The following field names are reserved in feature tables
--
-
event_timestamp-
- datetime-
- created_timestamp-
- ingestion_id-
- job_id-
-
| Metric | -Description | -Tags | -
|---|---|---|
feast_ingestion_feature_row_lag_ms_{BUCKET}
- |
- Lag time in milliseconds between succeeding ingested Feature Rows. | -
-
|
-
feast_ingestion_feature_value_lag_ms_{BUCKET}
- |
- Lag time in milliseconds between succeeding ingested values for each Feature. | -
-
|
-
feast_ingestion_feature_value_{BUCKET}
- |
- Last value feature for each Feature. | -feast_store, feature_project_name, feast_feature_name,feast_featureSet_name, ingest_job_name, metrics_namepace
- |
-
feast_ingestion_feature_row_ingested_count
- |
- No. of Ingested Feature Rows | -
-
|
-
feast_ingestion_feature_value_missing_count
- |
- No. of times a ingested Feature values did not provide a value for the - Feature. | -
-
|
-
feast_ingestion_deadletter_row_count
- |
- No. of Feature Rows that that the Ingestion Job did not successfully write - to store. | -feast_store, feast_project_name,feast_featureSet_name,ingestion_job_name
- |
-
The following field names are reserved in feature tables
--
-
event_timestamp-
- datetime-
- created_timestamp-
- ingestion_id-
- job_id-
-
| Metric | -Description | -Tags | -
|---|---|---|
feast_ingestion_feature_row_lag_ms_{BUCKET}
- |
- Lag time in milliseconds between succeeding ingested Feature Rows. | -
-
|
-
feast_ingestion_feature_value_lag_ms_{BUCKET}
- |
- Lag time in milliseconds between succeeding ingested values for each Feature. | -
-
|
-
feast_ingestion_feature_value_{BUCKET}
- |
- Last value feature for each Feature. | -feast_store, feature_project_name, feast_feature_name,feast_featureSet_name, ingest_job_name, metrics_namepace
- |
-
feast_ingestion_feature_row_ingested_count
- |
- No. of Ingested Feature Rows | -
-
|
-
feast_ingestion_feature_value_missing_count
- |
- No. of times a ingested Feature values did not provide a value for the - Feature. | -
-
|
-
feast_ingestion_deadletter_row_count
- |
- No. of Feature Rows that that the Ingestion Job did not successfully write - to store. | -feast_store, feast_project_name,feast_featureSet_name,ingestion_job_name
- |
-
+
+
+
+
+
+
+
+Retrieving historical features for resulting entity dataframe and persisting output as a saved dataset:
+
+
+```python
+job = store.get_historical_features(
+ entity_df=entity_df,
+ features=[
+ "trip_stats:total_miles_travelled",
+ "trip_stats:total_trip_seconds",
+ "trip_stats:total_earned",
+ "trip_stats:trip_count",
+ "on_demand_stats:avg_fare",
+ "on_demand_stats:avg_trip_seconds",
+ "on_demand_stats:avg_speed",
+ "on_demand_stats:earned_per_hour",
+ ]
+)
+
+store.create_saved_dataset(
+ from_=job,
+ name='my_training_ds',
+ storage=SavedDatasetFileStorage(path='my_training_ds.parquet')
+)
+```
+
+```python
+| + | taxi_id | +event_timestamp | +
|---|---|---|
| 0 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2019-06-01 | +
| 1 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2019-06-02 | +
| 2 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2019-06-03 | +
| 3 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2019-06-04 | +
| 4 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2019-06-05 | +
| ... | +... | +... | +
| 156979 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2019-06-27 | +
| 156980 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2019-06-28 | +
| 156981 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2019-06-29 | +
| 156982 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2019-06-30 | +
| 156983 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2019-07-01 | +
156984 rows × 2 columns
+
+
+
+
+
+
+
+
+Feast uses [Great Expectations](https://docs.greatexpectations.io/docs/) as a validation engine and [ExpectationSuite](https://legacy.docs.greatexpectations.io/en/latest/autoapi/great_expectations/core/expectation_suite/index.html#great_expectations.core.expectation_suite.ExpectationSuite) as a dataset's profile. Hence, we need to develop a function that will generate ExpectationSuite. This function will receive instance of [PandasDataset](https://legacy.docs.greatexpectations.io/en/latest/autoapi/great_expectations/dataset/index.html?highlight=pandasdataset#great_expectations.dataset.PandasDataset) (wrapper around pandas.DataFrame) so we can utilize both Pandas DataFrame API and some helper functions from PandasDataset during profiling.
+
+
+```python
+DELTA = 0.1 # controlling allowed window in fraction of the value on scale [0, 1]
+
+@ge_profiler
+def stats_profiler(ds: PandasDataset) -> ExpectationSuite:
+ # simple checks on data consistency
+ ds.expect_column_values_to_be_between(
+ "avg_speed",
+ min_value=0,
+ max_value=60,
+ mostly=0.99 # allow some outliers
+ )
+
+ ds.expect_column_values_to_be_between(
+ "total_miles_travelled",
+ min_value=0,
+ max_value=500,
+ mostly=0.99 # allow some outliers
+ )
+
+ # expectation of means based on observed values
+ observed_mean = ds.trip_count.mean()
+ ds.expect_column_mean_to_be_between("trip_count",
+ min_value=observed_mean * (1 - DELTA),
+ max_value=observed_mean * (1 + DELTA))
+
+ observed_mean = ds.earned_per_hour.mean()
+ ds.expect_column_mean_to_be_between("earned_per_hour",
+ min_value=observed_mean * (1 - DELTA),
+ max_value=observed_mean * (1 + DELTA))
+
+
+ # expectation of quantiles
+ qs = [0.5, 0.75, 0.9, 0.95]
+ observed_quantiles = ds.avg_fare.quantile(qs)
+
+ ds.expect_column_quantile_values_to_be_between(
+ "avg_fare",
+ quantile_ranges={
+ "quantiles": qs,
+ "value_ranges": [[None, max_value] for max_value in observed_quantiles]
+ })
+
+ return ds.get_expectation_suite()
+```
+
+Testing our profiler function:
+
+
+```python
+ds.get_profile(profiler=stats_profiler)
+```
+ 02/02/2022 02:43:47 PM INFO: 5 expectation(s) included in expectation_suite. result_format settings filtered.
+ | + | total_earned | +avg_trip_seconds | +taxi_id | +total_miles_travelled | +trip_count | +earned_per_hour | +event_timestamp | +total_trip_seconds | +avg_fare | +avg_speed | +
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | +68.25 | +2270.000000 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +24.70 | +2.0 | +54.118943 | +2019-06-01 00:00:00+00:00 | +4540.0 | +34.125000 | +19.585903 | +
| 1 | +221.00 | +560.500000 | +7a4a6162eaf27805aef407d25d5cb21fe779cd962922cb... | +54.18 | +24.0 | +59.143622 | +2019-06-01 00:00:00+00:00 | +13452.0 | +9.208333 | +14.499554 | +
| 2 | +160.50 | +1010.769231 | +f4c9d05b215d7cbd08eca76252dae51cdb7aca9651d4ef... | +41.30 | +13.0 | +43.972603 | +2019-06-01 00:00:00+00:00 | +13140.0 | +12.346154 | +11.315068 | +
| 3 | +183.75 | +697.550000 | +c1f533318f8480a59173a9728ea0248c0d3eb187f4b897... | +37.30 | +20.0 | +47.415956 | +2019-06-01 00:00:00+00:00 | +13951.0 | +9.187500 | +9.625116 | +
| 4 | +217.75 | +1054.076923 | +455b6b5cae6ca5a17cddd251485f2266d13d6a2c92f07c... | +69.69 | +13.0 | +57.206451 | +2019-06-01 00:00:00+00:00 | +13703.0 | +16.750000 | +18.308692 | +
| ... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +... | +
| 156979 | +38.00 | +1980.000000 | +0cccf0ec1f46d1e0beefcfdeaf5188d67e170cdff92618... | +14.90 | +1.0 | +69.090909 | +2019-07-01 00:00:00+00:00 | +1980.0 | +38.000000 | +27.090909 | +
| 156980 | +135.00 | +551.250000 | +beefd3462e3f5a8e854942a2796876f6db73ebbd25b435... | +28.40 | +16.0 | +55.102041 | +2019-07-01 00:00:00+00:00 | +8820.0 | +8.437500 | +11.591837 | +
| 156981 | +NaN | +NaN | +9a3c52aa112f46cf0d129fafbd42051b0fb9b0ff8dcb0e... | +NaN | +NaN | +NaN | +2019-07-01 00:00:00+00:00 | +NaN | +NaN | +NaN | +
| 156982 | +63.00 | +815.000000 | +08308c31cd99f495dea73ca276d19a6258d7b4c9c88e43... | +19.96 | +4.0 | +69.570552 | +2019-07-01 00:00:00+00:00 | +3260.0 | +15.750000 | +22.041718 | +
| 156983 | +NaN | +NaN | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +NaN | +NaN | +NaN | +2019-07-01 00:00:00+00:00 | +NaN | +NaN | +NaN | +
156984 rows × 10 columns
+
+
+
+
+
+
+
+```python
+job = store.get_historical_features(
+ entity_df=entity_df,
+ features=[
+ "trip_stats:total_miles_travelled",
+ "trip_stats:total_trip_seconds",
+ "trip_stats:total_earned",
+ "trip_stats:trip_count",
+ "on_demand_stats:avg_fare",
+ "on_demand_stats:avg_trip_seconds",
+ "on_demand_stats:avg_speed",
+ "on_demand_stats:earned_per_hour",
+ ]
+)
+```
+
+Execute retrieval job with validation reference:
+
+
+```python
+try:
+ df = job.to_df(validation_reference=validation_reference)
+except ValidationFailed as exc:
+ print(exc.validation_report)
+```
+
+ 02/02/2022 02:43:58 PM INFO: 5 expectation(s) included in expectation_suite. result_format settings filtered.
+ 02/02/2022 02:43:59 PM INFO: Validating data_asset_name None with expectation_suite_name default
+
+ [
+ {
+ "expectation_config": {
+ "expectation_type": "expect_column_mean_to_be_between",
+ "kwargs": {
+ "column": "trip_count",
+ "min_value": 10.387244591346153,
+ "max_value": 12.695521167200855,
+ "result_format": "COMPLETE"
+ },
+ "meta": {}
+ },
+ "meta": {},
+ "result": {
+ "observed_value": 6.692920555429092,
+ "element_count": 35448,
+ "missing_count": 31055,
+ "missing_percent": 87.6071992778154
+ },
+ "exception_info": {
+ "raised_exception": false,
+ "exception_message": null,
+ "exception_traceback": null
+ },
+ "success": false
+ },
+ {
+ "expectation_config": {
+ "expectation_type": "expect_column_mean_to_be_between",
+ "kwargs": {
+ "column": "earned_per_hour",
+ "min_value": 52.320624975640214,
+ "max_value": 63.94743052578249,
+ "result_format": "COMPLETE"
+ },
+ "meta": {}
+ },
+ "meta": {},
+ "result": {
+ "observed_value": 68.99268345164135,
+ "element_count": 35448,
+ "missing_count": 31055,
+ "missing_percent": 87.6071992778154
+ },
+ "exception_info": {
+ "raised_exception": false,
+ "exception_message": null,
+ "exception_traceback": null
+ },
+ "success": false
+ },
+ {
+ "expectation_config": {
+ "expectation_type": "expect_column_quantile_values_to_be_between",
+ "kwargs": {
+ "column": "avg_fare",
+ "quantile_ranges": {
+ "quantiles": [
+ 0.5,
+ 0.75,
+ 0.9,
+ 0.95
+ ],
+ "value_ranges": [
+ [
+ null,
+ 16.4
+ ],
+ [
+ null,
+ 26.229166666666668
+ ],
+ [
+ null,
+ 36.4375
+ ],
+ [
+ null,
+ 42.0
+ ]
+ ]
+ },
+ "result_format": "COMPLETE"
+ },
+ "meta": {}
+ },
+ "meta": {},
+ "result": {
+ "observed_value": {
+ "quantiles": [
+ 0.5,
+ 0.75,
+ 0.9,
+ 0.95
+ ],
+ "values": [
+ 19.5,
+ 28.1,
+ 38.0,
+ 44.125
+ ]
+ },
+ "element_count": 35448,
+ "missing_count": 31055,
+ "missing_percent": 87.6071992778154,
+ "details": {
+ "success_details": [
+ false,
+ false,
+ false,
+ false
+ ]
+ }
+ },
+ "exception_info": {
+ "raised_exception": false,
+ "exception_message": null,
+ "exception_traceback": null
+ },
+ "success": false
+ }
+ ]
+
+
+Validation failed since several expectations didn't pass:
+* Trip count (mean) decreased more than 10% (which is expected when comparing Dec 2020 vs June 2019)
+* Average Fare increased - all quantiles are higher than expected
+* Earn per hour (mean) increased more than 10% (most probably due to increased fare)
+
diff --git a/docs/user-guide/define-and-ingest-features.md b/docs/user-guide/define-and-ingest-features.md
deleted file mode 100644
index d55fcb1d85..0000000000
--- a/docs/user-guide/define-and-ingest-features.md
+++ /dev/null
@@ -1,56 +0,0 @@
-# Define and ingest features
-
-In order to retrieve features for both training and serving, Feast requires data being ingested into its offline and online stores.
-
-{% hint style="warning" %}
-Feast 0.8 does not have an offline store. Only Online storage support exists currently. Feast 0.9 will have offline storage support. In Feast 0.8, historical data is retrieved directly from batch sources.
-{% endhint %}
-
-Users are expected to already have either a batch or stream source with data stored in it, ready to be ingested into Feast. Once a feature table \(with the corresponding sources\) has been registered with Feast, it is possible to load data from this source into stores.
-
-The following depicts an example ingestion flow from a data source to the online store.
-
-### Batch Source to Online Store
-
-```python
-from feast import Client
-from datetime import datetime, timedelta
-
-client = Client(core_url="localhost:6565")
-driver_ft = client.get_feature_table("driver_trips")
-
-# Initialize date ranges
-today = datetime.now()
-yesterday = today - timedelta(1)
-
-# Launches a short-lived job that ingests data over the provided date range.
-client.start_offline_to_online_ingestion(
- driver_ft, yesterday, today
-)
-```
-
-### Stream Source to Online Store
-
-```python
-from feast import Client
-from datetime import datetime, timedelta
-
-client = Client(core_url="localhost:6565")
-driver_ft = client.get_feature_table("driver_trips")
-
-# Launches a long running streaming ingestion job
-client.start_stream_to_online_ingestion(driver_ft)
-```
-
-### Batch Source to Offline Store
-
-{% hint style="danger" %}
-Not supported in Feast 0.8
-{% endhint %}
-
-### Stream Source to Offline Store
-
-{% hint style="danger" %}
-Not supported in Feast 0.8
-{% endhint %}
-
diff --git a/docs/user-guide/getting-online-features.md b/docs/user-guide/getting-online-features.md
deleted file mode 100644
index c16dc08a01..0000000000
--- a/docs/user-guide/getting-online-features.md
+++ /dev/null
@@ -1,54 +0,0 @@
-# Getting online features
-
-Feast provides an API through which online feature values can be retrieved. This allows teams to look up feature values at low latency in production during model serving, in order to make online predictions.
-
-{% hint style="info" %}
-Online stores only maintain the current state of features, i.e latest feature values. No historical data is stored or served.
-{% endhint %}
-
-```python
-from feast import Client
-
-online_client = Client(
- core_url="localhost:6565",
- serving_url="localhost:6566",
-)
-
-entity_rows = [
- {"driver_id": 1001},
- {"driver_id": 1002},
-]
-
-# Features in | + | taxi_id | +event_timestamp | +
|---|---|---|
| 0 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2020-12-01 | +
| 1 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2020-12-02 | +
| 2 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2020-12-03 | +
| 3 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2020-12-04 | +
| 4 | +91d5288487e87c5917b813ba6f75ab1c3a9749af906a2d... | +2020-12-05 | +
| ... | +... | +... | +
| 35443 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2020-12-03 | +
| 35444 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2020-12-04 | +
| 35445 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2020-12-05 | +
| 35446 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2020-12-06 | +
| 35447 | +7ebf27414a0c7b128e7925e1da56d51a8b81484f7630cf... | +2020-12-07 | +
35448 rows × 2 columns
+`-Xms2048m -Xmx2048m` | -| livenessProbe.enabled | bool | `true` | Flag to enabled the probe | -| livenessProbe.failureThreshold | int | `5` | Min consecutive failures for the probe to be considered failed | -| livenessProbe.initialDelaySeconds | int | `60` | Delay before the probe is initiated | -| livenessProbe.periodSeconds | int | `10` | How often to perform the probe | -| livenessProbe.successThreshold | int | `1` | Min consecutive success for the probe to be considered successful | -| livenessProbe.timeoutSeconds | int | `5` | When the probe times out | -| logLevel | string | `"WARN"` | Default log level, use either one of `DEBUG`, `INFO`, `WARN` or `ERROR` | -| logType | string | `"Console"` | Log format, either `JSON` or `Console` | -| nodeSelector | object | `{}` | Node labels for pod assignment | -| podLabels | object | `{}` | Labels to be added to Feast Serving pods | -| readinessProbe.enabled | bool | `true` | Flag to enabled the probe | -| readinessProbe.failureThreshold | int | `5` | Min consecutive failures for the probe to be considered failed | -| readinessProbe.initialDelaySeconds | int | `15` | Delay before the probe is initiated | -| readinessProbe.periodSeconds | int | `10` | How often to perform the probe | -| readinessProbe.successThreshold | int | `1` | Min consecutive success for the probe to be considered successful | -| readinessProbe.timeoutSeconds | int | `10` | When the probe times out | -| replicaCount | int | `1` | Number of pods that will be created | -| resources | object | `{}` | CPU/memory [resource requests/limit](https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/#resource-requests-and-limits-of-pod-and-container) | -| secrets | list | `[]` | List of Kubernetes secrets to be mounted. These secrets will be mounted on /etc/secrets/
`-Xms2048m -Xmx2048m` | +| livenessProbe.enabled | bool | `true` | Flag to enabled the probe | +| livenessProbe.failureThreshold | int | `5` | Min consecutive failures for the probe to be considered failed | +| livenessProbe.initialDelaySeconds | int | `60` | Delay before the probe is initiated | +| livenessProbe.periodSeconds | int | `10` | How often to perform the probe | +| livenessProbe.successThreshold | int | `1` | Min consecutive success for the probe to be considered successful | +| livenessProbe.timeoutSeconds | int | `5` | When the probe times out | +| logLevel | string | `"WARN"` | Default log level, use either one of `DEBUG`, `INFO`, `WARN` or `ERROR` | +| logType | string | `"Console"` | Log format, either `JSON` or `Console` | +| nodeSelector | object | `{}` | Node labels for pod assignment | +| podAnnotations | object | `{}` | Annotations to be added to Feast Serving pods | +| podLabels | object | `{}` | Labels to be added to Feast Serving pods | +| readinessProbe.enabled | bool | `true` | Flag to enabled the probe | +| readinessProbe.failureThreshold | int | `5` | Min consecutive failures for the probe to be considered failed | +| readinessProbe.initialDelaySeconds | int | `15` | Delay before the probe is initiated | +| readinessProbe.periodSeconds | int | `10` | How often to perform the probe | +| readinessProbe.successThreshold | int | `1` | Min consecutive success for the probe to be considered successful | +| readinessProbe.timeoutSeconds | int | `10` | When the probe times out | +| replicaCount | int | `1` | Number of pods that will be created | +| resources | object | `{}` | CPU/memory [resource requests/limit](https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container/#resource-requests-and-limits-of-pod-and-container) | +| secrets | list | `[]` | List of Kubernetes secrets to be mounted. These secrets will be mounted on /etc/secrets/
+
-The above architecture is the minimal Feast deployment. Want to run the full Feast on GCP/AWS? Click [here](https://docs.feast.dev/how-to-guides/feast-gcp-aws).
+The above architecture is the minimal Feast deployment. Want to run the full Feast on Snowflake/GCP/AWS? Click [here](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws).
## 🐣 Getting Started
@@ -133,7 +133,7 @@ pprint(feature_vector)
Please refer to the official documentation at [Documentation](https://docs.feast.dev/)
* [Quickstart](https://docs.feast.dev/getting-started/quickstart)
* [Tutorials](https://docs.feast.dev/tutorials/tutorials-overview)
- * [Running Feast with GCP/AWS](https://docs.feast.dev/how-to-guides/feast-gcp-aws)
+ * [Running Feast with Snowflake/GCP/AWS](https://docs.feast.dev/how-to-guides/feast-snowflake-gcp-aws)
* [Change Log](https://github.com/feast-dev/feast/blob/master/CHANGELOG.md)
* [Slack (#Feast)](https://slack.feast.dev/)
diff --git a/java/CONTRIBUTING.md b/java/CONTRIBUTING.md
index 1694b3f33f..86eacfef41 100644
--- a/java/CONTRIBUTING.md
+++ b/java/CONTRIBUTING.md
@@ -5,7 +5,6 @@
### Overview
This guide is targeted at developers looking to contribute to Feast components in
the feast-java Repository:
-- [Feast Core](#feast-core)
- [Feast Serving](#feast-serving)
- [Feast Java Client](#feast-java-client)
@@ -15,11 +14,14 @@ the feast-java Repository:
#### Common Setup
Common Environment Setup for all feast-java Feast components:
-1. . Ensure following development tools are installed:
-- Java SE Development Kit 11, Maven 3.6, `make`
+
+Ensure following development tools are installed:
+- Java SE Development Kit 11
+- Maven 3.6
+- `make`
#### Code Style
-feast-java's codebase conforms to the [Google Java Style Guide](https://google.github.io/styleguide/javaguide.html).
+Feast's Java codebase conforms to the [Google Java Style Guide](https://google.github.io/styleguide/javaguide.html).
Automatically format the code to conform the style guide by:
@@ -59,82 +61,8 @@ Specifically, proto-generated code is not indexed by IntelliJ. To fix this, navi
- target/generated-sources/protobuf/java
- target/generated-sources/annotations
-
-## Feast Core
-### Environment Setup
-Setting up your development environment for Feast Core:
-1. Complete the feast-java [Common Setup](#common-setup)
-2. Boot up a PostgreSQL instance (version 11 and above). Example of doing so via Docker:
-```sh
-# spawn a PostgreSQL instance as a Docker container running in the background
-docker run \
- --rm -it -d \
- --name postgres \
- -e POSTGRES_DB=postgres \
- -e POSTGRES_USER=postgres \
- -e POSTGRES_PASSWORD=password \
- -p 5432:5432 postgres:12-alpine
-```
-
-### Configuration
-Feast Core is configured using it's [application.yml](https://docs.feast.dev/reference/configuration-reference#1-feast-core-and-feast-online-serving).
-
-### Building and Running
-1. Build / Compile Feast Core with Maven to produce an executable Feast Core JAR
-```sh
-mvn package -pl core --also-make -Dmaven.test.skip=true
-```
-
-2. Run Feast Core using the built JAR:
-```sh
-# where X.X.X is the version of the Feast Core JAR built
-java -jar core/target/feast-core-X.X.X-exec.jar
-```
-
-### Unit / Integration Tests
-Unit & Integration Tests can be used to verify functionality:
-```sh
-# run unit tests
-mvn test -pl core --also-make
-# run integration tests
-mvn verify -pl core --also-make
-```
-
## Feast Serving
-### Environment Setup
-Setting up your development environment for Feast Serving:
-1. Complete the feast-java [Common Setup](#common-setup)
-2. Boot up a Redis instance (version 5.x). Example of doing so via Docker:
-```sh
-docker run --name redis --rm -it -d -p 6379:6379 redis:5-alpine
-```
-
-> Feast Serving requires a running Feast Core instance to retrieve Feature metadata
-> in order to serve features. See the [Feast Core section](#feast-core) for
-> how to get a Feast Core instance running.
-
-### Configuration
-Feast Serving is configured using it's [application.yml](https://docs.feast.dev/reference/configuration-reference#1-feast-core-and-feast-online-serving).
-
-### Building and Running
-1. Build / Compile Feast Serving with Maven to produce an executable Feast Serving JAR
-```sh
-mvn package -pl serving --also-make -Dmaven.test.skip=true
-
-2. Run Feast Serving using the built JAR:
-```sh
-# where X.X.X is the version of the Feast serving JAR built
-java -jar serving/target/feast-serving-X.X.X-exec.jar
-```
-
-### Unit / Integration Tests
-Unit & Integration Tests can be used to verify functionality:
-```sh
-# run unit tests
-mvn test -pl serving --also-make
-# run integration tests
-mvn verify -pl serving --also-make
-```
+See instructions [here](serving/README.md) for developing.
## Feast Java Client
### Environment Setup
@@ -144,9 +72,6 @@ Setting up your development environment for Feast Java SDK:
> Feast Java Client is a Java Client for retrieving Features from a running Feast Serving instance.
> See the [Feast Serving Section](#feast-serving) section for how to get a Feast Serving instance running.
-### Configuration
-Feast Java Client is [configured as code](https://docs.feast.dev/v/master/reference/configuration-reference#4-feast-java-and-go-sdk)
-
### Building
1. Build / Compile Feast Java Client with Maven:
diff --git a/java/README.md b/java/README.md
index 8d6141faa8..ff5a1b8553 100644
--- a/java/README.md
+++ b/java/README.md
@@ -1,5 +1,4 @@
# Feast Java components
-[](https://github.com/feast-dev/feast-java/actions/workflows/complete.yml)
### Overview
@@ -19,4 +18,4 @@ Guides on Contributing:
- [Development Guide for feast-java (this repository)](CONTRIBUTING.md)
### Installing using Helm
-Please see the Helm charts in [charts](https://github.com/feast-dev/feast-helm-charts).
+Please see the Helm charts in [infra/charts/feast](../infra/charts/feast).
diff --git a/java/common/pom.xml b/java/common/pom.xml
index 0c5651876e..e5a648a7f9 100644
--- a/java/common/pom.xml
+++ b/java/common/pom.xml
@@ -33,13 +33,14 @@