performance（性能）

Performance Optimization - Critical Best Practices

NEVER Fetch Full Datasets

CRITICAL RULE: Never fetch entire datasets, especially large ones (100k+ rows). Always use targeted queries with only the columns needed for each visualization.

Performance Impact

// ❌ TERRIBLE - Fetches 300k rows × 30 columns = 9M data points
const allData = await new Query()
  .select(['col1', 'col2', 'col3', ...]) // All columns
  .fetch('dataset');

// ✅ GOOD - Fetches 300k rows × 2 columns = 600k data points (93% reduction)
const totals = await new Query()
  .select(['Transactions', 'Total Amount (USD)'])
  .fetch('dataset');
// Then aggregate client-side

// ✅ BEST - Server-side aggregation, returns only aggregated results
const totals = await new Query()
  .groupBy('Merchant Category', { 'Total Amount (USD)': 'sum' })
  .fetch('dataset');
// Returns only ~10-20 rows (one per category)

Query Optimization Strategy

1. Use Server-Side Aggregation When Possible

Priority order:

1. Best: .groupBy() with aggregations (server-side)
2. Good: .select() with only needed columns, then aggregate client-side
3. Acceptable: .select() with multiple columns if aggregation isn't possible
4. Never: Fetch all columns or entire dataset

// ✅ BEST - Server aggregates, returns minimal data
const salesByRegion = await new Query()
  .groupBy('region', { 'Sales_Amount': 'sum', 'Order_Count': 'count' })
  .fetch('sales');

// ✅ GOOD - Only fetch columns needed, aggregate client-side
const salesData = await new Query()
  .select(['region', 'Sales_Amount'])
  .fetch('sales');
const totalSales = salesData.reduce((sum, row) => sum + row.Sales_Amount, 0);

// ❌ BAD - Fetches all columns
const allData = await new Query().fetch('sales');

2. One Query Per Visualization

Each visualization should have its own optimized query:

// ✅ CORRECT - Separate optimized queries
const quickStats = await fetchQuickStats();      // Only Account Key, Account Status
const riskMetrics = await fetchRiskMetrics();    // Only Transactions, KYC Status, etc.
const categories = await fetchCategories();      // Grouped by Merchant Category

// ❌ WRONG - One query for everything
const allData = await fetchAllData();
const quickStats = calculateFromAll(allData);
const riskMetrics = calculateFromAll(allData);
const categories = calculateFromAll(allData);

3. Column Selection Strategy

Always specify columns explicitly:

• Only select columns needed for the specific calculation
• Don't use .select() without arguments (fetches all columns)
• For aggregations, only select the grouping column + aggregated columns

// ✅ CORRECT - Only 2 columns
const data = await new Query()
  .select(['Account Key', 'Account Status'])
  .fetch('dataset');

// ❌ WRONG - Fetches all columns
const data = await new Query().fetch('dataset');

Common Patterns

Pattern 1: Totals Without Grouping

When you need totals but no grouping column:

// Option A: Select only needed columns, sum client-side
const totalsData = await new Query()
  .select(['Transactions', 'Total Amount (USD)'])
  .fetch('dataset');
const totalTransactions = totalsData.reduce((sum, row) => sum + (row.Transactions || 0), 0);
const totalVolume = totalsData.reduce((sum, row) => sum + (row['Total Amount (USD)'] || 0), 0);

// Option B: Use a constant grouping column (if dataset has one)
// Not always possible, but more efficient if available

Pattern 2: Counts with Filters

// ✅ CORRECT - Only fetch the column needed for counting
const kycData = await new Query()
  .select(['KYC Status'])
  .fetch('dataset');
const approvedCount = kycData.filter(row => row['KYC Status'] === 'APPROVED').length;

// Better if possible: Use server-side count aggregation
// (See Query API limitations below)

Pattern 3: Unique Values

// ✅ CORRECT - Only fetch the column needed
const statesData = await new Query()
  .select(['State'])
  .fetch('dataset');
const uniqueStates = new Set(statesData.map(row => row.State)).size;

Query API Limitations

.aggregate() Doesn't Work

CRITICAL: The .aggregate() method shown in some documentation does not work in practice. It causes error: DA0057: An alias list was provided but it could not be parsed.

// ❌ DOES NOT WORK - Causes DA0057 error
const totals = await new Query()
  .aggregate({ 'Transactions': 'sum', 'Total Amount (USD)': 'sum' })
  .fetch('dataset');

// ✅ WORKAROUND - Use .groupBy() with a grouping column
const totals = await new Query()
  .groupBy('some_column', { 'Transactions': 'sum', 'Total Amount (USD)': 'sum' })
  .fetch('dataset');

// ✅ ALTERNATIVE - Select columns and aggregate client-side
const totals = await new Query()
  .select(['Transactions', 'Total Amount (USD)'])
  .fetch('dataset');
// Then sum client-side

.groupBy() Requires a Grouping Column

You cannot use .groupBy() with only aggregations - you must provide a grouping column:

// ❌ DOES NOT WORK - No grouping column
.groupBy({ 'Transactions': 'sum' })

// ✅ WORKS - Has grouping column
.groupBy('region', { 'Transactions': 'sum' })

// ✅ WORKS - Multiple groupBy calls
.groupBy('region')
.groupBy({ 'Transactions': 'sum' })

Performance Monitoring

Always check:

1. Network tab - How much data is being transferred?
2. Console - Any warnings about large queries?
3. Response times - Are queries taking too long?

If a query is slow or returns too much data:

• Reduce columns selected
• Add filters to reduce rows
• Use server-side aggregation instead of client-side
• Consider pagination for large result sets