MultiPooling Design Confirmation: A Developer's Guide

Hey guys! As Ben, a developer here, I'm on a mission to nail down the design for MultiPooling with all our stakeholders. This is super crucial to make sure we're all on the same page and that the final product rocks. We want to capture every user requirement and get a solid confirmation on how we're going to implement this thing. So, let's dive into what we need to do!

Acceptance Criteria: What We Need to Deliver

To make sure we're hitting the mark, we've got some key outputs we need to produce from our research. These aren't just nice-to-haves; they're essential for a successful implementation. Let's break them down:

1. Multi Pooling User Interaction Proposal

This is where we get to put our heads together and figure out how users will actually interact with MultiPooling. We're talking about creating a proposal that outlines the user experience from start to finish.

• Rough Mock-ups: We need to create some mock-ups of the page and index pages. These don't need to be pixel-perfect masterpieces, but they should give us a clear idea of the layout and functionality. The goal here is to confirm what data users need, how they expect to interact with it, and how they expect to access and edit it. Think wireframes and basic UI elements – the raw bones of the interface.
• Data Needs: What information do users need to see at a glance? How do they want to filter and sort data? We need to ensure the interface is intuitive and provides the right information at the right time. Consider the different types of users who will interact with the system and their specific needs. For example, a lab technician might need quick access to sample details, while a data analyst might be more interested in aggregated results. Make sure the design caters to all these perspectives.
• Interaction Expectations: How do users expect to navigate the system? What actions should be easy to perform? Are there any workflows that need special attention? We need to map out the user journey and identify any potential pain points. User interviews and surveys can be invaluable here. By understanding how users think and work, we can design an interface that feels natural and efficient.
• Access and Editing: How will users access MultiPooling features? How will they edit data? We need to think about permissions, roles, and the overall flow of information. Consider features like bulk editing, version control, and audit trails. Security is paramount, so we need to ensure that sensitive data is protected and access is appropriately controlled. Also, think about the different devices users might use, from desktop computers to tablets and mobile phones. A responsive design that adapts to different screen sizes is essential.

2. ERD Diagram for Traction Data

Next up, we need to create an Entity Relationship Diagram (ERD) or a similar visual representation of how MultiPooling will work within Traction from a data perspective. This is crucial for understanding the relationships between different data entities and ensuring our database design is solid.

• Data Relationships: An ERD will help us visualize how different data entities (like pools, samples, and metadata) are related to each other. This is vital for maintaining data integrity and ensuring efficient queries. We need to map out the entities, their attributes, and the relationships between them. Think about cardinality (one-to-one, one-to-many, many-to-many) and how this impacts the data model. For instance, a pool might contain multiple samples, and each sample might have multiple metadata entries. Understanding these relationships is fundamental to designing a robust and scalable system.
• Traction Integration: How will MultiPooling integrate with existing data structures in Traction? We need to make sure our new data model plays nicely with the old. Consider the existing tables, fields, and relationships. How will we avoid conflicts and ensure data consistency? A well-thought-out integration strategy is crucial for a smooth transition and minimal disruption to existing workflows. We also need to think about data migration – how will we move existing data into the new model? This might involve scripting, data transformation, and careful testing to ensure data integrity.
• Scalability: Can our data model handle future growth and increased data volumes? We need to design for the long term. Think about indexing, partitioning, and other techniques to optimize performance. A scalable data model is one that can adapt to changing needs without requiring major redesigns. This includes considering the size of the data, the number of users, and the frequency of access. Planning for scalability from the outset will save us headaches down the road.

3. Architectural Diagram for Front-End Pooling Abstraction

Finally, we need to create an architectural diagram that outlines the front-end pooling abstraction in Traction. This diagram should be pipeline agnostic, meaning it should work regardless of the specific pipeline being used. This is where we get into the nitty-gritty of the system's architecture.

• Pooling Abstraction: How will we abstract the pooling logic in the front-end? This is key to creating a flexible and maintainable system. We need to define the interfaces and components that will handle pooling operations. Think about how different modules will interact with each other and how data will flow through the system. Abstraction allows us to change the underlying implementation without affecting the rest of the application. This is crucial for adapting to new requirements and technologies in the future.
• Pipeline Agnostic Design: The design should not be tied to any specific pipeline. This allows us to reuse the pooling logic across different workflows and avoid duplication of code. Consider using design patterns like the Strategy pattern or the Template Method pattern to achieve pipeline agnosticism. The goal is to create a system that is flexible and adaptable to different workflows without requiring significant code changes.
• Front-End Components: What front-end components will be involved in MultiPooling? How will they interact with the back-end? We need to map out the components and their responsibilities. Think about the UI elements, the data services, and the state management. A clear understanding of the front-end architecture is essential for building a robust and maintainable application. This includes considering the use of frameworks and libraries, such as React, Angular, or Vue.js, and how they can help us structure the front-end code.

Primary Contacts

So, who are the go-to people for this project? This is where we need to fill in the blanks. Knowing the primary contacts ensures clear communication and efficient decision-making. We need to identify the individuals who will be responsible for different aspects of the project, such as design, development, and testing. Having a clear point of contact for each area will help streamline the process and ensure that questions and issues are addressed promptly.

Knowledge and Stakeholders

For this project, Ben T and Steve I are key stakeholders. They have the knowledge and insights we need to make this a success. But it's not just about these two. We need to think about everyone who has a stake in MultiPooling. Stakeholders might include end-users, project managers, and other developers. Engaging with stakeholders early and often is crucial for gathering requirements, addressing concerns, and ensuring that the final product meets everyone's needs.

Additional Context: The Bigger Picture

MultiPooling is the decided architecture from research story https://github.com/sanger/traction-ui/issues/2244. This means we're building on previous work and decisions. It's super important to check out the story comments for all the details. This context helps us understand the rationale behind MultiPooling and how it fits into the overall system architecture. It also provides valuable insights into the challenges and considerations that were taken into account during the research phase. By understanding the bigger picture, we can make informed decisions and avoid repeating mistakes.

Let's Get This Done!

Alright guys, that's the plan! We've got a lot to do, but with a clear understanding of our goals and the steps we need to take, we can make MultiPooling a huge success. Remember, collaboration and communication are key. Let's keep the conversation flowing, share our ideas, and work together to create something awesome.