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How G1 Garbage Collector is working in JAVA 9? | #JAVA9 #GC
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#JAVA9 #GC
Oracle’s Java 9 Hotspot VM ships with the Garbage First (G1) GC as its default garbage collector. This GC, first introduced in Java 7, has the unique ability to efficiently and concurrently deal with very large heaps.
It can also be configured to not exceed a maximum pause time. In this post we’ll take a look at how the G1 works compared to other collectors and why it can so easily outperform other state-of-the-art GCs on large heaps.
Garbage-First G1 Collector is a server-style garbage collector, targeted for multiprocessors with large memories, that meets a soft real-time goal with high probability, while achieving high throughput.
G1 preferentially collects regions with the least amount of live data, or "garbage first".
G1 is the long term replacement of CMS. Whole-heap operations, such as global marking, are performed concurrently with the application threads, to prevent interruptions proportional to heap or live-data size. Concurrent marking provides both collection completeness and identifies regions ripe for reclamation via compacting evacuation. This evacuation is performed in parallel on multiprocessors, to decrease pause times and increase throughput.
Read full details here:
Oracle’s Java 9 Hotspot VM ships with the Garbage First (G1) GC as its default garbage collector. This GC, first introduced in Java 7, has the unique ability to efficiently and concurrently deal with very large heaps.
It can also be configured to not exceed a maximum pause time. In this post we’ll take a look at how the G1 works compared to other collectors and why it can so easily outperform other state-of-the-art GCs on large heaps.
Garbage-First G1 Collector is a server-style garbage collector, targeted for multiprocessors with large memories, that meets a soft real-time goal with high probability, while achieving high throughput.
G1 preferentially collects regions with the least amount of live data, or "garbage first".
G1 is the long term replacement of CMS. Whole-heap operations, such as global marking, are performed concurrently with the application threads, to prevent interruptions proportional to heap or live-data size. Concurrent marking provides both collection completeness and identifies regions ripe for reclamation via compacting evacuation. This evacuation is performed in parallel on multiprocessors, to decrease pause times and increase throughput.
Read full details here:
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