| .. | ||
| component-library/src/main/scala/com/twitter/product_mixer/component_library | ||
| core/src/main | ||
| shared-library/src/main/scala/com/twitter/product_mixer/shared_library | ||
| README.md | ||
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:
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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.
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Service Framework: A common service skeleton for teams to host their Product Mixer products.
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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.