Data Retrieval

Accessing and extracting specific data records from the system for analysis and reporting.

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🧩 Overview

Data Retrieval involves fetching data from a database or system to:

• Display in the user interface
• Support decision-making and reporting
• Feed external systems or APIs
• Export for offline analysis

Effective retrieval is optimized for accuracy, performance, and relevance, often involving filtering, sorting, pagination, and security constraints.

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🔍 Retrieval Techniques

Method	Description
Direct Query	Raw SQL for complex, flexible filtering
API Endpoint	Abstracted REST/GraphQL interface for apps
View/Materialized View	Predefined result sets for reuse
Search Indexes	Full-text or metadata search (e.g., ElasticSearch)

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🧪 Example: Patient Search by Name and Date

SELECT id, name, dob, gender
FROM patients
WHERE name LIKE '%john%' AND dob >= '1990-01-01'
ORDER BY name ASC
LIMIT 20 OFFSET 0;

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⚙️ REST API Retrieval Pattern

Endpoint:

GET /api/patients?name=john&dob_after=1990-01-01&page=1&limit=20

Response:

{
  "data": [{ "id": 1, "name": "John Doe", "dob": "1990-02-12" }],
  "pagination": {
    "page": 1,
    "limit": 20,
    "total": 145
  }
}

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🧠 Filtering Strategies

• String match: LIKE, ILIKE, REGEXP
• Range queries: dates, numbers
• Boolean flags: is_active = 1
• Inclusion: IN (...) lists
• Advanced: subqueries, joins, computed columns

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📊 UI Features to Support Retrieval

• Search box with debouncing
• Dropdown filters (e.g., date range, status)
• Pagination or infinite scroll
• Sortable columns (ascending/descending)
• Download/export options (CSV, Excel, PDF)

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🚀 Performance Optimization

• Use indexes on frequently queried fields
• Avoid SELECT *, explicitly select required columns
• Use pagination (LIMIT, OFFSET) to reduce data load
• Cache heavy queries or precompute data if needed
• Use EXPLAIN plans to inspect query cost

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🛡️ Security and Access Control

• Apply row-level filtering based on user role

  SELECT * FROM patient_records WHERE organization_id = :user_org_id;
• Mask or restrict sensitive fields (e.g., SSN, salary)
• Rate-limit API requests to prevent misuse
• Log and monitor access patterns for auditing

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🧬 Common Use Cases

Purpose	Example
Patient record lookup	By name, mobile, or ID
Financial reports	Filtered by date, department
Result dashboards	Aggregated from test or service logs
Exporting datasets	Based on dynamic user filters
Audit trail views	By user, action type, and time

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💡 Advanced Retrieval Patterns

• Joins: Enrich data from related tables

  SELECT a.id, a.name, b.department_name
  FROM employees a
  JOIN departments b ON a.department_id = b.id;

• Aggregations: Totals, averages, groupings

  SELECT doctor_id, COUNT(*) AS total_visits
  FROM appointments
  GROUP BY doctor_id;

• Search & Rank: Full-text search with scoring

  SELECT * FROM articles WHERE MATCH(title, body) AGAINST ('+lab +test' IN BOOLEAN MODE);

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🔚 Summary

The Data Retrieval workflow ensures that end users and systems can access just the right data, at the right time, in a secure and performant way. It’s the foundation of dashboards, search tools, exports, and intelligent workflows.

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