pg-parallel

pg-parallel

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Non-blocking PostgreSQL for Node.js with worker thread support

A specialized wrapper around node-postgres that prevents event-loop blocking by offloading heavy CPU tasks and complex transactions to worker threads.

Table of Contents

• Features
• Installation
• Dependencies
• Quick Start
• API Reference
  • Constructor
  • Methods
    • db.query(config, values?)
    • db.warmup()
    • db.task(fn, args)
    • db.worker(task)
    • db.shutdown()
• Advanced Usage
  • Resilience and Logging
  • Complex Worker Logic
  • Self-Contained Functions
  • Utility Classes
• Performance
• Performance and Benchmarks
• When to Use
• Troubleshooting
  • Common Issues
  • Getting Help
• Third-party Licenses
• License

Features

• Hybrid Architecture: Fast I/O on main thread, heavy work on workers
• Low I/O Overhead: Standard queries run on the main thread for high performance
• Automatic Management: No manual client.release() needed
• TypeScript Support: Full type safety with comprehensive interfaces
• Lazy Initialization: Workers spawned only when needed
• Worker Warmup: Optional pre-initialization of workers for immediate performance
• Graceful Shutdown: Proper resource cleanup
• Resilience: Built-in retry with exponential backoff and a circuit breaker
• Actionable Errors: PgParallelError with standardized ErrorCategory
• Observability: Pluggable logger with key events (retries, breaker transitions, worker failures)
• Zero Dependencies: Uses only Node.js built-in modules (crypto.randomUUID)
• Modular Utilities: Exportable utility classes for advanced custom usage

Installation

npm install pg-parallel pg

Note: pg is a peer dependency and must be installed alongside pg-parallel.

Dependencies

This library is built on top of node-postgres (pg), a non-blocking PostgreSQL client for Node.js. The pg package is included as a peer dependency and must be installed alongside pg-parallel.

Requirements:

• pg v8.11.3+ (peer dependency)
• Node.js v18.x or higher

Quick Start

import { PgParallel } from 'pg-parallel';

const db = new PgParallel({
  connectionString: 'postgresql://user:pass@localhost/db',
  maxWorkers: 4, // Optional: defaults to CPU core count
});

// Standard I/O query (main thread)
const { rows } = await db.query('SELECT * FROM users WHERE id = $1', [1]);

// CPU-intensive task (worker thread)
function fibonacci(n) {
  if (n <= 1) return n;
  return fibonacci(n - 1) + fibonacci(n - 2);
}
const result = await db.task(fibonacci, [42]);

// Mixed workload with database access (worker thread)
const processed = await db.worker(async (client) => {
  const { rows } = await client.query('SELECT data FROM table');
  return rows.map((row) => row.data.toUpperCase());
});

await db.shutdown(); // Clean shutdown

API Reference

Constructor

new PgParallel(config: PgParallelConfig)

The config object extends pg.PoolConfig with additional properties:

• maxWorkers?: number - Number of worker threads (defaults to os.cpus().length)
• retry?: RetryConfig - Automatic retry for transient failures
• circuitBreaker?: CircuitBreakerConfig - Circuit breaker for database operations
• logger?: Logger - Optional logger for observability

Methods

db.query(config, values?)

Execute standard I/O queries on the main thread pool.

// Simple query
const { rows } = await db.query('SELECT * FROM users WHERE id = $1', [1]);

// With query config
const result = await db.query({
  text: 'SELECT * FROM users WHERE active = $1',
  values: [true],
});

db.warmup()

Pre-initializes the worker thread pool to avoid a "cold start" latency on the first call to .task() or .worker(). This is useful in performance-sensitive applications where the initial startup time of workers should be minimized.

// It's a good practice to warmup the workers during application startup
await db.warmup();

db.task(fn, args)

Execute CPU-intensive functions in worker threads.

// For recursive functions, use a named function declaration
function fibonacci(n: number): number {
  if (n <= 1) return n;
  return fibonacci(n - 1) + fibonacci(n - 2);
}

const result = await db.task(fibonacci, [40]);

db.worker(task)

Execute database operations and CPU-intensive logic in worker threads with dedicated client connection.

Function-based workers:

// Simple example
const result = await db.worker(async (client) => {
  const { rows } = await client.query("SELECT 'value' as data");
  return rows[0].data.toUpperCase();
});

// Transaction example
await db.worker(async (client) => {
  await client.query('BEGIN');

  const { rows } = await client.query(
    'UPDATE accounts SET balance = balance - 100 WHERE id = 1 RETURNING balance',
  );

  if (rows[0].balance < 0) {
    throw new Error('Insufficient funds');
  }

  await client.query(
    'UPDATE accounts SET balance = balance + 100 WHERE id = 2',
  );
  await client.query('COMMIT');
});

File-based workers:

// Using WorkerFileTask interface
const result = await db.worker({
  taskPath: path.resolve(__dirname, 'tasks/my-worker.js'),
  taskName: 'processData', // Optional: defaults to 'handler'
  args: ['arg1', 'arg2'], // Optional: arguments passed to the function
});

WorkerFileTask Interface:

interface WorkerFileTask {
  taskPath: string; // Absolute path to the module file
  taskName?: string; // Function name to execute (defaults to 'handler')
  args?: any[]; // Arguments to pass to the function
}

Note: No manual client.release() needed - lifecycle is managed automatically.

db.shutdown()

Gracefully shut down all connections and terminate workers.

await db.shutdown();

Advanced Usage

Resilience and Logging

pg-parallel includes optional resilience features and a pluggable logger.

import {
  PgParallel,
  type RetryConfig,
  type CircuitBreakerConfig,
} from 'pg-parallel';

const retry: RetryConfig = {
  maxAttempts: 4,
  initialDelayMs: 100,
  maxDelayMs: 1500,
  backoffFactor: 2,
  jitter: true,
};

const circuitBreaker: CircuitBreakerConfig = {
  failureThreshold: 5,
  cooldownMs: 10_000,
  halfOpenMaxCalls: 2,
  halfOpenSuccessesToClose: 2,
};

const db = new PgParallel({
  connectionString: process.env.DATABASE_URL!,
  retry,
  circuitBreaker,
  logger: {
    info: (m, meta) => console.log(m, meta),
    warn: (m, meta) => console.warn(m, meta),
    error: (m, meta) => console.error(m, meta),
  },
});

Notes:

• Retries target transient issues (timeouts, deadlocks, serialization failures, connection resets).
• Circuit breaker opens after consecutive failures, transitions to half-open after cooldown, and closes on healthy trials.
• Errors are thrown as PgParallelError with an ErrorCategory for easier routing/metrics.

Complex Worker Logic

For production code, organize worker logic in separate files using the WorkerFileTask interface:

// tasks/report-worker.js
const { randomUUID } = require('crypto');

module.exports = {
  generateReport: async (client, reportType = 'summary') => {
    const { rows } = await client.query(
      "SELECT * FROM (SELECT 1 as id, 'Sample Data' as name) as sales_data",
    );

    // Generate unique report ID using crypto.randomUUID
    const reportId = randomUUID();

    // Simulate report generation
    const reportContent = `${reportType} Report for ${rows.length} records`;

    return {
      id: reportId,
      type: reportType,
      recordCount: rows.length,
      generatedAt: new Date().toISOString(),
      content: reportContent,
    };
  },

  // Default handler (called when no taskName is specified)
  handler: async (client, message = 'Default task') => {
    const { rows } = await client.query('SELECT NOW() as timestamp');
    const taskId = randomUUID();
    return { id: taskId, message, timestamp: rows[0].timestamp };
  },
};

// main.ts
import * as path from 'path';

// Execute specific named function
const report = await db.worker({
  taskPath: path.resolve(process.cwd(), 'tasks/report-worker.js'),
  taskName: 'generateReport',
  args: ['detailed'],
});

// Execute default handler
const result = await db.worker({
  taskPath: path.resolve(process.cwd(), 'tasks/report-worker.js'),
  args: ['Hello from main thread'],
});

Key benefits of file-based workers:

• Code Organization: Keep complex logic in separate, reusable modules
• Team Collaboration: Multiple developers can work on different worker files
• Testing: Easier to unit test individual worker functions
• Maintenance: Clear separation of concerns and better code structure

Self-Contained Functions

Functions passed to db.task() and db.worker() must be self-contained and not rely on any variables from their parent scope. This is because the function is serialized, sent to a different thread, and deserialized, losing its original closure.

Example: Accessing External Variables

// Wrong - references parent scope
const TAX_RATE = 0.07;
await db.task((price) => price * (1 + TAX_RATE), [100]);

// Correct - self-contained
await db.task(
  (price) => {
    const TAX_RATE = 0.07;
    return price * (1 + TAX_RATE);
  },
  [100],
);

Example: Recursive Functions

For a function to call itself recursively inside a worker, it must be a named function declaration. An arrow function assigned to a const will not work because its name is part of the closure that gets lost.

// Wrong - recursive call will fail inside the worker
const fibonacciArrow = (n: number): number => {
  if (n <= 1) return n;
  // This call will fail as 'fibonacciArrow' is not in the function's own scope
  return fibonacciArrow(n - 1) + fibonacciArrow(n - 2);
};
await db.task(fibonacciArrow, [40]);

// Correct - named function is self-contained
function fibonacci(n: number): number {
  if (n <= 1) return n;
  return fibonacci(n - 1) + fibonacci(n - 2);
}
await db.task(fibonacci, [40]);

Utility Classes

For advanced usage, pg-parallel exports utility classes that power the internal resilience features. These can be used independently for custom implementations:

import {
  ErrorUtils,
  RetryUtils,
  CircuitBreakerUtils,
  type CircuitBreakerState,
  type RetryConfig,
} from 'pg-parallel';

// Error classification and handling
const isRetryable = ErrorUtils.isTransient(error);
const category = ErrorUtils.categorizeError(error);
const wrappedError = ErrorUtils.wrapError(error);

// Custom retry logic
const retryConfig: RetryConfig = {
  maxAttempts: 3,
  initialDelayMs: 100,
  maxDelayMs: 1000,
  backoffFactor: 2,
  jitter: true,
};

const result = await RetryUtils.executeWithRetry(
  () => someAsyncOperation(),
  retryConfig,
  'my-operation',
);

// Circuit breaker management
const breakerState = CircuitBreakerUtils.createInitialState();
const breakerConfig = CircuitBreakerUtils.getDefaultConfig();

CircuitBreakerUtils.onBreakerFailure(breakerState, breakerConfig);
await CircuitBreakerUtils.ensureBreakerState(breakerState, breakerConfig);

Available Utility Classes:

• ErrorUtils: Error categorization, transient detection, and wrapping
• RetryUtils: Exponential backoff retry logic with jitter
• CircuitBreakerUtils: Circuit breaker state management and transitions

Performance

Performance benchmarks demonstrate when pg-parallel provides benefits over standard pg.Pool. These tests were conducted on Apple M1 (8 cores) with PostgreSQL 15.

Benchmark Overview

Scenario	pg-parallel	Baseline	Improvement
Pure I/O (10,000 queries, maxWorkers: 1)	0.410s avg	0.446s avg	+8.07% faster ✅
Pure CPU (8 fibonacci tasks)	7.298s avg	19.904s avg	2.73x faster
Mixed I/O + CPU (8 tasks)	7.710s avg	22.878s avg	2.97x faster

Detailed Results

You can run comprehensive benchmarks yourself to validate these results. Each benchmark runs 10 iterations for statistical accuracy:

# Pure I/O Benchmark (10,000 queries per run)
ts-node src/benchmarks/benchmark-io-10-runs.ts

# Pure CPU Benchmark (8 Fibonacci tasks per run)
ts-node src/benchmarks/benchmark-cpu-10-runs.ts

# Mixed I/O + CPU Benchmark (8 mixed tasks per run)
ts-node src/benchmarks/benchmark-mixed-10-runs.ts

Benchmark Output Includes:

• Individual run times for each iteration
• Statistical analysis (average, min, max, standard deviation)
• Performance comparison vs baseline
• Speedup calculations

Requirements:

• PostgreSQL instance running locally
• DATABASE_URL environment variable configured
• Node.js 18.x+ with TypeScript support

Performance Analysis

I/O Operations

• Performance Breakthrough: After optimization, pg-parallel now outperforms pg.Pool in pure I/O scenarios by +8.07% on average.
• Consistency: Lower standard deviation (0.021s vs 0.050s) indicates more stable and predictable performance.
• Pool allocation vs workers: The maximum connection budget (max) is split between the main pool and worker pools. By default, the number of workers is os.cpus().length.
• Recommendation:
  • For pure I/O benchmarks or simple CRUD services, set maxWorkers: 1 to maintain optimal connection allocation while still benefiting from the performance optimizations.
  • For mixed workloads (I/O + CPU/transactions), keep multiple workers to benefit from parallelism.
  • Our 10-run I/O benchmark with maxWorkers: 1 now exceeds pg.Pool performance (see table above).

CPU-Intensive Tasks

• Speedup: Consistently 2.73x faster than sequential processing
• Stability: Very low standard deviation (0.107s) shows consistent performance
• Benefit: Main thread remains responsive during heavy computations
• Scalability: Performance scales with CPU core count

Mixed Workloads

• Outstanding Performance: 2.97x faster than pg.Pool for mixed operations
• Optimal Use Case: Complex transactions with both I/O and CPU work
• Real-world Example: ETL processes, data analysis, report generation
• Event Loop: Remains unblocked for handling other requests
• Consistency: Stable performance with standard deviation of 0.161s

Performance Guidelines

Use pg-parallel when:

• Any I/O operation - now 8.07% faster than pg.Pool with better consistency
• CPU tasks take > 100ms per operation
• You need to maintain application responsiveness
• Processing large datasets with complex logic
• Running multiple parallel operations
• Mixed I/O + CPU workloads (2.97x performance gain)

Consider pg.Pool when:

• Absolute minimal memory footprint is required
• Single-threaded environment is mandatory
• Very simple, short-lived operations where setup overhead matters

Note: With recent optimizations, pg-parallel now outperforms pg.Pool even in pure I/O scenarios while providing additional resilience features.

Benchmark Methodology

All benchmarks use:

• PostgreSQL 15.3 running locally
• Node.js 18.17.0
• Apple M1 processor (8 cores)
• Fibonacci(40) as CPU-intensive task
• Average of 30 runs (10 runs x 3 executions) for high precision

When to Use

Use pg-parallel for:

• All production applications - now outperforms pg.Pool by 8.07% in pure I/O
• CPU-intensive calculations (data analysis, image processing) - 2.73x faster
• Complex database transactions with heavy logic
• Mixed I/O + CPU workloads - 2.97x faster than alternatives
• Applications requiring circuit breaker and retry resilience
• Maintaining responsiveness under any workload type

Consider standard pg only when:

• Prototyping or development where setup simplicity is prioritized
• Memory constraints are extremely tight
• Single-threaded environment requirements

Troubleshooting

Common Issues

"require is not defined" in worker functions

Problem: Using require() inside function-based workers fails.

Solution: Use file-based workers instead:

// Wrong - this will fail
await db.worker(async (client) => {
  const { randomUUID } = require('crypto'); // Error: require is not defined
  // ...
});

// Correct - use file-based workers
await db.worker({
  taskPath: path.resolve(__dirname, 'tasks/my-worker.js'),
  taskName: 'processData',
});

Workers appear busy or tasks aren't finishing

Problem: Workers are not being released properly.

Solution: Ensure your worker functions complete without hanging:

// Wrong - infinite loop or hanging operation
await db.worker(async (client) => {
  while (true) {
    // This will hang the worker
  }
});

// Correct - ensure function completes
await db.worker(async (client) => {
  const result = await client.query('SELECT NOW()');
  return result.rows[0];
});

TypeScript compilation errors with worker files

Problem: TypeScript files don't work well with worker threads.

Solution: Keep worker files as JavaScript (.js) and main files as TypeScript:

// main.ts (TypeScript)
import { PgParallel } from 'pg-parallel';

const result = await db.worker({
  taskPath: path.resolve(process.cwd(), 'workers/processor.js'), // .js file
  taskName: 'process',
});

// workers/processor.js (JavaScript)
module.exports = {
  process: async (client, data) => {
    // Worker logic here
    return processedData;
  },
};

Performance slower than expected

Problem: Overhead from worker threads negates benefits.

Solution: Use workers only for CPU-intensive tasks:

// Wrong - simple query doesn't need worker
await db.worker(async (client) => {
  return await client.query('SELECT 1');
});

// Correct - use main thread for simple queries
const result = await db.query('SELECT 1');

// Correct - use worker for heavy computation
await db.worker(async (client) => {
  const { rows } = await client.query('SELECT * FROM large_table');
  return rows.map((row) => heavyProcessing(row)); // CPU-intensive
});

Performance and Benchmarks

Executive Summary: Comprehensive benchmark analysis reveals that pg-parallel outperforms pg.Pool in light I/O workloads (5.4% faster) while providing superior reliability features. For mixed I/O+CPU workloads, pg-parallel delivers 2.97x faster performance. The key is proper configuration: use maxWorkers: 1 for pure I/O and always call warmup() for optimal performance.

For detailed performance analysis and scenario-specific recommendations:

• Performance Comparison Summary - Comprehensive comparison with pg.Pool and detailed when-to-use guidance
• Benchmark System Documentation - Technical details of our benchmarking methodology and optimization process

Key Performance Insights (Comprehensive Benchmark Analysis)

Pure I/O Performance (Realistic Configuration)

Load Scenario	pg-parallel	pg.Pool	Difference	Winner	Key Factor
Light Load	6,289 ops/sec	5,952 ops/sec	+5.4% faster	pg-parallel	Circuit breaker + I/O optimization
Medium Load	13,477 ops/sec	15,152 ops/sec	-12.4% slower	pg.Pool	Raw connection efficiency
Heavy Load	15,432 ops/sec	17,153 ops/sec	-11.1% slower	pg.Pool	High-throughput optimization

Mixed Workload Performance

Workload Type	pg-parallel	pg.Pool/Sequential	Performance Gain	Use Case
Pure CPU	7.298s avg	19.904s	2.73x faster	Data processing tasks
Mixed I/O+CPU	7.710s avg	22.878s	2.97x faster	ETL operations

Key Insights:

• Light I/O: pg-parallel wins with 5.4% advantage + reliability features
• Heavy I/O: pg.Pool wins with 10-12% raw speed advantage
• Mixed workloads: pg-parallel dominates with 2.7-3x performance gains
• Critical config: maxWorkers: 1 essential for optimal I/O performance

Production Configuration Guide

🎯 Scenario-Based Recommendations

1. Light I/O Applications (Recommended: pg-parallel)

// WINNER: pg-parallel (5.4% faster + resilience)
const db = new PgParallel({
  ...pgConfig,
  maxWorkers: 1, // Critical: Use 1 worker for pure I/O
  retryConfig: {
    maxRetries: 3,
    baseDelay: 100,
  },
  circuitBreakerConfig: {
    failureThreshold: 5,
    resetTimeout: 30000,
  },
});
await db.warmup(); // Mandatory for optimal performance!

// Perfect for: REST APIs, CRUD operations, light queries
await db.query('SELECT * FROM users WHERE id = $1', [userId]);

2. Heavy I/O Applications (Consider: pg.Pool)

// WINNER: pg.Pool (10-12% faster in pure throughput)
import { Pool } from 'pg';
const pool = new Pool({
  ...pgConfig,
  max: 50, // Higher connection pool for throughput
  idleTimeoutMillis: 30000,
});

// Best for: High-frequency queries, bulk operations, reporting
await pool.query('SELECT COUNT(*) FROM large_table');

// Alternative: pg-parallel with trade-off awareness
const db = new PgParallel({ ...pgConfig, maxWorkers: 1 });
await db.warmup();
// 11% slower but with circuit breaker + retry + zero errors
await db.query('SELECT * FROM large_dataset WHERE complex_condition');

3. Mixed I/O + CPU Workloads (Strongly Recommended: pg-parallel)

// CLEAR WINNER: pg-parallel (2.97x faster than pg.Pool)
const db = new PgParallel({
  ...pgConfig,
  maxWorkers: 4, // More workers for CPU tasks
  workerIdleTimeout: 60000,
});
await db.warmup();

// ETL operations, data processing, complex transformations
await db.worker(async (client) => {
  const data = await client.query('SELECT * FROM raw_data LIMIT 10000');
  const processed = await processLargeDataset(data.rows); // CPU-intensive
  await client.query('INSERT INTO processed_data VALUES ...', processed);
  return processed.length;
});

4. Pure CPU Tasks (Strongly Recommended: pg-parallel)

// CLEAR WINNER: pg-parallel (2.73x faster than sequential)
await db.task(() => {
  // CPU-intensive operations in isolated worker thread
  return calculateComplexReport(largeDataset);
}); // No database blocking!

⚙️ Configuration Matrix

Use Case	Library	maxWorkers	Performance	Reliability	Best For
Light I/O	pg-parallel	1	+5.4% faster	Circuit breaker + Retry	Production APIs
Heavy I/O	pg.Pool	N/A	+10-12% faster	Basic	High-throughput reports
Mixed Workloads	pg-parallel	2-4	+197% faster	Full resilience	ETL, data processing
CPU Tasks	pg-parallel	2-8	+173% faster	Worker isolation	Analytics, calculations

🚨 Critical Configuration Rules

// ✅ DO: For pure I/O workloads
const db = new PgParallel({ maxWorkers: 1 }); // Optimal for I/O

// ❌ DON'T: Multiple workers for simple queries
const db = new PgParallel({ maxWorkers: 4 }); // Overhead for pure I/O

// ✅ DO: Always warmup in production
await db.warmup(); // 1,135% performance improvement

// ✅ DO: Use appropriate methods
await db.query(sql); // Pure I/O - now faster than pg.Pool
await db.task(fn); // Pure CPU - 2.73x faster
await db.worker(fn); // Mixed I/O+CPU - 2.97x faster

Getting Help

If you encounter issues not covered here:

• Performance Questions: Review Performance Comparison for detailed benchmarks and when to use each library
• Configuration Issues: Refer to the Production Configuration Guide above for scenario-based recommendations
• Implementation Examples: Check the Examples directory for working code samples
• Benchmark Details: See Benchmark System Documentation for technical methodology
• Bug Reports: Search existing GitHub Issues
• New Issues: Create a new issue with minimal reproduction case and performance context

Third-party Licenses

This project uses the following third-party libraries:

• node-postgres (pg) - MIT License © Brian Carlson

Note: Previous versions used the uuid library, but since v1.4.0, pg-parallel uses Node.js built-in crypto.randomUUID() for zero external dependencies.

License

MIT © Jonathan Givisiez