offline analyticsSQLbrowserDuckDBlocal processing
Offline SQL Analytics: Complete Guide to Browser-Based Data Analysis
LakeClient Team••35 min read
Master offline SQL analytics with browser-based tools. Learn to analyze data locally without internet connectivity using DuckDB, WebAssembly, and modern web technologies.
Offline SQL Analytics: Complete Guide to Browser-Based Data Analysis
In an increasingly connected world, the ability to perform sophisticated data analytics offline has become more valuable than ever. This comprehensive guide explores how to build and use offline SQL analytics capabilities that work entirely in your browser, without any internet connection.
The Power of Offline Analytics
Why Offline Analytics Matter
Critical Scenarios
- Remote fieldwork with unreliable internet
- Secure environments with air-gapped networks
- Travel and mobile analysis
- Disaster recovery situations
- Privacy-sensitive environments
- Cost-sensitive operations avoiding cloud fees
Key Advantages
- Complete independence: No reliance on external services
- Ultimate privacy: Data never transmitted anywhere
- Consistent performance: No network latency issues
- Zero ongoing costs: No cloud or subscription fees
- Regulatory compliance: Simplified data governance
Technology Foundation
Modern browsers provide powerful capabilities that enable offline analytics:
- WebAssembly (WASM): Native-speed computation
- Service Workers: Offline application functionality
- IndexedDB: Local persistent storage
- File System Access API: Direct file reading
- Web Workers: Background processing
- SharedArrayBuffer: High-performance memory sharing
Setting Up Offline SQL Analytics
Core Technology: DuckDB-WASM
DuckDB-WASM is the foundation for high-performance offline SQL analytics
This complete HTML application demonstrates how to build a fully functional offline SQL analytics platform. The interface handles file loading, query execution, and results display without requiring any internet connection after the initial page load.
The application automatically initializes DuckDB when the page loads and provides intuitive file handling for CSV, Parquet, and JSON formats
<!DOCTYPE html>
<html>
<head>
<title>Offline SQL Analytics</title>
<meta charset="utf-8">
</head>
<body>
<h1>Offline SQL Analytics Platform</h1>
<div id="file-input">
<input type="file" id="dataFiles" multiple accept=".csv,.parquet,.json">
<button onclick="loadFiles()">Load Data Files</button>
</div>
<div id="query-interface">
<textarea id="sqlQuery" rows="8" cols="80" placeholder="Enter your SQL query here...">
SELECT * FROM data LIMIT 10;
</textarea>
<br>
<button onclick="executeQuery()" id="executeBtn">Execute Query</button>
<button onclick="clearResults()">Clear Results</button>
</div>
<div id="results">
<!-- Query results will appear here -->
</div>
<script type="module">
import * as duckdb from 'https://cdn.jsdelivr.net/npm/@duckdb/duckdb-wasm@latest/+esm';
let db, conn;
let loadedFiles = new Set();
// Initialize DuckDB for offline use
async function initializeOfflineDB() {
try {
console.log('Initializing offline DuckDB...');
const JSDELIVR_BUNDLES = duckdb.getJsDelivrBundles();
const bundle = await duckdb.selectBundle(JSDELIVR_BUNDLES);
// Create worker for background processing
const worker = await duckdb.createWorker(bundle.mainWorker);
const logger = new duckdb.ConsoleLogger();
// Initialize database
db = new duckdb.AsyncDuckDB(logger, worker);
await db.instantiate(bundle.mainModule);
conn = await db.connect();
console.log('Offline DuckDB initialized successfully');
updateStatus('Ready for offline analysis');
} catch (error) {
console.error('Failed to initialize DuckDB:', error);
updateStatus('Initialization failed: ' + error.message);
}
}
// Load files for offline processing
window.loadFiles = async function() {
const fileInput = document.getElementById('dataFiles');
const files = Array.from(fileInput.files);
if (files.length === 0) {
alert('Please select files to load');
return;
}
updateStatus('Loading files...');
for (const file of files) {
try {
await loadSingleFile(file);
loadedFiles.add(file.name);
} catch (error) {
console.error(`Failed to load ${file.name}:`, error);
alert(`Failed to load ${file.name}: ${error.message}`);
}
}
updateStatus(`Loaded ${files.length} file(s) - Ready for analysis`);
updateFileList();
};
// Load individual file based on type
async function loadSingleFile(file) {
const fileName = file.name;
const extension = fileName.split('.').pop().toLowerCase();
switch (extension) {
case 'csv':
const csvText = await file.text();
await db.registerFileText(fileName, csvText);
break;
case 'parquet':
const parquetBuffer = await file.arrayBuffer();
await db.registerFileBuffer(fileName, new Uint8Array(parquetBuffer));
break;
case 'json':
const jsonText = await file.text();
await db.registerFileText(fileName, jsonText);
break;
default:
throw new Error(`Unsupported file type: ${extension}`);
}
console.log(`Loaded ${fileName} for offline analysis`);
}
// Execute SQL query offline
window.executeQuery = async function() {
const sqlQuery = document.getElementById('sqlQuery').value.trim();
const executeBtn = document.getElementById('executeBtn');
const resultsDiv = document.getElementById('results');
if (!sqlQuery) {
alert('Please enter a SQL query');
return;
}
if (!conn) {
alert('Database not initialized. Please refresh and try again.');
return;
}
try {
executeBtn.disabled = true;
executeBtn.textContent = 'Executing...';
updateStatus('Executing query...');
const startTime = performance.now();
const result = await conn.query(sqlQuery);
const endTime = performance.now();
const data = result.toArray();
const executionTime = Math.round(endTime - startTime);
displayResults(data, executionTime);
updateStatus(`Query completed in ${executionTime}ms - ${data.length} rows returned`);
} catch (error) {
console.error('Query execution error:', error);
resultsDiv.innerHTML = `<div style="color: red; padding: 10px; border: 1px solid red; margin: 10px 0;">
<strong>Query Error:</strong> ${error.message}
</div>`;
updateStatus('Query failed: ' + error.message);
} finally {
executeBtn.disabled = false;
executeBtn.textContent = 'Execute Query';
}
};
// Display query results
function displayResults(data, executionTime) {
const resultsDiv = document.getElementById('results');
if (data.length === 0) {
resultsDiv.innerHTML = `<div style="padding: 20px; text-align: center; color: #666;">
No results found. Query executed in ${executionTime}ms.
</div>`;
return;
}
const columns = Object.keys(data[0]);
const maxRowsToShow = 1000; // Limit display for performance
const displayData = data.slice(0, maxRowsToShow);
let html = `
<div style="margin: 10px 0; padding: 10px; background: #f5f5f5; border-radius: 4px;">
<strong>Query Results:</strong> ${data.length} rows returned in ${executionTime}ms
${data.length > maxRowsToShow ? ` (showing first ${maxRowsToShow} rows)` : ''}
</div>
<div style="overflow-x: auto;">
<table style="width: 100%; border-collapse: collapse; margin: 10px 0;">
<thead>
<tr style="background-color: #f0f0f0;">
`;
columns.forEach(col => {
html += `<th style="border: 1px solid #ddd; padding: 8px; text-align: left;">${col}</th>`;
});
html += '</tr></thead><tbody>';
displayData.forEach((row, index) => {
const bgColor = index % 2 === 0 ? '#fafafa' : '#ffffff';
html += `<tr style="background-color: ${bgColor};">`;
columns.forEach(col => {
const value = row[col];
const displayValue = value === null ? '<em>null</em>' : String(value);
html += `<td style="border: 1px solid #ddd; padding: 8px;">${displayValue}</td>`;
});
html += '</tr>';
});
html += '</tbody></table></div>';
resultsDiv.innerHTML = html;
}
// Clear results display
window.clearResults = function() {
document.getElementById('results').innerHTML = '';
updateStatus('Results cleared');
};
// Update status display
function updateStatus(message) {
// Create status div if it doesn't exist
let statusDiv = document.getElementById('status');
if (!statusDiv) {
statusDiv = document.createElement('div');
statusDiv.id = 'status';
statusDiv.style.cssText = 'margin: 10px 0; padding: 8px; background: #e3f2fd; border-left: 4px solid #2196f3; font-family: monospace;';
document.body.insertBefore(statusDiv, document.getElementById('file-input'));
}
statusDiv.textContent = `Status: ${message} (${new Date().toLocaleTimeString()})`;
}
// Update loaded files list
function updateFileList() {
let fileListDiv = document.getElementById('file-list');
if (!fileListDiv) {
fileListDiv = document.createElement('div');
fileListDiv.id = 'file-list';
fileListDiv.style.cssText = 'margin: 10px 0; padding: 10px; background: #f5f5f5; border-radius: 4px;';
document.getElementById('file-input').appendChild(fileListDiv);
}
if (loadedFiles.size > 0) {
fileListDiv.innerHTML = `<strong>Loaded Files:</strong> ${Array.from(loadedFiles).join(', ')}`;
} else {
fileListDiv.innerHTML = '<em>No files loaded</em>';
}
}
// Initialize when page loads
document.addEventListener('DOMContentLoaded', function() {
initializeOfflineDB();
updateFileList();
});
// Make functions globally available for HTML onclick
window.initializeOfflineDB = initializeOfflineDB;
</script>
<style>
body {
font-family: Arial, sans-serif;
margin: 20px;
line-height: 1.6;
}
#query-interface {
margin: 20px 0;
}
#sqlQuery {
width: 100%;
max-width: 800px;
font-family: 'Courier New', monospace;
font-size: 14px;
padding: 10px;
border: 1px solid #ddd;
border-radius: 4px;
}
button {
padding: 10px 20px;
margin: 5px;
font-size: 14px;
border: none;
border-radius: 4px;
cursor: pointer;
background-color: #4CAF50;
color: white;
}
button:hover {
background-color: #45a049;
}
button:disabled {
background-color: #cccccc;
cursor: not-allowed;
}
#results {
margin-top: 20px;
}
table {
font-size: 12px;
}
.offline-indicator {
position: fixed;
top: 10px;
right: 10px;
padding: 5px 10px;
background: #ff9800;
color: white;
border-radius: 4px;
font-size: 12px;
}
.online-indicator {
background: #4caf50;
}
</style>
</body>
</html>
Service Worker for True Offline Capability
Create a service worker to enable complete offline functionality
Service workers provide the backbone for true offline capability by intercepting network requests and serving cached resources when internet connectivity is unavailable. This implementation ensures your analytics platform continues working even in remote locations.
The service worker caches all necessary resources during the first visit, then serves them locally for subsequent offline usage
// offline-analytics-sw.js
const CACHE_NAME = 'offline-analytics-v1';
const urlsToCache = [
'/',
'/offline-analytics.html',
'/offline-analytics.css',
'/offline-analytics.js',
// DuckDB WASM files
'https://cdn.jsdelivr.net/npm/@duckdb/duckdb-wasm@latest/dist/duckdb-browser.js',
'https://cdn.jsdelivr.net/npm/@duckdb/duckdb-wasm@latest/dist/duckdb-browser.wasm',
// Other assets
];
// Install service worker and cache resources
self.addEventListener('install', event => {
event.waitUntil(
caches.open(CACHE_NAME)
.then(cache => {
console.log('Opened cache');
return cache.addAll(urlsToCache);
})
);
});
// Fetch event - serve from cache when offline
self.addEventListener('fetch', event => {
event.respondWith(
caches.match(event.request)
.then(response => {
// Return cached version or fetch from network
if (response) {
return response;
}
// If not in cache, try to fetch from network
return fetch(event.request).then(response => {
// Don't cache non-successful responses
if (!response || response.status !== 200 || response.type !== 'basic') {
return response;
}
// Clone the response
const responseToCache = response.clone();
// Add to cache for future offline use
caches.open(CACHE_NAME)
.then(cache => {
cache.put(event.request, responseToCache);
});
return response;
});
})
.catch(() => {
// Return offline page if available
if (event.request.destination === 'document') {
return caches.match('/offline.html');
}
})
);
});
// Update service worker
self.addEventListener('activate', event => {
event.waitUntil(
caches.keys().then(cacheNames => {
return Promise.all(
cacheNames.map(cacheName => {
if (cacheName !== CACHE_NAME) {
return caches.delete(cacheName);
}
})
);
})
);
});
// Handle offline analytics specific events
self.addEventListener('message', event => {
if (event.data && event.data.type === 'CACHE_ANALYTICS_DATA') {
// Cache user's analytics data for offline access
cacheAnalyticsData(event.data.payload);
}
});
async function cacheAnalyticsData(data) {
const cache = await caches.open(`${CACHE_NAME}-data`);
const response = new Response(JSON.stringify(data));
await cache.put('/analytics-data', response);
}
Register the service worker
Service worker registration enables your application to function offline by installing background scripts that manage network requests and resource caching. This code detects online/offline status and provides visual feedback to users.
The implementation includes proper error handling and status monitoring to ensure users understand their connectivity state
// Register service worker for offline capability
if ('serviceWorker' in navigator) {
window.addEventListener('load', () => {
navigator.serviceWorker.register('/offline-analytics-sw.js')
.then(registration => {
console.log('SW registered: ', registration);
updateOfflineStatus(true);
})
.catch(registrationError => {
console.log('SW registration failed: ', registrationError);
});
});
}
// Monitor online/offline status
function updateOfflineStatus(hasServiceWorker) {
const indicator = document.createElement('div');
indicator.className = navigator.onLine ? 'online-indicator' : 'offline-indicator';
indicator.textContent = navigator.onLine ?
(hasServiceWorker ? 'Online (Offline Capable)' : 'Online') :
'Offline Mode';
// Remove existing indicator
const existing = document.querySelector('.online-indicator, .offline-indicator');
if (existing) existing.remove();
document.body.appendChild(indicator);
}
// Listen for online/offline events
window.addEventListener('online', () => updateOfflineStatus(true));
window.addEventListener('offline', () => updateOfflineStatus(true));
Advanced Offline Analytics Patterns
Local Data Storage and Management
Sophisticated data management becomes crucial for offline analytics applications that need to persist datasets across browser sessions. This implementation provides comprehensive data storage, retrieval, and management capabilities using IndexedDB for persistence.
The system automatically handles different data formats and provides intelligent memory management to prevent browser crashes with large datasets
// Advanced offline data management
class OfflineDataManager {
constructor() {
this.db = null;
this.conn = null;
this.storage = new OfflineStorage();
this.dataRegistry = new Map();
}
async initialize() {
// Initialize DuckDB
const JSDELIVR_BUNDLES = duckdb.getJsDelivrBundles();
const bundle = await duckdb.selectBundle(JSDELIVR_BUNDLES);
const worker = await duckdb.createWorker(bundle.mainWorker);
const logger = new duckdb.ConsoleLogger();
this.db = new duckdb.AsyncDuckDB(logger, worker);
await this.db.instantiate(bundle.mainModule);
this.conn = await this.db.connect();
// Load previously stored data
await this.loadStoredData();
}
// Persistent local storage for datasets
async storeDataset(name, data, metadata = {}) {
try {
// Store in IndexedDB for persistence
await this.storage.storeDataset(name, data, metadata);
// Register with DuckDB for immediate use
await this.registerDataset(name, data);
// Update registry
this.dataRegistry.set(name, {
size: data.length,
lastModified: new Date(),
metadata: metadata
});
console.log(`Dataset '${name}' stored for offline access`);
return true;
} catch (error) {
console.error(`Failed to store dataset '${name}':`, error);
return false;
}
}
async registerDataset(name, data) {
// Determine data type and register appropriately
if (typeof data === 'string') {
if (data.trim().startsWith('[') || data.trim().startsWith('{')) {
// JSON data
await this.db.registerFileText(`${name}.json`, data);
} else {
// CSV data
await this.db.registerFileText(`${name}.csv`, data);
}
} else if (data instanceof Uint8Array) {
// Binary data (Parquet)
await this.db.registerFileBuffer(`${name}.parquet`, data);
}
}
async loadStoredData() {
const storedDatasets = await this.storage.listDatasets();
for (const dataset of storedDatasets) {
try {
const data = await this.storage.getDataset(dataset.name);
await this.registerDataset(dataset.name, data);
this.dataRegistry.set(dataset.name, dataset);
console.log(`Restored dataset '${dataset.name}' from offline storage`);
} catch (error) {
console.error(`Failed to restore dataset '${dataset.name}':`, error);
}
}
}
// Query with offline-optimized execution
async query(sql, options = {}) {
const startTime = performance.now();
try {
// Validate query for offline execution
this.validateOfflineQuery(sql);
// Execute with memory management
const result = await this.executeWithMemoryManagement(sql, options);
const endTime = performance.now();
console.log(`Offline query executed in ${Math.round(endTime - startTime)}ms`);
return result;
} catch (error) {
console.error('Offline query failed:', error);
throw error;
}
}
validateOfflineQuery(sql) {
const upperSQL = sql.toUpperCase();
// Check for operations that require network
const networkOperations = ['HTTP', 'HTTPS', 'FTP', 'REMOTE'];
for (const op of networkOperations) {
if (upperSQL.includes(op)) {
throw new Error(`Network operation '${op}' not available offline`);
}
}
// Validate table references exist locally
const tableMatches = sql.match(/FROM\s+([a-zA-Z_][a-zA-Z0-9_]*)/gi);
if (tableMatches) {
for (const match of tableMatches) {
const tableName = match.replace(/FROM\s+/i, '');
if (!this.dataRegistry.has(tableName)) {
throw new Error(`Table '${tableName}' not available offline`);
}
}
}
}
async executeWithMemoryManagement(sql, options) {
const memoryBefore = this.getMemoryUsage();
// Set memory limit for offline execution
if (options.memoryLimit) {
await this.conn.query(`SET memory_limit='${options.memoryLimit}'`);
}
const result = await this.conn.query(sql);
const data = result.toArray();
const memoryAfter = this.getMemoryUsage();
console.log(`Memory usage: ${memoryBefore}MB -> ${memoryAfter}MB`);
return { data, executionStats: { memoryUsed: memoryAfter - memoryBefore } };
}
getMemoryUsage() {
if (performance.memory) {
return Math.round(performance.memory.usedJSHeapSize / 1024 / 1024);
}
return 0;
}
// Export results for offline use
async exportResults(data, format = 'csv') {
switch (format.toLowerCase()) {
case 'csv':
return this.exportToCSV(data);
case 'json':
return this.exportToJSON(data);
case 'parquet':
return await this.exportToParquet(data);
default:
throw new Error(`Unsupported export format: ${format}`);
}
}
exportToCSV(data) {
if (data.length === 0) return '';
const columns = Object.keys(data[0]);
const csvLines = [columns.join(',')];
data.forEach(row => {
const values = columns.map(col => {
const value = row[col];
if (value === null) return '';
if (typeof value === 'string' && value.includes(',')) {
return `"${value.replace(/"/g, '""')}"`;
}
return String(value);
});
csvLines.push(values.join(','));
});
return csvLines.join('\n');
}
exportToJSON(data) {
return JSON.stringify(data, null, 2);
}
}
// IndexedDB wrapper for persistent offline storage
class OfflineStorage {
constructor() {
this.dbName = 'OfflineAnalyticsDB';
this.version = 1;
this.db = null;
}
async init() {
return new Promise((resolve, reject) => {
const request = indexedDB.open(this.dbName, this.version);
request.onerror = () => reject(request.error);
request.onsuccess = () => {
this.db = request.result;
resolve(this.db);
};
request.onupgradeneeded = (event) => {
const db = event.target.result;
// Create object store for datasets
if (!db.objectStoreNames.contains('datasets')) {
const store = db.createObjectStore('datasets', { keyPath: 'name' });
store.createIndex('lastModified', 'lastModified', { unique: false });
}
// Create object store for query history
if (!db.objectStoreNames.contains('queries')) {
const queryStore = db.createObjectStore('queries', { keyPath: 'id', autoIncrement: true });
queryStore.createIndex('timestamp', 'timestamp', { unique: false });
}
};
});
}
async storeDataset(name, data, metadata = {}) {
if (!this.db) await this.init();
const transaction = this.db.transaction(['datasets'], 'readwrite');
const store = transaction.objectStore('datasets');
const dataset = {
name,
data,
metadata,
lastModified: new Date(),
size: typeof data === 'string' ? data.length : data.byteLength
};
return new Promise((resolve, reject) => {
const request = store.put(dataset);
request.onsuccess = () => resolve(request.result);
request.onerror = () => reject(request.error);
});
}
async getDataset(name) {
if (!this.db) await this.init();
const transaction = this.db.transaction(['datasets'], 'readonly');
const store = transaction.objectStore('datasets');
return new Promise((resolve, reject) => {
const request = store.get(name);
request.onsuccess = () => {
const result = request.result;
resolve(result ? result.data : null);
};
request.onerror = () => reject(request.error);
});
}
async listDatasets() {
if (!this.db) await this.init();
const transaction = this.db.transaction(['datasets'], 'readonly');
const store = transaction.objectStore('datasets');
return new Promise((resolve, reject) => {
const request = store.getAll();
request.onsuccess = () => resolve(request.result);
request.onerror = () => reject(request.error);
});
}
async deleteDataset(name) {
if (!this.db) await this.init();
const transaction = this.db.transaction(['datasets'], 'readwrite');
const store = transaction.objectStore('datasets');
return new Promise((resolve, reject) => {
const request = store.delete(name);
request.onsuccess = () => resolve(request.result);
request.onerror = () => reject(request.error);
});
}
}
Offline Query Builder Interface
Visual query builders make SQL accessible to non-technical users while maintaining the power of direct SQL for experts. This implementation provides a complete interface for building complex analytical queries through point-and-click interactions.
The query builder automatically generates SQL based on user selections and provides immediate feedback, making advanced analytics accessible to business users
// Visual query builder for offline analytics
class OfflineQueryBuilder {
constructor(dataManager) {
this.dataManager = dataManager;
this.currentQuery = {
select: [],
from: '',
where: [],
groupBy: [],
orderBy: [],
limit: null
};
}
renderInterface() {
return `
<div class="query-builder">
<h3>Visual Query Builder (Offline)</h3>
<!-- Table Selection -->
<div class="builder-section">
<label>Select Table:</label>
<select id="tableSelect" onchange="queryBuilder.selectTable(this.value)">
<option value="">Choose a table...</option>
${this.renderTableOptions()}
</select>
</div>
<!-- Column Selection -->
<div class="builder-section" id="columnSection" style="display: none;">
<label>Select Columns:</label>
<div id="columnCheckboxes"></div>
</div>
<!-- WHERE Conditions -->
<div class="builder-section" id="whereSection" style="display: none;">
<label>Where Conditions:</label>
<div id="whereConditions">
<button onclick="queryBuilder.addWhereCondition()">Add Condition</button>
</div>
</div>
<!-- GROUP BY -->
<div class="builder-section" id="groupBySection" style="display: none;">
<label>Group By:</label>
<div id="groupByColumns"></div>
</div>
<!-- ORDER BY -->
<div class="builder-section" id="orderBySection" style="display: none;">
<label>Order By:</label>
<div id="orderByColumns"></div>
</div>
<!-- LIMIT -->
<div class="builder-section" id="limitSection" style="display: none;">
<label>Limit Results:</label>
<input type="number" id="limitInput" placeholder="Enter limit..."
onchange="queryBuilder.setLimit(this.value)">
</div>
<!-- Generated SQL -->
<div class="builder-section">
<label>Generated SQL:</label>
<textarea id="generatedSQL" readonly rows="4" cols="80"></textarea>
<br>
<button onclick="queryBuilder.executeQuery()">Execute Query</button>
<button onclick="queryBuilder.resetQuery()">Reset</button>
</div>
</div>
`;
}
renderTableOptions() {
const tables = Array.from(this.dataManager.dataRegistry.keys());
return tables.map(table =>
`<option value="${table}">${table}</option>`
).join('');
}
async selectTable(tableName) {
if (!tableName) return;
this.currentQuery.from = tableName;
// Get table schema for column selection
try {
const schema = await this.getTableSchema(tableName);
this.renderColumnSelection(schema);
this.showSections();
this.updateGeneratedSQL();
} catch (error) {
console.error('Failed to get table schema:', error);
}
}
async getTableSchema(tableName) {
const schemaQuery = `DESCRIBE ${tableName}`;
const result = await this.dataManager.query(schemaQuery);
return result.data.map(row => ({
name: row.column_name,
type: row.column_type
}));
}
renderColumnSelection(schema) {
const columnSection = document.getElementById('columnCheckboxes');
columnSection.innerHTML = schema.map(col => `
<label style="display: block; margin: 5px 0;">
<input type="checkbox" value="${col.name}"
onchange="queryBuilder.toggleColumn('${col.name}', this.checked)">
${col.name} (${col.type})
</label>
`).join('');
}
showSections() {
['columnSection', 'whereSection', 'groupBySection', 'orderBySection', 'limitSection']
.forEach(id => document.getElementById(id).style.display = 'block');
}
toggleColumn(columnName, isSelected) {
if (isSelected) {
if (!this.currentQuery.select.includes(columnName)) {
this.currentQuery.select.push(columnName);
}
} else {
this.currentQuery.select = this.currentQuery.select.filter(col => col !== columnName);
}
this.updateGeneratedSQL();
}
addWhereCondition() {
const whereDiv = document.getElementById('whereConditions');
const conditionId = 'condition_' + Date.now();
const conditionHtml = `
<div id="${conditionId}" style="margin: 5px 0; padding: 5px; border: 1px solid #ccc;">
<select onchange="queryBuilder.updateWhereCondition('${conditionId}')">
<option value="">Select column...</option>
${this.currentQuery.select.map(col =>
`<option value="${col}">${col}</option>`
).join('')}
</select>
<select onchange="queryBuilder.updateWhereCondition('${conditionId}')">
<option value="=">=</option>
<option value="!=">!=</option>
<option value=">">></option>
<option value="<"><</option>
<option value=">=">>=</option>
<option value="<="><=</option>
<option value="LIKE">LIKE</option>
</select>
<input type="text" placeholder="Value..."
onchange="queryBuilder.updateWhereCondition('${conditionId}')">
<button onclick="queryBuilder.removeWhereCondition('${conditionId}')">Remove</button>
</div>
`;
whereDiv.insertAdjacentHTML('beforeend', conditionHtml);
}
updateWhereCondition(conditionId) {
// Update where conditions based on form values
this.updateGeneratedSQL();
}
removeWhereCondition(conditionId) {
document.getElementById(conditionId).remove();
this.updateGeneratedSQL();
}
setLimit(value) {
this.currentQuery.limit = value ? parseInt(value) : null;
this.updateGeneratedSQL();
}
updateGeneratedSQL() {
const sql = this.generateSQL();
document.getElementById('generatedSQL').value = sql;
}
generateSQL() {
if (!this.currentQuery.from) return '';
let sql = 'SELECT ';
// SELECT clause
if (this.currentQuery.select.length === 0) {
sql += '*';
} else {
sql += this.currentQuery.select.join(', ');
}
// FROM clause
sql += ` FROM ${this.currentQuery.from}`;
// WHERE clause
if (this.currentQuery.where.length > 0) {
sql += ' WHERE ' + this.currentQuery.where.join(' AND ');
}
// GROUP BY clause
if (this.currentQuery.groupBy.length > 0) {
sql += ' GROUP BY ' + this.currentQuery.groupBy.join(', ');
}
// ORDER BY clause
if (this.currentQuery.orderBy.length > 0) {
sql += ' ORDER BY ' + this.currentQuery.orderBy.join(', ');
}
// LIMIT clause
if (this.currentQuery.limit) {
sql += ` LIMIT ${this.currentQuery.limit}`;
}
return sql;
}
async executeQuery() {
const sql = document.getElementById('generatedSQL').value;
if (!sql) return;
try {
const result = await this.dataManager.query(sql);
this.displayResults(result.data);
} catch (error) {
alert('Query failed: ' + error.message);
}
}
displayResults(data) {
// Implementation depends on your UI framework
console.log('Query results:', data);
}
resetQuery() {
this.currentQuery = {
select: [],
from: '',
where: [],
groupBy: [],
orderBy: [],
limit: null
};
document.getElementById('tableSelect').value = '';
document.getElementById('generatedSQL').value = '';
['columnSection', 'whereSection', 'groupBySection', 'orderBySection', 'limitSection']
.forEach(id => document.getElementById(id).style.display = 'none');
}
}
Real-World Offline Analytics Use Cases
Field Research Data Analysis
Field research often occurs in remote locations with unreliable internet connectivity, making offline analytics essential for real-time data collection and analysis. This implementation demonstrates how to build specialized analytics for scientific research applications.
The system combines GPS tracking, sensor data collection, and real-time species identification with offline analytical capabilities to support fieldwork
// Scientific field research offline analytics
class FieldResearchAnalytics {
constructor() {
this.dataManager = new OfflineDataManager();
this.gpsTracker = new OfflineGPSTracker();
this.sensorData = new Map();
}
async initializeFieldSetup() {
await this.dataManager.initialize();
// Pre-load reference data for offline use
await this.loadReferenceData();
console.log('Field research analytics ready for offline use');
}
async loadReferenceData() {
// Species identification database
const speciesDB = await fetch('/data/species-reference.parquet');
const speciesData = await speciesDB.arrayBuffer();
await this.dataManager.storeDataset('species_reference', new Uint8Array(speciesData));
// Geographic reference data
const geoData = await fetch('/data/geographic-zones.csv');
const geoText = await geoData.text();
await this.dataManager.storeDataset('geographic_zones', geoText);
// Weather patterns
const weatherData = await fetch('/data/historical-weather.json');
const weatherText = await weatherData.text();
await this.dataManager.storeDataset('weather_patterns', weatherText);
}
async recordObservation(observation) {
const enrichedObservation = {
...observation,
timestamp: new Date().toISOString(),
gps_location: await this.gpsTracker.getCurrentLocation(),
device_id: this.getDeviceId(),
session_id: this.getSessionId()
};
// Store in local buffer
await this.storeObservation(enrichedObservation);
// Run real-time analysis
await this.analyzeCurrentSession();
return enrichedObservation;
}
async analyzeCurrentSession() {
const sessionQuery = `
WITH session_data AS (
SELECT
species_name,
COUNT(*) as observation_count,
AVG(confidence_score) as avg_confidence,
ST_Centroid(ST_Collect(gps_location)) as center_point
FROM observations
WHERE session_id = '${this.getSessionId()}'
GROUP BY species_name
),
species_info AS (
SELECT
s.*,
r.conservation_status,
r.typical_habitat,
r.seasonal_patterns
FROM session_data s
LEFT JOIN species_reference r ON s.species_name = r.name
)
SELECT * FROM species_info
ORDER BY observation_count DESC
`;
const results = await this.dataManager.query(sessionQuery);
this.updateFieldSummary(results.data);
return results.data;
}
async generateFieldReport() {
const reportQuery = `
WITH daily_summary AS (
SELECT
DATE(timestamp) as survey_date,
COUNT(DISTINCT species_name) as species_count,
COUNT(*) as total_observations,
AVG(confidence_score) as avg_confidence,
MIN(timestamp) as start_time,
MAX(timestamp) as end_time
FROM observations
GROUP BY DATE(timestamp)
),
species_breakdown AS (
SELECT
species_name,
conservation_status,
COUNT(*) as observation_count,
ROUND(AVG(confidence_score), 2) as avg_confidence,
STRING_AGG(DISTINCT habitat_type, ', ') as habitats_found
FROM observations o
LEFT JOIN species_reference r ON o.species_name = r.name
GROUP BY species_name, conservation_status
ORDER BY observation_count DESC
),
conservation_summary AS (
SELECT
conservation_status,
COUNT(DISTINCT species_name) as species_count,
SUM(observation_count) as total_observations
FROM species_breakdown
WHERE conservation_status IS NOT NULL
GROUP BY conservation_status
)
SELECT
'daily_summary' as report_section,
TO_JSON(array_agg(d.*)) as data
FROM daily_summary d
UNION ALL
SELECT
'species_breakdown' as report_section,
TO_JSON(array_agg(s.*)) as data
FROM species_breakdown s
UNION ALL
SELECT
'conservation_summary' as report_section,
TO_JSON(array_agg(c.*)) as data
FROM conservation_summary c
`;
const reportData = await this.dataManager.query(reportQuery);
return this.formatFieldReport(reportData.data);
}
formatFieldReport(rawData) {
const report = {
generated: new Date().toISOString(),
summary: {},
details: {}
};
rawData.forEach(section => {
const sectionName = section.report_section;
const sectionData = JSON.parse(section.data);
report.details[sectionName] = sectionData;
});
// Generate summary statistics
report.summary = this.calculateSummaryStats(report.details);
return report;
}
}
// GPS tracking for offline field work
class OfflineGPSTracker {
constructor() {
this.currentLocation = null;
this.trackingActive = false;
this.locationHistory = [];
}
async startTracking() {
if (!navigator.geolocation) {
throw new Error('Geolocation not supported');
}
this.trackingActive = true;
// Get initial position
await this.updateLocation();
// Set up continuous tracking
this.watchId = navigator.geolocation.watchPosition(
position => this.handleLocationUpdate(position),
error => this.handleLocationError(error),
{
enableHighAccuracy: true,
maximumAge: 30000,
timeout: 15000
}
);
console.log('GPS tracking started for offline field work');
}
async getCurrentLocation() {
if (this.currentLocation) {
return this.currentLocation;
}
return new Promise((resolve, reject) => {
navigator.geolocation.getCurrentPosition(
position => resolve(this.formatLocation(position)),
error => reject(error)
);
});
}
formatLocation(position) {
return {
latitude: position.coords.latitude,
longitude: position.coords.longitude,
accuracy: position.coords.accuracy,
altitude: position.coords.altitude,
timestamp: new Date(position.timestamp).toISOString()
};
}
}
Offline Business Intelligence
Business intelligence dashboards need to function reliably regardless of internet connectivity, especially for organizations with remote operations. This implementation provides comprehensive BI capabilities that work entirely offline.
The dashboard automatically refreshes data when connectivity is available but continues providing insights using cached data during offline periods
// Offline BI dashboard for remote locations
class OfflineBIDashboard {
constructor() {
this.dataManager = new OfflineDataManager();
this.kpiCalculator = new KPICalculator();
this.chartRenderer = new OfflineChartRenderer();
}
async initializeDashboard() {
await this.dataManager.initialize();
// Load business data for offline analysis
await this.loadBusinessData();
// Set up dashboard refresh intervals
this.setupPeriodicRefresh();
console.log('Offline BI dashboard initialized');
}
async loadBusinessData() {
// Load sales data
await this.loadDataset('sales_data', '/data/sales.parquet');
// Load customer data
await this.loadDataset('customers', '/data/customers.csv');
// Load product catalog
await this.loadDataset('products', '/data/products.json');
// Load financial data
await this.loadDataset('financial_metrics', '/data/financial.csv');
}
async loadDataset(name, url) {
try {
const response = await fetch(url);
if (url.endsWith('.parquet')) {
const buffer = await response.arrayBuffer();
await this.dataManager.storeDataset(name, new Uint8Array(buffer));
} else {
const text = await response.text();
await this.dataManager.storeDataset(name, text);
}
console.log(`Loaded ${name} for offline BI`);
} catch (error) {
console.error(`Failed to load ${name}:`, error);
}
}
async generateExecutiveSummary() {
const summaryQuery = `
WITH revenue_metrics AS (
SELECT
DATE_TRUNC('month', order_date) as month,
SUM(amount) as monthly_revenue,
COUNT(DISTINCT customer_id) as unique_customers,
COUNT(*) as total_orders,
AVG(amount) as avg_order_value
FROM sales_data
WHERE order_date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY month
),
growth_metrics AS (
SELECT
month,
monthly_revenue,
LAG(monthly_revenue) OVER (ORDER BY month) as prev_month_revenue,
unique_customers,
LAG(unique_customers) OVER (ORDER BY month) as prev_month_customers
FROM revenue_metrics
),
customer_segments AS (
SELECT
CASE
WHEN total_spent > 5000 THEN 'VIP'
WHEN total_spent > 1000 THEN 'High Value'
WHEN total_orders > 5 THEN 'Regular'
ELSE 'New'
END as segment,
COUNT(*) as customer_count,
AVG(total_spent) as avg_ltv,
AVG(total_orders) as avg_orders
FROM (
SELECT
customer_id,
SUM(amount) as total_spent,
COUNT(*) as total_orders
FROM sales_data
GROUP BY customer_id
) customer_summary
GROUP BY segment
),
product_performance AS (
SELECT
p.category,
p.name as product_name,
SUM(s.quantity) as units_sold,
SUM(s.amount) as revenue,
COUNT(DISTINCT s.customer_id) as unique_buyers
FROM sales_data s
JOIN products p ON s.product_id = p.id
WHERE s.order_date >= CURRENT_DATE - INTERVAL '3 months'
GROUP BY p.category, p.name
ORDER BY revenue DESC
LIMIT 10
)
SELECT
'revenue_trends' as metric_type,
TO_JSON(array_agg(r.*)) as data
FROM growth_metrics r
WHERE prev_month_revenue IS NOT NULL
UNION ALL
SELECT
'customer_segments' as metric_type,
TO_JSON(array_agg(c.*)) as data
FROM customer_segments c
UNION ALL
SELECT
'top_products' as metric_type,
TO_JSON(array_agg(p.*)) as data
FROM product_performance p
`;
const results = await this.dataManager.query(summaryQuery);
return this.formatExecutiveSummary(results.data);
}
async generateSalesAnalytics() {
const analyticsQuery = `
WITH daily_sales AS (
SELECT
DATE(order_date) as sale_date,
SUM(amount) as daily_revenue,
COUNT(*) as daily_orders,
COUNT(DISTINCT customer_id) as daily_customers
FROM sales_data
WHERE order_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY DATE(order_date)
ORDER BY sale_date
),
sales_by_region AS (
SELECT
c.region,
SUM(s.amount) as region_revenue,
COUNT(*) as region_orders,
COUNT(DISTINCT s.customer_id) as region_customers
FROM sales_data s
JOIN customers c ON s.customer_id = c.id
WHERE s.order_date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY c.region
ORDER BY region_revenue DESC
),
seasonal_patterns AS (
SELECT
EXTRACT('hour', order_timestamp) as hour_of_day,
EXTRACT('dayofweek', order_date) as day_of_week,
COUNT(*) as order_count,
AVG(amount) as avg_order_value
FROM sales_data
WHERE order_date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY hour_of_day, day_of_week
ORDER BY hour_of_day, day_of_week
)
SELECT 'daily_trends' as analysis_type, TO_JSON(array_agg(d.*)) as data FROM daily_sales d
UNION ALL
SELECT 'regional_breakdown' as analysis_type, TO_JSON(array_agg(r.*)) as data FROM sales_by_region r
UNION ALL
SELECT 'seasonal_patterns' as analysis_type, TO_JSON(array_agg(s.*)) as data FROM seasonal_patterns s
`;
const results = await this.dataManager.query(analyticsQuery);
return this.processSalesAnalytics(results.data);
}
setupPeriodicRefresh() {
// Refresh dashboard every 5 minutes when online
setInterval(async () => {
if (navigator.onLine) {
try {
await this.refreshData();
console.log('Dashboard data refreshed');
} catch (error) {
console.log('Failed to refresh data, continuing with offline data');
}
}
}, 5 * 60 * 1000);
}
async refreshData() {
// Attempt to fetch fresh data when online
const datasets = ['sales_data', 'customers', 'products', 'financial_metrics'];
for (const dataset of datasets) {
try {
await this.loadDataset(dataset, `/data/${dataset}`);
} catch (error) {
console.log(`Failed to refresh ${dataset}, using cached version`);
}
}
}
}
Performance Optimization for Offline Analytics
Memory Management Strategies
Memory management becomes critical when processing large datasets in browser environments with limited resources. This implementation provides sophisticated memory monitoring, compression, and optimization techniques.
The system automatically detects memory pressure and applies optimization strategies like data compression and cache cleanup to maintain performance
// Advanced memory management for offline analytics
class OfflineMemoryManager {
constructor(maxMemoryMB = 512) {
this.maxMemory = maxMemoryMB * 1024 * 1024;
this.currentUsage = 0;
this.dataCache = new Map();
this.compressionWorker = new CompressionWorker();
}
async optimizeMemoryUsage() {
// Check current memory usage
const memoryInfo = this.getMemoryInfo();
if (memoryInfo.used > this.maxMemory * 0.8) {
console.log('High memory usage detected, optimizing...');
// Clear unused cached data
await this.clearUnusedCache();
// Compress large datasets
await this.compressLargeDatasets();
// Force garbage collection if available
if (window.gc) {
window.gc();
}
console.log('Memory optimization completed');
}
}
getMemoryInfo() {
if (performance.memory) {
return {
used: performance.memory.usedJSHeapSize,
total: performance.memory.totalJSHeapSize,
limit: performance.memory.jsHeapSizeLimit
};
}
return { used: 0, total: 0, limit: 0 };
}
async clearUnusedCache() {
const now = Date.now();
const maxAge = 30 * 60 * 1000; // 30 minutes
for (const [key, value] of this.dataCache.entries()) {
if (now - value.lastAccessed > maxAge) {
this.dataCache.delete(key);
console.log(`Cleared cached data: ${key}`);
}
}
}
async compressLargeDatasets() {
for (const [key, value] of this.dataCache.entries()) {
if (value.size > 10 * 1024 * 1024 && !value.compressed) {
try {
const compressed = await this.compressionWorker.compress(value.data);
this.dataCache.set(key, {
...value,
data: compressed,
compressed: true,
size: compressed.byteLength
});
console.log(`Compressed dataset: ${key}`);
} catch (error) {
console.error(`Failed to compress ${key}:`, error);
}
}
}
}
// Streaming query processor for large datasets
async processLargeDatasetStream(datasetName, query, chunkSize = 10000) {
const results = [];
let offset = 0;
while (true) {
const chunkQuery = `
${query}
LIMIT ${chunkSize}
OFFSET ${offset}
`;
const chunkResult = await this.dataManager.query(chunkQuery);
const chunkData = chunkResult.data;
if (chunkData.length === 0) break;
// Process chunk
const processedChunk = await this.processChunk(chunkData);
results.push(processedChunk);
offset += chunkSize;
// Yield control to prevent blocking
await new Promise(resolve => setTimeout(resolve, 0));
// Check memory usage
await this.optimizeMemoryUsage();
}
return this.aggregateChunkResults(results);
}
}
// Web Worker for data compression
class CompressionWorker {
constructor() {
this.worker = new Worker(this.getWorkerScript());
}
getWorkerScript() {
const script = `
importScripts('https://cdn.jsdelivr.net/npm/pako@2.0.4/dist/pako.min.js');
self.onmessage = function(e) {
const { id, action, data } = e.data;
try {
switch (action) {
case 'compress':
const compressed = pako.deflate(data);
self.postMessage({ id, success: true, result: compressed });
break;
case 'decompress':
const decompressed = pako.inflate(data, { to: 'string' });
self.postMessage({ id, success: true, result: decompressed });
break;
default:
self.postMessage({ id, success: false, error: 'Unknown action' });
}
} catch (error) {
self.postMessage({ id, success: false, error: error.message });
}
};
`;
return URL.createObjectURL(new Blob([script], { type: 'application/javascript' }));
}
async compress(data) {
return this.sendMessage('compress', data);
}
async decompress(data) {
return this.sendMessage('decompress', data);
}
sendMessage(action, data) {
return new Promise((resolve, reject) => {
const id = Math.random().toString(36).substr(2, 9);
const handler = (e) => {
if (e.data.id === id) {
this.worker.removeEventListener('message', handler);
if (e.data.success) {
resolve(e.data.result);
} else {
reject(new Error(e.data.error));
}
}
};
this.worker.addEventListener('message', handler);
this.worker.postMessage({ id, action, data });
});
}
}
Query Optimization for Offline Performance
Query optimization takes on special importance in offline environments where computational resources are limited to the local device. These examples demonstrate proven techniques for efficient local data processing.
Optimized queries reduce memory usage, improve performance, and prevent browser crashes when working with large datasets
-- Optimized queries for offline analytics
-- 1. Use column selection to reduce memory usage
SELECT customer_id, order_date, amount -- Instead of SELECT *
FROM sales_data
WHERE order_date >= '2025-01-01';
-- 2. Apply filters early to reduce dataset size
SELECT *
FROM (
SELECT * FROM large_dataset
WHERE important_filter = true
) filtered_data
WHERE secondary_condition = 'value';
-- 3. Use appropriate aggregation for memory efficiency
SELECT
DATE_TRUNC('month', order_date) as month,
SUM(amount) as total_revenue,
COUNT(*) as order_count
FROM sales_data
WHERE order_date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY month
ORDER BY month;
-- 4. Utilize window functions efficiently
SELECT
customer_id,
order_date,
amount,
-- Efficient running total
SUM(amount) OVER (
PARTITION BY customer_id
ORDER BY order_date
ROWS UNBOUNDED PRECEDING
) as customer_ltv
FROM sales_data
WHERE customer_id IN (SELECT id FROM top_customers);
-- 5. Use CTEs for complex analysis with memory management
WITH monthly_metrics AS (
SELECT
DATE_TRUNC('month', order_date) as month,
SUM(amount) as revenue,
COUNT(DISTINCT customer_id) as customers
FROM sales_data
WHERE order_date >= CURRENT_DATE - INTERVAL '24 months'
GROUP BY month
),
growth_calculation AS (
SELECT
month,
revenue,
LAG(revenue) OVER (ORDER BY month) as prev_revenue,
customers,
LAG(customers) OVER (ORDER BY month) as prev_customers
FROM monthly_metrics
)
SELECT
month,
revenue,
ROUND((revenue - prev_revenue) * 100.0 / prev_revenue, 2) as revenue_growth_pct,
customers,
ROUND((customers - prev_customers) * 100.0 / prev_customers, 2) as customer_growth_pct
FROM growth_calculation
WHERE prev_revenue IS NOT NULL
ORDER BY month DESC;
-- 6. Optimized cohort analysis for offline processing
WITH first_purchase AS (
SELECT
customer_id,
DATE_TRUNC('month', MIN(order_date)) as cohort_month
FROM sales_data
GROUP BY customer_id
),
monthly_activity AS (
SELECT
f.cohort_month,
DATE_TRUNC('month', s.order_date) as activity_month,
COUNT(DISTINCT s.customer_id) as active_customers
FROM first_purchase f
JOIN sales_data s ON f.customer_id = s.customer_id
GROUP BY f.cohort_month, DATE_TRUNC('month', s.order_date)
),
cohort_sizes AS (
SELECT cohort_month, COUNT(*) as cohort_size
FROM first_purchase
GROUP BY cohort_month
)
SELECT
ma.cohort_month,
ma.activity_month,
ma.active_customers,
cs.cohort_size,
ROUND(ma.active_customers * 100.0 / cs.cohort_size, 2) as retention_rate
FROM monthly_activity ma
JOIN cohort_sizes cs ON ma.cohort_month = cs.cohort_month
WHERE ma.activity_month >= ma.cohort_month
ORDER BY ma.cohort_month, ma.activity_month;
Testing and Validation
Offline Analytics Testing Framework
Thorough testing ensures offline analytics applications work reliably across different environments and scenarios. This testing framework validates core functionality, performance characteristics, and error handling capabilities.
The test suite covers both normal operations and edge cases, ensuring your offline analytics platform performs reliably in production
// Comprehensive testing for offline analytics
class OfflineAnalyticsTestSuite {
constructor() {
this.testResults = [];
this.dataManager = new OfflineDataManager();
}
async runAllTests() {
console.log('Starting offline analytics test suite...');
// Core functionality tests
await this.testDatabaseInitialization();
await this.testDataLoading();
await this.testQueryExecution();
await this.testMemoryManagement();
// Offline-specific tests
await this.testOfflineCapabilities();
await this.testDataPersistence();
await this.testPerformance();
// Error handling tests
await this.testErrorHandling();
await this.testRecovery();
return this.generateTestReport();
}
async testDatabaseInitialization() {
try {
await this.dataManager.initialize();
this.recordTest('Database Initialization', true, 'Successfully initialized DuckDB');
} catch (error) {
this.recordTest('Database Initialization', false, error.message);
}
}
async testDataLoading() {
const testData = this.generateTestCSV(1000);
try {
await this.dataManager.storeDataset('test_data', testData);
// Verify data was loaded
const result = await this.dataManager.query('SELECT COUNT(*) as count FROM test_data');
const count = result.data[0].count;
this.recordTest('Data Loading', count === 1000,
`Expected 1000 rows, got ${count}`);
} catch (error) {
this.recordTest('Data Loading', false, error.message);
}
}
async testQueryExecution() {
const testQueries = [
'SELECT * FROM test_data LIMIT 10',
'SELECT COUNT(*) FROM test_data',
'SELECT AVG(value) FROM test_data',
`SELECT category, COUNT(*) as count
FROM test_data
GROUP BY category
ORDER BY count DESC`
];
let passedQueries = 0;
for (const query of testQueries) {
try {
const result = await this.dataManager.query(query);
if (result.data && result.data.length >= 0) {
passedQueries++;
}
} catch (error) {
console.error(`Query failed: ${query}`, error);
}
}
this.recordTest('Query Execution', passedQueries === testQueries.length,
`${passedQueries}/${testQueries.length} queries executed successfully`);
}
async testMemoryManagement() {
const memoryBefore = this.getMemoryUsage();
// Load large dataset
const largeData = this.generateTestCSV(100000);
await this.dataManager.storeDataset('large_test_data', largeData);
// Execute memory-intensive query
await this.dataManager.query(`
SELECT
category,
AVG(value) as avg_value,
STDDEV(value) as stddev_value,
COUNT(*) as count
FROM large_test_data
GROUP BY category
`);
const memoryAfter = this.getMemoryUsage();
const memoryIncrease = memoryAfter - memoryBefore;
this.recordTest('Memory Management', memoryIncrease < 100,
`Memory increase: ${memoryIncrease}MB`);
}
async testOfflineCapabilities() {
// Simulate offline mode
const originalFetch = window.fetch;
window.fetch = () => Promise.reject(new Error('Network unavailable'));
try {
// Test that queries still work offline
const result = await this.dataManager.query('SELECT COUNT(*) FROM test_data');
this.recordTest('Offline Query Execution', true,
'Queries work without network connection');
} catch (error) {
this.recordTest('Offline Query Execution', false, error.message);
} finally {
// Restore fetch
window.fetch = originalFetch;
}
}
async testDataPersistence() {
const testKey = 'persistence_test';
const testValue = { timestamp: Date.now(), data: 'test data' };
try {
// Store data
const storage = new OfflineStorage();
await storage.init();
await storage.storeDataset(testKey, JSON.stringify(testValue));
// Retrieve data
const retrieved = await storage.getDataset(testKey);
const parsedData = JSON.parse(retrieved);
const matches = parsedData.timestamp === testValue.timestamp;
this.recordTest('Data Persistence', matches,
'Data persisted correctly in IndexedDB');
} catch (error) {
this.recordTest('Data Persistence', false, error.message);
}
}
async testPerformance() {
const performanceTests = [
{
name: 'Small Dataset Query',
query: 'SELECT * FROM test_data LIMIT 100',
maxTime: 100 // ms
},
{
name: 'Aggregation Query',
query: 'SELECT category, COUNT(*), AVG(value) FROM test_data GROUP BY category',
maxTime: 500 // ms
},
{
name: 'Complex Join',
query: `SELECT t1.*, t2.category
FROM test_data t1
LEFT JOIN test_data t2 ON t1.id = t2.id
LIMIT 1000`,
maxTime: 1000 // ms
}
];
for (const test of performanceTests) {
const startTime = performance.now();
try {
await this.dataManager.query(test.query);
const endTime = performance.now();
const executionTime = endTime - startTime;
this.recordTest(test.name, executionTime <= test.maxTime,
`Executed in ${Math.round(executionTime)}ms (limit: ${test.maxTime}ms)`);
} catch (error) {
this.recordTest(test.name, false, error.message);
}
}
}
async testErrorHandling() {
const errorTests = [
{
name: 'Invalid SQL Syntax',
query: 'SELCT * FORM test_data', // Intentional typos
expectError: true
},
{
name: 'Non-existent Table',
query: 'SELECT * FROM non_existent_table',
expectError: true
},
{
name: 'Invalid Column Reference',
query: 'SELECT non_existent_column FROM test_data',
expectError: true
}
];
for (const test of errorTests) {
try {
await this.dataManager.query(test.query);
this.recordTest(test.name, !test.expectError, 'Query unexpectedly succeeded');
} catch (error) {
this.recordTest(test.name, test.expectError, 'Error properly caught and handled');
}
}
}
generateTestCSV(rows) {
const categories = ['A', 'B', 'C', 'D'];
const csvLines = ['id,category,value,timestamp'];
for (let i = 1; i <= rows; i++) {
const category = categories[Math.floor(Math.random() * categories.length)];
const value = Math.round(Math.random() * 1000);
const timestamp = new Date(Date.now() - Math.random() * 365 * 24 * 60 * 60 * 1000).toISOString();
csvLines.push(`${i},${category},${value},${timestamp}`);
}
return csvLines.join('\n');
}
getMemoryUsage() {
if (performance.memory) {
return Math.round(performance.memory.usedJSHeapSize / 1024 / 1024);
}
return 0;
}
recordTest(name, passed, message) {
this.testResults.push({
name,
passed,
message,
timestamp: new Date().toISOString()
});
console.log(`${passed ? '✅' : '❌'} ${name}: ${message}`);
}
generateTestReport() {
const totalTests = this.testResults.length;
const passedTests = this.testResults.filter(t => t.passed).length;
const failedTests = totalTests - passedTests;
const report = {
summary: {
total: totalTests,
passed: passedTests,
failed: failedTests,
successRate: Math.round((passedTests / totalTests) * 100)
},
details: this.testResults,
generated: new Date().toISOString()
};
console.log(`\nTest Summary: ${passedTests}/${totalTests} tests passed (${report.summary.successRate}%)`);
return report;
}
}
Deployment and Distribution
PWA Deployment for Offline Analytics
// manifest.json for PWA deployment
{
"name": "Offline Analytics Platform",
"short_name": "OfflineAnalytics",
"description": "Privacy-first analytics that works completely offline",
"version": "1.0.0",
"start_url": "/",
"display": "standalone",
"background_color": "#ffffff",
"theme_color": "#2196f3",
"orientation": "portrait-primary",
"icons": [
{
"src": "/icons/icon-72x72.png",
"sizes": "72x72",
"type": "image/png"
},
{
"src": "/icons/icon-96x96.png",
"sizes": "96x96",
"type": "image/png"
},
{
"src": "/icons/icon-128x128.png",
"sizes": "128x128",
"type": "image/png"
},
{
"src": "/icons/icon-144x144.png",
"sizes": "144x144",
"type": "image/png"
},
{
"src": "/icons/icon-152x152.png",
"sizes": "152x152",
"type": "image/png"
},
{
"src": "/icons/icon-192x192.png",
"sizes": "192x192",
"type": "image/png"
},
{
"src": "/icons/icon-384x384.png",
"sizes": "384x384",
"type": "image/png"
},
{
"src": "/icons/icon-512x512.png",
"sizes": "512x512",
"type": "image/png"
}
],
"categories": ["business", "productivity", "utilities"],
"screenshots": [
{
"src": "/screenshots/desktop-1.png",
"sizes": "1280x720",
"type": "image/png",
"platform": "wide"
},
{
"src": "/screenshots/mobile-1.png",
"sizes": "360x640",
"type": "image/png",
"platform": "narrow"
}
],
"shortcuts": [
{
"name": "Quick Analysis",
"short_name": "Analyze",
"description": "Start analyzing data immediately",
"url": "/analyze",
"icons": [
{
"src": "/icons/shortcut-analyze.png",
"sizes": "96x96"
}
]
}
],
"file_handlers": [
{
"action": "/analyze",
"accept": {
"text/csv": [".csv"],
"application/json": [".json"],
"application/parquet": [".parquet"]
}
}
]
}
Security and Privacy Considerations
Client-Side Security for Offline Analytics
Security remains paramount even in offline environments, particularly when processing sensitive data. This implementation provides comprehensive security controls including encryption, access validation, and audit logging.
The security framework ensures data remains protected both in memory and in persistent storage, while maintaining detailed audit trails for compliance
// Security measures for offline analytics
class OfflineSecurityManager {
constructor() {
this.encryptionKey = null;
this.auditLogger = new SecurityAuditLogger();
this.accessValidator = new DataAccessValidator();
}
async initializeSecurity(userCredentials) {
// Generate or derive encryption key
this.encryptionKey = await this.deriveEncryptionKey(userCredentials);
// Set up Content Security Policy
this.enforceCSP();
// Initialize audit logging
await this.auditLogger.initialize();
console.log('Offline security initialized');
}
async deriveEncryptionKey(credentials) {
const encoder = new TextEncoder();
const keyMaterial = await crypto.subtle.importKey(
'raw',
encoder.encode(credentials.password),
'PBKDF2',
false,
['deriveBits', 'deriveKey']
);
return await crypto.subtle.deriveKey(
{
name: 'PBKDF2',
salt: encoder.encode(credentials.username),
iterations: 100000,
hash: 'SHA-256'
},
keyMaterial,
{ name: 'AES-GCM', length: 256 },
false,
['encrypt', 'decrypt']
);
}
enforceCSP() {
const csp = [
"default-src 'self'",
"script-src 'self' 'wasm-unsafe-eval'",
"connect-src 'self'",
"img-src 'self' data: blob:",
"style-src 'self' 'unsafe-inline'",
"font-src 'self'",
"object-src 'none'",
"base-uri 'none'"
].join('; ');
const meta = document.createElement('meta');
meta.setAttribute('http-equiv', 'Content-Security-Policy');
meta.setAttribute('content', csp);
document.head.appendChild(meta);
}
async encryptSensitiveData(data) {
const encoder = new TextEncoder();
const plaintext = encoder.encode(JSON.stringify(data));
const iv = crypto.getRandomValues(new Uint8Array(12));
const encryptedData = await crypto.subtle.encrypt(
{ name: 'AES-GCM', iv: iv },
this.encryptionKey,
plaintext
);
return {
encryptedData: new Uint8Array(encryptedData),
iv: iv
};
}
async decryptSensitiveData(encryptedPackage) {
const decryptedData = await crypto.subtle.decrypt(
{ name: 'AES-GCM', iv: encryptedPackage.iv },
this.encryptionKey,
encryptedPackage.encryptedData
);
const decoder = new TextDecoder();
return JSON.parse(decoder.decode(decryptedData));
}
validateDataAccess(datasetName, operation, userContext) {
return this.accessValidator.validate(datasetName, operation, userContext);
}
}
class SecurityAuditLogger {
constructor() {
this.auditTrail = [];
this.maxEntries = 10000;
}
async initialize() {
// Load existing audit trail from secure storage
await this.loadAuditTrail();
}
logSecurityEvent(event) {
const auditEntry = {
timestamp: new Date().toISOString(),
eventType: event.type,
details: event.details,
userContext: event.userContext,
severity: event.severity || 'info',
sessionId: this.getSessionId()
};
this.auditTrail.push(auditEntry);
// Maintain audit trail size
if (this.auditTrail.length > this.maxEntries) {
this.auditTrail = this.auditTrail.slice(-this.maxEntries);
}
// Persist audit trail
this.persistAuditTrail();
// Alert on high-severity events
if (event.severity === 'high' || event.severity === 'critical') {
this.alertSecurityEvent(auditEntry);
}
}
alertSecurityEvent(event) {
console.warn('Security Alert:', event);
// Could integrate with security monitoring systems
// when online connectivity is available
}
async loadAuditTrail() {
try {
const stored = localStorage.getItem('security_audit_trail');
if (stored) {
this.auditTrail = JSON.parse(stored);
}
} catch (error) {
console.error('Failed to load audit trail:', error);
}
}
async persistAuditTrail() {
try {
localStorage.setItem('security_audit_trail', JSON.stringify(this.auditTrail));
} catch (error) {
console.error('Failed to persist audit trail:', error);
}
}
getSessionId() {
if (!this.sessionId) {
this.sessionId = crypto.randomUUID();
}
return this.sessionId;
}
}
Conclusion
Offline SQL analytics represents a paradigm shift toward data sovereignty and privacy-first computing. By leveraging modern browser capabilities like WebAssembly, Service Workers, and local storage APIs, we can build powerful analytics applications that:
Ensure Complete Privacy
- Data never leaves the user's device
- No network transmission of sensitive information
- Local processing eliminates third-party risks
- Compliance with privacy regulations simplified
Deliver Superior Performance
- Eliminate network latency for instant results
- Scale with local hardware capabilities
- Work reliably in any connectivity environment
- Reduce operational costs and complexity
Provide Universal Access
- Run on any modern browser without installation
- Work across all platforms and devices
- Enable analytics in remote or restricted environments
- Democratize access to powerful data tools
The technologies and patterns presented in this guide enable you to build production-ready offline analytics solutions that rival cloud-based alternatives while providing superior privacy and reliability guarantees.
Whether you're building field research tools, business intelligence dashboards, or embedded analytics applications, offline SQL analytics provides the foundation for truly sovereign data processing.
Ready to implement offline analytics? Start with the code examples provided and gradually build toward your specific use case. The future of analytics is local, private, and powerful.
Explore more offline analytics capabilities with LakeClient's browser-based platform, or dive deeper into our guides on DuckDB-WASM tutorial and privacy-first analytics.
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