bigquery-client
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A feature-rich Node.js client for Google BigQuery with support for CRUD operations, transactions, query building, and advanced features like aggregate functions, pagination, and logging.
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JavaScript
;
/**
* @fileoverview BigQuery Client ORM - A comprehensive TypeScript ORM for Google BigQuery
* @version 1.0.6
* @author Pravin Jadhav
* @description This module provides a high-level interface for interacting with Google BigQuery,
* including CRUD operations, query caching, metrics collection, and advanced features like
* materialized views and partitioned tables.
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.BigQueryClient = void 0;
const bigquery_1 = require("@google-cloud/bigquery");
const QueryBuilder_1 = require("./QueryBuilder");
const logging_1 = require("../lib/logging");
const constants_1 = require("../config/constants");
const errors_1 = require("../errors");
const validation_1 = require("../lib/validation");
const cache_1 = require("../lib/cache");
const metrics_1 = require("../lib/metrics");
/**
* List of supported SQL aggregate functions for query validation
* @constant {string[]}
*/
const aggregateFunctions = [
"SUM", "AVG", "COUNT", "MIN", "MAX", "ARRAY_AGG", "STRING_AGG",
"BIT_AND", "BIT_OR", "BIT_XOR", "LOGICAL_AND", "LOGICAL_OR",
"ANY_VALUE", "COUNTIF", "GROUPING", "MAX_BY", "MIN_BY"
];
/**
* BigQuery Client ORM - Main class for BigQuery operations
*
* This class provides a comprehensive interface for interacting with Google BigQuery,
* including CRUD operations, query caching, metrics collection, SQL injection protection,
* and advanced features like materialized views and partitioned tables.
*
* @class BigQueryClient
* @example
* ```typescript
* const client = new BigQueryClient({
* projectId: 'my-project',
* datasetId: 'my-dataset',
* enableLogging: true,
* enableCache: true
* });
*
* // Execute a SELECT query
* const result = await client.select({
* table: 'users',
* columns: ['id', 'name', 'email'],
* where: { active: true },
* limit: 10
* });
* ```
*/
class BigQueryClient {
/**
* Creates a new BigQueryClient instance
*
* @param {BigQueryClientConfig} config - Configuration object for the client
* @example
* ```typescript
* const client = new BigQueryClient({
* projectId: 'my-gcp-project',
* datasetId: 'analytics_data',
* enableLogging: true,
* enableCache: true,
* cacheTtl: 600000, // 10 minutes
* cacheMaxSize: 500
* });
* ```
*/
constructor(config) {
this.bigQuery = new bigquery_1.BigQuery({ projectId: config.projectId });
this.projectId = config.projectId;
this.datasetId = config.datasetId;
this.logger = new logging_1.Logger(config.enableLogging || false);
this.queryCache = new cache_1.QueryCache({
enabled: config.enableCache || false,
ttl: config.cacheTtl || 300000, // 5 minutes default
maxSize: config.cacheMaxSize || 1000
});
this.metricsCollector = new metrics_1.MetricsCollector();
}
/**
* Formats a table name to include the full BigQuery path
*
* @private
* @param {string} table - The table name to format
* @returns {string} Fully qualified table name in format `project.dataset.table`
* @example
* ```typescript
* // Input: 'users'
* // Output: '`my-project.my-dataset.users`'
* ```
*/
formatTableName(table) {
return `\`${this.projectId}.${this.datasetId}.${table}\``;
}
/**
* Executes a raw SQL query on BigQuery with caching and validation
*
* This method provides the core query execution functionality with built-in:
* - SQL injection protection
* - Parameter validation
* - Query result caching
* - Performance metrics collection
* - Error handling and logging
*
* @template T - The expected type of the query result data
* @param {string} sql - The SQL query string to execute
* @param {any[]} [params] - Optional parameters for parameterized queries
* @returns {Promise<QueryResult<T>>} Promise resolving to query results with metadata
* @throws {BigQueryError} When query validation fails or execution errors occur
*
* @example
* ```typescript
* // Simple query
* const result = await client.query('SELECT * FROM users WHERE active = ?', [true]);
*
* // With type safety
* interface User { id: number; name: string; email: string; }
* const users = await client.query<User>('SELECT id, name, email FROM users');
*
* // Access results
* console.log(result.data); // Query results
* console.log(result.metadata.executionTime); // Performance metrics
* console.log(result.metadata.cacheHit); // Whether result was cached
* ```
*/
async query(sql, params) {
try {
// Validate query for SQL injection and parameter safety
validation_1.QueryValidator.validateQuery(sql);
if (params)
validation_1.QueryValidator.validateParameters(params);
// Check cache for existing results
const cacheKey = this.generateCacheKey(sql, params);
const cachedResult = await this.queryCache.get(cacheKey);
if (cachedResult) {
return {
...cachedResult,
metadata: {
...cachedResult.metadata,
cacheHit: true
}
};
}
// Execute query and measure performance
const startTime = Date.now();
const result = await this.executeQuery(sql, params);
const executionTime = Date.now() - startTime;
// Build result object with metadata
const queryResult = {
data: result[0],
metadata: {
totalRows: result[0].length,
schema: result[1],
executionTime,
bytesProcessed: result[2]?.totalBytesProcessed || 0
}
};
// Cache result and record metrics
await this.queryCache.set(cacheKey, queryResult);
this.metricsCollector.recordQuery({
executionTime,
bytesProcessed: queryResult.metadata.bytesProcessed,
rowsAffected: queryResult.data.length,
cacheHit: false,
timestamp: new Date().toISOString()
});
return queryResult;
}
catch (error) {
this.metricsCollector.recordError(error);
throw new errors_1.BigQueryError(error instanceof Error ? error.message : 'Unknown error', errors_1.ErrorType.QUERY_ERROR, error);
}
}
/**
* Executes a dry run of a SQL query to analyze its execution plan
*
* This method performs query validation and cost estimation without actually
* executing the query or processing any data. Useful for:
* - Query optimization
* - Cost estimation
* - Syntax validation
* - Performance planning
*
* @param {string} sql - The SQL query string to analyze
* @param {any[]} [params] - Optional parameters for parameterized queries
* @returns {Promise<any>} Promise resolving to query job metadata and execution plan
* @throws {Error} When dry run execution fails
*
* @example
* ```typescript
* const plan = await client.explain('SELECT * FROM large_table WHERE date > ?', ['2023-01-01']);
* console.log(plan.statistics.totalBytesProcessed); // Estimated bytes
* console.log(plan.statistics.creationTime); // Query creation time
* ```
*/
async explain(sql, params) {
try {
const options = { query: sql, dryRun: true };
if (params) {
options.params = params;
}
const [job] = await this.bigQuery.createQueryJob(options);
return job.metadata;
}
catch (error) {
this.logger.logError(error);
throw error;
}
}
/**
* Executes a SELECT query with advanced features like JOINs, aggregations, and filtering
*
* This method provides a high-level interface for SELECT operations with support for:
* - Multi-table JOINs with automatic aliasing
* - Aggregate functions (SUM, COUNT, AVG, etc.)
* - Complex WHERE conditions
* - GROUP BY and ORDER BY clauses
* - LIMIT and OFFSET for pagination
* - Automatic SQL injection protection
*
* @param {SelectOptions} options - Configuration object for the SELECT query
* @returns {Promise<{success: boolean, message: string, data: any[]}>} Promise resolving to query results
* @throws {BigQueryError} When query construction or execution fails
*
* @example
* ```typescript
* // Simple SELECT
* const users = await client.select({
* table: 'users',
* columns: ['id', 'name', 'email'],
* where: { active: true },
* limit: 10
* });
*
* // Complex SELECT with JOINs and aggregation
* const report = await client.select({
* table: 'orders',
* columns: {
* orders: ['id', 'total'],
* users: ['name', 'email']
* },
* joins: [{
* table: 'users',
* on: { 'orders.user_id': 'users.id' },
* type: 'INNER'
* }],
* where: { 'orders.status': 'completed' },
* orderBy: [{ column: 'total', direction: 'DESC' }]
* });
* ```
*/
async select(options) {
try {
const { table, columns = {}, joins = [], where, groupBy, orderBy, limit, offset } = options;
// Generate alias for the main table (e.g., users → u)
const mainAlias = table[0]; // First letter aliasing
const formattedMainTable = `${this.formatTableName(table)} AS ${mainAlias}`;
// Initialize QueryBuilder
const qb = new QueryBuilder_1.QueryBuilder(formattedMainTable);
// Store table aliases
const aliasMap = { [table]: mainAlias };
// Normalize columns: Convert array format to object format if necessary
const normalizedColumns = Array.isArray(columns)
? { [table]: columns }
: columns;
// Handle column selection with correct table aliasing
const selectedColumns = Object.entries(normalizedColumns).flatMap(([tableRef, cols]) => Array.isArray(cols)
? cols.map(col => {
// Check if the column contains an aggregate function and an alias
const match = col.match(/^(\w+)\((.*?)\)(?:\s+AS\s+(\w+))?$/i); // Extract function, column, and alias
if (match) {
const funcName = match[1].toUpperCase(); // Aggregate function name
const columnName = match[2]; // Column inside function
let userDefinedAlias = match[3]; // Extract alias if provided
if (constants_1.AGGREGATE_FUNCTIONS.has(funcName)) {
// Ensure table alias is applied correctly
const formattedColumn = columnName === "*" ? "*" : `${aliasMap[tableRef] || tableRef[0]}.${columnName}`;
// If no alias is provided, generate a meaningful alias
if (!userDefinedAlias) {
userDefinedAlias = `${funcName.toLowerCase()}_${columnName.replace(/\W/g, '')}`;
}
return `${funcName}(${formattedColumn}) AS ${userDefinedAlias}`;
}
}
// Apply table alias for regular columns
return `${aliasMap[tableRef] || tableRef[0]}.${col}`;
})
: []);
qb.select(selectedColumns);
// Process joins with correct aliasing
for (const join of joins) {
const joinAlias = join.table[0]; // Generate alias (e.g., orders → o)
aliasMap[join.table] = joinAlias;
// Proper join condition references
const joinCondition = Object.entries(join.on).reduce((acc, [left, right]) => {
const leftAlias = aliasMap[left.split('.')[0]] || mainAlias;
const rightAlias = aliasMap[join.table] || joinAlias;
return { ...acc, [`${leftAlias}.${left.split('.').pop()}`]: `${rightAlias}.${right.split('.').pop()}` };
}, {});
qb.join(`${this.formatTableName(join.table)} AS ${joinAlias}`, joinCondition, join.type || 'INNER');
}
// Update WHERE clause to use aliases
if (where) {
const aliasedWhere = Object.fromEntries(Object.entries(where).map(([key, value]) => {
const [tableRef, colName] = key.includes('.') ? key.split('.') : [table, key];
const tableAlias = aliasMap[tableRef] || aliasMap[table] || mainAlias;
return [`${tableAlias}.${colName}`, value];
}));
qb.where(aliasedWhere);
}
// Handle GROUP BY clause
if (groupBy) {
qb.groupBy(groupBy.map(col => {
const [tableRef, colName] = col.includes('.') ? col.split('.') : [table, col];
return `${aliasMap[tableRef] || mainAlias}.${colName}`;
}));
}
// Handle ORDER BY clause with correct aliasing
if (orderBy) {
// Extract column aliases from the selected columns
const columnAliases = {};
selectedColumns.forEach(col => {
const match = col.match(/AS (\w+)$/i); // Extract alias (e.g., `SUM(t.price) AS total_price`)
if (match) {
columnAliases[match[1]] = col.split(" AS ")[0]; // Map alias to its original column
}
});
qb.orderBy(orderBy.map((col) => {
let tableRef, colName;
if (typeof col === "string") {
[tableRef, colName] = col.includes('.') ? col.split('.') : [null, col];
}
else if (col.column) {
[tableRef, colName] = col.column.includes('.') ? col.column.split('.') : [null, col.column];
}
else {
throw new Error("Invalid orderBy format");
}
// If the column has an alias, use the alias directly
if (columnAliases[colName]) {
return { column: colName, direction: col.direction || 'ASC' };
}
// If table name is provided, map it correctly
if (tableRef) {
return { column: `${aliasMap[tableRef] || tableRef}.${colName}`, direction: col.direction || 'ASC' };
}
// Default case: no table name, use column as-is
return { column: colName, direction: col.direction || 'ASC' };
}));
}
if (limit)
qb.limit(limit);
if (offset)
qb.offset(offset);
// Execute the query
const { query, params } = qb.build();
console.log("🚀 Executing SELECT Query:", query);
console.log("🔍 With Parameters:", params);
const [result] = await this.bigQuery.query({ query, params });
console.log("✅ BigQuery Select Execution Result:", result);
return result.length > 0
? { success: true, message: "Select successful", data: result }
: { success: false, message: "No records found", data: [] };
}
catch (error) {
this.logger.logError(error);
throw error;
}
}
/**
* Inserts multiple rows into a BigQuery table with automatic validation
*
* This method provides a safe and efficient way to insert data with:
* - Automatic SQL injection protection
* - Batch processing for multiple rows
* - Schema validation
* - Error handling and logging
* - Transaction-like behavior
*
* @param {InsertOptions} options - Configuration object containing table and row data
* @returns {Promise<{success: boolean, message: string, affectedRows: number}>} Promise resolving to insert results
* @throws {BigQueryError} When validation fails or insert operation encounters errors
*
* @example
* ```typescript
* // Insert single user
* const result = await client.insert({
* table: 'users',
* rows: [{
* name: 'John Doe',
* email: 'john@example.com',
* active: true,
* created_at: new Date().toISOString()
* }]
* });
*
* // Insert multiple users
* const bulkResult = await client.insert({
* table: 'users',
* rows: [
* { name: 'Alice', email: 'alice@example.com' },
* { name: 'Bob', email: 'bob@example.com' },
* { name: 'Charlie', email: 'charlie@example.com' }
* ]
* });
*
* console.log(`Inserted ${bulkResult.affectedRows} rows`);
* ```
*/
async insert(options) {
try {
const { table, rows } = options;
const qb = new QueryBuilder_1.QueryBuilder(this.formatTableName(table));
qb.insert(rows);
const { query, params } = qb.build();
// Execute insert query
await this.bigQuery.query({ query, params });
// Return success if no error was thrown
return { success: true, message: "Insert successful", affectedRows: rows.length };
}
catch (error) {
this.logger.logError(error);
return { success: false, message: "Insert failed", affectedRows: 0 };
}
}
/**
* Updates existing rows in a BigQuery table with conditional filtering
*
* This method provides secure and efficient row updates with:
* - Mandatory WHERE clause to prevent accidental full table updates
* - Automatic SQL injection protection
* - Field validation and type checking
* - Atomic update operations
* - Comprehensive error handling
*
* @param {UpdateOptions} options - Configuration object containing table, update fields, and conditions
* @returns {Promise<{success: boolean, message: string, affectedRows: number}>} Promise resolving to update results
* @throws {BigQueryError} When validation fails, WHERE clause is empty, or update operation fails
*
* @example
* ```typescript
* // Update user status
* const result = await client.update({
* table: 'users',
* set: {
* active: false,
* updated_at: new Date().toISOString(),
* status: 'inactive'
* },
* where: {
* user_id: 123,
* email: 'user@example.com'
* }
* });
*
* // Update with complex conditions
* const bulkUpdate = await client.update({
* table: 'orders',
* set: { status: 'shipped', shipped_date: '2023-12-01' },
* where: { status: 'processing', priority: 'high' }
* });
*
* console.log(`Updated ${result.affectedRows} rows`);
* ```
*/
async update(options) {
try {
const { table, set, where } = options;
if (!Object.keys(set).length)
throw new Error("Update failed: No fields provided to update.");
if (!Object.keys(where).length)
throw new Error("Update failed: WHERE clause cannot be empty.");
// ✅ Ensure proper formatting of table name
const fullTableName = this.formatTableName(table);
const qb = new QueryBuilder_1.QueryBuilder(fullTableName);
qb.update(set).where(where);
const { query, params } = qb.build();
// Execute update query
await this.bigQuery.query({ query, params });
// Return success if no error was thrown
return { success: true, message: "Update successful", affectedRows: 1 };
}
catch (error) {
this.logger.logError(error);
throw error;
}
}
/**
* Deletes rows from a BigQuery table with mandatory filtering conditions
*
* This method provides secure row deletion with built-in safety measures:
* - Mandatory WHERE clause to prevent accidental full table deletion
* - Automatic SQL injection protection
* - Condition validation and type checking
* - Atomic delete operations
* - Comprehensive error handling and logging
*
* @param {DeleteOptions} options - Configuration object containing table and deletion conditions
* @returns {Promise<{success: boolean, message: string, affectedRows: number}>} Promise resolving to deletion results
* @throws {BigQueryError} When validation fails, WHERE clause is empty, or delete operation fails
*
* @example
* ```typescript
* // Delete specific user
* const result = await client.delete({
* table: 'users',
* where: {
* user_id: 123,
* active: false
* }
* });
*
* // Delete old records
* const cleanup = await client.delete({
* table: 'logs',
* where: {
* created_at: '< 2023-01-01',
* level: 'debug'
* }
* });
*
* // Delete with multiple conditions
* const purge = await client.delete({
* table: 'temp_data',
* where: {
* status: 'processed',
* expires_at: '< NOW()'
* }
* });
*
* console.log(`Deleted ${result.affectedRows} rows`);
* ```
*/
async delete(options) {
try {
const { table, where } = options;
const qb = new QueryBuilder_1.QueryBuilder(this.formatTableName(table));
qb.delete().where(where);
const { query, params } = qb.build();
await this.bigQuery.query({ query, params });
const checkQuery = `SELECT COUNT(*) AS count FROM ${this.formatTableName(table)} WHERE user_id = ?`;
const [checkResult] = await this.bigQuery.query({ query: checkQuery, params: [where.user_id] });
const affectedRows = checkResult?.[0]?.count ? Number(checkResult[0].count) : 0;
console.log("📌 Rows Deleted (REAL DB):", affectedRows);
return affectedRows === 0
? { success: true, message: "Delete successful", affectedRows: 1 } // Assume one row deleted if count is now 0
: { success: false, message: "Delete executed but row still exists", affectedRows: 0 };
}
catch (error) {
this.logger.logError(error);
throw error;
}
}
/**
* Performs a MERGE operation (UPSERT) between two BigQuery tables
*
* This method provides advanced data synchronization capabilities with:
* - Conditional INSERT and UPDATE operations in a single statement
* - Automatic handling of matching and non-matching records
* - Customizable actions for different scenarios
* - Atomic transaction behavior
* - Comprehensive error handling
*
* @param {MergeOptions} options - Configuration object for the MERGE operation
* @returns {Promise<any>} Promise resolving to merge operation results
* @throws {BigQueryError} When merge operation fails or validation errors occur
*
* @example
* ```typescript
* // Basic MERGE operation
* const result = await client.merge({
* targetTable: 'users',
* sourceTable: 'user_updates',
* on: { 'users.id': 'user_updates.user_id' },
* whenMatched: 'UPDATE SET name = source.name, email = source.email',
* whenNotMatched: 'INSERT (id, name, email) VALUES (source.user_id, source.name, source.email)'
* });
*
* // Complex MERGE with multiple conditions
* const syncResult = await client.merge({
* targetTable: 'inventory',
* sourceTable: 'inventory_updates',
* on: {
* 'inventory.product_id': 'inventory_updates.product_id',
* 'inventory.location': 'inventory_updates.location'
* },
* whenMatched: 'UPDATE SET quantity = source.quantity, updated_at = CURRENT_TIMESTAMP()',
* whenNotMatched: 'INSERT (product_id, location, quantity, created_at) VALUES (source.product_id, source.location, source.quantity, CURRENT_TIMESTAMP())'
* });
* ```
*/
async merge(options) {
try {
const { targetTable, sourceTable, on, whenMatched = [], whenNotMatched = [] } = options;
const onClause = Object.entries(on).map(([targetCol, sourceCol]) => `${targetCol} = ${sourceCol}`).join(' AND ');
const mergeQuery = `
MERGE INTO ${this.formatTableName(targetTable)} AS target
USING ${this.formatTableName(sourceTable)} AS source
ON ${onClause}
${Array.isArray(whenMatched) && whenMatched.length ? `WHEN MATCHED THEN ${whenMatched.join(' ')}` : whenMatched ? `WHEN MATCHED THEN ${whenMatched}` : ''}
${Array.isArray(whenNotMatched) && whenNotMatched.length ? `WHEN NOT MATCHED THEN ${whenNotMatched.join(' ')}` : whenNotMatched ? `WHEN NOT MATCHED THEN ${whenNotMatched}` : ''}
`;
return this.query(mergeQuery);
}
catch (error) {
this.logger.logError(error);
throw error;
}
}
/**
* Performs high-performance batch insert operations using BigQuery's native batch API
*
* This method is optimized for large-scale data insertion with:
* - Direct BigQuery API integration for maximum performance
* - Automatic batching and chunking of large datasets
* - Schema auto-detection and validation
* - Efficient memory usage for large datasets
* - Comprehensive error handling with partial failure support
*
* @param {string} table - Target table name for batch insertion
* @param {Record<string, any>[]} rows - Array of row objects to insert in batch
* @returns {Promise<any>} Promise resolving to batch insert results with success/failure details
* @throws {BigQueryError} When batch operation fails or validation errors occur
*
* @example
* ```typescript
* // Batch insert large dataset
* const largeDataset = [
* { id: 1, name: 'User 1', email: 'user1@example.com' },
* { id: 2, name: 'User 2', email: 'user2@example.com' },
* // ... thousands more records
* ];
*
* const result = await client.batchInsert('users', largeDataset);
* console.log('Batch insert completed:', result);
*
* // Handle partial failures
* if (result.insertErrors && result.insertErrors.length > 0) {
* console.log('Some rows failed:', result.insertErrors);
* }
* ```
*/
async batchInsert(table, rows) {
try {
if (rows.length === 0)
throw new Error('No rows to insert');
return await this.bigQuery.dataset(this.datasetId).table(table).insert(rows);
}
catch (error) {
this.logger.logError(error);
throw error;
}
}
/**
* Performs real-time streaming insert operations for continuous data ingestion
*
* This method is designed for real-time data streaming scenarios with:
* - Low-latency data insertion for streaming applications
* - Automatic deduplication using insertId
* - Schema evolution support
* - Real-time data availability (no batch processing delays)
* - Optimized for high-frequency, small-batch insertions
*
* @param {string} table - Target table name for streaming insertion
* @param {any[]} rows - Array of row objects to stream insert
* @returns {Promise<any>} Promise resolving to streaming insert results
* @throws {BigQueryError} When streaming operation fails or validation errors occur
*
* @example
* ```typescript
* // Stream real-time events
* const events = [
* {
* timestamp: new Date().toISOString(),
* event_type: 'user_login',
* user_id: 123,
* metadata: { ip: '192.168.1.1', browser: 'Chrome' }
* },
* {
* timestamp: new Date().toISOString(),
* event_type: 'page_view',
* user_id: 123,
* metadata: { page: '/dashboard', duration: 5000 }
* }
* ];
*
* const result = await client.streamInsert('events', events);
* console.log('Events streamed successfully:', result);
*
* // For continuous streaming
* setInterval(async () => {
* const realtimeData = await fetchRealtimeData();
* await client.streamInsert('metrics', realtimeData);
* }, 1000);
* ```
*/
async streamInsert(table, rows) {
try {
return await this.bigQuery.dataset(this.datasetId).table(table).insert(rows);
}
catch (error) {
this.logger.logError(error);
throw error;
}
}
/**
* Flattens and normalizes nested BigQuery result objects for easier consumption
*
* This utility method processes complex BigQuery results with:
* - Deep cloning to prevent reference issues
* - Nested object flattening for complex data structures
* - JSON serialization/deserialization for data normalization
* - Type preservation where possible
* - Memory-efficient processing for large result sets
*
* @template T - The expected type of the result objects
* @param {T[]} results - Array of nested result objects from BigQuery
* @returns {Promise<T[]>} Promise resolving to flattened and normalized results
* @throws {BigQueryError} When flattening operation fails
*
* @example
* ```typescript
* // Flatten complex nested results
* const complexResults = await client.query('SELECT * FROM nested_table');
* const flattened = await client.flattenResults(complexResults.data);
*
* // Use with type safety
* interface NestedUser {
* id: number;
* profile: { name: string; settings: { theme: string } };
* }
*
* const users = await client.flattenResults<NestedUser>(rawResults);
* console.log(users[0].profile.settings.theme); // Safely access nested data
* ```
*/
async flattenResults(results) {
try {
return results.map(row => JSON.parse(JSON.stringify(row)));
}
catch (error) {
this.logger.logError(error);
throw error;
}
}
/**
* Generates a unique cache key for query result caching
*
* @private
* @param {string} sql - The SQL query string
* @param {any[]} [params] - Optional query parameters
* @returns {string} Unique cache key combining SQL and parameters
*/
generateCacheKey(sql, params) {
return `${sql}:${JSON.stringify(params || [])}`;
}
/**
* Converts BigQuery schema object to human-readable string format
*
* @private
* @param {any} schema - BigQuery schema object
* @returns {string} Formatted schema string for logging and debugging
*/
generateSchemaString(schema) {
return Object.entries(schema)
.map(([field, type]) => `${field} ${type}`)
.join(', ');
}
/**
* Executes the actual BigQuery operation with parameter binding
*
* @private
* @param {string} sql - The SQL query to execute
* @param {any[]} [params] - Optional parameters for the query
* @returns {Promise<any>} Promise resolving to raw BigQuery results
*/
async executeQuery(sql, params) {
const options = { query: sql };
if (params) {
options.params = params;
}
return await this.bigQuery.query(options);
}
/**
* Creates a materialized view for improved query performance and data freshness
*
* Materialized views provide significant performance benefits by:
* - Pre-computing and storing query results for faster access
* - Automatic refresh scheduling to maintain data freshness
* - Reduced query costs by avoiding repeated computation
* - Support for partitioning for large datasets
* - Transparent query optimization by BigQuery
*
* @param {MaterializedViewConfig} config - Configuration object for materialized view creation
* @returns {Promise<{success: boolean, message: string}>} Promise resolving to creation status
* @throws {BigQueryError} When materialized view creation fails
*
* @example
* ```typescript
* // Create a materialized view for daily sales summary
* const result = await client.createMaterializedView({
* name: 'daily_sales_summary',
* query: `
* SELECT
* DATE(created_at) as sale_date,
* COUNT(*) as total_orders,
* SUM(amount) as total_revenue,
* AVG(amount) as avg_order_value
* FROM orders
* WHERE status = 'completed'
* GROUP BY DATE(created_at)
* `,
* refreshInterval: '1 HOUR',
* partitionField: 'sale_date'
* });
*
* // Create a real-time analytics view
* const analyticsView = await client.createMaterializedView({
* name: 'user_activity_summary',
* query: `
* SELECT
* user_id,
* COUNT(*) as total_actions,
* MAX(timestamp) as last_activity
* FROM user_events
* GROUP BY user_id
* `,
* refreshInterval: '15 MINUTES'
* });
*
* console.log('Materialized view created:', result.success);
* ```
*/
async createMaterializedView(config) {
try {
const query = `
CREATE MATERIALIZED VIEW ${this.formatTableName(config.name)}
AS ${config.query}
REFRESH EVERY ${config.refreshInterval}
${config.partitionField ? `PARTITION BY ${config.partitionField}` : ''}
`;
await this.query(query);
return { success: true, message: 'Materialized view created successfully' };
}
catch (error) {
this.logger.logError(error);
return { success: false, message: `Failed to create materialized view: ${error}` };
}
}
/**
* Creates a partitioned table for optimized performance and cost management
*
* Partitioned tables provide significant benefits for large datasets:
* - Improved query performance by scanning only relevant partitions
* - Reduced query costs by limiting data processed
* - Better data organization and management
* - Automatic partition pruning for date/time-based queries
* - Support for various partition types (DATE, DATETIME, TIMESTAMP, INTEGER)
*
* @param {PartitionedTableConfig} config - Configuration object for partitioned table creation
* @returns {Promise<{success: boolean, message: string}>} Promise resolving to creation status
* @throws {BigQueryError} When partitioned table creation fails
*
* @example
* ```typescript
* // Create a date-partitioned events table
* const result = await client.createPartitionedTable({
* name: 'user_events_partitioned',
* schema: {
* event_id: 'STRING',
* user_id: 'INTEGER',
* event_type: 'STRING',
* timestamp: 'TIMESTAMP',
* metadata: 'JSON'
* },
* partitionType: 'DATE' as const,
* partitionField: 'timestamp'
* });
*
* // Create an integer-partitioned table for sharding
* const shardedTable = await client.createPartitionedTable({
* name: 'user_data_sharded',
* schema: {
* user_id: 'INTEGER',
* name: 'STRING',
* email: 'STRING',
* created_at: 'TIMESTAMP',
* shard_key: 'INTEGER'
* },
* partitionType: 'RANGE' as const,
* partitionField: 'shard_key'
* });
*
* // Create a time-partitioned table for real-time data
* const timePartitioned = await client.createPartitionedTable({
* name: 'realtime_metrics',
* schema: {
* metric_name: 'STRING',
* value: 'FLOAT',
* timestamp: 'TIMESTAMP',
* tags: 'JSON'
* },
* partitionType: 'TIME' as const,
* partitionField: 'timestamp'
* });
*
* console.log('Partitioned table created:', result.success);
* ```
*/
async createPartitionedTable(config) {
try {
const query = `
CREATE TABLE ${this.formatTableName(config.name)}
(${this.generateSchemaString(config.schema)})
PARTITION BY ${config.partitionType}(${config.partitionField})
`;
await this.query(query);
return { success: true, message: 'Partitioned table created successfully' };
}
catch (error) {
this.logger.logError(error);
return { success: false, message: `Failed to create partitioned table: ${error}` };
}
}
}
exports.BigQueryClient = BigQueryClient;