9 Patterns and Coding Styles That Impact Java GC Performance

Introduction

In Java, Garbage Collection (GC) is a mechanism for automatically managing memory, freeing up unused objects to prevent memory leaks. However, while GC simplifies memory management, it can introduce latency due to the unpredictable pauses required to reclaim memory. For low-latency applications (like high-frequency trading or real-time analytics), frequent or long GC pauses can severely impact performance, as they introduce delays that disrupt the timing of critical operations.

Image Source: JVM Architecture – https://oracle.com

Why GC Affects Low-Latency Applications

Unpredictable Pauses

• GC runs at intervals determined by JVM heuristics, which means applications can experience pauses at seemingly random times. In latency-sensitive applications, even a minor pause (e.g., in the range of a few milliseconds) can mean missing a trading opportunity or delaying a time-critical response.

Stop-the-World Events

During some GC operations, the JVM pauses all application threads (a “stop-the-world” event) to perform memory cleanup. For low-latency applications, these pauses can be unacceptable, as they delay processing.

CPU Overhead

GC cycles consume CPU resources, which can compete with the main application, increasing latency and reducing throughput.

Addressing the Patterns and Coding Styles

When building low-latency Java applications, understanding how coding choices impact garbage collection (GC) behaviour is crucial. Certain patterns and coding styles can inadvertently increase memory churn, triggering more frequent GC events that disrupt performance. In this section, we’ll explore 9 common patterns and practices that can add strain to Java’s GC system. By identifying and optimising these areas, you can minimise GC overhead and improve your application’s responsiveness in latency-sensitive environments. Let’s dive into the patterns that every Java developer working on real-time applications should keep in mind.

Certain patterns and coding styles can inadvertently increase memory churn, triggering more frequent GC events that disrupt performance.

1. Frequent Object Creation and Disposal:

Constantly creating and discarding objects puts pressure on the young generation space in the heap, triggering frequent minor GCs. This is particularly problematic when many temporary objects (like boxed primitives) are created in loops or recursive methods.

Solutions:

• Reuse objects where possible, such as by using object pools or mutable structures.
• Avoid creating unnecessary temporary objects in tight loops or latency-sensitive code.

Young generation space: A section of the heap where new objects are allocated, optimised for rapid garbage collection as objects are short-lived.

Minor GCs: Garbage collection events that clean up the young generation space, occurring frequently but designed to be fast and minimally disruptive.

Boxed primitives: Primitive data types wrapped as objects (e.g., Integer for int), which increases memory usage and GC load due to additional object creation.

2. Heavy Use of Boxed Primitives

Java collections like ArrayList and HashMap don’t support primitive types directly, which leads to auto-boxing (e.g., converting an int to an Integer). Boxed primitives increase memory overhead and generate garbage.

Solutions:

• Use libraries like fastutil or HPPC that provide collections supporting primitives directly, reducing the need for boxing and the memory footprint of these collections.

3. Inefficient Use of Standard Collections

Standard collections (like ArrayList, LinkedList, HashMap) often resize or reallocate memory, which creates additional garbage and can lead to unpredictable GC behaviour.

Solutions:

• Pre-size collections when possible to avoid resizing
• Alternatively, use specialised data structures optimised for low-latency applications, such as Chronicle Map or Agrona collections.

4. Heavy Use of Strings

Strings are immutable in Java, so each modification creates a new object. Constant string manipulation (e.g., in concatenations or logging) creates garbage quickly, putting pressure on the GC.

Solutions:

• Use StringBuilder for string concatenations in performance-critical areas. Consider alternative data formats (e.g., binary protocols instead of JSON/XML) to reduce string parsing and manipulation.
• For large datasets, explore off-heap storage (e.g., with libraries like Chronicle).

Off-heap memory: Storage / Memory managed outside the JVM heap, allocated and freed manually to avoid garbage collection, providing more control and predictable performance for high-volume data.

5. Unoptimized Object References and Large Object Graphs

Complex object graphs (e.g., nested collections or large data structures) increase the work the GC must do to traverse references and identify unreachable objects, especially during full GCs.

Solutions:

• Simplify data structures where possible, avoiding deep nesting.
• For collections of objects that don’t need to be individually managed by GC, consider using flat data structures (e.g., arrays) or off-heap memory with libraries like Agrona or Chronicle Map.

Object graph: A network of interconnected objects in memory, representing the relationships and dependencies between them.

6. Excessive Use of Synchronisation and Locks: Synchronisation can cause objects to “escape” (i.e., become accessible by multiple threads), which prevents the JVM from easily garbage-collecting them. Escaped objects tend to get promoted to the old generation, leading to longer GC times.

Solutions:

• Minimise synchronisation in latency-sensitive code by using concurrent data structures like ConcurrentHashMap or lock-free structures from libraries like JCTools.
• Use thread-local variables or immutable objects to reduce the need for synchronisation.

Escaped objects: Objects that are accessible by multiple threads or live beyond their initial scope, making garbage collection more complex.

7. Misuse of Finalizers and Weak/Soft References

finalize() methods add overhead to object cleanup, as GC must perform additional work to run finalizers. Weak or soft references can also be challenging for GC, as they introduce indirect memory management.

Solutions:

• Avoid finalize() whenever possible. For resource cleanup, use try-with-resources and AutoCloseable instead.
• If weak or soft references are essential, monitor their use closely and ensure they are necessary.

Weak or soft references: Special references that allow objects to be garbage-collected when memory is low, aiding efficient memory management.

8. Improper Use of Caches

Caches can accumulate stale objects if not managed properly, which can increase the memory footprint and lead to larger GCs.

Solutions:

• Use bounded caches or apply an eviction policy (like LRU). Consider memory-efficient caches, such as Caffeine or Chronicle Map.
• For time-bound data, consider off-heap caching solutions.

LRU (Least Recently Used): A caching algorithm that evicts the least recently accessed items first to make space for new data.

9. Using Large Arrays or Collections in the Old Generation

Large arrays and collections that get promoted to the old generation (from long-lived objects or high churn) require full GC for reclamation, causing significant latency.

Solutions:

• Partition large collections where possible, or use off-heap storage solutions like DirectByteBuffer.
• For frequently accessed data, consider segregating hot and cold data to keep critical objects in the young generation.

Old generation: A memory area in the Java heap for long-lived objects, requiring less frequent but longer garbage collection cycles.

GC Optimization Techniques for Low-Latency Applications

Choose the Right GC Algorithm

Low-latency applications can benefit from garbage collectors optimised for minimal pause times, like ZGC or Shenandoah GC. These collectors are designed to operate with low and consistent pause times, making them suitable for real-time applications.

Tune the Heap Size and Generation Ratios

Customise the JVM heap configuration to reduce GC frequency. For instance, setting a larger young generation can help avoid minor GCs, while fine-tuning the survivor spaces can prevent premature promotion to the old generation.

Enable Escape Analysis

Escape analysis is a JVM optimization that allows objects to be allocated on the stack instead of the heap if they don’t escape the current scope, reducing GC load. Modern JVMs enable this by default, but it’s good to be aware of the optimization.

Closing

In Java, frequent or unoptimized GC cycles are a significant challenge for low-latency applications. Patterns like excessive object creation, inefficient use of collections, and complex data structures can lead to increased GC pressure. By understanding and addressing these patterns – using alternatives like off-heap storage, primitive collections, object pooling, and optimised GC settings – you can reduce latency, avoid unpredictable pauses, and achieve more consistent application performance.