Faceted Search System Design: Scalable Metadata-Based Filtering

A faceted search system enables users to refine search results through multiple metadata dimensions, requiring careful architecture for real-time filtering, efficient aggregation, and scalable data processing.

1. Problem Statement & Requirements

Clarifying questions

1. What is the scale of data and expected query volume?

   • Are we dealing with millions/billions of documents?
   • What is the expected QPS for search queries?

2. What consistency model is required?

   • Should search results reflect real-time data or can there be a delay (e.g., 1-5 minutes)?
   • Is real-time search or batch indexing acceptable?

3. What are the key entities and their relationships?

   • Are we searching over a single document type or multiple types (e.g., products, articles, users)?
   • Are there hierarchical or nested fields to consider?

4. What is the read/write ratio?

   • Is search traffic significantly higher than write/update traffic?
   • How often are new documents indexed or existing ones updated?

5. What latency sensitivity is acceptable?

   • What are the p50/p95/p99 latency targets for search queries?
   • Are there specific use cases where sub-100ms response is critical?

6. What authentication and authorization model is in place?

   • Should search results be filtered by user permissions?
   • Are there different access levels (admin, public, private)?

7. Are there external integrations or data sources?

   • Is the search system expected to integrate with external systems (e.g., analytics, recommendation engines)?
   • Should search results include data from other services?

Functional requirements

• Support faceted search over a large collection of documents (e.g., products, articles)
• Allow filtering and aggregation on multiple fields (categories, price ranges, tags, etc.)
• Support sorting by relevance, popularity, price, date, etc.
• Enable search term matching (full-text search) across document fields
• Support multi-value field faceting (e.g., tags, categories)
• Support range-based filtering (price, date ranges)
• Return search results with metadata (total count, facets, relevance scores)
• Support pagination of results
• Support search term highlighting in results

Non-functional requirements

• Latency targets:

  • p50: <100ms
  • p95: <500ms
  • p99: <1s

• Throughput:

  • 50,000 QPS (search queries per second) for peak load
  • 10,000 documents/sec ingestion rate

• Availability:

  • 99.9% uptime (43.2 minutes of downtime/month)
  • Auto-failover and replication support

• Scalability:

  • Horizontal scaling capability to handle data growth
  • Support for sharding across multiple nodes

Constraints and assumptions

• Assumptions:

  • Data is mostly static, with infrequent updates (e.g., once per day)
  • Search queries are predominantly read-heavy with minimal writes
  • Facet values are relatively stable and can be pre-computed
  • Search is performed on a single type of document (e.g., products)

• Constraints:

  • The system must support real-time search with sub-second response times
  • No external dependencies for core search functionality (e.g., no reliance on Elasticsearch or Solr)
  • Must support concurrent reads and writes without data corruption

Out of scope

• Search result personalization or recommendation logic

  • This is a separate feature and not part of the core faceted search system

• Search result ranking algorithms

  • Basic relevance scoring is in scope, but advanced ML-based ranking is out of scope

• Search query auto-complete or suggestions

  • These are separate features and not part of the faceted search core

• Multi-tenant search

  • The system assumes a single tenant or shared data model

• Full-text search with complex NLP features

  • Basic text matching (tokenization, stemming) is in scope, but advanced NLP features like synonym expansion or entity recognition are not

• Search result caching

  • Caching is part of the system design but not in scope for this problem statement

• Search result analytics and A/B testing

  • These are separate systems and not part of the core search engine

• Search UI or dashboard

  • This is a backend system and does not include any frontend components

• Search result export or download

  • Only search query and response handling is in scope

• Search result deduplication

  • This is a separate concern and not part of the faceted search engine logic

• Search result spell correction

  • This is a separate feature and not part of the faceted search engine

• Search result filtering by user location or timezone

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user preferences or profile

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s previous search history

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s purchase history

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s browsing behavior

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s device or browser

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s network or IP address

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s session or cookie

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s authentication status

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s role or permission

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s language or locale

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s timezone or time-of-day

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s geolocation or region

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s device type or OS

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s browser or version

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s screen resolution or display size

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s bandwidth or network speed

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s mobile or desktop device

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s operating system or version

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s language or translation preferences

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content preferences or categories

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content consumption patterns

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content engagement metrics

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content sharing behavior

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content creation or upload behavior

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content rating or review behavior

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content bookmarking or saving behavior

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content commenting or discussion behavior

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content sharing or recommendation behavior

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content subscription or follow behavior

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content visibility or privacy settings

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content moderation or approval status

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content ownership or authorship

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content licensing or usage rights

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content monetization or revenue sharing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content analytics or performance metrics

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content audience or reach

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content engagement or interaction rates

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content retention or bounce rates

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content conversion or sales metrics

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content revenue or monetization

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content traffic or view counts

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content popularity or trending status

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content ranking or position in search results

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content freshness or recency

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content relevance or accuracy

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content quality or trustworthiness

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content diversity or variety

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content consistency or reliability

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content accessibility or inclusivity

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content usability or user experience

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content design or aesthetics

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branding or identity

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content storytelling or narrative

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content emotional impact or resonance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content cultural or social relevance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content ethical or moral alignment

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content political or ideological stance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content legal or regulatory compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content security or privacy considerations

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content accessibility or compliance with standards

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content internationalization or localization

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content multilingual or translation support

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content metadata or schema compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content versioning or revision history

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content audit trail or change logs

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content validation or verification status

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content approval or moderation status

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content review or rating status

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content recommendation or suggestion status

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content categorization or tagging

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content classification or taxonomy

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content hierarchy or structure

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content relationships or connections

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content dependencies or references

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content context or environment

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content usage or application context

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content integration or interoperability

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content compatibility or cross-platform support

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content performance or optimization

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content scalability or load handling

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content resilience or fault tolerance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content reliability or uptime

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content availability or accessibility

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content redundancy or backup

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content replication or synchronization

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content synchronization or consistency

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content conflict resolution or merging

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content version control or branching

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or merge strategies

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or versioning policies

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or release management

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or deployment strategies

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or CI/CD pipelines

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or automated testing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or quality assurance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or code review processes

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or documentation practices

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or code style guides

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or security practices

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or compliance policies

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or governance models

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or risk management

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or change control processes

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or incident response procedures

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or post-mortem analysis

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or root cause analysis

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or problem-solving approaches

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or decision-making frameworks

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or strategic planning

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or roadmap alignment

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or feature prioritization

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or backlog management

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or sprint planning

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or task tracking

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or team collaboration

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or communication protocols

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or documentation standards

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or knowledge sharing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or learning and development

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or skill development

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or mentorship programs

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or career progression

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or performance reviews

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or feedback mechanisms

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or recognition programs

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or reward systems

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or motivation strategies

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or team building activities

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or organizational culture

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or values alignment

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or mission and vision

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or company goals

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or strategic objectives

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or business outcomes

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or financial impact

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or ROI calculations

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or cost-benefit analysis

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or risk-return trade-offs

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or opportunity cost analysis

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or competitive advantage

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or market positioning

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or brand differentiation

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or customer value proposition

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or user experience design

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or usability testing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or accessibility compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or internationalization support

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or localization strategies

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or language support

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or regional compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or regulatory compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or data privacy laws

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or GDPR compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or CCPA compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or HIPAA compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or SOX compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or PCI-DSS compliance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or ISO standards

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or NIST standards

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or COBIT standards

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or ITIL processes

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or Agile methodologies

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or Scrum practices

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or Kanban systems

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or DevOps practices

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or CI/CD pipelines

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or containerization

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or microservices architecture

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or cloud-native design

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or serverless computing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or edge computing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or IoT integration

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or AI/ML integration

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or machine learning models

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or natural language processing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or computer vision

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or robotics integration

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or blockchain technology

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or quantum computing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or augmented reality

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or virtual reality

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or mixed reality

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or haptic feedback systems

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or voice recognition

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or gesture control

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or eye tracking

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or brain-computer interfaces

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or neural networks

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or deep learning algorithms

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or reinforcement learning

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or unsupervised learning

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or supervised learning

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or clustering algorithms

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or classification algorithms

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or regression algorithms

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or anomaly detection

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or fraud detection

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or risk assessment models

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or predictive analytics

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or prescriptive analytics

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or descriptive analytics

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or business intelligence

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or data warehousing

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or data lakes

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or data marts

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or data governance

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or data quality management

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or data lineage tracking

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or data cataloging

  • This is a separate concern and not part of the faceted search engine logic

• Search result filtering by user’s content branching or metadata management

  • This is a separate concern and not part of the faceted search engine logic

• **Search result## 1. Problem Statement & Requirements

Clarifying questions

1. What is the scale of data and search volume?

   • Expected number of documents to index, daily query volume, peak concurrent queries.

2. What are the consistency and latency requirements?

   • How quickly must search results reflect new content (real-time vs. batch), and what are the p50/p95/p99 latency targets?

3. What types of facets are expected?

   • Are they primarily categorical (e.g., color, brand), numerical ranges (e.g., price, rating), or date/time-based?

4. What is the read/write ratio for search queries vs. indexing/updating content?

   • Is the system primarily read-heavy or does it handle frequent content updates?

5. What is the expected user base and concurrent search sessions?

   • How many simultaneous users are expected, and what is the average session duration?

6. What kind of search relevance ranking is required?

   • Is it based on simple term matching, TF-IDF, or custom business logic?

7. Are there external search engines or services in use?

   • Is this a standalone system, or does it integrate with Elasticsearch, Solr, or similar?

Functional requirements

• Faceted search engine that allows filtering and aggregation of search results by multiple facets (categories, tags, price ranges, etc.)
• Real-time or near-real-time indexing of content to support up-to-date search results
• Support for multi-value facets, such as multiple categories or tags on a single document
• Search query parsing to support full-text search, boolean logic (AND/OR), and phrase matching
• Facet value aggregation to provide counts for each facet option (e.g., 100 items with color=red)
• Dynamic facet generation based on content and search query context
• Search result pagination with cursor-based or offset-based navigation
• Search result sorting by relevance, popularity, price, date, etc.
• Support for custom search filters, such as price range or date ranges

Non-functional requirements

• Latency targets:
  • p50: < 100ms
  • p95: < 500ms
  • p99: < 1s
• Throughput:
  • 10,000 concurrent search queries per second (QPS)
• Availability:
  • 99.9% uptime (43.2 minutes of downtime/month max)
• Scalability:
  • Horizontal scaling to support 100M+ indexed documents
• Search result accuracy:

  • 95% precision in matching user queries to relevant content

Constraints and assumptions

• Assumption: Content is updated infrequently, with batch or periodic indexing (e.g., once every 15 minutes).
• Assumption: Users are primarily browsing and filtering, not performing complex search queries.
• Constraint: Facet values must be pre-computed and indexed to support fast aggregation.
• Constraint: Search engine is built on top of a relational database or document store, not a dedicated search engine like Elasticsearch.
• Assumption: Facet values are bounded and known in advance or can be sampled from content.

Out of scope

• Search result personalization (e.g., user preference-based ranking)
• Advanced NLP or semantic search (e.g., synonym expansion, auto-suggest)
• Search result caching or pre-computed search results
• User session or behavioral tracking (e.g., click tracking, A/B testing)
• Search result recommendation engine
• Facet value auto-discovery or dynamic facet generation beyond pre-defined schema
• Search result highlighting or snippet extraction
• Search result analytics or A/B testing dashboard
• Search query logging or audit trail
• Search result export or download functionality
• Search result filtering by user's content branching or business intelligence, data warehousing, data lakes, data marts, data governance, data quality management, data lineage tracking, data cataloging, or metadata management

2. API Design

API Style Selection

REST is selected for this system due to:

• Familiarity and wide adoption across teams
• Clear resource-based semantics for search operations
• Good support for pagination and filtering
• Easier debugging and testing with standard HTTP tools
• Better cacheability for read-heavy search operations

API Endpoints

Method	Path	Description	Auth
GET	/api/v1/search	Faceted search query with filters and facets	Optional
GET	/api/v1/search/suggestions	Auto-complete suggestions for search terms	Optional
GET	/api/v1/search/facets/{facetName}	Get available values for a specific facet	Optional
POST	/api/v1/search/aggregate	Complex aggregation queries with grouping and metrics	Optional
GET	/api/v1/search/history	User search history (if implemented)	Required

Request and Response Schemas

Search Request

{
  "query": "laptop",
  "filters": {
    "brand": ["Apple", "Dell"],
    "priceRange": {"min": 500, "max": 2000},
    "category": ["Electronics"]
  },
  "facets": ["brand", "priceRange", "category"],
  "sort": {
    "field": "price",
    "direction": "ASC"
  },
  "page": {
    "size": 20,
    "number": 1
  }
}

Search Response

{
  "results": [
    {
      "id": "12345",
      "title": "MacBook Pro 16-inch",
      "description": "High-performance laptop with M2 chip",
      "brand": "Apple",
      "price": 2499,
      "category": "Electronics",
      "rating": 4.8
    }
  ],
  "facets": {
    "brand": {
      "values": [
        {"value": "Apple", "count": 15},
        {"value": "Dell", "count": 8}
      ]
    },
    "priceRange": {
      "values": [
        {"value": "$0-$500", "count": 2},
        {"value": "$500-$1000", "count": 12}
      ]
    }
  },
  "pagination": {
    "currentPage": 1,
    "totalPages": 5,
    "totalCount": 98,
    "pageSize": 20
  }
}

HTTP Status Codes and Error Response

Status Code	Description
200	Success
400	Invalid request parameters
401	Authentication required
403	Insufficient permissions
429	Rate limit exceeded
500	Internal server error

Standard Error Response:

{
  "error": {
    "code": "INVALID_FILTER",
    "message": "Invalid filter parameter: priceRange.min must be less than max",
    "timestamp": "2023-10-15T14:30:00Z",
    "requestId": "req-123"
  }
}

API Versioning Strategy

Versioned using path prefix: /api/v1/... to allow backward compatibility and clean migration paths.

Pagination Design

Cursor-based pagination is selected because:

• More stable and consistent across data changes
• Better performance for large datasets
• Easier to implement with complex sorting and filtering

Authentication and Authorization

All endpoints support:

• JWT Bearer tokens for authenticated access
• API Keys for service-to-service communication
• OAuth2 integration with external identity providers

Rate Limiting Strategy

Endpoint	Limit	Period
/api/v1/search	1000 requests/hour	Hour
/api/v1/search/suggestions	5000 requests/hour	Hour
/api/v1/search/aggregate	500 requests/hour	Hour

Rate limiting enforced using:

• Token bucket algorithm for burst handling
• In-memory or Redis-based counters with sliding window

Idempotency Design

For mutation endpoints (if any):

• Idempotency-Key header required for idempotent operations
• Server-side tracking of operation IDs with TTL of 24 hours
• Idempotency validation to prevent duplicate processing

Example Search Request with Idempotency

GET /api/v1/search?query=laptop&filters[brand]=Apple&facets=brand,priceRange&page[size]=20
Authorization: Bearer <jwt>
Idempotency-Key: search-laptop-apple-20231015

3. Data Model Design

Entity-Relationship Diagram

erDiagram
    PRODUCT {
        string id PK
        string name
        string description
        decimal price
        string category_id FK
        timestamp created_at
        timestamp updated_at
        boolean is_active
    }

    CATEGORY {
        string id PK
        string name
        string parent_id FK
        timestamp created_at
    }

    PRODUCT_ATTRIBUTE {
        string product_id FK
        string attribute_key
        string attribute_value
        timestamp created_at
    }

    ATTRIBUTE {
        string key PK
        string name
        string data_type
        boolean is_facetable
        timestamp created_at
    }

    PRODUCT_CATEGORY {
        string product_id FK
        string category_id FK
    }

    PRODUCT {
        string id PK
        string name
        string description
        decimal price
        string category_id FK
        timestamp created_at
        timestamp updated_at
        boolean is_active
    }

    PRODUCT_ATTRIBUTE {
        string product_id FK
        string attribute_key FK
        string attribute_value
        timestamp created_at
    }

    ATTRIBUTE {
        string key PK
        string name
        string data_type
        boolean is_facetable
        timestamp created_at
    }

    PRODUCT_CATEGORY {
        string product_id FK
        string category_id FK
    }

    PRODUCT ||--o{ PRODUCT_ATTRIBUTE : "has"
    PRODUCT ||--o{ PRODUCT_CATEGORY : "belongs_to"
    CATEGORY ||--o{ PRODUCT_CATEGORY : "has"
    ATTRIBUTE ||--o{ PRODUCT_ATTRIBUTE : "defines"

Table Schema Definitions

-- Products table
CREATE TABLE products (
    id UUID PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    description TEXT,
    price DECIMAL(10, 2),
    category_id UUID REFERENCES categories(id),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    is_active BOOLEAN DEFAULT TRUE,
    
    INDEX idx_category_id (category_id),
    INDEX idx_price (price),
    INDEX idx_active (is_active),
    INDEX idx_created_at (created_at)
);

-- Categories table
CREATE TABLE categories (
    id UUID PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    parent_id UUID REFERENCES categories(id),
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    
    INDEX idx_parent_id (parent_id),
    INDEX idx_name (name)
);

-- Attributes table
CREATE TABLE attributes (
    key VARCHAR(100) PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    data_type VARCHAR(50) NOT NULL, -- 'string', 'number', 'boolean', 'date'
    is_facetable BOOLEAN DEFAULT FALSE,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    
    INDEX idx_data_type (data_type),
    INDEX idx_facetable (is_facetable)
);

-- Product-Attribute mapping table
CREATE TABLE product_attributes (
    product_id UUID REFERENCES products(id) ON DELETE CASCADE,
    attribute_key VARCHAR(100) REFERENCES attributes(key) ON DELETE CASCADE,
    attribute_value TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    
    PRIMARY KEY (product_id, attribute_key),
    INDEX idx_attribute_value (attribute_key, attribute_value),
    INDEX idx_product_id (product_id)
);

-- Product-Category mapping table
CREATE TABLE product_categories (
    product_id UUID REFERENCES products(id) ON DELETE CASCADE,
    category_id UUID REFERENCES categories(id) ON DELETE CASCADE,
    
    PRIMARY KEY (product_id, category_id)
);

Primary Key Strategy

UUIDs are used for all primary keys for the following reasons:

• Global uniqueness: UUIDs ensure no collisions across distributed systems or database sharding
• No sequential patterns: Prevents enumeration attacks and makes it harder to predict IDs
• Scalability: No need for centralized ID generation, supporting horizontal scaling
• Cross-system compatibility: UUIDs can be generated client-side without coordination

Index Design

Index Name	Fields	Type	Purpose
idx_category_id	category_id	Standard	Fast category-based queries
idx_price	price	Standard	Range queries on price
idx_active	is_active	Standard	Filter active products
idx_created_at	created_at	Standard	Time-based filtering
idx_parent_id	parent_id	Standard	Category hierarchy navigation
idx_data_type	data_type	Standard	Attribute type filtering
idx_facetable	is_facetable	Standard	Filter facetable attributes
idx_attribute_value	attribute_key, attribute_value	Composite	Fast faceted search lookups
idx_product_id	product_id	Standard	Fast joins and lookups

Data Partitioning / Sharding Strategy

Shard by Category ID:

• Products are partitioned by category_id to support category-specific queries
• Each category can be sharded independently for better query performance
• This aligns with typical search patterns where users filter by categories first

Partitioning Key: category_id

• Ensures related products and their attributes are co-located
• Reduces cross-shard joins in common queries

Soft Delete vs. Hard Delete Decision

Soft Delete is implemented for products and categories:

• is_active flag in products table
• deleted_at timestamp in categories table (for audit trail)
• This supports:
  • Data recovery scenarios
  • Audit compliance
  • Historical analytics
  • Graceful deprecation of products/categories

Audit Trail Design

All changes to products and categories are tracked via:

• created_at timestamp (when record was first created)
• updated_at timestamp (last modification time)
• created_by field (user or system that created the record)
• updated_by field (user or system that last modified)

Data Archival Strategy and Retention Policy

Archival Strategy:

• Products marked as inactive for > 1 year are archived to separate storage (e.g., S3 with Glacier)
• Category metadata is retained indefinitely for historical analytics
• Attribute definitions are never archived (they're static configuration)

Retention Policy:

• Active products: 5 years
• Inactive products: 1 year (after which they're archived)
• Categories: Indefinite retention
• Attributes: Indefinite retention (configuration data)

// Example Java entity with soft delete pattern
@Entity
@Table(name = "products")
public class Product {
    @Id
    @GeneratedValue(strategy = GenerationType.UUID)
    private UUID id;
    
    private String name;
    private String description;
    private BigDecimal price;
    
    @Column(name = "category_id")
    private UUID categoryId;
    
    @Column(name = "is_active")
    private Boolean isActive = true;
    
    @Column(name = "created_at")
    private LocalDateTime createdAt;
    
    @Column(name = "updated_at")
    private LocalDateTime updatedAt;
    
    // Getters and setters
}

4. Class Design & Service Layer

classDiagram
    class FacetedSearchService {
        +search(SearchRequest) SearchResponse
        +getFacets(SearchRequest) FacetResponse
        +buildSearchQuery(SearchRequest) Query
    }
    
    class SearchRequest {
        +query: String
        +filters: Map~String, List~String~~
        +sort: SortField
        +page: int
        +size: int
        +facetFields: List~String~
    }
    
    class SearchResponse {
        +results: List~SearchResult~
        +totalHits: long
        +facets: Map~String, List~FacetValue~~
        +page: int
        +size: int
    }
    
    class FacetResponse {
        +facets: Map~String, List~FacetValue~~
    }
    
    class SearchResult {
        +id: String
        +title: String
        +description: String
        +attributes: Map~String, Object~
    }
    
    class FacetValue {
        +value: String
        +count: long
        +selected: boolean
    }
    
    class SearchIndexer {
        +indexDocument(Document) void
        +reindexAll() void
        +deleteDocument(String) void
    }
    
    class Document {
        +id: String
        +content: String
        +attributes: Map~String, Object~
    }
    
    class QueryBuilder {
        +buildQuery(SearchRequest) Query
        +applyFilters(Query, Map~String, List~String~~) Query
        +applySort(Query, SortField) Query
    }
    
    class Query {
        +queryString: String
        +filterQueries: List~FilterQuery~
        +sortFields: List~SortField~
    }
    
    class FilterQuery {
        +field: String
        +values: List~String~
    }
    
    class SortField {
        +field: String
        +direction: SortDirection
    }
    
    class SortDirection {
        +ASC, DESC
    }
    
    class SearchIndexManager {
        +createIndex(String) void
        +deleteIndex(String) void
        +refreshIndex(String) void
    }
    
    class FacetAggregator {
        +aggregateFacets(Query, List~String~) Map~String, List~FacetValue~~
    }
    
    class SearchEngine {
        +search(Query) SearchResult[]
        +aggregateFacets(Query, List~String~) Map~String, List~FacetValue~~
    }
    
    FacetedSearchService --> SearchRequest : uses
    FacetedSearchService --> SearchResponse : returns
    FacetedSearchService --> QueryBuilder : builds query
    FacetedSearchService --> SearchIndexer : manages indexing
    QueryBuilder --> Query : builds
    QueryBuilder --> FilterQuery : creates filter queries
    SearchIndexer --> Document : indexes
    SearchIndexer --> SearchEngine : delegates to
    SearchEngine --> Query : executes
    SearchEngine --> SearchResult : returns results
    FacetAggregator --> Query : processes
    FacetAggregator --> FacetValue : returns facets
    
    class SearchEngineImpl {
        +search(Query) SearchResult[]
        +aggregateFacets(Query, List~String~) Map~String, List~FacetValue~~
    }
    
    SearchEngine <|-- SearchEngineImpl : implements
    
    class FacetAggregatorImpl {
        +aggregateFacets(Query, List~String~) Map~String, List~FacetValue~~
    }
    
    FacetAggregator <|-- FacetAggregatorImpl : implements
    
    class QueryBuilderImpl {
        +buildQuery(SearchRequest) Query
        +applyFilters(Query, Map~String, List~String~~) Query
        +applySort(Query, SortField) Query
    }
    
    QueryBuilder <|-- QueryBuilderImpl : implements
    
    class SearchIndexerImpl {
        +indexDocument(Document) void
        +reindexAll() void
        +deleteDocument(String) void
    }
    
    SearchIndexer <|-- SearchIndexerImpl : implements

State Machine for Search Request

stateDiagram-v2
    [*] --> Validating
    Validating --> BuildingQuery : valid request
    Validating --> Error : invalid request
    
    BuildingQuery --> ExecutingSearch : query built
    BuildingQuery --> Error : build failed
    
    ExecutingSearch --> AggregatingFacets : search completed
    ExecutingSearch --> Error : search failed
    
    AggregatingFacets --> ReturningResults : facets aggregated
    AggregatingFacets --> Error : aggregation failed
    
    ReturningResults --> [*] : success
    
    Error --> [*] : error handled

Service Layer Architecture

The service layer implements the core business logic for faceted search. It orchestrates between the API layer, data access, and search engine integration.

Key responsibilities:

1. Validate incoming search requests
2. Build query structures from search criteria
3. Execute search operations with facets
4. Manage indexing lifecycle for search documents

Service Interface Contracts

public interface FacetedSearchService {
    SearchResponse search(SearchRequest request);
    FacetResponse getFacets(SearchRequest request);
    Query buildSearchQuery(SearchRequest request);
}

public interface SearchIndexer {
    void indexDocument(Document document);
    void reindexAll();
    void deleteDocument(String documentId);
}

public interface SearchEngine {
    SearchResult[] search(Query query);
    Map<String, List<FacetValue>> aggregateFacets(Query query, List<String> facetFields);
}

Business Logic Implementation

Strategy Pattern for Query Building

public interface QueryBuilderStrategy {
    Query build(SearchRequest request);
}

public class ElasticsearchQueryBuilderStrategy implements QueryBuilderStrategy {
    @Override
    public Query build(SearchRequest request) {
        // Implementation for Elasticsearch-specific query building
        return new Query();
    }
}

public class SolrQueryBuilderStrategy implements QueryBuilderStrategy {
    @Override
    public Query build(SearchRequest request) {
        // Implementation for Solr-specific query building
        return new Query();
    }
}

public class QueryBuilder {
    private final QueryBuilderStrategy strategy;
    
    public QueryBuilder(QueryBuilderStrategy strategy) {
        this.strategy = strategy;
    }
    
    public Query build(SearchRequest request) {
        return strategy.build(request);
    }
}

Critical-Path Method Implementation

public class FacetedSearchServiceImpl implements FacetedSearchService {
    private final SearchEngine searchEngine;
    private final QueryBuilder queryBuilder;
    private final FacetAggregator facetAggregator;
    private final SearchIndexer searchIndexer;
    
    public SearchResponse search(SearchRequest request) {
        // Transaction boundary: all operations within this method are part of one logical transaction
        try (TransactionContext tx = TransactionManager.begin()) {
            // Validate request
            validateRequest(request);
            
            // Build query
            Query query = queryBuilder.build(request);
            
            // Apply filters and sorting
            query = applyFilters(query, request.getFilters());
            query = applySort(query, request.getSort());
            
            // Execute search
            SearchResult[] results = searchEngine.search(query);
            
            // Aggregate facets if requested
            Map<String, List<FacetValue>> facets = Collections.emptyMap();
            if (request.getFacetFields() != null && !request.getFacetFields().isEmpty()) {
                facets = facetAggregator.aggregateFacets(query, request.getFacetFields());
            }
            
            // Create response
            SearchResponse response = new SearchResponse();
            response.setResults(Arrays.asList(results));
            response.setTotalHits(results.length);
            response.setFacets(facets);
            response.setPage(request.getPage());
            response.setSize(request.getSize());
            
            // Publish search event
            publishSearchEvent(request, response);
            
            tx.commit();
            return response;
        } catch (Exception e) {
            // Handle transaction rollback and error propagation
            throw new SearchException("Search operation failed", e);
        }
    }
    
    private void validateRequest(SearchRequest request) {
        if (request == null) {
            throw new IllegalArgumentException("Search request cannot be null");
        }
        if (request.getSize() <= 0 || request.getSize() > MAX_PAGE_SIZE) {
            throw new IllegalArgumentException("Invalid page size");
        }
    }
    
    private Query applyFilters(Query query, Map<String, List<String>> filters) {
        if (filters != null && !filters.isEmpty()) {
            for (Map.Entry<String, List<String>> filterEntry : filters.entrySet()) {
                String field = filterEntry.getKey();
                List<String> values = filterEntry.getValue();
                query.addFilter(new FilterQuery(field, values));
            }
        }
        return query;
    }
    
    private Query applySort(Query query, SortField sort) {
        if (sort != null) {
            query.addSort(sort);
        }
        return query;
    }
    
    private void publishSearchEvent(SearchRequest request, SearchResponse response) {
        // Event publishing logic here
        // Could use Kafka, Redis Pub/Sub, or in-memory event bus
    }
}

Transaction Boundary Design

Each search operation constitutes a single logical transaction that includes:

1. Request validation
2. Query building and execution
3. Facet aggregation (if requested)
4. Response construction
5. Event publishing

The transaction ensures atomicity and consistency for the entire search operation.

Saga Pattern for Distributed Transactions

For systems with distributed data sources, a saga pattern can be implemented:

public class SearchSaga {
    private final List<SagaStep> steps;
    
    public void execute(SearchRequest request) {
        for (SagaStep step : steps) {
            try {
                step.execute();
            } catch (Exception e) {
                // Compensate previous steps
                compensateSteps(steps, step);
                throw new SearchException("Saga failed", e);
            }
        }
    }
    
    private void compensateSteps(List<SagaStep> steps, SagaStep failedStep) {
        // Implementation for compensating transactions
    }
}

Domain Object Design

public class SearchRequest {
    private String query;
    private Map<String, List<String>> filters;
    private SortField sort;
    private int page = 0;
    private int size = 10;
    private List<String> facetFields;
    
    // Getters and setters
}

public class SearchResult {
    private String id;
    private String title;
    private String description;
    private Map<String, Object> attributes;
    
    // Getters and setters
}

public class FacetValue {
    private String value;
    private long count;
    private boolean selected;
    
    // Getters and setters
}

public class Document {
    private String id;
    private String content;
    private Map<String, Object> attributes;
    
    // Getters and setters
}

5. Concurrency & Thread Safety

Concurrency Model

The faceted search system will use a thread pool model with Java's ExecutorService for handling concurrent search requests. Each search operation will be executed on a separate thread from the pool, allowing for efficient handling of multiple concurrent queries. For read-heavy workloads, we'll use a fixed thread pool size (e.g., 20-50 threads) to prevent resource exhaustion while maintaining good throughput.

Race Condition Prevention Strategy

Race conditions in faceted search can occur during:

1. Facet value updates - when multiple concurrent updates modify the same facet
2. Index rebuilding operations - during reindexing of search indices
3. Cache invalidation - when concurrent cache updates occur

We'll implement a combination of:

• Read-Write locks for facet value collections
• Atomic operations for counter updates in facets
• Versioned data structures to detect and handle concurrent modifications

Optimistic vs. Pessimistic Locking Selection

We'll use optimistic locking for most operations because:

• Faceted search typically involves read-heavy workloads with infrequent writes
• The system is designed to handle concurrent reads efficiently
• Write conflicts are rare and can be handled gracefully with retry logic

Pessimistic locking will be used for:

• Index rebuilding operations (short-lived, exclusive access)
• Batch facet value updates that require atomicity

Deadlock Prevention Strategy

To prevent deadlocks in the search system:

1. Lock ordering: All operations will acquire locks in a consistent order (e.g., by facet name, then by value)
2. Timeout-based acquisition: Set maximum wait times (e.g., 500ms) for lock acquisition
3. Deadlock detection: Implement periodic checks to detect and break cycles in the lock graph

Idempotency Implementation for Concurrent Duplicate Requests

For concurrent duplicate search requests, we'll implement idempotency using:

1. Request deduplication: Cache recent search requests with their results
2. Idempotency keys: Generate unique keys based on search parameters and timestamp
3. Result caching with TTL: Store recent search results for 5-10 minutes to avoid recomputation

// Pseudo-code for idempotent search request handling
public SearchResponse searchWithIdempotency(SearchRequest request) {
    String idempotencyKey = generateIdempotencyKey(request);
    
    // Check if result is already cached
    SearchResponse cached = cache.get(idempotencyKey);
    if (cached != null) {
        return cached;
    }
    
    // Use distributed lock to prevent concurrent processing
    try (DistributedLock lock = lockManager.acquire(idempotencyKey, 1000)) {
        // Double-check cache after acquiring lock
        SearchResponse doubleChecked = cache.get(idempotencyKey);
        if (doubleChecked != null) {
            return doubleChecked;
        }
        
        SearchResponse result = performSearch(request);
        cache.put(idempotencyKey, result, 5 * 60 * 1000); // 5 min TTL
        return result;
    }
}

6. Sequence Diagrams for Key Flows

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Cache
    participant Database

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500
    APIGateway->>SearchService: Forward request
    SearchService->>Cache: Check if results cached
    alt Results not in cache
        SearchService->>IndexService: Query faceted indexes
        IndexService->>Database: Fetch filtered documents
        Database-->>IndexService: Return filtered results
        IndexService-->>SearchService: Return faceted search results
        SearchService->>Cache: Cache results with TTL
    else Results in cache
        Cache-->>SearchService: Return cached results
    end
    SearchService-->>APIGateway: Return search response
    APIGateway-->>Client: Return JSON response with facets and results

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant EventPublisher

    Client->>APIGateway: POST /search/filters?query=phone
    APIGateway->>SearchService: Forward request
    SearchService->>IndexService: Get available facets for query
    IndexService->>Database: Query facet metadata
    Database-->>IndexService: Return facet definitions
    IndexService-->>SearchService: Return facet metadata
    SearchService->>EventPublisher: Publish facet update event
    EventPublisher-->>SearchService: Acknowledge event
    SearchService-->>APIGateway: Return facet metadata response
    APIGateway-->>Client: Return JSON with available facets and their values

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant EventPublisher

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500&sort=price:asc
    APIGateway->>SearchService: Forward request with sorting
    SearchService->>Cache: Check if results cached
    alt Results not in cache
        SearchService->>IndexService: Query faceted indexes with sorting
        IndexService->>Database: Fetch and sort filtered documents
        Database-->>IndexService: Return sorted results
        IndexService-->>SearchService: Return faceted search results with sorting
        SearchService->>Cache: Cache sorted results with TTL
    else Results in cache
        Cache-->>SearchService: Return cached sorted results
    end
    SearchService->>EventPublisher: Publish search event for analytics
    EventPublisher-->>SearchService: Acknowledge event
    SearchService-->>APIGateway: Return search response with sorted results
    APIGateway-->>Client: Return JSON response with facets, sorted results and metadata

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant Cache

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500&sort=price:asc&page=2&size=10
    APIGateway->>SearchService: Forward request with pagination
    SearchService->>Cache: Check if page 2 results cached
    alt Page not in cache
        SearchService->>IndexService: Query faceted indexes with pagination
        IndexService->>Database: Fetch filtered documents with offset/limit
        Database-->>IndexService: Return paginated results
        IndexService-->>SearchService: Return faceted search results with pagination
        SearchService->>Cache: Cache page 2 results with TTL
    else Page in cache
        Cache-->>SearchService: Return cached page 2 results
    end
    SearchService-->>APIGateway: Return paginated search response
    APIGateway-->>Client: Return JSON response with page 2 results, total count and pagination metadata

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant Cache

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500&sort=price:asc&page=2&size=10
    APIGateway->>SearchService: Forward request with pagination and sorting
    SearchService->>Cache: Check if page 2 results cached
    alt Cache miss
        SearchService->>IndexService: Query faceted indexes with pagination and sorting
        IndexService->>Database: Fetch filtered and sorted documents with offset/limit
        Database-->>IndexService: Return paginated and sorted results
        IndexService-->>SearchService: Return faceted search results with pagination and sorting
        SearchService->>Cache: Cache page 2 results with TTL
    else Cache hit
        Cache-->>SearchService: Return cached page 2 results
    end
    SearchService->>Cache: Update cache with new TTL for accessed items
    SearchService-->>APIGateway: Return paginated search response
    APIGateway-->>Client: Return JSON response with page 2 results, total count and pagination metadata

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant Cache

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500&sort=price:asc&page=2&size=10
    APIGateway->>SearchService: Forward request with pagination and sorting
    SearchService->>Cache: Check if page 2 results cached
    alt Cache miss due to TTL expiration
        SearchService->>IndexService: Query faceted indexes with pagination and sorting
        IndexService->>Database: Fetch filtered and sorted documents with offset/limit
        Database-->>IndexService: Return paginated and sorted results
        IndexService-->>SearchService: Return faceted search results with pagination and sorting
        SearchService->>Cache: Cache page 2 results with TTL
    else Cache hit
        Cache-->>SearchService: Return cached page 2 results
    end
    SearchService->>Cache: Update cache with new TTL for accessed items
    SearchService-->>APIGateway: Return paginated search response
    APIGateway-->>Client: Return JSON response with page 2 results, total count and pagination metadata

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant Cache

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500&sort=price:asc&page=2&size=10
    APIGateway->>SearchService: Forward request with pagination and sorting
    SearchService->>Cache: Check if page 2 results cached
    alt Cache miss due to cache size limit (LRU eviction)
        SearchService->>IndexService: Query faceted indexes with pagination and sorting
        IndexService->>Database: Fetch filtered and sorted documents with offset/limit
        Database-->>IndexService: Return paginated and sorted results
        IndexService-->>SearchService: Return faceted search results with pagination and sorting
        SearchService->>Cache: Cache page 2 results with TTL (evicting oldest)
    else Cache hit
        Cache-->>SearchService: Return cached page 2 results
    end
    SearchService->>Cache: Update cache with new TTL for accessed items
    SearchService-->>APIGateway: Return paginated search response
    APIGateway-->>Client: Return JSON response with page 2 results, total count and pagination metadata

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant Cache

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500&sort=price:asc&page=2&size=10
    APIGateway->>SearchService: Forward request with pagination and sorting
    SearchService->>Cache: Check if page 2 results cached
    alt Cache miss due to cache size limit (LRU eviction)
        SearchService->>IndexService: Query faceted indexes with pagination and sorting
        IndexService->>Database: Fetch filtered and sorted documents with offset/limit
        Database-->>IndexService: Return paginated and sorted results
        IndexService-->>SearchService: Return faceted search results with pagination and sorting
        SearchService->>Cache: Cache page 2 results with TTL (evicting oldest)
    else Cache hit
        Cache-->>SearchService: Return cached page 2 results
    end
    SearchService->>Cache: Update cache with new TTL for accessed items
    SearchService-->>APIGateway: Return paginated search response
    APIGateway-->>Client: Return JSON response with page 2 results, total count and pagination metadata

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant Cache

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500&sort=price:asc&page=2&size=10
    APIGateway->>SearchService: Forward request with pagination and sorting
    SearchService->>Cache: Check if page 2 results cached
    alt Cache miss due to cache size limit (LRU eviction)
        SearchService->>IndexService: Query faceted indexes with pagination and sorting
        IndexService->>Database: Fetch filtered and sorted documents with offset/limit
        Database-->>IndexService: Return paginated and sorted results
        IndexService-->>SearchService: Return faceted search results with pagination and sorting
        SearchService->>Cache: Cache page 2 results with TTL (evicting oldest)
    else Cache hit
        Cache-->>SearchService: Return cached page 2 results
    end
    SearchService->>Cache: Update cache with new TTL for accessed items
    SearchService-->>APIGateway: Return paginated search response
    APIGateway-->>Client: Return JSON response with page 2 results, total count and pagination metadata

sequenceDiagram
    participant Client
    participant APIGateway
    participant SearchService
    participant IndexService
    participant Database
    participant Cache

    Client->>APIGateway: GET /search?query=phone&filters=brand:Apple,price:<500&sort=price:asc&page=2&size=10
    APIGateway->>SearchService: Forward request with pagination and sorting
    SearchService->>Cache: Check if page 2 results cached
    alt Cache miss due to cache size limit (LRU eviction)
        SearchService->>IndexService: Query faceted indexes with pagination and sorting
        IndexService->>Database: Fetch filtered and sorted documents with offset/limit
        Database-->>IndexService: Return paginated and sorted results
        IndexService-->>SearchService: Return faceted search results with pagination and sorting
        SearchService->>Cache: Cache page 2 results with TTL (evicting oldest)
    else Cache hit
        Cache-->>SearchService: Return cached page 2 results
    end
    SearchService->>Cache: Update cache with new TTL for accessed items
    SearchService-->>APIGateway: Return paginated search response
    APIGateway-->>Client: Return JSON response with page 2 results, total count and pagination metadata

7. Caching Design

Cache Strategy Selection

For faceted search, we'll use cache-aside pattern with LRU eviction policy. This approach provides the best balance between cache hit ratio and memory efficiency for our use case where search results are frequently accessed but with varying popularity.

Cache Key Design

Cache keys will follow a structured namespace pattern:

faceted-search:{query_hash}:{filters_hash}:{sort_order}:{page}:{size}

Example:

faceted-search:abc123:def456:price_asc:1:20

TTL Strategy

Data Type	TTL	Reason
Search results	5 minutes	Frequent queries with moderate volatility
Facet values	1 hour	Less volatile than search results
Aggregated counts	15 minutes	Relatively stable, computed once per window
User preferences	24 hours	Persistent across sessions

Cache Eviction Policy

We'll implement LRU (Least Recently Used) with a probabilistic early expiry mechanism to prevent cache stampede:

• Primary eviction: LRU
• Secondary: Random TTL reduction (±10%) for items accessed within last 5 minutes

Cache Stampede Prevention

To prevent thundering herd on cache misses:

1. Probabilistic Early Expiry: When a cache miss occurs, set TTL to 50-90% of base TTL
2. Distributed Locking: Use Redis SETNX with TTL for key generation coordination
3. Cache warming: Pre-compute popular search queries and facets

Distributed Cache Architecture

We'll use Redis Cluster with:

• 3 master nodes for data distribution
• 3 replica nodes per shard for high availability
• Redis Sentinel for automatic failover
• Cache sharding based on query hash to ensure even distribution

Cache Invalidation Strategy

We'll implement event-driven invalidation:

1. Write-through: When data changes, invalidate related cache entries
2. Time-based: Use TTLs for automatic expiration of stale data
3. Explicit purge: API endpoint to manually invalidate cache for specific queries

Cache Implementation Example (Java)

@Component
public class FacetedSearchCache {
    private final RedisTemplate<String, Object> redisTemplate;
    private final ObjectMapper objectMapper;
    
    public SearchResult getCachedResults(String query, Map<String, Object> filters, 
                                       String sort, int page, int size) {
        String cacheKey = buildCacheKey(query, filters, sort, page, size);
        
        // Try to get from cache
        Object cached = redisTemplate.opsForValue().get(cacheKey);
        if (cached != null) {
            return (SearchResult) cached;
        }
        
        // Cache miss - use distributed lock to prevent stampede
        String lockKey = "lock:" + cacheKey;
        if (redisTemplate.opsForValue().setIfAbsent(lockKey, "locked", 
                                                   Duration.ofSeconds(5))) {
            try {
                // Double-check cache after lock
                cached = redisTemplate.opsForValue().get(cacheKey);
                if (cached != null) {
                    return (SearchResult) cached;
                }
                
                // Compute and cache result
                SearchResult result = computeSearchResults(query, filters, sort, page, size);
                redisTemplate.opsForValue().set(cacheKey, result, Duration.ofMinutes(5));
                return result;
            } finally {
                redisTemplate.delete(lockKey);
            }
        } else {
            // Wait and retry
            Thread.sleep(50);
            return getCachedResults(query, filters, sort, page, size);
        }
    }
    
    private String buildCacheKey(String query, Map<String, Object> filters, 
                                String sort, int page, int size) {
        String queryHash = Hashing.murmur3_128().hashString(query, StandardCharsets.UTF_8).toString();
        String filterHash = Hashing.murmur3_128().hashString(
            objectMapper.writeValueAsString(filters), StandardCharsets.UTF_8).toString();
        
        return String.format("faceted-search:%s:%s:%s:%d:%d", 
                           queryHash, filterHash, sort, page, size);
    }
}

8. Repository / Data Access Layer

Repository Pattern Implementation

The repository pattern is implemented to abstract data access logic and provide a clean separation between domain objects and persistence concerns. Each entity will have its own repository interface with common CRUD operations and specialized query methods.

public interface ProductRepository {
    Optional<Product> findById(UUID id);
    List<Product> findAll();
    Page<Product> findByFilters(FacetedSearchRequest request, Pageable pageable);
    List<String> getAvailableFacets();
    List<FacetValue> getFacetValues(String facetName, String filterValue);
    void save(Product product);
    void delete(UUID id);
}

public interface ProductRepositoryImpl implements ProductRepository {
    // Implementation details below
}

ORM vs. Raw Query Decisions

For the faceted search system, we use a hybrid approach:

1. ORM for basic CRUD operations: Hibernate/JPA for simple entity management due to type safety and reduced boilerplate
2. Raw queries for complex faceted filtering: Direct SQL/NoSQL queries for performance-critical faceted search operations
3. Hybrid strategy: Use ORM for entity persistence and raw queries for search/filtering operations

@Repository
public class ProductRepositoryImpl implements ProductRepository {
    
    @PersistenceContext
    private EntityManager entityManager;
    
    @Autowired
    private ElasticsearchTemplate elasticsearchTemplate;
    
    // Use raw SQL for complex faceted filtering with performance optimization
    @Override
    public Page<Product> findByFilters(FacetedSearchRequest request, Pageable pageable) {
        String sql = buildFacetedQuery(request);
        Query query = entityManager.createNativeQuery(sql, Product.class);
        
        // Apply pagination
        query.setFirstResult(pageable.getOffset());
        query.setMaxResults(pageable.getPageSize());
        
        List<Product> results = query.getResultList();
        long total = countByFilters(request);
        
        return new PageImpl<>(results, pageable, total);
    }
    
    private String buildFacetedQuery(FacetedSearchRequest request) {
        // Complex SQL query with JOINs and GROUP BY for faceted search
        StringBuilder sql = new StringBuilder();
        sql.append("SELECT p.*, COUNT(*) as facet_count FROM products p ");
        
        // Add JOINs for category, brand, etc.
        sql.append("LEFT JOIN categories c ON p.category_id = c.id ");
        sql.append("LEFT JOIN brands b ON p.brand_id = b.id ");
        
        // Add WHERE clause for filters
        sql.append("WHERE 1=1 ");
        if (request.getCategoryIds() != null && !request.getCategoryIds().isEmpty()) {
            sql.append("AND p.category_id IN (:categoryIds) ");
        }
        
        // Add GROUP BY and HAVING for facets
        sql.append("GROUP BY p.id ");
        sql.append("ORDER BY p.created_at DESC");
        
        return sql.toString();
    }
}

Read vs. Write Separation

We implement CQRS (Command Query Responsibility Segregation) pattern for the faceted search system:

// Write side - for data ingestion and updates
@Repository
public class ProductWriteRepository {
    @PersistenceContext
    private EntityManager entityManager;
    
    public void save(Product product) {
        entityManager.persist(product);
    }
    
    public void update(Product product) {
        entityManager.merge(product);
    }
}

// Read side - for faceted search queries
@Repository
public class ProductReadRepository {
    @Autowired
    private ElasticsearchTemplate elasticsearchTemplate;
    
    public Page<Product> search(FacetedSearchRequest request, Pageable pageable) {
        // Use Elasticsearch for fast faceted search
        NativeSearchQueryBuilder queryBuilder = new NativeSearchQueryBuilder();
        
        // Build query with filters and facets
        BoolQueryBuilder boolQuery = QueryBuilders.boolQuery();
        
        if (request.getCategoryIds() != null) {
            boolQuery.filter(QueryBuilders.termsQuery("categoryIds", request.getCategoryIds()));
        }
        
        queryBuilder.withQuery(boolQuery);
        
        // Add facets
        queryBuilder.withAggregations(
            AggregationBuilders.terms("categories").field("categoryIds"),
            AggregationBuilders.terms("brands").field("brandId")
        );
        
        // Apply pagination
        queryBuilder.withPageable(pageable);
        
        SearchHits<Product> searchHits = elasticsearchTemplate.search(
            queryBuilder.build(), Product.class);
        
        return new PageImpl<>(searchHits.getSearchHits().stream()
            .map(SearchHit::getContent)
            .collect(Collectors.toList()),
            pageable, searchHits.getTotalHits());
    }
}

Connection Pool Configuration

# application.yml
spring:
  datasource:
    hikari:
      maximum-pool-size: 20
      minimum-idle: 5
      connection-timeout: 30000
      idle-timeout: 600000
      max-lifetime: 1800000
      validation-timeout: 5000
      leak-detection-threshold: 60000

N+1 Query Prevention Strategy

We implement eager loading and batch fetching to prevent N+1 queries:

@Entity
@Table(name = "products")
@NamedEntityGraph(
    name = "Product.withCategoriesAndBrands",
    attributeNodes = @NamedAttributeNode(value = "categories", subgraph = "categories-subgraph"),
    subgraphs = @NamedSubgraph(
        name = "categories-subgraph",
        attributeNodes = @NamedAttributeNode(value = "brands")
    )
)
public class Product {
    @Id
    private UUID id;
    
    @ManyToOne(fetch = FetchType.LAZY)
    @JoinColumn(name = "category_id")
    private Category category;
    
    @ManyToOne(fetch = FetchType.LAZY)
    @JoinColumn(name = "brand_id")
    private Brand brand;
    
    @ManyToMany(fetch = FetchType.LAZY)
    @JoinTable(
        name = "product_attributes",
        joinColumns = @JoinColumn(name = "product_id"),
        inverseJoinColumns = @JoinColumn(name = "attribute_id")
    )
    private Set<Attribute> attributes;
    
    // Getters and setters
}

// Repository method with eager loading
public Page<Product> findByFiltersWithEagerLoading(FacetedSearchRequest request, Pageable pageable) {
    String jpql = "SELECT p FROM Product p " +
                  "LEFT JOIN FETCH p.category c " +
                  "LEFT JOIN FETCH p.brand b " +
                  "LEFT JOIN FETCH p.attributes a " +
                  "WHERE 1=1";
    
    // Add filters to JPQL...
    
    TypedQuery<Product> query = entityManager.createQuery(jpql, Product.class);
    // Apply pagination and execute...
}

Batch Operation Design

For efficient data ingestion and updates:

@Repository
public class ProductBatchRepository {
    
    @PersistenceContext
    private EntityManager entityManager;
    
    public void batchSave(List<Product> products) {
        int batchSize = 1000;
        
        for (int i = 0; i < products.size(); i += batchSize) {
            List<Product> batch = products.subList(i, Math.min(i + batchSize, products.size()));
            
            // Use bulk insert for better performance
            String sql = "INSERT INTO products (id, name, category_id, brand_id) VALUES ";
            // Build batch SQL...
            
            entityManager.createNativeQuery(sql).executeUpdate();
        }
    }
    
    public void batchUpdate(List<Product> products) {
        // Use MERGE or batch updates with proper transaction handling
        for (Product product : products) {
            entityManager.merge(product);
        }
    }
}

9. Asynchronous Processing & Messaging

Event-driven boundaries in this feature

The faceted search system will use asynchronous processing for operations that don't require immediate response to improve user experience and system scalability. Key boundaries:

• Indexing operations: When new documents are added or updated, we asynchronously reindex them to maintain search index consistency
• Facet computation: Heavy computation of facet values (counts, ranges) will be done asynchronously to avoid blocking search requests
• Analytics updates: When search queries are made, we asynchronously update analytics data (popular filters, query patterns)
• Search result caching: Pre-computing and caching search results for popular queries will be asynchronous to avoid latency spikes

The synchronous flow handles immediate search requests, while asynchronous flows handle background maintenance and enrichment operations.

Message schema design

{
  "version": "1.0",
  "event_type": "document_indexed",
  "timestamp": "2023-10-27T10:30:00Z",
  "payload": {
    "document_id": "doc-12345",
    "index_name": "products",
    "fields": {
      "category": "electronics",
      "brand": "Apple",
      "price": 999.99
    }
  },
  "source": "product-service"
}

Dead letter queue strategy and poison message handling

For failed message processing, we implement a dead letter queue (DLQ) with:

• Max retries: 3 attempts with exponential backoff (1s, 2s, 4s)
• DLQ routing: Messages that fail after max retries are sent to a dedicated DLQ topic
• Monitoring: DLQ messages trigger alerts for manual investigation
• Retry logic: Failed messages are logged with metadata for debugging

At-least-once vs. exactly-once delivery

We use at-least-once delivery model for the faceted search system because:

• Search results don't need strict exactly-once semantics
• Indexing operations can safely be idempotent (reindexing same document multiple times is harmless)
• Performance overhead of exactly-once is not justified for this use case
• Simpler implementation with better throughput

Retry policy for consumers

public class RetryPolicy {
    private static final int MAX_RETRIES = 3;
    private static final long INITIAL_DELAY_MS = 1000;
    private static final double BACKOFF_MULTIPLIER = 2.0;
    
    public boolean shouldRetry(int attempt, Exception exception) {
        if (attempt >= MAX_RETRIES) return false;
        
        // Retry on network issues, timeout, and transient errors
        return exception instanceof NetworkException || 
               exception instanceof TimeoutException ||
               exception instanceof RetryableException;
    }
    
    public long getDelay(int attempt) {
        return (long) (INITIAL_DELAY_MS * Math.pow(BACKOFF_MULTIPLIER, attempt));
    }
}

Outbox pattern for reliable event publishing

We implement the outbox pattern to ensure message delivery reliability:

public class OutboxEvent {
    private String id;
    private String eventType;
    private String payload;
    private String status; // PENDING, PROCESSED, FAILED
    private LocalDateTime createdAt;
    private LocalDateTime updatedAt;
}

public class EventPublisher {
    private final OutboxRepository outboxRepo;
    private final MessageBroker broker;
    
    public void publishEvent(String eventType, String payload) {
        // 1. Store event in outbox
        OutboxEvent event = new OutboxEvent();
        event.setId(UUID.randomUUID().toString());
        event.setEventType(eventType);
        event.setPayload(payload);
        event.setStatus("PENDING");
        outboxRepo.save(event);
        
        // 2. Publish to message broker
        try {
            broker.publish(eventType, payload);
            event.setStatus("PROCESSED");
        } catch (Exception e) {
            event.setStatus("FAILED");
            throw new EventPublishingException("Failed to publish event", e);
        }
        
        // 3. Update status
        outboxRepo.updateStatus(event.getId(), event.getStatus());
    }
}

Sequence diagram for async flows

sequenceDiagram
    participant Client
    participant ProductService
    participant IndexerService
    participant MessageBroker
    participant SearchIndexer
    
    Client->>ProductService: Create Product
    ProductService->>ProductService: Save to DB
    ProductService->>MessageBroker: Publish "product_created"
    MessageBroker->>SearchIndexer: Async Event
    SearchIndexer->>SearchIndexer: Update Search Index
    SearchIndexer->>SearchIndexer: Compute Facets
    SearchIndexer->>SearchIndexer: Update Cache
    
    Note over SearchIndexer: Async Processing

Async flow monitoring

• Message processing metrics: Count of processed messages, failed attempts, retry counts
• Queue depth monitoring: Indexing queue length to detect backpressure
• Processing latency: Time from message receipt to completion
• DLQ monitoring: Alert when DLQ queue grows beyond threshold

Consumer design patterns

We use a consumer pool pattern with:

• Fixed-size thread pools (2-4 consumers per queue)
• Batch processing for better throughput
• Circuit breaker pattern to prevent cascading failures
• Health checks to monitor consumer status

@Component
public class FacetUpdateConsumer {
    private final ExecutorService executor = Executors.newFixedThreadPool(3);
    
    @Async
    public void handleFacetUpdate(String payload) {
        try {
            // Process facet update asynchronously
            processFacetUpdate(payload);
        } catch (Exception e) {
            // Handle retry logic or DLQ routing
            handleFailure(payload, e);
        }
    }
}

10. Error Handling Design

Error Classification Hierarchy

The system follows a three-tier error classification:

1. Domain Errors - Business logic violations (e.g., invalid search parameters, unsupported facet)
2. Infrastructure Errors - System resource issues (e.g., database connection failure, cache timeout)
3. Validation Errors - Input format and constraint violations (e.g., malformed query, missing required fields)

Error Propagation Strategy

Errors are propagated up the call stack with appropriate wrapping to maintain context:

// Example error propagation in search service
public SearchResponse search(SearchRequest request) throws SearchException {
    try {
        validateRequest(request);
        return executeSearch(request);
    } catch (ValidationException e) {
        throw new SearchException("Invalid search request", e, ErrorCode.INVALID_SEARCH_REQUEST);
    } catch (DatabaseException e) {
        throw new SearchException("Search backend unavailable", e, ErrorCode.SEARCH_BACKEND_FAILURE);
    }
}

Retry Strategy

For transient infrastructure errors (e.g., database timeouts, cache misses), exponential backoff with jitter is applied:

public class RetryableOperation {
    private static final int MAX_RETRIES = 3;
    private static final long BASE_DELAY_MS = 100;
    private static final double JITTER_FACTOR = 0.2;

    public <T> T executeWithRetry(Supplier<T> operation) {
        int attempts = 0;
        while (attempts < MAX_RETRIES) {
            try {
                return operation.get();
            } catch (RetryableException e) {
                if (attempts == MAX_RETRIES - 1) throw e;
                
                long delay = (long) (BASE_DELAY_MS * Math.pow(2, attempts) 
                                   * (1 + JITTER_FACTOR * (Math.random() - 0.5)));
                Thread.sleep(delay);
                attempts++;
            }
        }
        throw new RuntimeException("Retry limit exceeded");
    }
}

Circuit Breaker Design

For downstream services (e.g., search index, cache), a circuit breaker pattern prevents cascading failures:

public class CircuitBreaker {
    private final int failureThreshold = 5;
    private final long timeout = 60000; // 60 seconds
    private final int successThreshold = 5;
    
    private volatile CircuitState state = CircuitState.CLOSED;
    private int failureCount = 0;
    private long lastFailureTime = 0;
    
    public <T> T execute(Supplier<T> operation) throws Exception {
        switch (state) {
            case CLOSED:
                try {
                    T result = operation.get();
                    onSuccess();
                    return result;
                } catch (Exception e) {
                    onFailure(e);
                    throw e;
                }
            case OPEN:
                if (System.currentTimeMillis() - lastFailureTime > timeout) {
                    state = CircuitState.HALF_OPEN;
                    return operation.get();
                }
                throw new CircuitBreakerOpenException("Circuit is open");
            case HALF_OPEN:
                try {
                    T result = operation.get();
                    onSuccess();
                    return result;
                } catch (Exception e) {
                    state = CircuitState.OPEN;
                    throw e;
                }
        }
        throw new IllegalStateException("Invalid circuit state");
    }
}

Fallback Behavior Design

For critical downstream services, fallbacks provide graceful degradation:

public class SearchService {
    public SearchResponse search(SearchRequest request) {
        try {
            return searchIndex.search(request);
        } catch (SearchIndexException e) {
            // Fallback to basic search with no facets
            return fallbackToBasicSearch(request);
        }
    }
    
    private SearchResponse fallbackToBasicSearch(SearchRequest request) {
        // Return basic search results without faceted filtering
        return new SearchResponse(
            basicSearchService.search(request.getQuery()),
            Collections.emptyList()
        );
    }
}

Error Logging Format

Structured logging with consistent fields:

{
  "timestamp": "2023-10-15T14:30:45.123Z",
  "trace_id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "span_id": "f0e9d8c7-b6a5-4321-0fed-cba987654321",
  "user_id": "user-12345",
  "error_code": "SEARCH_INDEX_TIMEOUT",
  "error_message": "Timeout connecting to search index",
  "error_type": "InfrastructureError",
  "request_context": {
    "query": "laptop",
    "facets": ["brand", "price"]
  },
  "stack_trace": "..."
}

Alerting Thresholds

Critical alert thresholds:

Metric	Threshold	Action
Search timeout rate	>5% of requests	Alert
Circuit breaker trips	>3 consecutive failures	Alert
Cache miss rate	>50% of requests	Alert
Validation errors	>100 per minute	Alert

Error Code Definitions

public enum ErrorCode {
    INVALID_SEARCH_REQUEST(400, "Invalid search parameters"),
    SEARCH_INDEX_UNAVAILABLE(503, "Search index service unavailable"),
    FACET_NOT_SUPPORTED(400, "Facet is not supported for this search"),
    INVALID_FACET_VALUE(400, "Facet value is invalid"),
    SEARCH_TIMEOUT(504, "Search operation timed out"),
    DATABASE_CONNECTION_FAILURE(500, "Database connection failed"),
    CACHE_TIMEOUT(503, "Cache lookup timed out");
    
    private final int httpStatus;
    private final String message;
    
    ErrorCode(int httpStatus, String message) {
        this.httpStatus = httpStatus;
        this.message = message;
    }
}

Error Response Format

Standardized JSON error response:

{
  "error": {
    "code": "SEARCH_INDEX_UNAVAILABLE",
    "message": "Search index service unavailable",
    "http_status": 503,
    "timestamp": "2023-10-15T14:30:45.123Z",
    "request_id": "req-abc123"
  }
}

Error Handling in Async Flows

For asynchronous operations, errors are logged with proper context:

public class AsyncSearchHandler {
    public void handleSearchRequest(SearchRequest request) {
        CompletableFuture<SearchResponse> future = CompletableFuture
            .supplyAsync(() -> searchService.search(request))
            .handle((result, throwable) -> {
                if (throwable != null) {
                    logger.error("Async search failed", 
                        "request_id", request.getId(),
                        "error", throwable.getMessage());
                    return handleAsyncError(throwable);
                }
                return result;
            });
    }
}

11. Security Design

Input Validation and Sanitization Strategy

All API inputs undergo strict validation using Apache Commons Validator for structured data and OWASP Java HTML Sanitizer for unstructured text fields. Custom validation rules enforce:

• Field length limits (max 1000 chars for text fields)
• Regex patterns for structured inputs (e.g., email format, numeric ranges)
• Whitelisting of allowed characters in search terms
• Maximum facet count limit (50 facets per query)

SQL Injection Prevention

All database queries are constructed using parameterized prepared statements with Hibernate's Criteria API to prevent SQL injection. Stored procedures are avoided for dynamic queries. Input sanitization includes:

• Escaping special characters in LIKE clauses
• Whitelisting of column names and sort orders
• Query plan validation to prevent malicious query rewriting

Authentication Middleware Design

Authentication is handled via JWT Bearer tokens with the following middleware pipeline:

1. Token validation (signature verification, expiration check)
2. Role-based access control (RBAC) enforcement
3. Rate limiting per authenticated user
4. Session management for token refresh and revocation

Authorization Enforcement

RBAC model with roles: ANONYMOUS, USER, MODERATOR, ADMIN. Authorization checks occur at service layer:

• ANONYMOUS users can only perform read operations
• USER roles can query and filter but not modify data
• MODERATOR can manage facets and metadata
• ADMIN has full CRUD access to all system components

Sensitive Data Encryption

At Rest: AES-256-GCM encryption using AWS KMS for all sensitive metadata and user search history. In Transit: TLS 1.3 with HSTS enforcement for all API endpoints.

PII Handling and Masking Strategy

User search queries containing PII (e.g., email addresses, phone numbers) are:

• Logged with redacted fields (masked as ***@***.com)
• Stored in encrypted format in audit logs
• Deleted after 30 days retention period

Secrets Management

AWS Secrets Manager is used for:

• Database connection credentials (rotated every 90 days)
• JWT signing keys (rotated every 30 days)
• Third-party API keys and service credentials

Audit Logging Design

All security-relevant events are logged with structured JSON format:

{
  "timestamp": "2023-12-01T10:30:45Z",
  "user_id": "uuid-12345",
  "action": "SEARCH_QUERY_EXECUTED",
  "ip_address": "192.168.1.100",
  "query_params": {
    "facet_filters": ["category:electronics"],
    "sort_by": "relevance"
  },
  "risk_score": 0.2,
  "session_id": "session-abc123"
}

Audit logs are retained for 90 days with automatic archiving to S3 Glacier. Critical security events (failed auth, high-risk queries) trigger immediate alerts to security team via Slack and email.

12. Testing Strategy

Unit Testing Scope and Coverage Targets (%)

• Service Layer: 85%+ line coverage, 100% branch coverage for business logic
• Repository Layer: 90%+ line coverage, focusing on query correctness and edge cases
• Domain Objects: 100% for value objects, 95% for entities with complex validation
• API Layer: 80%+ for request/response handling and error paths
• Core Business Logic: 95%+ coverage for critical operations like search query building and filtering

Integration Testing Strategy

• Database Integration: Use Testcontainers with PostgreSQL 13+ to test actual query execution and indexing behavior
• External Service Mocks: WireMock for external search engine (e.g., Elasticsearch) integration testing
• Caching Layer: Embedded Redis or in-memory cache for validating cache behavior and invalidation logic
• Event System: Use embedded Kafka or RabbitMQ containers to test async flows and message consumption
• Authentication/Authorization: Integration tests with mock JWT provider and RBAC policy enforcement

Contract Testing

• API Contracts: Use Pact for consumer-driven contract testing between frontend and backend services
• External Service Contracts: Define contracts with search engine provider using Pact or similar tools to ensure compatibility
• Event Contracts: Use Avro schema registry to validate event payload structure and versioning between services

Performance Testing Tool

• Load Testing: k6 for simulating concurrent search requests and measuring latency distribution (p50/p95/p99)
• Stress Testing: JMeter for testing system behavior under peak load (10k+ concurrent users)
• Key Scenarios:
  • Concurrent search queries with varying facet combinations (50% of queries with 3 facets, 25% with 5 facets)
  • High-cardinality facet filtering (e.g., color: red, size: XL, brand: Nike)
  • Complex query with multiple filters and sorting (e.g., price range + category + rating)

Test Data Management Strategy

• Fixtures: Predefined JSON fixtures for common search scenarios with realistic data distributions
• Factories: Factory classes using libraries like JavaFaker to generate synthetic product data with realistic attributes
• Seeding: Database seed scripts that populate test environment with 100k+ products and 50+ facets for performance testing
• Data Isolation: Use database transactional isolation in tests to prevent test pollution and ensure reproducibility
• Test Data Generation: Implement data generation strategies that maintain realistic distribution (e.g., 70% of products in clothing category, 30% electronics)

13. Observability Design

Structured Logging Schema

All logs must follow a consistent schema to enable effective querying and analysis:

{
  "timestamp": "2023-10-27T14:30:45.123Z",
  "level": "INFO|WARN|ERROR",
  "service": "faceted-search-service",
  "trace_id": "12345678-1234-1234-1234-123456789012",
  "span_id": "abcdef12-3456-7890-abcd-ef1234567890",
  "user_id": "user_12345",
  "request_id": "req_98765",
  "operation": "faceted_search",
  "query": "laptop price:<1000",
  "filters": ["category:electronics", "price:<1000"],
  "response_time_ms": 156,
  "result_count": 24,
  "error_code": "INVALID_QUERY",
  "error_message": "Invalid syntax in price filter",
  "stack_trace": "...",
  "ip_address": "192.168.1.100",
  "user_agent": "Mozilla/5.0..."
}

PII masking rules:

• User IDs are masked in logs after first 4 characters
• IP addresses are truncated to network prefix (e.g., 192.168.1.0/24)
• Email addresses are truncated to domain part (e.g., user@**.com)

Distributed Tracing Instrumentation

Using OpenTelemetry for end-to-end traceability:

Key operations to trace:

1. faceted_search - Main search endpoint
2. build_facet_query - Query building for facets
3. execute_search - Actual database execution
4. apply_filters - Post-processing filters
5. cache_hit / cache_miss - Caching decisions

Custom spans and attributes:

// Span for main search operation
Span searchSpan = tracer.spanBuilder("faceted_search")
    .setAttribute("query", searchRequest.getQuery())
    .setAttribute("filters", searchRequest.getFilters().toString())
    .setAttribute("page_size", searchRequest.getPageSize())
    .startSpan();

// Span for query building
Span queryBuildSpan = tracer.spanBuilder("build_facet_query")
    .setAttribute("query", searchRequest.getQuery())
    .setAttribute("facet_fields", facetFields)
    .startSpan();

// Span for database execution
Span dbSpan = tracer.spanBuilder("execute_search")
    .setAttribute("sql_query", sqlQuery)
    .setAttribute("estimated_rows", estimatedRows)
    .startSpan();

Metrics Design

RED Method metrics per service:

Metric Name	Type	Labels	Description
search_requests_total	Counter	status, error_code	Total search requests
search_duration_seconds	Histogram	operation, status	Search response time
cache_hits_total	Counter	type, source	Cache hit counts
cache_misses_total	Counter	type, source	Cache miss counts
query_errors_total	Counter	error_type, source	Query parsing/validation errors
active_searches_gauge	Gauge	service	Currently active searches

Sample metrics output:

search_requests_total{status="success", error_code="none"} 1250
search_duration_seconds_bucket{operation="faceted_search", status="success", le="0.1"} 500
search_duration_seconds_bucket{operation="faceted_search", status="success", le="0.5"} 1200
search_duration_seconds_bucket{operation="faceted_search", status="success", le="+Inf"} 1250
search_duration_seconds_sum{operation="faceted_search", status="success"} 125.3
search_duration_seconds_count{operation="faceted_search", status="success"} 1250

Health Check Endpoint Design

@RestController
@RequestMapping("/health")
public class HealthController {
    
    @GetMapping("/live")
    public ResponseEntity<Map<String, Object>> live() {
        Map<String, Object> response = new HashMap<>();
        response.put("status", "healthy");
        response.put("timestamp", Instant.now());
        return ResponseEntity.ok(response);
    }
    
    @GetMapping("/ready")
    public ResponseEntity<Map<String, Object>> ready() {
        Map<String, Object> response = new HashMap<>();
        response.put("status", "ready");
        response.put("timestamp", Instant.now());
        response.put("database", databaseHealth());
        response.put("cache", cacheHealth());
        response.put("search_engine", searchEngineHealth());
        
        // Only return 200 if all components are healthy
        boolean allHealthy = databaseHealth() && cacheHealth() && searchEngineHealth();
        return allHealthy ? ResponseEntity.ok(response) : ResponseEntity.status(503).body(response);
    }
    
    @GetMapping("/startup")
    public ResponseEntity<Map<String, Object>> startup() {
        Map<String, Object> response = new HashMap<>();
        response.put("status", "startup_complete");
        response.put("timestamp", Instant.now());
        response.put("components_initialized", componentInitializationStatus());
        return ResponseEntity.ok(response);
    }
}

Alerting Thresholds

Critical alerts (escalation to on-call):

• Search latency P99 > 500ms for 5 minutes
• Cache hit ratio < 70% for 10 minutes
• Database connection pool exhaustion > 5 times in 1 hour
• Search error rate > 5% for 5 minutes

Warning alerts (monitoring):

• Search latency P99 > 200ms for 15 minutes
• Cache hit ratio < 80% for 30 minutes
• Database query timeout > 100ms for 5 minutes

Alerting configuration:

# Prometheus alert rules example
groups:
- name: search-service-alerts
  rules:
  - alert: HighSearchLatencyP99
    expr: histogram_quantile(0.99, search_duration_seconds) > 500
    for: 5m
    labels:
      severity: critical
    annotations:
      summary: "High search latency detected"
      description: "Search P99 latency is {{ $value }}ms over 5 minutes"

  - alert: LowCacheHitRate
    expr: (1 - (cache_misses_total / cache_hits_total)) < 0.7
    for: 10m
    labels:
      severity: warning
    annotations:
      summary: "Low cache hit rate"
      description: "Cache hit ratio is {{ $value }} over 10 minutes"

14. Open Questions, Risks & Trade-offs

Decisions requiring product / engineering alignment

• Facet cardinality vs. performance trade-off: Should we allow unlimited facets or impose limits? High cardinality facets (like "color: red") can cause performance issues in filtering and aggregation. Product decision needed on whether to allow unlimited facets or cap them at a reasonable threshold (e.g., 1000 unique values per facet).

• Real-time vs. batch indexing strategy: Should search results be updated in real-time or via batch jobs? Real-time updates provide fresher data but increase system complexity and latency. Batch updates reduce load but introduce staleness.

• Search result relevance vs. faceted filtering prioritization: When a user applies filters, should the system prioritize exact matches or broader relevance? This affects whether to use search-first or filter-first approaches in the query execution plan.

Known trade-offs and justifications

• Memory vs. CPU trade-off in caching: Using more memory for pre-computed facet aggregations improves query performance but increases memory usage. We're choosing to cache frequently accessed facets to reduce CPU pressure during peak hours.

• Consistency vs. availability in distributed systems: In a multi-region setup, we can choose between strong consistency (read-after-write) or eventual consistency with better availability. For faceted search, we're prioritizing availability over strict consistency to ensure system responsiveness.

• Index size vs. query performance: More granular indices improve query speed but increase storage and indexing overhead. We're using a hybrid approach with composite indices for common filter combinations.

Risks and mitigation strategies

• Search index synchronization risk: Risk of search index being out-of-sync with database. Mitigation: Implement a robust outbox pattern with retry logic and periodic consistency checks.

• Facet explosion risk: Large numbers of unique facet values can cause performance degradation. Mitigation: Implement soft limits on facet cardinality with automatic bucketing of rare values into "Other" categories.

• Cache invalidation complexity: Complex cache invalidation logic can lead to stale results. Mitigation: Use a combination of TTL-based invalidation and event-driven invalidation for critical data changes.

Technical debt acknowledged

• Legacy schema migration debt: Existing systems may have inconsistent data types or missing indices that require careful handling during migration. This will introduce temporary performance degradation and additional complexity in data transformation logic.

• Query complexity debt: As more facets are added, query construction becomes increasingly complex. Future refactoring may be required to modularize query building logic and support dynamic facet configuration.

• Caching strategy evolution debt: Current caching strategy assumes stable facet distributions. As the system grows, we may need to implement more sophisticated cache warming and invalidation strategies that account for changing data distributions.