[Alpha] Go feature server
Overview
The 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.
CLI
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
.
Installation
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 docs.
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.
Alpha features
Feature logging
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.
Python SDK retrieval
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.
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