Anti-patterns related to input/output operations that can lead to performance issues.
I/O Bottlenecks Overview
Input/Output (I/O) operations are often the slowest part of an application, whether it’s reading from or writing to files, databases, networks, or other external resources. Inefficient I/O patterns can lead to significant performance degradation, resource exhaustion, and poor user experience.
Common I/O-related performance issues include:
This guide covers common anti-patterns related to I/O operations, along with best practices for optimizing I/O performance across different programming languages and application types.
Blocking I/O in Responsive Applications
Performing blocking I/O operations on the main thread or UI thread can lead to unresponsive applications, poor user experience, and in some cases, application crashes or “Not Responding” states.
To avoid blocking I/O in responsive applications:
Inefficient File Reading Patterns
Inefficient file reading patterns, such as reading one character at a time, using inappropriate buffer sizes, or loading entire large files into memory, can lead to poor performance and excessive memory usage.
To optimize file reading:
Excessive Database Queries
Excessive database queries, particularly the N+1 query problem where a single query is followed by N additional queries (one for each result), can lead to significant performance degradation and database load.
To optimize database queries:
Inefficient Network Communication
Inefficient network communication, such as making many small requests instead of batched requests, not reusing connections, or failing to implement proper error handling and retries, can lead to poor performance and reliability issues.
To optimize network communication:
Improper Resource Management
Improper resource management, such as failing to close files, database connections, or network sockets, can lead to resource leaks, degraded performance, and eventually application crashes.
To implement proper resource management:
Inefficient Logging Practices
Inefficient logging practices, such as excessive logging, string concatenation in log statements, or performing expensive operations regardless of log level, can lead to significant performance overhead, especially in high-throughput applications.
To optimize logging:
Inefficient Serialization/Deserialization
Inefficient serialization and deserialization, such as repeatedly creating serializer instances, performing unnecessary conversions, or using inefficient formats, can lead to significant performance overhead, especially when dealing with large data sets.
To optimize serialization/deserialization:
Inefficient Buffering Strategies
Inefficient buffering strategies, such as using buffers that are too small or too large, unnecessary flushing, or not considering the characteristics of the underlying storage system, can lead to poor I/O performance.
To optimize buffering strategies:
Synchronous I/O in Event Loops
Performing synchronous I/O operations in event-loop-based systems (Node.js, Netty, etc.) can block the event loop, preventing it from processing other events and leading to reduced throughput and responsiveness.
To avoid blocking the event loop:
I/O Performance Best Practices Checklist
Optimizing I/O performance is critical for building responsive and scalable applications. By following best practices for different types of I/O operations, you can significantly improve throughput, reduce latency, and enhance overall system performance.
Key principles for I/O optimization: