How to Evaluate and Select Technical Solutions in Projects

Problem Description
During project initiation or execution, teams often face choices between multiple technical solutions. For example: choosing between microservices or monolithic architecture, selecting a database type (SQL vs. NoSQL), or adopting specific third-party tools (such as message queue selection). This topic examines how to systematically evaluate and select technical solutions to ensure they align with project objectives, resource constraints, and long-term maintenance needs.

Solution Process

1. Clarify Business Requirements and Technical Goals

   • First, outline core business requirements (e.g., high concurrency, data consistency, rapid iteration).
   • Define technical goals: scalability, performance, security, development efficiency, cost (e.g., licensing fees, operational investment).
   • Example: If the business requires rapid market validation (MVP), solutions with high development efficiency may be prioritized; for financial systems, strong data consistency is essential.

2. List Feasible Solutions and Quantify Criteria

   • Based on requirements, list all candidate solutions (e.g., Architecture A: Microservices + NoSQL; Architecture B: Monolithic + SQL).
   • Establish evaluation dimensions (weights can be adjusted based on project priorities):
     • Functionality: Whether core requirements are met (e.g., transaction support, interface compatibility).
     • Performance: Response time, throughput, resource consumption (compared via stress testing or benchmark data).
     • Maintainability: Code structure, documentation completeness, team technical alignment.
     • Cost: Direct costs (software licensing, cloud services) and indirect costs (learning curve, operational manpower).
     • Risk: Technology maturity, community support, vendor stability (e.g., activity level of open-source projects).

3. In-Depth Research and Prototype Validation

   • Conduct empirical validation for critical dimensions:
     • If performance is in doubt, build a minimal prototype for benchmark testing (e.g., comparing database read/write speeds).
     • If the team is unfamiliar with a technology, arrange small pilot projects to assess development efficiency.
   • Collect data: For example, a microservices solution may require additional investment in gateways and monitoring tools, necessitating quantification of extra workload.

4. Comprehensive Scoring and Decision-Making

   • Use a decision matrix (e.g., weighted scoring table) for quantitative comparison:

     Dimension	Weight	Solution A Score	Solution B Score
     Performance	30%	8	9
     Maintainability	25%	7	6
     Cost	20%	6	9
     Risk	25%	7	8

     • Weighted Total Score: Solution A = 0.3×8 + 0.25×7 + 0.2×6 + 0.25×7 = 7.1; Solution B = 7.55 → Solution B prevails.
   • Incorporate non-quantitative factors (e.g., team preference, strategic alignment) to adjust decisions, avoiding over-reliance on scores.

5. Document Conclusions and Plan Transition

   • Produce a selection report detailing the trade-off process, expected advantages/disadvantages, and mitigation measures (e.g., Solution B has lower cost but weaker scalability, requiring future refactoring plans).
   • If migration is involved, develop a phased implementation plan (e.g., trial in new modules first, gradually replacing old systems).

Summary
Technical selection requires balancing data-driven approaches with practical realities, avoiding the pursuit of a "perfect solution." The core is to reduce subjective bias through a structured process, ensuring choices support project success rather than mere technological novelty.