Here are some general tips for improving performance at the code level:
Avoid Premature Optimization
First, make sure to not optimize unnecessarily. Code that is clean and readable is preferable to overly complex “optimized” code. Only optimize once bottlenecks are identified through profiling.
Size Data Structures Appropriately
When declaring arrays, collections, and other data structures initialize them at an appropriate size instead of letting them resize dynamically. This avoids unnecessary memory churn and resizing computations.
Avoid excessive null checking which can be costly. But also prevent NullPointerExceptions which are more expensive. Use annotations like @NotNull to enforce non-nullability when applicable.
Streamline Common Cases
Structure code so the common, typical scenarios are handled efficiently with minimum decision points. Handle edge cases separately.
Use Primitive Types Instead of Objects
Primitive types like int and boolean allocate less memory and avoid boxing conversions. Prefer them over object wrapper types when possible.
Avoid Slow Operations in Loops
Move expensive operations like file/network I/O outside of loops to improve throughput. Extract invariant loop calculations into local variables.
Limit Scope of Variables
Declare variables in the narrowest practical scope. Method-local variables are accessed faster than instance/static fields.
These tips will improve performance across all types of Java applications. Now let’s look specifically at tuning garbage collection.
Tuning Garbage Collection
The JVM’s garbage collector manages automatic memory reclamation. While essential, poorly configured GC can lead to pauses and throughput issues.
Here are techniques to optimize garbage collection:
Choose the Right Collector
Select server-mode collectors like G1GC for long-running applications. Avoid incremental collectors for latency-sensitive apps.
Use New Generation Collectors
Generational collectors like G1GC are optimized for short-lived objects and perform better in most cases.
Size Heap Appropriately
Allocate enough heap to hold live data and some overhead. Avoid drastic resize pauses by sizing upfront based on usage.
Adjust Garbage Collector Flags
Tuning options like -XX:MaxGCPauseMillis control frequency and duration of GC cycles. Profile to find optimal settings.
Reduce Object Churn
Reuse objects and use object pools to minimize allocation/deallocation. This results in fewer minor GCs.
Use Weak/Soft References
Use weak and soft references for caching to avoid retaining objects after use. This reduces heap size.
With tuned GC you can significantly cut down pauses and throughput issues. Next let’s discuss concurrency.
Modern Java applications rely heavily on concurrency. Using threads effectively can boost throughput and responsiveness. Here are some concurrency performance tips:
Use Thread Pools
Cached thread pools avoid creating new threads per task which has high overhead. Use pools with a bounded number of threads.
Avoid Blocking I/O Calls
Use non-blocking NIO to prevent blocking entire threads for disk/network I/O. Utilize asynchronous processing when possible.
Reduce Lock Contention
Design with non-blocking data structures and avoid locks in hot code paths. This minimizes lock contention.
Leverage Concurrent Data Structures
Take advantage of optimized concurrent collections like ConcurrentHashMap. But limit modifications for thread-safety.
Isolate Expensive Resources
Isolate synchronized access to expensive resources like JDBC connections to avoid contention.
Control Thread Priority
Increase priority on important threads and lower for background tasks to improve responsiveness.
Writing concurrent Java code properly will enable your apps to scale across many CPU cores for higher throughput.
Next we will explore caching techniques.
Retrieving data over the network or from disk can be orders of magnitude slower than memory access. An effective caching strategy is critical for performance. Here are some pointers:
Cache Frequently Used Data
Look for hot spots that generate frequent reads, like reference tables, metadata, user sessions. Cache these aggressively.
Avoid stale data by automatically evicting cache entries after a TTL or on write invalidation events. Keep cache fresh.
Cache at Multiple Levels
Use a hierarchy with local CPU caches, distributed caches, and hybrid caches for varying performance needs.
Read and Write Through Cache
For consistency, update underlying data source on cache misses and writes. This avoids stale reads.
Use Async Warming
Proactively prepopulate cache in the background to speed up initial requests.
Weigh Cache Eviction Policies
LRU, LFU, FIFO have different tradeoffs. Tune policies based on access patterns.
A holistic caching strategy optimizes reuse and avoids redundant I/O. Let’s now discuss code profiling.
Profiling for Optimization
Profilers give you insights into where your application is spending time and resources. This data is invaluable for identifying optimization opportunities. Here are some profiler tips:
Use Sampling Profiling
Sampling profilers like Async Profiler have very low overhead. Use during load tests to identify hotspots.
Profile in Staging
Test profiling on a staging environment first to avoid overhead on production.
Look for Hot Methods
Identify the critical code paths where most time is spent based on the profiler output. Focus optimization efforts there.
Find Slow Algorithms
Look for custom algorithms that may have poor time complexity. Profilers can pinpoint these clearly.
Check Memory Usage
Memory profiles help uncover leaks and inefficient allocation. Pay attention to GC stats.
Compare profiling data across builds/deployments to measure improvements from optimizations.
Proper profiling guides and validates optimization work. It should be the first step in any performance tuning exercise.
Now let’s explore actually measuring improvements with performance tests.
Performance Testing Approaches
Rigorous performance testing is required to validate optimizations. Here are different ways to performance test:
Vary the load like concurrent users to identify scaling limits. Look for throughput caps or high latency.
Increase load past normal levels to find crashes or stuck threads at peak usage.
Simulate spikes in traffic to see if the system gracefully handles bursts.
Run sustained long-load tests to uncover memory leaks and accumulation issues.
Monitoring in Production
Record metrics like response times, error rates to compare against benchmarks.
Test on Varied Hardware
Run tests across different hardware setups to find platform specific bottlenecks.
Automated performance tests prevent regressions and validate improvements from tuning work. They are well worth the investment for any high-scale system.
Finally, let’s go over some useful tools and libraries for optimizing Java apps.
Here are some useful resources, tools and libraries to improve Java performance:
- Async Profiler – Sampling profiler useful for microbenchmarking code.
- VisualVM – GUI profiler included in JDK. Provides memory, CPU and thread analysis.
- jMH – Microbenchmarking framework for rigorously testing small code snippets.
- Byteman – Injects faults like latency to allow testing failure scenarios.
- Caffeine – High-performance Java caching library. Simple API.
- Chronicle – Fast persistent queue and logging libraries.
- Disruptor – Inter-thread messaging library focused on throughput and low latency.
Make use of tools like profilers and load injectors to pinpoint bottlenecks before tuning. Leverage optimized libraries for caching, I/O, concurrency, etc to boost performance.
Improving Java application performance requires a holistic approach across architecture, coding practices, testing methodology and tooling.
Key techniques include:
- Diagnosing root causes with profiling
- Optimizing algorithms, data structures and garbage collection
- Improving concurrency with asynchronous, non-blocking code
- Implementing caching to avoid redundant I/O
- Validating improvements with load tests and metrics
Mastering these performance tuning practices will enable you to build high-throughput and low-latency Java systems that handle demanding workloads with ease.
Hopefully this comprehensive guide has provided you a wealth of tips, tricks and techniques to analyze and boost the speed of your Java applications!