kb.pub

📖 READER VIEW (Read-Only, Public Access)

Poorly Optimized Database Schema

جلسة عامةالجلسة #85916‏/1‏/2026👁️ 35 مشاهداتإدارة وتحسين قواعد البيانات
🤖

AI Analysis Summary

The user is experiencing performance issues and inefficient storage due to a poorly designed database schema. They believe a redesign is necessary to improve these aspects.

المشكلة المبلغ عنها

My database schema seems to be poorly designed, leading to inefficient data retrieval and storage. I believe a redesign could significantly improve performance and maintainability.

أسئلة

5 أسئلة
Q1

What are the primary symptoms of inefficient data retrieval you are observing? (ID: 4397)

Slow query execution times

Q2

Can you describe the types of queries that are performing poorly? (ID: 4398)

Complex joins, large table scans

Q3

What is the approximate size of your database (in GB or TB)? (ID: 4399)

500 GB

Q4

Are you currently using any indexing strategies? (ID: 4400)

Yes, but maybe not enough

Q5

Which database system are you using? (ID: 4401)

PostgreSQL

Identified Causes

4 أسباب
#1

فهارس مفقودة أو غير فعالة

80%

الأعمدة الحاسمة المستخدمة في عبارات WHERE، أو شروط JOIN، أو عبارات ORDER BY قد تفتقر إلى الفهارس المناسبة، مما يجبر على إجراء عمليات مسح كاملة للجدول.

#2

مشاكل عدم التطبيع / إلغاء التطبيع

75%

قد يكون المخطط مفرطًا في إلغاء التطبيع، مما يؤدي إلى تكرار البيانات وشذوذ التحديث، أو قد يكون مفرطًا في التطبيع، مما يتسبب في عمليات ربط مفرطة للاستعلامات البسيطة.

#3

أنواع بيانات غير مناسبة

60%

استخدام أنواع بيانات كبيرة جدًا أو غير مناسبة (مثل VARCHAR للسلاسل ذات الطول الثابت، وأنواع رقمية كبيرة للقيم الصغيرة) يمكن أن يهدر مساحة التخزين ويبطئ العمليات.

#4

علاقات ضعيفة البنية (مثل السجلات اليتيمة، المفاتيح الخارجية المفرطة)

50%

يمكن أن تؤدي العلاقات المعقدة أو المعرفة بشكل غير صحيح بين الجداول إلى اختناقات في الأداء ومشكلات في سلامة البيانات.

Recommended Solutions

8 حلول
💡

Identify Missing Indexes

95%Official
Use database performance monitoring tools to find queries performing full table scans and identify candidate columns for indexing.

🤖 AI Analysis

"The user explicitly mentions 'large table scans' and 'slow query execution times', which are direct indicators that missing indexes are a primary cause of inefficiency. Identifying and implementing new indexes is a crucial step to address this."

💡

Simplify Complex Joins

90%Official
Re-evaluate table structures and relationships to simplify queries that involve many joins.

🤖 AI Analysis

"The user states 'complex joins' are performing poorly. This solution directly addresses the simplification of such joins, which is a common cause of database performance issues and often stems from schema design."

💡

Implement Strategic Denormalization

85%Official
For read-heavy operations, selectively denormalize by adding redundant data to reduce join complexity.

🤖 AI Analysis

"Given the mention of 'complex joins' and 'read-heavy operations' (implied by inefficient data retrieval), denormalization can significantly improve performance by reducing the need for joins. This is a strong candidate for optimizing retrieval."

💡

Analyze Normalization Levels

80%Official
Review the schema for redundancy and identify opportunities to apply appropriate normalization or denormalization.

🤖 AI Analysis

"The problem statement is about a 'poorly designed database schema'. Analyzing normalization levels is a fundamental step in understanding and improving schema design, which directly impacts data retrieval and storage efficiency."

💡

Optimize Existing Indexes

75%Official
Review existing indexes for redundancy, unused indexes, or composite indexes that could be improved.

🤖 AI Analysis

"The user states they are 'using indexing strategies, but maybe not enough'. This implies that existing indexes might not be optimal. Reviewing and optimizing them is a logical next step after identifying missing ones."

💡

Review Data Type Usage

60%Official
Audit all columns to ensure the most efficient and appropriate data types are being used.

🤖 AI Analysis

"Inefficient data types can lead to larger storage and slower processing, especially with a 500GB database. Reviewing data types is a good practice for overall schema optimization, though perhaps less immediately impactful than indexing or join simplification for the described symptoms."

💡

Consider Data Type Conversion

55%Official
Plan and execute the conversion of columns to more suitable data types, considering potential data loss or application impact.

🤖 AI Analysis

"This is a follow-up to reviewing data types. If inefficient types are found, conversion might be necessary. It's a relevant step but depends on the findings from solution 5221."

💡

Validate Foreign Key Constraints

40%Official
Ensure all relationships are properly defined with foreign key constraints to maintain data integrity and aid query optimization.

🤖 AI Analysis

"While foreign key constraints are important for data integrity and can aid query planners, the user's primary complaints are about performance due to complex joins and table scans, not data integrity issues. This solution is less directly relevant to the stated performance problems."

مساعدة الآخرين