mirror of
https://github.com/twitter/the-algorithm.git
synced 2024-12-23 10:41:50 +01:00
42 lines
2.4 KiB
Markdown
42 lines
2.4 KiB
Markdown
Product Mixer
|
|
=============
|
|
|
|
## Overview
|
|
|
|
Product Mixer is a common service framework and set of libraries that make it easy to build,
|
|
iterate on, and own product surface areas. It consists of:
|
|
|
|
- **Core Libraries:** A set of libraries that enable you to build execution pipelines out of
|
|
reusable components. You define your logic in small, well-defined, reusable components and focus
|
|
on expressing the business logic you want to have. Then you can define easy to understand pipelines
|
|
that compose your components. Product Mixer handles the execution and monitoring of your pipelines
|
|
allowing you to focus on what really matters, your business logic.
|
|
|
|
- **Service Framework:** A common service skeleton for teams to host their Product Mixer products.
|
|
|
|
- **Component Library:** A shared library of components made by the Product Mixer Team, or
|
|
contributed by users. This enables you to both easily share the reusable components you make as well
|
|
as benefit from the work other teams have done by utilizing their shared components in the library.
|
|
|
|
## Architecture
|
|
|
|
The bulk of a Product Mixer can be broken down into Pipelines and Components. Components allow you
|
|
to break business logic into separate, standardized, reusable, testable, and easily composable
|
|
pieces, where each component has a well defined abstraction. Pipelines are essentially configuration
|
|
files specifying which Components should be used and when. This makes it easy to understand how your
|
|
code will execute while keeping it organized and structured in a maintainable way.
|
|
|
|
Requests first go to Product Pipelines, which are used to select which Mixer Pipeline or
|
|
Recommendation Pipeline to run for a given request. Each Mixer or Recommendation
|
|
Pipeline may run multiple Candidate Pipelines to fetch candidates to include in the response.
|
|
|
|
Mixer Pipelines combine the results of multiple heterogeneous Candidate Pipelines together
|
|
(e.g. ads, tweets, users) while Recommendation Pipelines are used to score (via Scoring Pipelines)
|
|
and rank the results of homogenous Candidate Pipelines so that the top ranked ones can be returned.
|
|
These pipelines also marshall candidates into a domain object and then into a transport object
|
|
to return to the caller.
|
|
|
|
Candidate Pipelines fetch candidates from underlying Candidate Sources and perform some basic
|
|
operations on the Candidates, such as filtering out unwanted candidates, applying decorations,
|
|
and hydrating features.
|