AdoptOpenJDK 11.24 High CPU usage from excessive logging with PrintStream (like System. out. printing). It can slow down applications, especially under heavy loads. To fix it, reduce the amount of logging or switch to a more efficient method.
Introduction:
One of the most popular programming languages, Java, has various implementations. Among them are OpenJDK and AdoptOpenJDK. The latter’s 11.24 version drew a lot of attention after high CPU consumption was reported on many issues, especially when it concerns the usage of `PrintStream.`
This has caused huge problems for developers and system administrators who must cope with performance optimization and maintaining stable applications on this particular JDK version.
What is AdoptOpenJDK?
AdoptOpenJDK is a community-led effort that makes pre-built OpenJDK binaries available for development and production. It ensures users’ smooth availability of OpenJDK distributions while providing them with stable, reliable builds. OpenJDK 11 comes with support for the longest period possible, thus its popularity for enterprise applications.
The Problem: High CPU Usage!
Users have identified that applications built on AdoptOpenJDK 11.24 show high CPU consumption, especially when using PrintStream to print log or data output. This seems especially observable in logging-intensive applications or those frequently calling `System.out.println()`. Symptoms may include:
Peaked CPU usage: The CPUs can be plugged in at nearly 100%, thereby degrading application performance.
Slowness in logging operations: This can be reflected in log entries that take longer than expected, impacting debugging and monitoring activities.
Increased latency: Other system processes may be impacted as a result of competing for CPU resources, leading to general system slowness.
About PrintStream’s Function:
PrintStream in Java is used to print data to output streams, which includes writing to the console or files. The problem may be that `PrintStream` operations are synchronous and may not be optimized for high-frequency writes. This can become a performance bottleneck for applications logging large volumes of data.
Possible Causes
1. Buffering Problems:
If the output is not buffered properly, or if the output buffer is filled too quickly, Java will have increased CPU cycles to handle these writes when writing to the console or a file.
2. Concurrent Access:
In multi-threaded applications, several threads may try to write to the `PrintStream` almost at the same time, causing contention and high CPU usage caused by Java’s synchronization of writing to the stream.
3. Garbage Collection:
The frequent creation of new string objects while logging further contributes to heavy garbage collection activities, which again consumes more CPU.
How can I stop 100% CPU usage?
Optimize the code and minimize the amount of logging to reduce the CPU usage. Profiling your application can be a valuable means of identifying performance bottlenecks that need further optimization. If the demand for an application justifies the requirement for more hardware resources, increase those accordingly.
Monitor performance with tools like Java VisualVM or analyze thread dumps to see what the threads are doing. Profiling helps you identify CPU-intensive areas in your code, giving you insights into where to make optimizations.
How do I troubleshoot performance issues in Java applications?
Troubleshooting performance issues in Java applications involves a step-by-step process of identifying and resolving bottlenecks. Here’s a brief guide:
1. Monitor Resource Usage
Use tools like Java VisualVM, JConsole, or Perf4J to monitor CPU, memory, and thread usage in real time. Set up profiling tools like YourKit, JProfiler, or Eclipse MAT to analyze resource consumption and performance metrics.
2. Analyze Thread Activity
Generate thread dumps to examine what threads are doing when CPU spikes occur. Use `stack` or VisualVM for this. Look for threads stuck in waiting states or deadlock situations, which may hint at thread contention issues.
3. Profile and Optimize Code
Use profiling tools to identify hotspots in your application where CPU or memory usage is concentrated. Look for inefficient algorithms, excessive object creation, or frequent garbage collections that can be optimized.
4. Inspect Garbage Collection
Analyze garbage collection (GC) logs to understand their impact on performance. Use flags like `-XX:+PrintGCDetails` to enable detailed logging. Tweak JVM memory settings to the best of the application’s behavior and workload by tuning `-Xms,` `-Xmx,` and GC algorithms.
5. Review Application Logic
Review for architectural software issues that cause performance issues, such as too much data fetching in loops or redundant calculations. Verify that external service calls (for example, database queries or API calls) are efficient and do not become a bottleneck.
6. Perform Load Testing
Run load testing with tools like Apache JMeter or Gatling to simulate user traffic and identify performance bottlenecks under load. Analyze how your application scales under different loads to ensure it meets performance expectations.
7. Check Environment Factors
Ensure that your Java environment is using optimal configurations and versions of Java and supporting libraries. Analyze the system resource limitations, like CPU and memory allocation, and network configurations that may affect performance.
8. Tune JVM Parameters
Experiment with different JVM parameters based on your application’s needs. Tuning heap size, GC settings, and thread pool sizes can yield significant improvements.
Test the impact of these changes in a staging environment before applying to production.
Mitigation and Solutions:
To mitigate high CPU usage from AdoptOpenJDK 11.24, following are some workarounds:
1. Buffering Output:
Instead of using `System.out.println()`, use a logging framework like SLF4J with Logback or Log4j2 that can be configured for asynchronous logging. This can greatly reduce contention and CPU load.
2. Reducing Log Levels:
Reduce log verbosity in production to avoid excessive log writes.
3. Upgrade or Downgrade:
Some users reported that upgrading to a later version of AdoptOpenJDK or downgrading to an earlier version, such as OpenJDK 11.0.17, can solve these performance issues. New versions should always be tested in a staging environment before deployment to production.
4. Review Threading Models:
The application design should minimize contention around logging. Thread-safe logging mechanisms allow for efficient logging without locking bottlenecks.
FAQs:
Why is Java taking up so much CPU?
Inefficient code, heavy logging, or garbage collection can cause high CPU usage in Java. Infinite loops and resource contention among threads also cause CPU usage spikes. Analyze your application to identify and resolve these issues.
How to check Java thread CPU usage?
For monitoring CPU usage, you can use tools such as Java VisualVM, stack, and jconsole. These tools help visualize thread activity and pinpoint which thread is consuming the most CPU resources. Continuous monitoring is the key to managing performance.
Why does my CPU usage spike to 100% when I open programs?
Spikes often occur for resource-intensive applications that require a lot of CPU power. Background processes also compete for resources, increasing usage when new programs are loaded. A lack of proper system resources makes this worse.
Is it safe to use OpenJDK?
OpenJDK is the most popular, safe Java implementation for production, being open-source. Maintain a high version that gets updated regularly to eliminate the risk of security attacks. Always review compatibility with applications and environments.
How can I make Java use less RAM?
To control heap size, you can adjust JVM memory settings with options like `-Xmx` and `-Xms`. Optimize data structures and algorithms to reduce memory footprints. Tuning garbage collection settings can also improve memory efficiency.
Conclusion:
AdoptOpenJDK 11.24’s high CPU usage due to `PrintStream` can be solved by reducing logging, using efficient logging frameworks, and optimizing application design. Even changing to a different version of JDK can be of help. These ensure smoother performance and stability.
