Tuesday, July 27, 2010

DGC II: The JVM Tuning

This blog post is part of the DevOps Guide to Confluence series. In this chapter of the guide, I’ll be focusing on JVM tuning with the aim to make our Confluence perform well and operate reliably.

JDK Version

First things first: use a recent JDK. Java 5 (1.5) has been EOLed 1.5 years ago, there is absolutely no reason for you to use it with Confluence. As George pointed out in his presentation, there are some significant performance gains to be made just by switching to Java 6 and you can get another performance boost if you upgrade from an older JDK 6 release to a recent one. JDK 6u21 is currently the latest release and that’s what I would pick if I were to set up a production Confluence server today.

If you are wondering about which Java VM to use, I suggest that you stick with Sun’s HotSpot (also known as Sun JDK). It’s the only VM supported by Atlassian and I really don’t see any point in using anything else at the moment.

Lastly it goes without saying that you should use -server JVM option to enable the server VM. This usually happens automatically on server grade hardware, but it's safer to set it explicitly.

VM Observability

For me using JDK 6 is not just about performance, but also about observability of the VM. Java 6 contains many enhancements in the monitoring, debugging and probing arena that make JDK 5 and its VM look like an obsolete black box.

Just to mention some enhancements, the amount of interesting VM telemetry data exposed via JMX is amazing, just point a VisualVM to a local Java VM to see for yourself (no restart or configuration needed). Be sure to install VisualGC plugin for VisualVM. In order to allow remote connections you’ll need to start the JVM with these flags:

Unless you make the port available only on some special admin-only network, you should password protect the JMX endpoint as well as use SSL. The JMX interface is very powerful and in the wrong hands could result in security issues or outages caused by inappropriate actions.

For more info about all the options available read this document.

In addition to JMX, on some platforms there is also good DTrace integration which helped me troubleshoot some Confluence issues in production without disrupting our users.
And lastly there is BTrace that allowed me to troubleshoot a nasty hibernate issue once. It's a very handy tool that as opposed to DTrace, works on all OSes.

I can’t stress enough how important continuous monitoring of your Confluence JVMs is. Only if you know how your JVMs and app are doing, you can tell if your tuning has any effect. George Barnett has also a set of automated performance tests which are handy to load test your test instance and compare results before and after you make some tweaks.

Heap and Garbage Collection Must Haves

After upgrading the JDK version, the next best thing you can do is to give Confluence lots of memory. In the infrastructure chapter of the guide, I mentioned that you should prepare your HW for this, so let’s put this memory to use.

Before we set the heap size, we should decide between 32-bit JVM and 64-bit JVM. 64-bit VM is theoretically a bit slower, but allows you to create huge heaps. 32-bit JVM has heap size limited by the available 32-bit address space and other factors. 32bit OSes will allow you to create heaps up to only 1.6-2.0 GB. 64bit Solaris will allow you to create 32bit JVMs with up to 4GB heap (more info). For anything bigger than that you have to go 64bit. It’s not a big deal, if your OS is 64bit already. The option to start the VM in 64bit mode is -d64. On almost all platforms the default is -d32.

Before I go into any detail, I should explain what are the main objectives of heap and garbage collection tuning for Confluence. The objectives are:
  • heap size - we need to tell JVM how much memory to use
  • garbage collector latency - garbage collection often requires that the JVM stops your application, this is GC pauses are often invisible, but with large heaps and under certain conditions might become very significant (30-60+ seconds)

Additionally we should also know a thing or two about how Confluence uses the heap. The main points are:
  • Objects created by Confluence and stored on the heap generally fall into three categories:
    • short-lived objects - life-cycle of these is bound to a http request
    • medium-lived objects - usually represent cache entries with shorter TTL
    • long-lived objects - represent cache entries with big TTL, settings and infrastructure objects (plugin framework, rendering engine, etc), cache entries taking most of the space.
  • Confluence creates lots of short-lived objects per request
  • Half or more of the heap will be used by long-lived cache objects

By combining our objectives with our knowledge of Confluences heap profile, our tuning should focus on providing enough heap space for the application to have space for the cache, short-lived objects, as well as some extra buffer. Given that long-lived objects will (eventually) reside in the old generation of the heap, we want to avoid promoting short-lived objects there, because otherwise we’ll then need to do massive garbage collections of the old generation unnecessarily. Instead we should try to limit the promotion from young generation only to those objects, that will likely belong to the long-lived category.

We’ll also need to figure out how much heap you need to use. Unfortunately there isn’t an easy way to find this out, except for some educated guessing and trial & error. You can also read this HW Requirements document from Atlassian that can give you an idea about some starting points. I believe we started at 1GB, but over time went through 2GB, 3GB, 3.5GB, 4GB, 5GB all the way to 6GB.

The Confluence heap size depends on the number of concurrent users and the amount of content you have. This is mainly because Confluence uses a massive (well, in our case it is) in-process cache that is stored on the heap. We’ll get to Confluence and cache tuning in a later chapter of this guide.

So let’s set the max heap size. This is done via -Xmx JVM option:
The additional -Xms parameter says that the JVM should reserve all 6GB at startup — this is to avoid heap resizing which can be slow, especially when dealing with large heaps.

The rest of the heap settings in this post are based on 6GB heap size, you might need to make appropriate changes to adjust for your total heap size.

The next JVM option is -Xmn, which specifies how much of the heap should be dedicated to young generation (you should read up on generational gc if you don’t know what I’m talking about). The default is something like 25% or 33%, I set the young generation to ~45% of the entire heap:

Increasing the permanent generation size is also usually required given the number of classes that Confluence loads. This is done via -XX:MaxPermSize option:

Given that determining the right heap size for your environment is non-trivial task for larger instances, especially if occasional memory leaks start consuming the precious memory, you always want to have as much data as possible to debug memory exhaustion issues. Aside from good monitoring (which I mentioned in the previous chapter) you should also configure your JVM to dump the heap, when an OutOfMemoryException occurs. You can then analyze this heap dump for potential memory leaks.

Since we are dealing with relatively big heaps, make sure you have enough space on the disk (heap dumps for 6GB heap usually take 2-4GB). I’ve had a very good experience using Eclipse Memory Analyzer to analyze these large heaps (VisuaVM or jhat are not up for analyzing heaps of this size). The relevant JVM options are:

While trying to minimize gc latency in order to avoid situations when users have to wait several seconds for the stop-the-world (STW) gc to finish before their pages render is a commendable thing to do, the main reason why you want to do this is to avoid Confluence cluster panics.

Confluence has this “wonderful” cluster safety mechanism that is sensitive to any latency bigger than a few tens of seconds. In case a major STW gc occurs, the cluster safety code might announce cluster panic and shut down all the nodes (that’s right, all the nodes, not just the one that is misbehaving).

In order to be informed of any latencies caused by gc, you need to turn on gc logging. This is the magic combination of switches that works well for me:

Unfortunately the file specified via -Xloggc will get overwritten during a jvm restart, so make sure you preserve it either manually before a restart or automatically via some restart script. Additionally reading the gc log is a tough job that requires some practice and since the format varies a lot depending on your JDK version and garbage collector, I’m not going to describe it here.

Performance tweaks

The first performance boosting JVM option I'd like to mention is -XX:+AggressiveOpts, which will turn on performance enhancements that are expected to be on by default in the future JVM versions (more info).

If you are using 64bit JVM then -XX:+UseCompressedOops will make a big difference and will virtually eliminate the performance penalty you pay for switching from 32bit to 64bit JVM.

And lastly there is -XX:+DoEscapeAnalysis which will boost the performance by another few percents.

Optional Heap and GC tweaks

To slow down object promotion into the old generation, you might want to tune the sizes of the survivor space (a heap generation within the young generation). To achieve this, we want the survivor space to be slightly bigger than the default. Additionally I also want to keep the promotion rate down (objects that survive a specific number of collections in the survivor space will be be promoted to the older generation), so I use these options:

I also found that by using parallel gc for the young generation and concurrent mark and sweep gc for the older generation I can practically eliminate any significant SWT gc pauses. Your mileage might vary on this one, so do some testing before you use it in production. These are the settings I use:


The information above was gather from years of experience as well as various sources, including the following:

Running Multiple Web Apps in one VM

Don't do that. Really. Don't. Bad things will happen if you do (OOME, classloading issues etc).


Your JVM should now be in a good shape to host Confluence and serve your clients. In the next chapter of this guide I'll write about Confluence configuration, tuning, upgrades and more.


Tim Moore said...


I just wanted to say that I'm really enjoying this blog series, and I'm learning a lot! Thanks for writing all of this up.

I was also wondering if you've tried Adaptavist's Visual VM plugin for confluence: https://www.adaptavist.com/display/ADAPTAVIST/2010/06/10/Adaptavist+announce+Visual+VMm+plugin

I've just played with a little bit, but it's pretty nice. It puts some of the monitoring graphs from Visual VM right into a web page in the Confluence admin console.

Anyway, I'll keep making my way through the rest of the posts you've made already, and I'm looking forward to what comes next.

-- Tim

Igor Minar said...

Hi Tim,

I'm glad that you are finding the info useful.

I did read about the plugin but haven't had the opportunity to try it myself.

It's a very slick plugin, but in my opinion has two major flaws (correct me if I'm wrong):
1) it runs in-process only (within the same JVM as Confluence) - this means that if the VM is running out of resources (memory, cpu) the plugin won't work properly at the time when you need it the most. You'll likely won't be able to get to its UI and if it records any data for future viewing, it's likely that some information won't be recorded at all during the problematic period.
2) I didn't find any information on how this plugin works in clustered environment, so I'm assuming that the monitoring is done on per JVM basis and can be viewed only by logging to all of the individual nodes. In other words there is no central management or aggregation going on.

The Adaptavist came up with something very interesting, but from what I've seen it's again something suitable only for smaller non-clustered instances.

However! Going from what they have to a standalone product that runs outside of the Confluence VM and gathers data from several nodes should not be that difficult. Especially if they are really reusing the VisualVM apis. If they added data archival, aggregation and some fancy statistics they would have a really slick version of what my team build for our internal monitoring.


Anonymous said...

Thank you for this