Chapter 18: Performance Optimization

In this section

• 18.1 Identifying Performance Bottlenecks
  • Profiling in Clojure: Identifying Performance Bottlenecks
    Learn the importance of profiling in Clojure applications, how to identify performance bottlenecks, and the methodologies to optimize your code effectively.
  • Common Sources of Bottlenecks in Clojure Applications
    Explore common performance bottlenecks in Clojure applications, including memory allocations, I/O latency, and inefficient algorithms, with comparisons to Java.
  • Setting Performance Goals for Clojure Applications
    Learn how to set realistic performance goals for Clojure applications, leveraging your Java experience to optimize functional programming performance.
• 18.2 Profiling Clojure Applications
  • Using VisualVM and Other Profilers for Clojure Performance Optimization
    Learn how to use VisualVM, JProfiler, and YourKit to profile and optimize Clojure applications running on the JVM.
  • Collecting and Analyzing Metrics for Clojure Performance Optimization
    Learn how to collect and analyze performance metrics in Clojure applications, including CPU usage, memory consumption, and garbage collection statistics, to identify and resolve performance issues.
  • Clojure-Specific Profiling Tools for Performance Optimization
    Explore Clojure-specific profiling tools like clj-async-profiler and criterium to gain insights into performance at the language level.
• 18.3 Optimizing Function Calls
  • Optimizing Clojure Performance: Avoiding Reflection
    Learn how to optimize Clojure performance by avoiding reflection through type hints and other techniques. Enhance your Clojure applications by understanding and eliminating reflection overhead.
  • Inlining Functions for Performance Optimization in Clojure
    Explore how inlining functions in Clojure can enhance performance, with a focus on using the `inline` metadata for optimization.
  • Optimizing Clojure Performance: Reducing Function Call Overhead
    Explore strategies to reduce function call overhead in Clojure, enhancing performance by leveraging transducers, avoiding unnecessary function recreation, and more.
• 18.4 Efficient Use of Data Structures
  • Choosing the Right Data Structures for Performance Optimization in Clojure
    Explore how to select the most efficient data structures in Clojure for optimal performance, drawing parallels with Java and emphasizing functional programming principles.
  • Persistent Data Structures in Clojure: A Guide for Java Developers
    Explore how Clojure's persistent data structures work, their internal mechanisms, and how to use them efficiently. Understand the impact of structural sharing on performance.
  • Utilizing Transients for Local Mutability in Clojure
    Explore how to use transients in Clojure for efficient local mutability, enhancing performance while maintaining functional programming principles.
• 18.5 Leveraging Concurrency for Performance
  • Parallel Processing with pmap: Leveraging Concurrency for Performance in Clojure
    Explore how to use pmap and other parallel processing functions in Clojure to efficiently utilize multiple CPU cores for computationally intensive tasks.
  • Asynchronous Processing in Clojure: Boosting Performance with Concurrency
    Explore how asynchronous programming in Clojure, using core.async, enhances performance by managing tasks concurrently without blocking threads. Learn to leverage Clojure's unique concurrency features for efficient asynchronous processing.
  • Effective Use of Atoms and Agents in Clojure Concurrency
    Master the use of Atoms and Agents in Clojure to manage state efficiently in concurrent applications, minimizing contention and synchronization overhead.
• 18.6 Interacting with Native Code
  • Java Interop for Performance Optimization in Clojure
    Explore how leveraging Java interoperability can enhance performance in Clojure applications, with practical examples and comparisons.
  • JNI and JNA: Interacting with Native Code for Performance Optimization
    Explore Java Native Interface (JNI) and Java Native Access (JNA) for integrating native code with Clojure, enhancing performance and leveraging existing C/C++ libraries.
  • Trade-offs and Risks of Interacting with Native Code in Clojure
    Explore the trade-offs and risks of integrating native code with Clojure, including maintenance challenges and memory safety concerns.
• 18.7 Performance in JVM vs. Clojure
  • JVM Performance Model: Mastering Memory, JIT, and Garbage Collection for Clojure
    Explore the intricacies of the JVM performance model, focusing on memory management, JIT compilation, and garbage collection, and their impact on Clojure applications.
  • Exploiting JVM Optimizations for Clojure Performance
    Learn how to write Clojure code that leverages JVM optimizations to enhance performance, focusing on avoiding dynamic code paths and effective use of polymorphism.
  • Comparing Clojure and Java Performance: A Comprehensive Analysis
    Explore the performance characteristics of Clojure compared to Java, focusing on dynamic typing, immutability, and JVM optimizations.
• 18.8 Memory Management and Garbage Collection
  • Understanding Garbage Collection in JVM for Clojure Developers
    Explore the intricacies of garbage collection in the JVM, its impact on performance, and how it relates to Clojure development.
  • Minimizing Memory Allocation: Strategies for Efficient Clojure Programming
    Explore strategies for reducing memory allocation in Clojure, including data structure reuse, avoiding unnecessary object creation, and leveraging primitives.
  • Optimizing JVM Garbage Collection for Clojure Applications
    Learn how to tune JVM garbage collection settings to enhance performance in Clojure applications, including heap size adjustments and GC algorithm selection.
• 18.9 Case Studies
  • Performance Optimization in a Clojure Web Application: A Comprehensive Guide
    Explore a detailed case study on identifying and resolving performance issues in a Clojure web application. Learn about profiling, findings, and optimizations.
  • High-Performance Data Processing in Clojure: Optimizing Data Pipelines
    Explore a comprehensive case study on optimizing data processing pipelines in Clojure, leveraging concurrency and efficient data transformations for high performance.
  • Performance Optimization Lessons Learned: Key Insights for Java Developers Transitioning to Clojure
    Explore key lessons from performance optimization case studies in Clojure, emphasizing the importance of measurement, understanding problems, and applying targeted optimizations for Java developers.
• 18.10 Tools and Best Practices
  • Regular Performance Testing: Ensuring Optimal Clojure Application Performance
    Learn how to incorporate regular performance testing into your Clojure development process to detect regressions early and automate performance benchmarks.
  • Monitoring in Production: Ensuring Optimal Performance for Clojure Applications
    Learn how to effectively monitor Clojure applications in production environments, leveraging tools and best practices to ensure optimal performance and reliability.
  • Keeping Up with Updates: Clojure, Libraries, and JVM
    Stay informed about updates in Clojure, libraries, and the JVM to leverage performance improvements and maintain cutting-edge applications.
  • Code Reviews and Knowledge Sharing: Enhancing Performance Optimization in Clojure
    Explore the critical role of code reviews and knowledge sharing in optimizing Clojure performance, drawing parallels with Java practices.