting features from a feature group is a lazy operation, returning a query w
=== "Python"
```python
- rain_fg = fs.get_feature_group("rain_fg")
+ credit_card_transactions_fg = fs.get_feature_group("credit_card_transactions")
# Returns Query
- feature_join = rain_fg.select(["location_id", "weekly_rainfall"])
+ selected_features = credit_card_transactions_fg.select(["amount", "latitude", "longitude"])
```
=== "Scala"
```Scala
- val rainFg = fs.getFeatureGroup("rain_fg")
+ val creditCardTransactionsFg = fs.getFeatureGroup("credit_card_transactions")
# Returns Query
- val featureJoin = rainFg.select(Seq("location_id", "weekly_rainfall"))
+ val selectedFeatures = creditCardTransactionsFg.select(Seq("amount", "latitude", "longitude"))
```
#### Join
@@ -75,35 +88,103 @@ By default, Hopsworks will use the maximal matching subset of the primary keys o
=== "Python"
```python
# Returns Query
- feature_join = rain_fg.join(temperature_fg)
+ selected_features = credit_card_transactions_fg.join(account_details_fg)
```
=== "Scala"
```Scala
// Returns Query
- val featureJoin = rainFg.join(temperatureFg)
+ val selectedFeatures = creditCardTransactionsFg.join(accountDetailsFg)
```
More complex joins are possible by selecting subsets of features from the joined feature groups and by specifying a join key and type.
-Possible join types are "inner", "left" or "right". Furthermore, it is possible to specify different features for the join key of the left and right feature group.
-The join key lists should contain the names of the features to join on.
+Possible join types are "inner", "left" or "right". By default`join_type` is `"left". Furthermore, it is possible to specify different
+features for the join key of the left and right feature group. The join key lists should contain the names of the features to join on.
=== "Python"
```python
- feature_join = rain_fg.select_all() \
- .join(temperature_fg.select_all(), on=["date", "location_id"]) \
- .join(location_fg.select_all(), left_on=["location_id"], right_on=["id"], join_type="left")
+ selected_features = credit_card_transactions_fg.select_all() \
+ .join(account_details_fg.select_all(), on=["cc_num"]) \
+ .join(merchant_details_fg.select_all(), left_on=["merchant_id"], right_on=["id"], join_type="inner")
```
=== "Scala"
```scala
- val featureJoin = (rainFg.selectAll()
- .join(temperatureFg.selectAll(), Seq("date", "location_id"))
- .join(locationFg.selectAll(), Seq("location_id"), Seq("id"), "left"))
+ val selectedFeatures = (creditCardTransactionsFg.selectAll()
+ .join(accountDetailsFg.selectAll(), Seq("cc_num"))
+ .join(merchantDetailsFg.selectAll(), Seq("merchant_id"), Seq("id"), "inner"))
+ ```
+
+### Data modeling in Hopsworks
+
+Since v4.0 Hopsworks Feature selection API supports both Star and Snowflake Schema data models.
+
+#### Star schema data model
+
+When choosing Star Schema data model all tables are children of the parent (the left most) feature group, which has all
+foreign keys for its child feature groups.
+
+
+
+
+ Star schema data model
+
+
+
+=== "Python"
+ ```python
+ selected_features = credit_card_transactions.select_all()
+ .join(aggregated_cc_transactions.select_all())
+ .join(account_details.select_all())
+ .join(merchant_details.select_all())
+ .join(cc_issuer_details.select_all())
```
-!!! error "Nested Joins"
-The API currently does not support nested joins. That is joins of joins.
-You can fall back to Spark DataFrames to cover these cases. However, if you have to use joins of joins, most likely there is potential to optimise your feature group structure.
+In online inference, when you want to retrieve features in your online model, you have to provide all foreign key values,
+known as the serving_keys, from the parent feature group to retrieve your precomputed feature values using the feature view.
+
+=== "Python"
+ ```python
+ feature vector = feature_view.get_feature_vector({
+ ‘cc_num’: “1234 5555 3333 8888”,
+ ‘issuer_id’: 20440455,
+ ‘merchant_id’: 44208484,
+ ‘account_id’: 84403331
+ })
+ ```
+
+#### Snowflake schema
+Hopsworks also provides the possibility to define a feature view that consists of a nested tree of children (to up to a depth of 20)
+from the root (left most) feature group. This is called Snowflake Schema data model where you need to build nested tables (subtrees) using joins, and then join the
+subtrees to their parents iteratively until you reach the root node (the leftmost feature group in the feature selection):
+
+
+
+
+ Snowflake schema data model
+
+
+
+=== "Python"
+ ```python
+ nested_selection = aggregated_cc_transactions.select_all()
+ .join(account_details.select_all())
+ .join(cc_issuer_details.select_all())
+
+ selected_features = credit_card_transactions.select_all()
+ .join(nested_selection)
+ .join(merchant_details.select_all())
+ ```
+
+Now, you have the benefit that in online inference you only need to pass two serving key values (the foreign keys of the leftmost feature group)
+to retrieve the precomputed features:
+
+=== "Python"
+ ```python
+ feature vector = feature_view.get_feature_vector({
+ ‘cc_num’: “1234 5555 3333 8888”,
+ ‘merchant_id’: 44208484,
+ })
+ ```
#### Filter
@@ -114,48 +195,48 @@ For the Scala part of the API, equivalent methods are available in the `Feature`
=== "Python"
```python
- filtered_rain = rain_fg.filter(rain_fg.location_id == 10)
+ filtered_credit_card_transactions = credit_card_transactions_fg.filter(credit_card_transactions_fg.category == "Grocery")
```
=== "Scala"
```scala
- val filteredRain = rainFg.filter(rainFg.getFeature("location_id").eq(10))
+ val filteredCreditCardTransactions = creditCardTransactionsFg.filter(creditCardTransactionsFg.getFeature("category").eq("Grocery"))
```
Filters are fully compatible with joins:
=== "Python"
```python
- feature_join = rain_fg.select_all() \
- .join(temperature_fg.select_all(), on=["date", "location_id"]) \
- .join(location_fg.select_all(), left_on=["location_id"], right_on=["id"], join_type="left") \
- .filter((rain_fg.location_id == 10) | (rain_fg.location_id == 20))
+ selected_features = credit_card_transactions_fg.select_all() \
+ .join(account_details_fg.select_all(), on=["cc_num"]) \
+ .join(merchant_details_fg.select_all(), left_on=["merchant_id"], right_on=["id"]) \
+ .filter((credit_card_transactions_fg.category == "Grocery") | (credit_card_transactions_fg.category == "Restaurant/Cafeteria"))
```
=== "Scala"
```scala
- val featureJoin = (rainFg.selectAll()
- .join(temperatureFg.selectAll(), Seq("date", "location_id"))
- .join(locationFg.selectAll(), Seq("location_id"), Seq("id"), "left")
- .filter(rainFg.getFeature("location_id").eq(10).or(rainFg.getFeature("location_id").eq(20))))
+ val selectedFeatures = (creditCardTransactionsFg.selectAll()
+ .join(accountDetailsFg.selectAll(), Seq("cc_num"))
+ .join(merchantDetailsFg.selectAll(), Seq("merchant_id"), Seq("id"), "left")
+ .filter(creditCardTransactionsFg.getFeature("category").eq("Grocery").or(creditCardTransactionsFg.getFeature("category").eq("Restaurant/Cafeteria"))))
```
The filters can be applied at any point of the query:
=== "Python"
```python
- feature_join = rain_fg.select_all() \
- .join(temperature_fg.select_all().filter(temperature_fg.avg_temp >= 22), on=["date", "location_id"]) \
- .join(location_fg.select_all(), left_on=["location_id"], right_on=["id"], join_type="left") \
- .filter(rain_fg.location_id == 10)
+ selected_features = credit_card_transactions_fg.select_all() \
+ .join(accountDetails_fg.select_all().filter(accountDetails_fg.avg_temp >= 22), on=["cc_num"]) \
+ .join(merchant_details_fg.select_all(), left_on=["merchant_id"], right_on=["id"]) \
+ .filter(credit_card_transactions_fg.category == "Grocery")
```
=== "Scala"
```scala
- val featureJoin = (rainFg.selectAll()
- .join(temperatureFg.selectAll().filter(temperatureFg.getFeature("avg_temp").ge(22)), Seq("date", "location_id"))
- .join(locationFg.selectAll(), Seq("location_id"), Seq("id"), "left")
- .filter(rainFg.getFeature("location_id").eq(10)))
+ val selectedFeatures = (creditCardTransactionsFg.selectAll()
+ .join(accountDetailsFg.selectAll().filter(accountDetailsFg.getFeature("avg_temp").ge(22)), Seq("cc_num"))
+ .join(merchantDetailsFg.selectAll(), Seq("merchant_id"), Seq("id"), "left")
+ .filter(creditCardTransactionsFg.getFeature("category").eq("Grocery")))
```
#### Joins and/or Filters on feature view query
@@ -166,23 +247,23 @@ However, this operation will not update the metadata and persist the updated que
=== "Python"
```python
fs = ...
- wind_speed_fg = fs.get_feature_group(name="wind_speed_fg", version=1)
- rain_fg = fs.get_feature_group(name="rain_fg", version=1)
- feature_view = fs.get_feature_view(“rain_dataset”, version=1)
+ merchant_details_fg = fs.get_feature_group(name="merchant_details", version=1)
+ credit_card_transactions_fg = fs.get_feature_group(name="credit_card_transactions", version=1)
+ feature_view = fs.get_feature_view(“credit_card_fraud”, version=1)
feature_view.query \
- .join(wind_speed_fg.select_all()) \
- .filter((rain_fg.location_id == 54)
+ .join(merchant_details_fg.select_all()) \
+ .filter((credit_card_transactions_fg.category == "Cash Withdrawal")
```
=== "Scala"
```scala
val fs = ...
- val windSpeedFg = fs.getFeatureGroup("wind_speed_fg", 1)
- val rainFg = fs.getFeatureGroup("rain_fg", 1)
- val featureView = fs.getFeatureView(“rain_dataset”, 1)
+ val merchantDetailsFg = fs.getFeatureGroup("merchant_details", 1)
+ val creditCardTransactionsFg = fs.getFeatureGroup("credit_card_transactions", 1)
+ val featureView = fs.getFeatureView(“credit_card_fraud”, 1)
featureView.getQuery()
- .join(windSpeedFg.selectAll())
- .filter(rainFg.getFeature("location_id").eq(54))
+ .join(merchantDetailsFg.selectAll())
+ .filter(creditCardTransactionsFg.getFeature("category").eq("Cash Withdrawal"))
```
!!! warning
@@ -192,45 +273,46 @@ However, this operation will not update the metadata and persist the updated que
=== "Python"
```python
fs = ...
- wind_speed_fg = fs.get_feature_group(name="wind_speed_fg", version=1)
- solar_irradiance_fg = fs.get_feature_group(name="solar_irradiance_fg", version=1)
- rain_fg = fs.get_feature_group(name="rain_fg", version=1)
+
+ merchant_details_fg = fs.get_feature_group(name="merchant_details", version=1)
+ account_details_fg = fs.get_feature_group(name="account_details", version=1)
+ credit_card_transactions_fg = fs.get_feature_group(name="credit_card_transactions", version=1)
# fetch new feature view and its query instance
- feature_view = fs.get_feature_view(“rain_dataset”, version=1)
+ feature_view = fs.get_feature_view(“credit_card_fraud”, version=1)
- # apply join/filter logic based on location and wind speed
- feature_view.query.join(wind_speed_fg.select_all()) \
- .filter((rain_fg.location_id == 54)
+ # apply join/filter logic based on purchase type
+ feature_view.query.join(merchant_details_fg.select_all()) \
+ .filter((credit_card_transactions_fg.category == "Cash Withdrawal")
- # to apply new logic independent of location and wind speed from above
+ # to apply new logic independent of purchase type from above
# re-fetch new feature view and its query instance
- feature_view = fs.get_feature_view(“rain_dataset”, version=1)
+ feature_view = fs.get_feature_view(“credit_card_fraud”, version=1)
- # apply new join/filter logic based on solar irradiance
- feature_view.query.join(solar_irradiance_fg.select_all()) \
- .filter(solar_irradiance_fg.location_id == 28)
+ # apply new join/filter logic based on account details
+ feature_view.query.join(merchant_details_fg.select_all()) \
+ .filter(account_details_fg.gender == "F")
```
=== "Scala"
```scala
fs = ...
- windSpeedFg = fs.getFeatureGroup("wind_speed_fg", 1)
- solarIrradianceFg = fs.getFeatureGroup("solar_irradiance_fg", 1)
- rainFg = fs.getFeatureGroup("rain_fg", 1)
+ merchantDetailsFg = fs.getFeatureGroup("merchant_details", 1)
+ accountDetailsFg = fs.getFeatureGroup("account_details", 1)
+ creditCardTransactionsFg = fs.getFeatureGroup("credit_card_transactions", 1)
// fetch new feature view and its query instance
- val featureView = fs.getFeatureView(“rain_dataset”, version=1)
+ val featureView = fs.getFeatureView(“credit_card_fraud”, version=1)
- // apply join/filter logic based on location and wind speed
- featureView.getQuery.join(windSpeedFg.selectAll())
- .filter(rainFg.getFeature("location_id").eq(54))
+ // apply join/filter logic based on purchase type
+ featureView.getQuery.join(merchantDetailsFg.selectAll())
+ .filter(creditCardTransactionsFg.getFeature("category").eq("Cash Withdrawal"))
- // to apply new logic independent of location and wind speed from above
+ // to apply new logic independent of purchase type from above
// re-fetch new feature view and its query instance
- val featureView = fs.getFeatureView(“rain_dataset”, 1)
+ val featureView = fs.getFeatureView(“credit_card_fraud”, 1)
- // apply new join/filter logic based on solar irradiance
- featureView.getQuery.join(solarIrradianceFg.selectAll())
- .filter(solarIrradianceFg.getFeature("location_id").eq(28))
+ // apply new join/filter logic based on account details
+ featureView.getQuery.join(merchantDetailsFg.selectAll())
+ .filter(accountDetailsFg.getFeature("gender").eq("F"))
```
+ 
+