The way Java manages the memory is by allocating chunks of memory in its “heap” space. Every so often, it will clean up

Some applications (like ours) have some infrequent but critical methods that will only be invoked a handful number of times but need to be extremely fast when they do (think of a risk or stop-loss process only called in emergencies).

The reason Java does not compile the code at start-up has to do with long-term performance optimisation. By observing the application run and analysing real-time methods invocations and class initialisations, Java compiles frequently called portions of code. It might even make some assumptions based on experience (this portion of code never gets called or this object is always a String).

When you restart your application with an existing profile, the Azul JVM immediately recalls its previous decisions and compiles the outlined methods directly, solving the Java warm-up issue.

Since Java 9, the G1 collector has been the default GC, the main idea being to slice up GC pauses according to user-supplied time targets. It usually offers shorter pause times but at the expense of lesser throughput. In addition, the pause time increases with the size of the heap.

Second issue, during a garbage collection, the whole application could freeze for anything between a few milliseconds to a few seconds (the delay increases with code complexity and heap size), and to make the matter worse, you have no way of controlling when this happens.

Multi-threading often requires locks, which cause synchronisation latencies and pauses (especially if they share resources). A popular design is a ring buffer queue system with many threads writing and reading in a lock-free setup (see the disruptor).

One old (and still used) technique is to use object pools of re-usable objects. A database connection pool, for example, will hold a reference to 10 opened connections ready to use as required.

A method needs to be called a certain number of times to reach the compilation threshold before it can be optimised and compiled (the limit is configurable but typically around 10,000 calls). Until then, unoptimised code is not running at “full speed”. There is a compromise between getting quicker compilation and getting high-quality compilation (if the assumptions were wrong there will be a cost of recompilation).

Java offers plenty of settings to tune its garbage collections (and the JVM in general) from the heap size to the collection algorithm, and the number threads allocated to GC. So, it’s quite common to see Java applications configured with a plethora of customised options:

Azul Zing addresses those issues by having its JVM “save” the state of compiled methods and classes in what it calls a profile. This unique feature named ReadyNow!® means Java applications are always running at optimum speed, even after a restart.

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elligence is the deep abiding knowledge of self which allows you to see the benefits and shortcomings of your emotions. A partner or a spouse with a high emotional intelligence can see how their emotions work, and they understand how the same emotions work in others. For this reason, they are able to approach those they love with a greater amount of compassion and understanding. It makes it easier for both parties to work together and remain focused on their future goals.

The GC impact is a big topic among Java developers; a full garbage collection is commonly referred to as a “stop-the-world pause” because it freezes the entire application.

Over the years, many GC algorithms have attempted to compromise throughput (how much CPU is spent on the actual application logic rather than on garbage collection) versus GC pauses (how long can I afford to pause my application for?).

Furthermore, you can build a profile in a development environment to mimic production behaviour. The optimised profile can then be deployed in production, knowing that all critical paths are compiled and optimised.

While pausing an application for a few milliseconds or even seconds may be acceptable for many Java applications, it is a disaster for low-latency ones, whether in automotive, aerospace, medical, or finance sectors.