Feast users can choose to retrieve features from a feature server, as opposed to through the Python SDK.
Python feature server[Alpha] Go feature server[Alpha] AWS Lambda feature serverThe Go feature server is an HTTP/gRPC endpoint that serves features. It is written in Go, and is therefore significantly faster than the Python feature server. See this blog post for more details on the comparison between Python and Go. In general, we recommend the Go feature server for all production use cases that require extremely low-latency feature serving. Currently only the Redis and SQLite online stores are supported.
By default, the Go feature server is turned off. To turn it on you can add go_feature_serving: True to your feature_store.yaml:
Then the feast serve CLI command will start the Go feature server. As with Python, the Go feature server uses port 6566 by default; the port be overridden with a --port flag. Moreover, the server uses HTTP by default, but can be set to use gRPC with --type=grpc.
Alternatively, if you wish to experiment with the Go feature server instead of permanently turning it on, you can just run feast serve --go.
The Go component comes pre-compiled when you install Feast with Python versions 3.8-3.10 on macOS or Linux (on x86). In order to install the additional Python dependencies, you should install Feast with
You must also install the Apache Arrow C++ libraries. This is because the Go feature server uses the cgo memory allocator from the Apache Arrow C++ library for interoperability between Go and Python, to prevent memory from being accidentally garbage collected when executing on-demand feature views. You can read more about the usage of the cgo memory allocator in these .
For macOS, run brew install apache-arrow. For linux users, you have to install libarrow-dev.
For developers, if you want to build from source, run make compile-go-lib to build and compile the go server. In order to build the go binaries, you will need to install the apache-arrow c++ libraries.
The Go feature server can log all requested entities and served features to a configured destination inside an offline store. This allows users to create new datasets from features served online. Those datasets could be used for future trainings or for feature validations. To enable feature logging we need to edit feature_store.yaml:
Feature logging configuration in feature_store.yaml also allows to tweak some low-level parameters to achieve the best performance:
All these parameters are optional.
The logic for the Go feature server can also be used to retrieve features during a Python get_online_features call. To enable this behavior, you must add go_feature_retrieval: True to your feature_store.yaml. You must also have all the dependencies installed as detailed above.
project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: redis
connection_string: "localhost:6379"
go_feature_serving: Truepip install feast[go]sudo apt update
sudo apt install -y -V ca-certificates lsb-release wget
wget https://apache.jfrog.io/artifactory/arrow/$(lsb_release --id --short | tr 'A-Z' 'a-z')/apache-arrow-apt-source-latest-$(lsb_release --codename --short).deb
sudo apt install -y -V ./apache-arrow-apt-source-latest-$(lsb_release --codename --short).deb
sudo apt update
sudo apt install -y -V libarrow-dev # For C++project: my_feature_repo
registry: data/registry.db
provider: local
online_store:
type: redis
connection_string: "localhost:6379"
go_feature_serving: True
feature_server:
feature_logging:
enabled: Truefeature_server:
feature_logging:
enabled: True
flush_interval_secs: 300
write_to_disk_interval_secs: 30
emit_timeout_micro_secs: 10000
queue_capacity: 10000The Python feature server is an HTTP endpoint that serves features with JSON I/O. This enables users to write and read features from the online store using any programming language that can make HTTP requests.
There is a CLI command that starts the server: feast serve. By default, Feast uses port 6566; the port be overridden with a --port flag.
One can deploy a feature server by building a docker image that bundles in the project's feature_store.yaml. See this for an example on how to run Feast on Kubernetes.
A on AWS Lambda is also available.
Here's an example of how to start the Python feature server with a local feature repo:
After the server starts, we can execute cURL commands from another terminal tab:
It's also possible to specify a feature service name instead of the list of features:
The Python feature server also exposes an endpoint for . This endpoint allows you to push data to the online and/or offline store.
The request definition for pushmode is a string parameter to where the options are: ["online", "offline", "online_and_offline"]. Note that timestamps need to be strings.
or equivalently from Python:
$ feast init feature_repo
Creating a new Feast repository in /home/tsotne/feast/feature_repo.
$ cd feature_repo
$ feast apply
Created entity driver
Created feature view driver_hourly_stats
Created feature service driver_activity
Created sqlite table feature_repo_driver_hourly_stats
$ feast materialize-incremental $(date +%Y-%m-%d)
Materializing 1 feature views to 2021-09-09 17:00:00-07:00 into the sqlite online store.
driver_hourly_stats from 2021-09-09 16:51:08-07:00 to 2021-09-09 17:00:00-07:00:
100%|████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 295.24it/s]
$ feast serve
09/10/2021 10:42:11 AM INFO:Started server process [8889]
INFO: Waiting for application startup.
09/10/2021 10:42:11 AM INFO:Waiting for application startup.
INFO: Application startup complete.
09/10/2021 10:42:11 AM INFO:Application startup complete.
INFO: Uvicorn running on http://127.0.0.1:6566 (Press CTRL+C to quit)
09/10/2021 10:42:11 AM INFO:Uvicorn running on http://127.0.0.1:6566 (Press CTRL+C to quit)$ curl -X POST \
"http://localhost:6566/get-online-features" \
-d '{
"features": [
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips"
],
"entities": {
"driver_id": [1001, 1002, 1003]
}
}' | jq
{
"metadata": {
"feature_names": [
"driver_id",
"conv_rate",
"avg_daily_trips",
"acc_rate"
]
},
"results": [
{
"values": [
1001,
0.7037263512611389,
308,
0.8724706768989563
],
"statuses": [
"PRESENT",
"PRESENT",
"PRESENT",
"PRESENT"
],
"event_timestamps": [
"1970-01-01T00:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z"
]
},
{
"values": [
1002,
0.038169607520103455,
332,
0.48534533381462097
],
"statuses": [
"PRESENT",
"PRESENT",
"PRESENT",
"PRESENT"
],
"event_timestamps": [
"1970-01-01T00:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z"
]
},
{
"values": [
1003,
0.9665873050689697,
779,
0.7793770432472229
],
"statuses": [
"PRESENT",
"PRESENT",
"PRESENT",
"PRESENT"
],
"event_timestamps": [
"1970-01-01T00:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z",
"2021-12-31T23:00:00Z"
]
}
]
}curl -X POST \
"http://localhost:6566/get-online-features" \
-d '{
"feature_service": <feature-service-name>,
"entities": {
"driver_id": [1001, 1002, 1003]
}
}' | jqcurl -X POST "http://localhost:6566/push" -d '{
"push_source_name": "driver_hourly_stats_push_source",
"df": {
"driver_id": [1001],
"event_timestamp": ["2022-05-13 10:59:42"],
"created": ["2022-05-13 10:59:42"],
"conv_rate": [1.0],
"acc_rate": [1.0],
"avg_daily_trips": [1000]
},
"to": "online_and_offline",
}' | jqimport json
import requests
import pandas as pd
from datetime import datetime
event_dict = {
"driver_id": [1001],
"event_timestamp": [str(datetime(2021, 5, 13, 10, 59, 42))],
"created": [str(datetime(2021, 5, 13, 10, 59, 42))],
"conv_rate": [1.0],
"acc_rate": [1.0],
"avg_daily_trips": [1000],
"string_feature": "test2",
}
push_data = {
"push_source_name":"driver_stats_push_source",
"df":event_dict,
"to":"online",
}
requests.post(
"http://localhost:6566/push",
data=json.dumps(push_data))Warning: This is an experimental feature. It's intended for early testing and feedback, and could change without warnings in future releases.
The AWS Lambda feature server is an HTTP endpoint that serves features with JSON I/O, deployed as a Docker image through AWS Lambda and AWS API Gateway. This enables users to get features from Feast using any programming language that can make HTTP requests. A local feature server is also available. A remote feature server on GCP Cloud Run is currently being developed.
The AWS Lambda feature server is only available to projects using the AwsProvider with registries on S3. It is disabled by default. To enable it, feature_store.yaml must be modified; specifically, the enable flag must be on and an execution_role_name must be specified. For example, after running feast init -t aws, changing the registry to be on S3, and enabling the feature server, the contents of feature_store.yaml should look similar to the following:
If enabled, the feature server will be deployed during feast apply. After it is deployed, the feast endpoint CLI command will indicate the server's endpoint.
Feast requires the following permissions in order to deploy and teardown AWS Lambda feature server:
The following inline policy can be used to grant Feast the necessary permissions:
After feature_store.yaml has been modified as described in the previous section, it can be deployed as follows:
After the feature server starts, we can execute cURL commands against it:
lambda:CreateFunction
lambda:GetFunction
lambda:DeleteFunction
lambda:AddPermission
lambda:UpdateFunctionConfiguration
arn:aws:lambda:<region>:<account_id>:function:feast-*
ecr:CreateRepository
ecr:DescribeRepositories
ecr:DeleteRepository
ecr:PutImage
ecr:DescribeImages
ecr:BatchDeleteImage
ecr:CompleteLayerUpload
ecr:UploadLayerPart
ecr:InitiateLayerUpload
ecr:BatchCheckLayerAvailability
ecr:GetDownloadUrlForLayer
ecr:GetRepositoryPolicy
ecr:SetRepositoryPolicy
ecr:GetAuthorizationToken
*
iam:PassRole
arn:aws:iam::<account_id>:role/
apigateway:*
arn:aws:apigateway:::/apis//routes//routeresponses
arn:aws:apigateway:::/apis//routes//routeresponses/
arn:aws:apigateway:::/apis//routes/
arn:aws:apigateway:::/apis//routes
arn:aws:apigateway:::/apis//integrations
arn:aws:apigateway:::/apis//stages//routesettings/
arn:aws:apigateway:::/apis/
arn:aws:apigateway:*::/apis
project: dev
registry: s3://feast/registries/dev
provider: aws
online_store:
region: us-west-2
offline_store:
cluster_id: feast
region: us-west-2
user: admin
database: feast
s3_staging_location: s3://feast/redshift/tests/staging_location
iam_role: arn:aws:iam::{aws_account}:role/redshift_s3_access_role
feature_server:
enabled: True
execution_role_name: arn:aws:iam::{aws_account}:role/lambda_execution_role{
"Statement": [
{
Action = [
"lambda:CreateFunction",
"lambda:GetFunction",
"lambda:DeleteFunction",
"lambda:AddPermission",
"lambda:UpdateFunctionConfiguration",
]
Effect = "Allow"
Resource = "arn:aws:lambda:<region>:<account_id>:function:feast-*"
},
{
Action = [
"ecr:CreateRepository",
"ecr:DescribeRepositories",
"ecr:DeleteRepository",
"ecr:PutImage",
"ecr:DescribeImages",
"ecr:BatchDeleteImage",
"ecr:CompleteLayerUpload",
"ecr:UploadLayerPart",
"ecr:InitiateLayerUpload",
"ecr:BatchCheckLayerAvailability",
"ecr:GetDownloadUrlForLayer",
"ecr:GetRepositoryPolicy",
"ecr:SetRepositoryPolicy",
"ecr:GetAuthorizationToken"
]
Effect = "Allow"
Resource = "*"
},
{
Action = "iam:PassRole"
Effect = "Allow"
Resource = "arn:aws:iam::<account_id>:role/<lambda-execution-role-name>"
},
{
Effect = "Allow"
Action = "apigateway:*"
Resource = [
"arn:aws:apigateway:*::/apis/*/routes/*/routeresponses",
"arn:aws:apigateway:*::/apis/*/routes/*/routeresponses/*",
"arn:aws:apigateway:*::/apis/*/routes/*",
"arn:aws:apigateway:*::/apis/*/routes",
"arn:aws:apigateway:*::/apis/*/integrations",
"arn:aws:apigateway:*::/apis/*/stages/*/routesettings/*",
"arn:aws:apigateway:*::/apis/*",
"arn:aws:apigateway:*::/apis",
]
},
],
"Version": "2012-10-17"
}$ feast apply
10/07/2021 03:57:26 PM INFO:Pulling remote image feastdev/feature-server-python-aws:aws:
10/07/2021 03:57:28 PM INFO:Creating remote ECR repository feast-python-server-key_shark-0_13_1_dev23_gb3c08320:
10/07/2021 03:57:29 PM INFO:Pushing local image to remote 402087665549.dkr.ecr.us-west-2.amazonaws.com/feast-python-server-key_shark-0_13_1_dev23_gb3c08320:0_13_1_dev23_gb3c08320:
10/07/2021 03:58:44 PM INFO:Deploying feature server...
10/07/2021 03:58:45 PM INFO: Creating AWS Lambda...
10/07/2021 03:58:46 PM INFO: Creating AWS API Gateway...
Registered entity driver_id
Registered feature view driver_hourly_stats
Deploying infrastructure for driver_hourly_stats
$ feast endpoint
10/07/2021 03:59:01 PM INFO:Feature server endpoint: https://hkosgmz4m2.execute-api.us-west-2.amazonaws.com
$ feast materialize-incremental $(date +%Y-%m-%d)
Materializing 1 feature views to 2021-10-06 17:00:00-07:00 into the dynamodb online store.
driver_hourly_stats from 2020-10-08 23:01:34-07:00 to 2021-10-06 17:00:00-07:00:
100%|█████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 16.89it/s]$ curl -X POST \
"https://hkosgmz4m2.execute-api.us-west-2.amazonaws.com/get-online-features" \
-H "Content-type: application/json" \
-H "Accept: application/json" \
-d '{
"features": [
"driver_hourly_stats:conv_rate",
"driver_hourly_stats:acc_rate",
"driver_hourly_stats:avg_daily_trips"
],
"entities": {
"driver_id": [1001, 1002, 1003]
},
"full_feature_names": true
}' | jq
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 1346 100 1055 100 291 3436 947 --:--:-- --:--:-- --:--:-- 4370
{
"field_values": [
{
"fields": {
"driver_id": 1001,
"driver_hourly_stats__conv_rate": 0.025330161675810814,
"driver_hourly_stats__avg_daily_trips": 785,
"driver_hourly_stats__acc_rate": 0.835975170135498
},
"statuses": {
"driver_hourly_stats__avg_daily_trips": "PRESENT",
"driver_id": "PRESENT",
"driver_hourly_stats__conv_rate": "PRESENT",
"driver_hourly_stats__acc_rate": "PRESENT"
}
},
{
"fields": {
"driver_hourly_stats__conv_rate": 0.7595187425613403,
"driver_hourly_stats__acc_rate": 0.1740121990442276,
"driver_id": 1002,
"driver_hourly_stats__avg_daily_trips": 875
},
"statuses": {
"driver_hourly_stats__acc_rate": "PRESENT",
"driver_id": "PRESENT",
"driver_hourly_stats__avg_daily_trips": "PRESENT",
"driver_hourly_stats__conv_rate": "PRESENT"
}
},
{
"fields": {
"driver_hourly_stats__acc_rate": 0.7785481214523315,
"driver_hourly_stats__conv_rate": 0.33832859992980957,
"driver_hourly_stats__avg_daily_trips": 846,
"driver_id": 1003
},
"statuses": {
"driver_id": "PRESENT",
"driver_hourly_stats__conv_rate": "PRESENT",
"driver_hourly_stats__acc_rate": "PRESENT",
"driver_hourly_stats__avg_daily_trips": "PRESENT"
}
}
]
}