2905 Performance Optimization
PHASE 10: EXPERT TOPICS - WEEKS 19-20Performance Optimization
Module Overview: Master the art and science of optimizing Tadabase applications for maximum performance. Learn how to handle millions of records, optimize queries, improve component performance, implement effective caching strategies, and design for scalability.
Performance Fundamentals
Performance Golden Rule: "Premature optimization is the root of all evil" - Donald Knuth. First make it work, then measure, then optimize where it matters.
Understanding Performance Bottlenecks
Common Performance Killers
| Bottleneck | Symptoms | Impact | Solution |
|---|---|---|---|
| Unindexed Queries | Slow page loads, timeouts | 10-100x slower | Add indexes to frequently queried fields |
| N+1 Query Problem | Page takes seconds to load lists | Grows with record count | Use connection fields instead of lookups |
| Large Result Sets | Memory errors, browser freezes | Exponential degradation | Implement pagination and filtering |
| Complex Equations | Reports timeout, exports fail | Increases linearly with records | Pre-calculate and cache results |
| Inefficient Workflows | Actions take > 30 seconds | User frustration, timeouts | Use background processing |
| Unoptimized Images | Slow page rendering | Wastes bandwidth | Compress and resize images |
Performance Measurement
Key Performance Metrics
- Page Load Time: Time from request to fully rendered page (target:
- Time to First Byte (TTFB): Server response time (target:
- Query Execution Time: Database query speed (target:
- Component Render Time: Time to display component (target:
- API Response Time: External API calls (target:
Measuring Performance in Tadabase:
BROWSER DEVELOPER TOOLS:
1. Open Chrome DevTools (F12)
2. Go to Network tab
3. Reload page
4. Analyze:
- Total page load time
- Number of requests
- Size of data transferred
- Slowest requests
TADABASE API LOGS:
Settings > API Logs
- View request duration
- Identify slow endpoints
- Track API usage patterns
CUSTOM PERFORMANCE LOGGING:
TABLE: Performance_Logs
- page_name
- user_id
- load_started_at
- load_completed_at
- duration_ms
- record_count
- filters_applied
RECORD RULE: On page load
CREATE_LOG({
page_name: {current_page.name},
user_id: {current_user.id},
load_started_at: NOW(),
record_count: COUNT({records_loaded})
})
Generate reports:
- Average load time by page
- Load time distribution (histogram)
- Slow pages requiring optimization
- Performance trends over time
Database Optimization
Query Optimization Strategies
1. Use Proper Indexing
Slow: Non-Indexed Search
FIELD: email (text field, not unique)
QUERY: Find customer by email
RESULT: Full table scan
TIME: 5-10 seconds on 1M records
Fast: Indexed Search
FIELD: email (text field, UNIQUE)
QUERY: Find customer by email
RESULT: Index lookup
TIME: 10-50ms on 1M records
IMPROVEMENT: 100-1000x faster
Fields to Make Unique (Indexed):
- Email addresses
- Order numbers
- SKUs / Product codes
- Customer IDs
- Invoice numbers
- Any field used in connection field relationships
- Any field frequently used in filters
2. Avoid the N+1 Query Problem
Slow: Lookup in Equation (N+1)
TABLE COMPONENT: Orders (showing 50 orders)
COLUMN: Customer Name
EQUATION: LOOKUP('Customers', {customer_id}, 'name')
EXECUTION:
- Query 1: Load 50 orders
- Query 2-51: Lookup customer name (50 times)
TOTAL: 51 queries
TIME: 2-5 seconds
Fast: Connection Field
TABLE COMPONENT: Orders (showing 50 orders)
COLUMN: Customer Name
FIELD: {customer_connection.name}
EXECUTION:
- Query 1: Load 50 orders with customer names (JOIN)
TOTAL: 1 query
TIME: 100-200ms
IMPROVEMENT: 10-50x faster
3. Implement Pagination
Slow: Load All Records
TABLE COMPONENT: Products
RECORDS: Load all 100,000 products
RESULT:
- 50 MB data transfer
- Browser freezes
- 30-60 second load time
- Users can't scroll/interact
Fast: Paginated View
TABLE COMPONENT: Products
RECORDS: Load 50 per page
FEATURES:
- Pagination controls
- Search/filter capability
- Sort options
RESULT:
- 50 KB data transfer
- Instant load (
Performance Impact: Pagination reduces load time by 90-99% and memory usage by 99%+ for large datasets.
Strategic Denormalization
When to Denormalize for Performance
Trade-off: Denormalization improves read performance but requires maintaining data consistency and uses more storage.
Example: Order Dashboard Performance
NORMALIZED (SLOW):
TABLE: Orders
- order_id
- customer_id (connection)
- order_date
DASHBOARD QUERY:
Display orders with customer name and total order count
REQUIRES:
- Join to Customers table (for name)
- Count aggregation (for order count)
- Multiple subqueries
RESULT: 2-5 seconds on 1M orders
---
DENORMALIZED (FAST):
TABLE: Orders
- order_id
- customer_id (connection)
- customer_name (TEXT - copied from customer)
- order_date
TABLE: Customers
- customer_id
- name
- total_orders (NUMBER - calculated count)
- last_order_date (DATE - cached)
- total_revenue (NUMBER - sum of orders)
DASHBOARD QUERY:
SELECT customer_name, total_orders FROM Orders
REQUIRES:
- Simple select, no joins
- No aggregations needed
- Direct field access
RESULT: 100-300ms on 1M orders
IMPROVEMENT: 10-50x faster
MAINTENANCE:
RECORD RULE: On Order Create/Update/Delete
UPDATE Customer:
- Recalculate total_orders
- Update last_order_date
- Recalculate total_revenue
UPDATE Order:
- Copy customer_name from Customer
SCHEDULED TASK: Daily Verification (2 AM)
- Verify denormalized counts match actual counts
- Fix any discrepancies
- Log inconsistencies
Data Partitioning
Separating Active and Archived Data
PROBLEM:
Orders table has 5 million records
- 95% are old/completed
- 5% are active/recent
- Users mostly work with active orders
- Loading all orders is slow
SOLUTION: Partition by Age
TABLE: Orders_Active (250,000 records)
- Contains orders from last 12 months
- Used for day-to-day operations
- Fast queries and updates
TABLE: Orders_Archive (4,750,000 records)
- Contains orders older than 12 months
- Read-only or rarely updated
- Optimized for storage and reporting
IMPLEMENTATION:
SCHEDULED TASK: Monthly Archive Process
// Move old orders to archive
old_orders = FIND_RECORDS("Orders_Active", {
order_date:
Component Performance
Table Component Optimization
Best Practices for Large Tables
- Enable pagination (25-50 records per page)
- Limit columns displayed (max 10-12 visible columns)
- Use connection fields instead of equation lookups
- Pre-calculate complex values in separate fields
- Implement server-side filtering
- Lazy load images and files
- Disable real-time updates for large datasets
- Use conditional formatting sparingly
Optimized Table Configuration:
TABLE COMPONENT: Customer Orders
PAGINATION:
✓ Enable pagination
Records per page: 50
Show page numbers: Yes
COLUMNS (limit to essential fields):
1. Order Number (indexed field)
2. Customer Name (connection field)
3. Order Date (date field)
4. Status (dropdown)
5. Total (number field)
6. Actions (action links)
AVOID in table view:
✗ Long text fields (description, notes)
✗ Complex equations (show in detail view)
✗ Multiple images per row
✗ Nested child records
✗ Real-time calculations
FILTERS:
Default filter: Status = "Active"
User can adjust:
- Date range
- Status
- Customer
- Amount range
SORTING:
Default sort: Order Date DESC (most recent first)
Allow sorting on: Order Number, Date, Total
RESULT:
Load time:
Chart Component Optimization
Optimizing Data Visualization
Slow: Raw Data Charting
CHART: Sales by Day (Last Year)
DATA SOURCE: Orders table (2M records)
PROCESSING:
- Load all 2M orders
- Group by day (365 groups)
- Sum totals for each day
- Render chart
TIME: 30-60 seconds
OFTEN: Timeout error
Fast: Pre-Aggregated Data
CHART: Sales by Day (Last Year)
DATA SOURCE: Daily_Sales_Summary (365 records)
TABLE: Daily_Sales_Summary
- date (indexed)
- total_orders
- total_revenue
- average_order_value
UPDATED BY: Scheduled task (nightly)
- Aggregates previous day's orders
- Stores in summary table
CHART CONFIGURATION:
- Query 365 pre-aggregated records
- No grouping needed
- No aggregation needed
- Direct visualization
TIME: 200-500ms
IMPROVEMENT: 100x faster
Pre-Aggregation Strategy:
SCHEDULED TASK: Daily_Sales_Aggregation (runs at 1 AM)
// Find or create today's summary record
today = TODAY()
summary = FIND_OR_CREATE("Daily_Sales_Summary", {date: today})
// Calculate totals from Orders
orders_today = FIND_RECORDS("Orders", {
order_date: today,
status: NOT "Cancelled"
})
summary.total_orders = COUNT(orders_today)
summary.total_revenue = SUM(orders_today.total)
summary.average_order_value = summary.total_revenue / summary.total_orders
summary.unique_customers = COUNT_DISTINCT(orders_today.customer_id)
UPDATE_RECORD(summary)
// Also update weekly and monthly summaries
UPDATE_WEEKLY_SUMMARY(today)
UPDATE_MONTHLY_SUMMARY(today)
RESULT:
- Dashboard charts load in
Form Component Optimization
Optimizing Large Forms
- Use tabs to organize fields (load one tab at a time)
- Lazy load connected records (load on demand)
- Minimize conditional field rules (slow on large forms)
- Use dropdown fields instead of connection fields when possible
- Defer non-essential validations to save time
- Load child records in separate components
SCENARIO: Complex Customer Form (100+ fields)
POOR DESIGN (SLOW):
- Single page with all 100 fields
- All fields visible at once
- All child records loaded (orders, contacts, notes)
- Real-time validation on every field
- Load time: 5-10 seconds
OPTIMIZED DESIGN (FAST):
TAB 1: Basic Information (10 fields)
- Name, email, phone, address
- Load time: 200ms
TAB 2: Company Details (15 fields)
- Loaded when tab is clicked
- Load time: 300ms
TAB 3: Orders (child table)
- Paginated table
- Loads only when tab is clicked
- Shows 25 most recent orders
- Load time: 400ms
TAB 4: Contacts (child table)
- Similar to orders
- On-demand loading
TAB 5: Documents & Files
- Lazy load file previews
- Only download when clicked
OVERALL IMPROVEMENT:
- Initial load: 200ms (vs 5-10 seconds)
- User perceives instant load
- Total data loaded on demand: ~ 1 second
- Much better user experience
Caching Strategies
Application-Level Caching
Caching Expensive Calculations
Pattern: Customer Lifetime Value Calculation
CALCULATION (EXPENSIVE):
Customer Lifetime Value (CLV) =
(Average Order Value) ×
(Purchase Frequency) ×
(Customer Lifespan) ×
(Profit Margin)
REQUIRES:
- Aggregate all customer orders
- Calculate averages
- Apply predictive formulas
- Compute for 1M customers: 10-30 minutes
SOLUTION: Cache with Scheduled Update
TABLE: Customers
- customer_id
- name
- email
- clv_amount (NUMBER - cached value)
- clv_calculated_at (DATE/TIME)
- clv_tier (TEXT - High/Medium/Low, derived from clv_amount)
SCHEDULED TASK: Update_Customer_CLV (runs nightly)
// Process in batches to avoid timeout
BATCH_SIZE = 1000
FOR EACH batch OF customers:
FOR EACH customer:
// Calculate CLV
clv = CALCULATE_CLV(customer.id)
// Update cached value
customer.clv_amount = clv
customer.clv_calculated_at = NOW()
// Update tier
customer.clv_tier = CASE
WHEN clv > 10000 THEN "High"
WHEN clv > 5000 THEN "Medium"
ELSE "Low"
END
UPDATE_CUSTOMER(customer)
END FOR
// Pause between batches to reduce load
SLEEP(5 seconds)
END FOR
USAGE:
Dashboard displays:
- Top 100 customers by CLV (instant query on cached field)
- CLV distribution chart (fast aggregation)
- Customer segmentation (based on cached tier)
ACCURACY:
- Values updated nightly
- "Fresh enough" for strategic decisions
- Don't need real-time CLV for most use cases
PERFORMANCE GAIN:
Before: Dashboard timeout after 60 seconds
After: Dashboard loads in
Browser Caching
Leveraging Client-Side Caching
- Static Assets: Images, logos, CSS cached in browser
- Reference Data: Dropdowns, lookup tables cached locally
- User Preferences: Settings stored in browser localStorage
Impact: Repeat page loads 50-90% faster due to cached assets.
Workflow Performance
Asynchronous Processing
Moving Long Operations to Background
Slow: Synchronous Processing
USER ACTION: Click "Generate Annual Report"
PROCESSING:
1. Query all orders (5M records)
2. Aggregate data
3. Generate PDF (200 pages)
4. Send email with PDF
WAIT TIME: 5-10 minutes
USER SEES: Spinning loader (frustrating)
RISK: Browser timeout, user clicks away
Fast: Asynchronous Processing
USER ACTION: Click "Generate Annual Report"
IMMEDIATE RESPONSE (
Batch Processing Optimization
Efficient Bulk Operations
SCENARIO: Send email to 100,000 customers
INEFFICIENT:
FOR EACH customer IN all_customers:
SEND_EMAIL(customer.email, subject, body)
// 100,000 individual API calls
// Takes: 5-10 hours
// Risk: API rate limits, timeouts
END FOR
OPTIMIZED:
// Group customers into batches of 1,000
BATCH_SIZE = 1000
total_customers = COUNT(all_customers)
batches = CEIL(total_customers / BATCH_SIZE)
FOR batch_num = 1 TO batches:
// Get batch of customers
customers_batch = GET_BATCH(batch_num, BATCH_SIZE)
// Send batch email (1 API call for 1,000 emails)
SEND_BATCH_EMAIL(customers_batch, subject, body)
// Update progress
UPDATE_JOB_PROGRESS(batch_num / batches * 100)
// Rate limit: pause between batches
SLEEP(10 seconds)
END FOR
IMPROVEMENTS:
- 100,000 API calls → 100 API calls (1000x reduction)
- Time: 30-60 minutes (vs 5-10 hours)
- No rate limit issues
- Progress tracking
- Can pause/resume
API Performance
Optimizing API Requests
API Best Practices
- Use pagination for large result sets (page=1&limit=100)
- Filter at the API level (don't load all then filter client-side)
- Request only needed fields (fields=id,name,email)
- Use bulk endpoints instead of individual calls
- Implement caching headers (Cache-Control, ETag)
- Compress responses (Accept-Encoding: gzip)
- Use HTTP/2 for multiplexing
- Implement retry logic with exponential backoff
Optimized API Usage:
INEFFICIENT:
// Load all customers, then filter client-side
GET /api/v1/data-tables/customers
// Returns 100,000 records (50 MB)
// Time: 30-60 seconds
client_side_filter(customers, {status: "active", country: "US"})
// Filters 100,000 records to 5,000
// Wasted 95% of data transfer
OPTIMIZED:
// Filter server-side with pagination
GET /api/v1/data-tables/customers?
filters[status]=active&
filters[country]=US&
fields=id,name,email,phone&
page=1&
limit=100
// Returns 100 records (50 KB)
// Time: 200-500ms
// IMPROVEMENT: 100x faster, 1000x less data
Rate Limiting and Throttling
Respecting API Limits
TADABASE API LIMITS:
- 1,000 requests per hour (standard)
- 10,000 requests per hour (enterprise)
IMPLEMENTATION: Respectful API Client
requests_made = 0
request_limit = 1000
time_window_start = NOW()
FUNCTION: MAKE_API_REQUEST(endpoint, params)
// Check if we've exceeded limit
IF requests_made >= request_limit:
elapsed_minutes = MINUTES_BETWEEN(time_window_start, NOW())
IF elapsed_minutes
Monitoring and Continuous Optimization
Performance Monitoring Dashboard
Building a Performance Dashboard
DASHBOARD: Application Performance Monitor
SECTION 1: Real-Time Metrics
- Current active users
- Average page load time (last hour)
- API calls per minute
- Database query time average
- Error rate
SECTION 2: Slow Queries Report
- List of queries taking > 1 second
- Frequency of slow queries
- Users affected
- Recommendations for optimization
SECTION 3: User Experience Metrics
- Pages with load time > 3 seconds
- Most accessed pages
- User sessions and duration
- Bounce rate by page
SECTION 4: Resource Usage
- Database size and growth rate
- File storage usage
- API quota consumption
- Memory usage patterns
SECTION 5: Performance Trends
- Load time trends (last 30 days)
- Query performance over time
- User growth vs. performance degradation
- Proactive alerts for degradation
ALERTS:
- Email when page load > 5 seconds
- Slack notification when error rate > 1%
- Weekly performance summary report
- Monthly capacity planning report
Summary and Next Steps
You've Mastered:
- Performance measurement and bottleneck identification
- Database optimization (indexing, query optimization, partitioning)
- Component-level performance tuning
- Strategic caching and denormalization
- Asynchronous and batch processing
- API optimization and rate limiting
- Performance monitoring and continuous improvement
Real-World Impact: Proper performance optimization can improve application speed by 10-100x, enable scaling to millions of records, and dramatically improve user satisfaction.
Next Lesson: Real-World Applications - See how to apply everything you've learned to build production-grade CRM, ERP, and industry-specific applications.
Practice Challenge: Take an existing Tadabase application with performance issues. Measure baseline performance, identify bottlenecks, apply optimization techniques, and measure improvement. Document before/after metrics and the specific optimizations that had the biggest impact.
We'd love to hear your feedback.