[英]the memory usage of netty normal in this dump?
for 13K users I have the following memory dump. 对于13K用户,我有以下内存转储。 I will paste the top 7 consumers.
我将粘贴前7位消费者。 Netty seems to consume too much memory.
Netty似乎消耗过多的内存。 Is this normal ?
这正常吗?
(Netty Version:3.2.7, implementing IdleStateAwareChannelUpstreamHandler,Total Memory Netty Memory Usage:2.5GB minimum ) (Netty版本:3.2.7,实现IdleStateAwareChannelUpstreamHandler,总内存Netty内存使用量:最小2.5GB)
num #instances #bytes class name
----------------------------------------------
1: 23086640 923465600 org.jboss.netty.util.internal.ConcurrentHashMap$Segment
2: 28649817 916794144 java.util.concurrent.locks.ReentrantLock$NonfairSync
3: 23086640 554864352 [Lorg.jboss.netty.util.internal.ConcurrentHashMap$HashEntry;
4: 118907 275209504 [I
5: 5184704 207388160 java.util.concurrent.ConcurrentHashMap$Segment
6: 5184704 130874832 [Ljava.util.concurrent.ConcurrentHashMap$HashEntry;
7: 1442915 115433200 [Lorg.jboss.netty.util.internal.ConcurrentHashMap$Segment;
It looks like the memory usage is not normal. 看起来内存使用不正常。
Here are some facts about Netty internal memory usage 这是有关Netty内部存储器使用情况的一些事实
One channel has two ReentrantLocks
, (one read lock,one write lock) 一个通道有两个
ReentrantLocks
,(一个读锁,一个写锁)
Channel stores all channel references in a org.jboss.netty.util.internal.ConcurrentHashMap
internally, and automatically removes on close (This is to assign unique channel ids). Channel将所有通道引用存储在
org.jboss.netty.util.internal.ConcurrentHashMap
内部,并在关闭时自动删除(这是分配唯一的通道ID)。
ChannelGroup stores channel references in a org.jboss.netty.util.internal.ConcurrentHashMap
on add() and automatically removes on close. ChannelGroup在add()上将频道引用存储在
org.jboss.netty.util.internal.ConcurrentHashMap
,并在关闭时自动删除。
There will be one ConcurrentHashMap$HashEntry
per item stored in org.jboss.netty.util.internal.ConcurrentHashMap
. 存储在
org.jboss.netty.util.internal.ConcurrentHashMap
每个项目将有一个ConcurrentHashMap$HashEntry
。
so you can calculate the expected memory usage, if your handlers are not leaking any references. 因此,如果您的处理程序没有泄漏任何引用,则可以计算预期的内存使用量。
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