--- title: "Define features and push velocity data" description: "Create a fraud feature repository with PushSource, run feast apply, and push a velocity snapshot." --- # Define features and push velocity data > For the complete documentation index see: [llms.txt](https://aerospike.com/docs/llms.txt) > > All documentation pages available in markdown. On this page, you define a push-backed feature view, register it with `feast apply`, and push one velocity snapshot to confirm the write path. The [Aerospike online store reference](https://github.com/feast-dev/feast/blob/master/docs/reference/online-stores/aerospike.md) and [Feast push source reference](https://docs.feast.dev/reference/data-sources/push) cover the full configuration options. ## Create the feature repository Create the project directory, feature store configuration, and the push-backed feature view definition. 1. Create a project directory: Terminal window ```shell mkdir -p fraud-feast-demo/data cd fraud-feast-demo ``` 2. Create `feature_store.yaml`: feature\_store.yaml ```yaml project: fraud_detection registry: data/registry.db provider: local online_store: type: aerospike hosts: - ["127.0.0.1", 3000] namespace: test ttl_seconds: 600 read_timeout_ms: 150 write_timeout_ms: 300 entity_key_serialization_version: 3 offline_store: type: file ``` `registry: data/registry.db` is Feast’s local metadata store for entity and feature view definitions. `offline_store: type: file` is required by Feast even in push-only demos. Feast validates its presence but does not use it for reading in this tutorial. `ttl_seconds: 600` covers a five-minute velocity window plus buffer. `provider: local`, `entity_key_serialization_version: 3`, and the `hosts` list format are explained in [Tutorial: Serve real-time Feast features with Aerospike](https://aerospike.com/docs/develop/feast-aerospike-online-store/step/3/part/0/define-and-materialize#create-the-feature-repository) on the Define and materialize page. 3. Create `fraud_repo.py`: fraud\_repo.py ```python from datetime import timedelta from feast import Entity, FeatureService, FeatureView, Field, FileSource, PushSource from feast.types import Float64, Int64 from feast.value_type import ValueType account = Entity( name="account", join_keys=["account_id"], value_type=ValueType.STRING, description="Bank or wallet account identifier", ) txn_velocity_batch = FileSource( path="data/txn_velocity.parquet", timestamp_field="event_timestamp", ) txn_velocity_push = PushSource( name="txn_velocity_push", batch_source=txn_velocity_batch, ) txn_velocity_fv = FeatureView( name="txn_velocity_5m", entities=[account], ttl=timedelta(minutes=10), schema=[ Field(name="txn_count_5m", dtype=Int64), Field(name="amount_sum_5m", dtype=Float64), Field(name="distinct_merchants_5m", dtype=Int64), ], online=True, source=txn_velocity_push, tags={"domain": "fraud", "window": "5m"}, # Descriptive only. Feast routing does not use tags. ) fraud_scoring_service = FeatureService( name="fraud_scoring_v1", features=[txn_velocity_fv], description="Velocity features for real-time fraud scoring", ) ``` `PushSource` allows `batch_source` to be omitted, but this tutorial defines features through `FeatureView`. Feast’s `FeatureView` constructor requires a `batch_source` on every stream source, including `PushSource`, so `txn_velocity_batch` is included here. That `FileSource` registers schema for `feast apply` and is not used when you write through `store.push()` at runtime. The Parquet file it references does not need to exist at apply time. `txn_velocity_push` is the source name you pass to `store.push()`. `txn_velocity_5m` is the feature view name Feast uses in the registry and feature references. `online=True` marks the feature view as eligible for online serving. It is the default value for `FeatureView`. Setting `online=False` restricts the view to offline retrieval and prevents `get_online_features` from reading it. `ttl=timedelta(minutes=10)` controls Feast materialization lookback and historical point-in-time retrieval. It does not make the standard Aerospike online read reject an old value. `ttl_seconds: 600` in `feature_store.yaml` is the serving-layer control: Aerospike expires the record ten minutes after its latest write. `FeatureService` wraps the feature view under a named service. Your request references `fraud_scoring_v1` rather than enumerating individual features. Coordinate service-membership changes with the model’s expected schema and version the service when its contract changes. 4. Register definitions: `feast apply` reads the Python definitions in your project directory and records them in the Feast registry at `data/registry.db`. From that point, commands and SDK calls know your entity, feature view, and feature service by name. It registers definitions only. It does not move any feature values. Terminal window ```shell feast apply ``` Expected result ```text No project found in the repository. Using project name fraud_detection defined in feature_store.yaml Applying changes for project fraud_detection Deploying infrastructure for txn_velocity_5m ``` 5. Confirm registration: - Output includes `Applying changes for project fraud_detection`. - Output includes `Deploying infrastructure for txn_velocity_5m`. ## Push a velocity snapshot Write a velocity snapshot into the Aerospike online store with `store.push()` and read it back to confirm the write and read paths work end to end. The values you push here stand in for the output of a real aggregation job. 1. Create `push_velocity.py`: push\_velocity.py ```python from datetime import datetime, timezone import pandas as pd from feast import FeatureStore from feast.data_source import PushMode store = FeatureStore(repo_path=".") snapshot = pd.DataFrame( { "account_id": ["acct-1001"], "event_timestamp": [datetime.now(timezone.utc)], "txn_count_5m": [7], "amount_sum_5m": [1243.50], "distinct_merchants_5m": [3], } ) store.push("txn_velocity_push", snapshot, to=PushMode.ONLINE) print("Pushed velocity snapshot for acct-1001") ``` `FeatureStore(repo_path=".")` loads the registry from `feature_store.yaml` in the current directory. `store.push()` takes a pandas `DataFrame`. It must include the entity join key (`account_id`), `event_timestamp`, and one `DataFrame` column for every feature field defined in `txn_velocity_5m`, with column names matching the `Field` names exactly. `event_timestamp` records when the velocity snapshot was computed, here set to the current system time. Production scoring code or monitoring can inspect that timestamp to enforce a stricter freshness limit than the record TTL. The first argument to `store.push()` is the `PushSource` name (`txn_velocity_push`), not the feature view name. Feast routes the pushed values to the right feature view through the source name. `PushMode.ONLINE` writes only to the Aerospike online store. `PushMode.ONLINE_AND_OFFLINE` also requires a writable batch source for offline history, as explained on the [Wrap up](https://aerospike.com/docs/develop/feast-aerospike-fraud-velocity/step/4/part/0/wrap-up) page. 2. Run it: Terminal window ```shell python3 push_velocity.py ``` Expected result ```plaintext Pushed velocity snapshot for acct-1001 ``` 3. Confirm the push: - The script prints `Pushed velocity snapshot for acct-1001` without errors. - `PushMode.ONLINE` wrote only to the Aerospike online store. On the next page, you read features through the `FeatureService` and optional HTTP feature server. ::: undefined - I’ve created the fraud feature repository. - I’ve run `feast apply` and pushed a velocity snapshot with `store.push()`. ::: [Previous Push sources and velocity features](https://aerospike.com/docs/develop/feast-aerospike-fraud-velocity/step/2/part/0/push-and-velocity) [Next Serve features to a fraud model](https://aerospike.com/docs/develop/feast-aerospike-fraud-velocity/step/3/part/1/serve-features)