Remote code execution in Java made trivial

SSH offers a very convenient way to execute shell scripts remotely. Code like

ssh myserver.acme.com << EOC
cd $APP_HOME
./start_my_stuff.sh
EOC

are fairly easy to write and read.

But while remote execution itself is easy, writing actual distributed code is total mess.

I’m a Java guy. I wish, I could run Java code remotely as easy as I can do it with shell.
And now, finally, I can.

@Test public void remote_hello_world() throws InterruptedException { ViManager cloud = CloudFactory.createSimpleSshCloud(); cloud.node("myserver.acme.com"); cloud.node("**").exec(new Callable<Void>() { @Override public Void call() throws Exception { String localHost = InetAddress.getLocalHost().toString(); System.out.println("Hi! I'm running on " + localHost); return null; } }); // Console output is transfered asynchronously, // so it is better to wait few seconds for it to arrive Thread.sleep(1000); }

And that snippet will work for you too, only two requirements are:

You should be able to SSH to target host without password (you have SSH key-pairs set up).

Remote host should have JDK installed, and java command on PATH.

That is it, there is no need to install anything special to target servers.

You can find more details in tutorial at GridKit site.

How it work?

A lot of black magic is happening behind the scene. In particular:

Classpath of current Java processes is replicated and cached at remote host using SFTP.

SSH is used to start remote Java process.

All communications between master and slave are tunneled in stdIn and stdOut (thus you can care less about NAT and firewall).

Modified version of RMI/serialization is used to allow anonymous classes to be run remotely.

By despite all that internal complexity, it just works. I’m using it to run agents on Linux and Solaris. I was using that stuff on Amazon EC2 and I cloud have master process running on my Windows desktop while slave scattered across Unixes.
It was long road. A lot of issues such as firewalls, bugs in JSch (java SSH client), subtle SSH limitation, etc has been solved along that path.
But now, I believe, it will “just work” for you too.

Java vs shell

But what is the need for such library in a first place? Below are just few of my reasons:

I’m a Java guy, I do not want to invest much in shell scripting skills.

Java is "really" cross platform, heck I can even debug stuff on Windows then run them on Linux.

I have to do a lot to distributed orchestration (starting/stopping processes in right order etc), it is so much easier to do in java.

It is hard to write reusable shell scripts, but easy for java (heck, I can even unit test pieces of deployment logic).

Remotting is just a remotting

Ability to effortlessly run remote java code is not much by itself. But it enables you to reach new levels of day-to-day automation (which was prohibitively expensive before).

But that would be topic for another post.

Coherence 101, Beware of cache listeners

Cache events facility is a quite useful feature of Oracle Coherence. For example, continuous queries and near cache features are build on top of cache event system.

Unfortunately it could be also abused easily. In particular, they are noticeably bad at scale unless you are very careful.

Please note. This article is covering only partitioned cache topology (distributed cache scheme).

Client side map listeners

UPDATE: I was very wrong in my previous description of client side synchronous map listeners. Section below was rewritten to reflect more accurate picture.

Client side map listeners are usually added via NamedCache API. They typically receive events from caches hosted on remote JVMs (storage nodes). But regardless of whenever cache event is produced at remote or local JVM, Coherence will deliver it to listeners using dedicated event dispatch thread (or service thread itself for listeners marked as synchronous).

Each cache service has only one event dispatch thread, and it could easily become a bottle neck, limiting speed of cache event processing on client.

Few tips to mitigate this design aspect are below.

• Do not do anything time consuming in listener itself, offload processing to other thread instead.
• Be careful with synchronization – avoid lock contention in listener code.
• When event hits your listener, its data are still in binary form. To avoid deserialization cost, do not access key or value in event dispatch thread, instead pass reference to map event object to own processing thread (or thread pool).

Last advice may not be intuitive, but deserialization of map event in Coherence’s event dispatch thread often becomes a bottleneck slowing down event processing rate.

Synchronous and normal map listeners

There is a marker interface SynchronousListener in com.tangosol.util package.
You could implement it in your map listener. But this wouldn’t make map event delivery to your listener synchronous with cache operation (as you may think), instead it would affect in which thread your listener is invoked.

Normal listeners are invoked in event dispatch thread.

“Synchronous” listeners would be invoked in service thread

What are the differences?

• Imagine you have near cache and you are using entry processor to update entry. If cache event would be processed in event dispatch thread, data in near cache may remain stale for short time between entry processor call have returned, but event is not processed yet.
  Using of synchronous listeners would solve this, because event would be guaranteed to be processed before processing response message from entry processor invocation.
• Time consuming custom map listeners could slow down event dispatch thread increasing event delays. This would affect Coherence build-in facilities such as near caches and CQC would be affected because they use synchronous listeners internally - you can consider it extra level of protection from misbehaving developer :)

But let me stress it again, for any type of listener events are delivered asynchronously relative to other cluster nodes.

Backing map listeners

Backing map listeners are used less often (but being abused more frequently). Backing map listeners are usually configured via XML cache configuration and work on storage side.

On storage side, Coherence could use pool of worker threads to perform operations in parallel. You may assume that you backing map listener would also be invoked in parallel …
… but that is wrong. Backing map listener could process one map event at time for given cache, regardless of thread pool size.

First time, I was also surprised by such behavior. This is not fundamental limitation of Coherence, but all out-of-box variations of backing map use cache global lock to dispatch map event. Even for partitioned backing map Coherence will use ObservableSplittingBackingMap wrapper which is, again, using global lock.

So, if you are using backing map listeners, be aware of that limitation. Live object pattern also relay on backing mapping listener and thus limited by this scalability constraint.

Map triggers

Fortunately map triggers work as a part of cache update transaction on cache service level. In other words map trigger would not harm performance more than entry processors do.

One possible workaround for baking map listeners concurrency issue could be invocation of map listener from map trigger.

Safepoints in HotSpot JVM

Term Stop-the-World pause is usually associated with garbage collection. Indeed GC is a major contributor to STW pauses, but not the only one.

Safepoints

In HotSpot JVM Stop-the-World pause mechanism is called safepoint. During safepoint all threads running java code are suspended. Threads running native code may continue to run as long as they do not interact with JVM (attempt to access Java objects via JNI, call Java method or return from native to java, will suspend thread until end of safepoint).

Stopping all threads are required to ensure what safepoint initiator have exclusive access to JVM data structures and can do crazy things like moving objects in heap or replacing code of method which is currently running (On-Stack-Replacement).

How safepoints work?

Safepoint protocol in HotSpot JVM is collaborative. Each application thread checks safepoint status and park itself in safe state in safepoint is required.

For compiled code, JIT inserts safepoint checks in code at certain points (usually, after return from calls or at back jump of loop). For interpreted code, JVM have two byte code dispatch tables and if safepoint is required, JVM switches tables to enable safepoint check.

Safepoint status check itself is implemented in very cunning way. Normal memory variable check would require expensive memory barriers. Though, safepoint check is implemented as memory reads a barrier. Then safepoint is required, JVM unmaps page with that address provoking page fault on application thread (which is handled by JVM’s handler). This way, HotSpot maintains its JITed code CPU pipeline friendly, yet ensures correct memory semantic (page unmap is forcing memory barrier to processing cores).

When safepoints are used?

Below are few reasons for HotSpot JVM to initiate a safepoint:

• Garbage collection pauses
• Code deoptimization
• Flushing code cache
• Class redefinition (e.g. hot swap or instrumentation)
• Biased lock revocation
• Various debug operation (e.g. deadlock check or stacktrace dump)

Trouble shooting safepoints

Normally safepoints just work. Thus, you can care less about them (most of them, except GC ones, are extremely quick). But if something can break it will break eventually, so here is useful diagnostic:

• -XX:+PrintGCApplicationStoppedTime – this will actually report pause time for all safepoints (GC related or not). Unfortunately output from this option lacks timestamps, but it is still useful to narrow down problem to safepoints.
• -XX:+PrintSafepointStatistics –XX:PrintSafepointStatisticsCount=1 – this two options will force JVM to report reason and timings after each safepoint (it will be reported to stdout, not GC log).

References

·         How does JVM handle locks – quick info about biased locking

·         HotSpot JVM thread management