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10 Reasons Your Java Apps are slow

Nastel Technologies®
May 12, 2020

1. Poor thread model where there is too much inter-thread synchronization or blocking on a common resource. Many developers overuse synchronized sections which slows down overall throughput.

2. Use of synchronous calls using fire-and-wait model. Many apps make users wait for a response. A better approach is to use asynchronous communication where requests are handled in the background and the user is notified when the request completes. The app can allow the user to do something else in the meantime.

3. Lack of effective caching. Many apps query databases to satisfy user requests. Common queries should be cached to dramatically improve the performance.

4. Poor memory management resulting in overactive GC (Garbage Collection) activity. Many applications create too many short-lived objects creating a lot of pressure on the Java garbage collector. These short-term objects must be cleaned up during GC cycles. Apps with high rates of object creation will slow down due to GC activity and GC pauses reducing overall throughput.

5. Overuse of application logging. Many applications use extensive logging and make calls to Log4j, slf4j or other logging frameworks. Logging calls can significantly slow apps down even when log calls are filtered out. Many of the log messages include parameters which are expanded using Object.toString() and could create lots of String instances & concatenation even when no log messages are logged.

6. Poor algorithm choice. Choose the most efficient algorithm for the job. Example: many apps make use of linear search based on assumptions that datasets are small. Performance will drop dramatically as the datasets grow. Use lock free algorithms in concurrent apps whenever possible to avoid synchronization blocks.

7. Too much I/O during critical execution paths. I/O (storage, network) is a lot slower than memory or CPU bound operations. Try to avoid IO during critical user interactions to improve latency profile.

8. Inefficient data models. Lots of applications are slow simply because of poor data models. This is especially true for database-based applications. Database operations will get slow due to non-existent indexes, poor index or queries requiring complex joins with many resulting in linear table scans. Performance will degrade significantly with larger datasets.

9. Deadlocks. These typically will halt your apps completely causing hangs and timeouts. Deadlocks are difficult to catch and troubleshoot, especially when they lock on resources external to the JVM. Carefully examine your threading model, locking sections to make sure you don’t get a deadlock.

10. Chatty apps. Too much inter-application chatter will dramatically reduce overall performance. Examine protocols used between your applications: how many messages were exchanged? How big are the messages? Are exchanges synchronous (sync) or asynchronous (async)? The objective is to make such exchanges as slim as possible. Chatty apps are typically slow apps.

 

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.

 

The Nastel i2M Platform provides:

Author

  • Albert Mavashev

    Innovator, Growth Hacker, Idea Generator. As Nastel’s CTO, Albert turns ideas into real-world solutions. Albert’s specialties include Integration Infrastructure Management, Application Performance Management, IT Service Management, Transaction Analytics, Performance Measurement & Practices, Streaming Analytics @ Scale, Clustered Computing, Big & Fast Data, Complex Event Processing (CEP), Messaging & Middleware, Ops & DevOps, Blockchain, cryptocurrencies, digital tokens/assets, and building & scaling & analyzing digital de-centralized economies.

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