Tuesday, May 07, 2013

JGroups 3.3.0.Final released

I'm happy to announce that JGroups 3.3.0.Final was released today.

It contains quite a few optimizations and new features, the most prominent ones being:
  • Message batching: messages received as bundles by the transport are passed up as batches. Compared to passing individual messages up the stack, the advantage is that we have to acquire resources (such as locks) only once per batch instead of once per message. This reduces the number of lock acquisitions and should lead to less context switching and better performance in contended scenarios.
  • Asynchronous Invocation API: This allows the recipient of a message in MessageDispatcher or RpcDispatcher to make the delivering thread return immediately (making it available for other requests) and to send the response later. The advantage is that it is the application which now decides how to deliver messages (e.g. sequentially or in parallel), and not JGroups. The documentation is here: http://www.jgroups.org/manual-3.x/html/user-building-blocks.html#AsyncInvocation
  • UNICAST3: this is a new reliable unicast protocol, combining the advantages of UNICAST (immediate delivery) and UNICAST3 (speed). It is the default in 3.3.
  • New internal thread pool: to be used for internal JGroups messages only. This way, important internal JGroups messages are not impeded by the delivery of application messages ahead of them in delivery order. While this pool is not supposed to be used by application messages, it will help for example to decrease unneeded blocking (by credit messages getting queued up behind application messages), or reduce false suspicions (due to heartbeats getting handled too late, or even getting dropped).
  • RELAY2 improvements: RELAY2 is the protocol which relays traffic between geographically separate sites. This will be the topic of my talk at Red Hat Summit in June.
  • New timer implementation: a better, simpler and faster implementation; the default in 3.3.
  • New message bundler: this new bundler handles sending of individual messages and message batches equally well. Default in 3.3.
A more detailed version of these release notes can be found at [1]. The documentation can be found at [2]. The JIRA is at [3].

Please post questions and feedback as usual on the mailing list.


[1] https://github.com/belaban/JGroups/blob/3.3/doc/ReleaseNotes-3.3.0.txt

[2]  http://www.jgroups.org/manual-3.x/html/index.html

[3] https://issues.jboss.org/browse/JGRP

Monday, April 22, 2013

JBossWorld 2013 in Boston is around the corner

FYI,

I'm going to have a talk at JBossWorld / Red Hat Summit 2013 in Boston on June 13: http://www.redhat.com/summit/sessions/index.html#54.

This will be about a feature in JBoss Data Grid (JDG) which provides geographic failover between sites. I'm going to run a couple of JDG clusters (Boston, London and San Francisco), and initially route all clients to London. Then, as London shuts down at the end of their day, I'll route all clients over to Boston, and finally to San Francisco.

I have a demo that can be run by anyone with internet access, in their browser. Users can punch in some data, and will see their clients fail over between sites seamlessly, without data loss.

Hope to see / meet some of you in Boston !
Cheers,

Tuesday, February 05, 2013

Performance of message batching


A quick heads up on performance of message batching [1] [2]: I ran MPerf and UPerf and got the results as shown at the bottom of [1].

The tests were run on a 4 node cluster; with cluster sizes of 6 and 8, I ran 2 processes on the same physical box.

MPerf shows that a slightly better perf for 2 and 4 nodes, but a significantly (10%) better perf when running more than 1 process on the same box (6 and 8 nodes). I think the reason is that under contention, the property of message batching to acquire fewer locks comes in to reduce lock contention.

UnicastTestRPC shows exactly the same perf for the old (no message batching) and the new code (with message batching). The main reason here is that we use synchronous RPCs and one sender, which doesn't take advantage of message batching at all, as no message bundles are sent across the wire.

UPerf shows a significantly better perf for 4 nodes (11%) and 8 nodes (16% better). I guess the reason here is that we do make use of message batching as we have multiple sender threads and higher contention than in the previous test.

This is not the end of the line, as I haven't implemented message batching in protocols above NAKACK2 and UNICAST2: currently, messages are sent up in batches from the transport to NAKACK2 (multicast messages) or UNICAST2 (unicast messages), but from there on, they're sent up individually.
This will get changed in [3], but because this is a lot of work and will affect many classes, I thought I split the work in two parts.

The first part has been merged with master (3.3) and it would be interesting to get feedback from people trying this out !

Cheers,

[1] https://issues.jboss.org/browse/JGRP-1564

[2] http://belaban.blogspot.ch/2013/01/buy-one-get-many-for-free-message.html

[3] https://issues.jboss.org/browse/JGRP-1581

Wednesday, January 30, 2013

Buy one, get many for free: message batching in JGroups

Just a quick heads-up of what's going on in JGroups 3.3 with message batching.

Currently, when the transport receives a message bundle (say 20 messages), it passes the bundle to the regular thread pool (OOB messages are never bundled). The thread which handles the bundle grabs each message, adds it to the retransmission table, and then removes as many messages as possible and passes them up one-by-one.

So, although we have a message bundle, we don't process it as a bundle, but rather add each message individually and then pass them up up one-by-one.

Message batching [1] changes this. It reads a message bundle directly into 2 MessageBatch instances: one for OOB messages and one for regular messages. (This already shows that OOB messages are now bundled, too, but more on this later). The OOB MessageBatch is passed to the OOB thread pool for handling, the regular batch to the regular pool.

A message batch is nothing more than a list of messages.

A message batch is handled by only 1 thread: the thread passes the entire batch up the stack. Each protocol can remove messages it consumes (e.g. FD_ALL), change messages in-place (e.g. COMPRESS or ENCRYPT), remove and add messages (e.g. FRAG2), remove messages and pass up a new batch (NAKACK2, UNICAST2) or even do nothing (SIZE, STATS).

The advantage is that a protocol can now handle many messages at once, amortizing (e.g.) lock acquisition costs. For example, NAKACK2 adds all 20 messages to the retransmission table at once, thereby acquiring the table lock only once rather than 20 times. This means that we incur the cost of 1 lock acquition, instead of 20. It goes without saying that this will also reduce lock contention, at least in this particular case, even if the lock duration will be slightly longer than before.

I'll present some performance numbers soon, but so far preliminary performance tests look promising !

So while message bundling queues messages and sends them across the wire as a list, but stops at the receiver's transport; message batching takes this idea further and passes that bundle up all the way to the channel. (Note that this will require another receive() callback in the Receiver, but this will be transparent by default).

Message batching will allow other cool things to happen, e.g.
  • OOB messages will be bundled too now. If no bundling is desired, tag a message as DONT_BUNDLE.
  • We can simplify the message bundler (on the sender side), see [2]. As a result, I might even be able to remove all existing 4 message bundlers. As you know, I like removing stuff, and making code easier to read !
  • RPC responses can be bundled [3]
  • UNICAST2 can now ack the 'last message' [4]
Cheers,


[1] https://issues.jboss.org/browse/JGRP-1564

[2] https://issues.jboss.org/browse/JGRP-1540

[3] https://issues.jboss.org/browse/JGRP-1566

[4] https://issues.jboss.org/browse/JGRP-1548

Wednesday, January 02, 2013

SUPERVISOR: detecting faults and fixing them automatically

I've added a new protocol SUPERVISOR [1] to master, which can periodically check for certain conditions and correct them if necessary. This will be in the next release (3.3) of JGroups.

You can think of SUPERVISOR as an automated rule-based system admin.

SUPERVISOR was born out of a discussion on the mailing list [2] where a bug in FD caused the failure detection task in FD to be stopped, so members would not get suspected and excluded anymore. This is bad if the suspected member was the coordinator itself, as new members would not be able to join anymore !

Of course, fixing the bug [3] was the first priority, but I felt that it would be good to also have a second line of defense that detected problems in a running stack. Even if a rule doesn't fix the problem, it can still be used to detect it and alert the system admin, so that the issue can be fixed manually.

The documentation for SUPERVISOR is here: [4].


[1] https://github.com/belaban/JGroups/blob/master/src/org/jgroups/protocols/rules/SUPERVISOR.java

[2] https://sourceforge.net/mailarchive/message.php?msg_id=30218296

[3] https://issues.jboss.org/browse/JGRP-1559

[4] http://www.jgroups.org/manual-3.x/html/user-advanced.html#Supervisor



Cross site replication: demo on YouTube

FYI,

I've recently published a video on cross-site replication [1] on Youtube: [2].

The video shows how to set up and configure cross-site replication in Infinispan, although the focus of the video is on running the performance test [3].

Cheers, and a belated happy new year to everyone !


[1] https://docs.jboss.org/author/display/ISPN/Cross+site+replication

[2] https://www.youtube.com/watch?v=owOs430vLZo

[3] https://github.com/belaban/IspnPerfTest

Friday, November 16, 2012

Persisting discovery responses with TCPPING

I've added a nifty little feature to JGroups which helps people who use TCPPING but can't list all of the cluster nodes in the static list.

So far I've always said that if someone needs dynamic discovery, they should use a dynamic discovery protocol such as PING / MPING (require IP multicasting), TCPGOSSIP (requires external GossipRouter process), FILE_PING (requires shared file system), S3_PING / AS_PING / SWIFT_PING / RACKSPACE_PING (requires to be running in a cloud) or JDBC_PING (requires a database).

I always said that TCPPING is for static clusters, ie. clusters where the membership is finite and is always known beforehand.

However, there are cases, where it makes sense to add a little dynamicity to TCPPING, and this is what PDC (Persistent Discovery Cache) does.

PDC is a new protocol that should be placed somewhere between the transport and the discovery protocol, e.g.

    <TCP />

    <PDC cache_dir="/tmp/jgroups"  />

    <TCPPING timeout="2000" num_initial_members="20"
            initial_hosts="192.168.1.5[7000]"
            port_range="0" return_entire_cache="true"
            use_disk_cache="true" />

Here, PDC is placed above TCP and below TCPPING. Note that we need to set use_disk_cache to true in the discovery protocol for it to use the persistent cache.

What PDC does is actually very simple: it intercepts discovery responses and persists them to disk. Whenever a discovery request is received, it also intercepts that request and adds its own results from disk to the response set.

Let's take a look at a use case (with TCPPING) that PDC solves:
  • The membership is {A,B,C}
  • TCPPING.initial_hosts="A"
  • A is started, the cluster is A|0={A}
  • B is started, the cluster is A|1={A,B}
  • C is started, the cluster is A|2={A,B,C}
  • A is killed, the cluster is B|3={B,C}
  • C leaves, the cluster is B|4={B}
  • C joins again
    • Without PDC, it doesn't get a response from A (which is the only node listed in TCPPING.initial_hosts), and forms a singleton cluster C|0={C}
    • With PDC, C discovers A and B and asks both of them for an initial discovery. B replies and therefore the new view is B|5={B,C}
The directory in which PDC stores its information is configured with PDC.cache_dir. If multiple cluster nodes are running on the same physical box, they can share that directory.

Feedback appreciated on the mailing list !
Cheers,
Bela

Friday, October 19, 2012

JGroups 3.2.0.Final released

I've released JGroups 3.2.0.Final, the most important features are:
  • RELAY2 
  • Internationalized logging
    •  The most important user-facing warnings and error messages (e.g. configuration errors) have been internationalized.
    • Error/warning translations are in jg-messages.properties. If someone wants to translate these into a different language, e.g. French, just copy jg-messages.properties into jg-messages_fr.properties and translate the messages. The new file now only needs to be added to the classpath, no changes to JGroups !
  • Reduction of error/warn messages
    • Sometimes there are a lot of recurring warnings or error messages, e.g. warnings about messages received from different clusters, or warnings about messages from members with different JGroups versions.
    • These can now be suppressed for a certain time, e.g. we can configure that there's only *one* warning every 60 seconds about messages from different clusters.
    • [https://issues.jboss.org/browse/JGRP-1518]

A full list of features and bug fixes is here.

The manual can be found at http://www.jgroups.org/manual-3.x/html/index.html.

Questions and feedback as usual on the mailing lists.

Enjoy !

Bela Ban
Kreuzlingen, Oct 2012

Friday, July 06, 2012

JGroups 3.0.11 and 3.1.0 released

I'm happy to announce that I've released JGroups versions 3.0.11 and 3.1.0 !

3.0.11 is the 3.0.x branch which is used by the newly released EAP 6 / JBoss 7.x application server. It consists mainly of bug fixes (and one or two performance enhancements) backported from the 3.1 branch.

The 3.1.0 release has 90+ issues which were resolved (some of them backported to 3.0.x).

Here's a short list of the major issues resolved in 3.1.0, for details consult [2]:

  • NAKACK, UNICAST and NAKACK2 now use a new internal data structure for message delivery and retransmission, which reduces the memory needed by JGroups
  • MERGE3: a new merge protocol for large clusters
  • RSVP: blocks the sender until a given message has been received by all members of the target set
  • A new Total Order Anycast (TOA) protocol needed by the next version of Infinispan to deliver messages to a cluster subset in total order
  • New discovery protocols for mod-cluster (not yet completely done), Rackspace and OpenStack
  • MPerf / UPerf: dynamic multicast and unicast performance tests
  • Concurrent joins to a non-existing cluster are faster, and there's less chances of a merge happening (optimization)
  •  TCP: socket creation doesn't block sending of regular messages (optimization)
Both JGroups 3.0.11 and 3.1.0 can be downloaded from [1].  The updated documentation can be found at [3].

As usual, use the mailing lists or fora for questions.

Enjoy !


[1] https://sourceforge.net/projects/javagroups/files/JGroups/

[2] https://github.com/belaban/JGroups/blob/master/doc/ReleaseNotes-3.1.0.txt

[3] http://www.jgroups.org/manual-3.x/html/index.html

Sunday, April 01, 2012

JBoss World 2012

I'm going to be speaking at JBossWorld 2012 (June 29th) on session clustering in EAP 6 (JBoss 7.1.x):
http://www.redhat.com/summit/sessions/best-of.html#18

The talk is a remake of the 2008 talk held by Brian Stansberry and me, and will show how clustering  performance has increased between JBoss 4 and 7. However, this is not all, I'll cover among other things:
  • Configuration of an EAP 6 cluster
  • Use of EAP 6 domains to start and stop JBoss instances in a cluster, to deploy applications across the entire cluster, and to disseminate configuration changes
  • Pros and cons of replication and distribution, and its effect on scalability and performance
  • Configuration and tuning of Infinispan and JGroups to achieve optimal performance
  • Setup of mod-cluster to dynamically add and remove JBoss instances and applications
  • Performance difference between EAP 5 and 6
I'll be in Boston Tuesday until Friday and hope to meet many users of JGroups/Infinispan/JBoss clustering, get feedback and experience reports on the good, bad and ugly, and in general have many good discussions !

Friday, February 10, 2012

JGroups 3.1.0.Alpha2 released

I'm happy to announce the release of JGroups 3.1.0.Alpha2 !

Don't be put off by the Alpha2 suffix; as a matter of fact, this release is very stable, and I might just go ahead and promote it to "Final" within a short time !

At the time of writing this, I still have a few issues open in 3.1, but because I think the current feature set is great, I might push them into a 3.2.

So what features and enhancements did 3.1 add ? In a nutshell:

  • A new protocol NAKACK2: this is a successor to NAKACK (which will get phased out over the next couple of releases). The 2 biggest changes are:
    • A new memory efficient data structure (Table) is used to store messages to be retransmitted. It can grow and shrink dynamically, and replaces NakReceiverWindow.
    • There is no Retransmitter associated with each table, and we don't create an entry *per* missing sequence number (seqno) or seqno range. Instead, we have *one* single retransmission task, which periodically (xmit_interval ms) scans through *all* tables, identifies gaps and triggers retransmission for missing messages. This is a significant code simplification and brings memory consumption down when we have missing messages.
  • Changes to UNICAST2 and UNICAST: in both cases, we switch from NakReceiverWindow  / AckSenderWindow / AckReceiverWindow to Table and instead of a retransmitter per member, we now have *one* retransmitter task for *all* members.
  • The changes in NAKACK2, UNICAST2 and UNICAST have several benefits:
    • Code simplification: having only one data structure (Table) instead of several ones (NakReceiverWindow, AckSenderWindow, AckReceiverWindow), plus removing all Retransmitter implementations leads to simpler code.
    • Code reduction: several classes can be removed, making the code base simpler to understand, and reducing complexity
    • Better maintainability: Table is now an important core data structure, and improvements to it will affect many parts of JGroups
    • Smaller memory footprint: especially for larger clusters, having less per-member data (e.g. retransmission tasks) should lead to better scalability in large clusters (e.g. 1000 nodes).
    • Smooth transition: we'll leave NAKACK (and therefore NakReceiverWindow and Retransmitter) in JGroups for some releases. NAKACK / NakReceiverWindow have served JGroups well for over a decade, and are battle-tested. When there is an issue with NAKACK2 / Table in  production, we can always fall back to NAKACK. I intend to phase out NAKACK after some releases and a good amount of time spent in production around the world, to be sure NAKACK2 works well
  • MERGE3: merging is frequent in large clusters. MERGE3 handles merging in large clusters better by
    • preventing (or reducing the chances of) concurrent merges
    • reducing traffic caused by merging
    • disseminating {UUID/physical address/logical name} information, so every node has this information, reducing the number of times we need to ask for it explicitly.
    • MERGE3 was written with UDP as transport in mind (which is the transport recommended for large clusters anyway), but it also works with TCP. 
  • Synchronous messages: they  block the sender until the receiver or receivers have ack'ed its delivery. This allows for 'partial flushing' in the sense that all messages sent by a member P prior to M will get delivered at all receivers before delivering M.
    This is related to FLUSH, but less costly and can be done per message. For example, if a unit of work is done, a sender could send an RSVP tagged message M and would be sure that - after the send() returns - all receivers have delivered M.
    To send an RSVP marked messages, Message.setFlag(Message.Flag.RSVP) has to be used.
    A new protocol (RSVP) needs to be added to the stack. See the documentation (link below) for details.
  • A new rackspace-based discovery protocol
  • Concurrent joining to a non-existing cluster is faster
  • Elimination (or reduction of) "no physical address for X; dropping message" warnings
  • Elimination of global JGroups ThreadGroup leaks
  • Elimination of socket leaks with TCPPING
The full list of changes is at [1], the manual can be found at [2] and 3.1 can be downloaded from [3].

Feedback is appreciated on the mailing lists, enjoy !


[1] https://github.com/belaban/JGroups/blob/master/doc/ReleaseNotes-3.1.0.txt
[2] http://www.jgroups.org/manual-3.x/html/index.html
[3] https://sourceforge.net/projects/javagroups/files/JGroups/3.1.0.Alpha2/



Tuesday, December 06, 2011

Repondez s'il vous plait !

No, this isn't a post in French (my school French would be too rusty for this !); this is about a new protocol in JGroups, called RSVP :-)

As the name possibly suggests, this feature allows for messages to get ack'ed by receivers before a message send returns. In other words, when A broadcasts a message M to {A,B,C,D}, then JChannel.send() will only return once itself, B, C and D have acknowledged that they delivered M to the application.

This differs from the default behavior of JGroups which always sends messages asynchronously, and guarantees that all non-faulty members will eventually receive the message. If we tag a message as RSVP, then we basically have 2 properties:
  1. The message send will only return when we've received all acks from the current members. Members leaving or crashing during the wait are treated as if they sent an ack. The send() method can also throw a (runtime) TimeoutException if a timeout was defined (in RSVP) and encountered.
  2. If A sent (asynchronous) messages #1-10, and tagged #10 as RSVP, then - when send() returns successfully - A is guaranteed that all members received A's message #10 and all messages prior to #10, that's #1-9.
This can be used for example when completing a unit of work, and needing to know that all current cluster members received all of the messages sent up to now by a given cluster member.

This is similar to FLUSH, but less strict in that it is a per-sender flush, there is no reconciliation phase, and it doesn't stop the world.

An alternative is to use a blocking RPC. However, I wanted to add the capability of synchronous messages directly into the base channel.

Note that this also solves another problem: if A sends messages #1-5, but some members drop #5, and A doesn't send more messages for some time, then A#5 won't get delivered at some members for quite a while (until stability (STABLE) kicks in).

RSVP will be available in JGroups 3.1. If you want to try it out, get the code from master [2]. The documentation is at [1], section 3.8.8.2.

For questions, I suggest one of the mailing lists.
Cheers,

[1] http://www.jgroups.org/manual-3.x/html/user-channel.html#SendingMessages

[2] https://github.com/belaban/JGroups


Thursday, November 17, 2011

JGroups 3.0.0.Final released

I'm happy to announce that JGroups 3.0.0.Final is here !

While originally intended to make only API changes (some of them queued for years), there are also several optimizations, most of them related to running JGroups in larger clusters.

For instance, the size of several messages has been reduced, and some protocol rounds have been eliminated, making JGroups more memory efficient and less chatty.

For the last couple of weeks, I've been working on making merging of 100-300 cluster nodes faster and making sure a merge never blocks. To this end, I've written a unit test, which creates N singleton nodes (= nodes which only see themselves in the cluster), then make them see each other and wait until a cluster of N has formed.

The test itself was a real challenge because I was hitting the max heap size pretty soon. For example, with 300 members, I had to increase the heap size to at least 900 MB, to make the test complete. This indicates that a JGroups member needs roughly a max of 3MBs of heap. Of course, I had to use shared thread pools, timers and do a fair amount of (memory) tuning on some of the protocols, to accommodate 300 members all running in the same JVM.

Running in such a memory constrained environment led to some more optimizations, which will benefit users, even if they're not running 300 members inside the same JVM ! :-)

One of them is that UNICAST / UNICAST2 maintain a structure for every member they talk to. So if member A sends a unicast to each and every member of a cluster of 300, it'll have 300 connections open.

The change is to close connections that have been idle for a given (configurable) time, and re-establish them when needed.

Further optimizations will be made in 3.1.

The release notes for 3.0.0.Final are here: https://github.com/belaban/JGroups/blob/master/doc/ReleaseNotes-3.0.0.txt

JGroups 3.0.0.Final can be downloaded here: https://sourceforge.net/projects/javagroups/files/JGroups/3.0.0.Final

As usual, if you have questions, use one of the mailing lists for questions.

Enjoy !


Monday, September 12, 2011

Publish-subscribe with JGroups

I've added a new demo program (org.jgroups.demos.PubSub), which shows how to use JGroups channels to do publish-subscribe.

Pub-sub is a pattern where instances subscribe to topics and receive only messages posted to those topics. For example, in a stock feed application, an instance could subscribe to topics "rht", "aapl" and "msft". Stock quote publishers could post to these topics to update a quote, and subscribers would get notified of the updates.

The simplest way to do this in JGroups is for each instance to join a cluster; publishers send topic posts as multicasts, and subscribers discard messages for topics to which they haven't subscribed.

The problem with this is that a lot of multicasts will make it all they way up to the application, only to be discarded there if the topic doesn't match. This means that a message is received by the transport protocols (by all instances in the cluster), passed up through all the protocols, and then handed over to the application. If the application discards the message, then all the work of fragmenting, retransmitting, ordering, flow-controlling, de-fragmenting, uncompressing and so on is unnecessary, resulting in wasted CPU cycles, lock acquisitions, cache and memory accesses, context switching and bandwidth.

A solution to this could be to do topic filtering at the publisher's side: a publisher maintains a hashmap of subscribers and topics they've subscribed to and sends updates only to instances which have a current subscription.

This has two drawbacks though: first the publishers have additional work maintaining those subscriptions, and the subscribers need to multicast subscribe or unsubscribe requests. In addition, new publishers need to somehow get the current subscriptions from an existing cluster member (via state transfer).

Secondly, to send updates only to instances with a subscription, we'd have to resort to unicasts: if 10 instances of a 100 instance cluster are subscribed to "rht", an update message to "rht" would entail sending 10 unicast messages rather than 1 multicast message. This generates more traffic than needed, especially when the cluster size increases.

Another solution, and that's the one chosen by PubSub, is to send all updates as multicast messages, but discard them as soon as possible at the receivers when there isn't a match. Instead of having to traverse the entire JGroups stack, a message that doesn't match is discarded directly by the transport, which is the first protocol that receives a message.

This is done by using a shared transport and creating a separate channel for each subscription: whenever a new topic is subscribed to, PubSub creates a new channel and joins a cluster whose name is the topic name. This is not overly costly, as the transport protocol - which contains almost all the resources of a stack, such as the thread pools, timers and sockets -  is only created once.

The first channel to join a cluster will create the shared transport. Subsequent channels will only link to the existing shared transport, but won't initialize it. Using reference counting, the last channel to leave the cluster will de-allocate the resources used by the shared transport and destroy it.

Every channel on top of the same shared transport will join a different cluster, named after the topic. PubSub maintains a hashmap of topic names as keys and channels as values. A "subscribe rht" operation simply creates a new channel (if there isn't one for topic "rht" yet), adds a listener, joins cluster "rht" and adds the topic/channel pair to the hashmap. An "unsubscribe rht" grabs the channel for "rht", closes it and removes it from the hashmap.

When a publishes posts an update for "rht", it essentially sends a multicast to the "rht" cluster.

The important point is that, when an update for "rht" is received by a shared transport, JGroups tries to find the channel which joined cluster "rht" and passes the message up to that channel (through its protocol stack), or discards it if there isn't a channel which joined cluster "rht".

For example, if we have 3 channels A, B and C over the same shared transport TP, and A joined cluster "rht", B joined "aapl" and C joined "msft", then when a message for "ibm" arrives, it will be discarded by TP as there is no cluster "ibm" present. When a message for "rht" arrives, it will be passed up the stack for "rht" to channel A.

As a non-matching message will be discarded at the transport level, and not the application level, we save the costs of passing the message up the stack, through all the protocols and delivering it to the application.

Note that PubSub uses the properties of IP multicasting, so the stack used by it should have UDP as shared transport. If TCP is used, then there are no benefits to the approach outlined above.

Wednesday, September 07, 2011

Speaking at the OpenBlend conference on Sept 15

FYI,

I'll be speaking at the OpenBlend conference in Ljubljana on Sept 15.

My talk will be about how to persist data without using a disk, by spreading it over a grid with a customizable degree of redundancy. Kind of the NoSQL stuff everybody and their grandmothers are talking about these days...

I'm excited to visit Ljubljana, as I've never been there before and I like seeing new towns.

The other reason, of course, is to beat Ales Justin's a**s in tennis :-)

If you happen to be in town, come and join us ! I mean not for tennis, but for the conference, or for a beer in the evening !

Cheers,
Bela

Thursday, September 01, 2011

Optimizations for large clusters

I've been working on making JGroups more efficient on large clusters. 'Large' is between 100 and 2000 nodes.

My focus has been on making the memory footprint smaller, and to reduce the wire size of certain types of messages.


Here are some of the optimizations that I implemented.

Discovery

Discovery is needed by a new member to find the coordinator when joining. It broadcasts a discovery request, and everybody in the cluster replies with a discovery response.

There were 2 problems with this: first, a cluster of 1000 nodes meant that a new joiner received 1000 messages at the same time, possibly clogging up network queues and causing messages to get dropped.

This was solved by staggering the sending of responses (stagger_timeout).

The second problem was that every discovery response included the current view. In a cluster of 1000, this meant that 1000 responses each contained a view of 1000 members !

The solution to this was that we only send back the address of the coordinator; as this is all that's needed to send a JOIN request to it. So instead of sending back (with every discovery response) 1000 addresses, we now only send back 1 address.


Digest

A digest used to contain the lowest, highest delivered and highest received sequence numbers (seqnos) for every member. They are sent back to a new joiner in a JOIN response, and they are also broadcast periodically by STABLE to purge messages delivered by everyone.

The wire size would be 2 longs for every address (UUID), and 3 longs for the 3 seqnos. That's roughly 1000 * 5 * 8 = 40000 bytes for a cluster of 1000 members. Bear in mind that that's the size of one digest; in a cluster of 1000, everyone broadcasts such a digest periodically (STABLE) !

The first optimization was to remove the 'low' seqno; I had to change some code in the retransmitters to allow for that, but - hey - who wouldn't do that to save 8 bytes / STABLE message ? :-)

This reduced the wire (and memory !) size of a 1000-member digest by another 8'000 bytes, down to 32'000 (from 40'000).

Having only highest delivered (HD) and highest received (HR) seqnos allowed for another optimization: HR is always >= HD, and the difference between HR and HD is usually small.

So the next optimization was to send HR as a delta to HD. So instead of sending 322649 | 322650, we'd send 322649 | 1.

The central optimization underlying that was that seqnos seldomly need 8 bytes: a seqno starts at 1 and increases monotonically. If a member sends 5 million messages, the seqno can still be encoded in 4 bytes (saving 4 bytes per seqno). If a member is restarted, the seqno starts again at 1 and can thus be encoded in 1 byte.

So now I could encode an HD/HR pair by sending a byte containing the number of bytes needed for the HD part in the lower 4 bits and the number of bytes needed for the delta in the higher 4 bits. The HD and the delta would then follow. Example: to encode HD=2000000 | HR=2000500, we'd generate the bytes:

| 50 | -128 | -124 | 30 | -12 | 1 |

  • 50 encodes a length of 3 for HD and 2 for HD-HR (500)
  • -128, -124 and 30 encode 2'000'000 in 3 bytes
  • -12 and 1 encode the delta (500)

So instead of using 16 bytes for the above sequence, we use only 6 bytes !

If we assume that we can encode 2 seqnos on average in 6 bytes, the wire size of a digest is now 1000 * (16 (UUID) + 6) = 22'000, that's down from 40'000 in a 1000 member cluster. In other words, we're saving almost 50% of the wire size of a digest !

Of course, we can not only encode seqno sequences, but also other longs, which is exactly what we did for another optimization. Examples of where this makes sense are:
  • Seqnos in NakackHeaders: every multicast message has such a header, so the savings here are significant
  • Range: this is used for retransmission requests, and is also a seqno sequence
  • RequestCorrelator IDs: used for every RPC
  • Fragmentation IDs (FRAG and FRAG2)
  • UNICAST and UNICAST2: sqnos and ranges
  • ViewId
An example of where this doesn't make sense are UUIDs: they are generated such that the bits are spread out over the entire 8 bytes, so encoding them would make 9 bytes out of 8 and that doesn't help.


JoinRsp

A JoinRsp used to contain a list of members twice: once in the view and once in the digest. The was eliminated, and now we're sending the member list only once. This also cut the wire size of a JoinRsp in half.



Further optimizations planned for 3.1 include delta views and better compressed STABLE messages:



Delta views

If we have a view of 1000 members, we always send the full address list with every view change. This is not necessary, as everybody has access to the previous view.

So, for example, when we have P, Q and R joining, and X and Y leaving in V22, then we can simply send a delta view; a view V22={V21+P+Q+R-X-Y}. This means, take the current view V21, remove members X and Y, and add members P, Q and R to the tail of the list, in order to generate a new view V22.

So, instead of sending a list of 1000 members, we simply send 5 members, and everybody creates the new view locally, based on the current view and the delta information.


Compressed STABLE messages

A STABLE message contains a digest with a list of all members and then the digest seqnos for HD and HR. Since STABLE messages are exchanged between members of the same cluster, they all have the same view, or else they would drop a STABLE message.

Hence, we can drop the View and instead send the ViewId, which is 1 address and a long. Everyone knows that the digest seqnos will be in order of the current view, e.g. seqno pair 1 belongs to the first member of the current view, seqno pair 2 to the second member and so on.

So instead of sending a list of 1000 members for a STABLE message, we only send 1 address.

This will reduce the wire size of a 1000-member digest sent by STABLE from roughly 40'000 bytes to ca. 6'000 bytes !



Download 3.0.0.CR1

The optimizations (exluding delta views and compressed STABLE messages) are available in JGroups 3.0.0.CR1, which can be downloaded from [1].

Enjoy (and feedback appreciated, on the mailing lists...) !

[1] https://sourceforge.net/projects/javagroups/files/JGroups/3.0.0.CR1

Tuesday, July 26, 2011

It's time for a change: JGroups 3.0

I'm happy to anounce that I just released a first beta of JGroups 3.0 !

It's been a long time since I released version 2.0 (Feb 2002); over 11 years and 77 2.x releases !

We've pushed a lot of API changes into 3.x, in order to provide more features, bug fixes and optimizations in 2.x releases, which were always (API) backwards compatible to previous 2.x releases.

However, now it was time to take that step and make all the changes we've accumulated over the years.

The bad thing is that 3.x will require code changes if you port your 2.x app to it... however I anticipate that those changes will be trivial. Please ask questions regarding porting on the JGroups mailing list (or forums), and also post suggestions for improvements !

The good thing is that I was able to remove a lot of code (ca. 25'000 lines compared to 2.12.1) and simplify JGroups significantly.

Just one example: the getState(OutputStream) callback in 2.x didn't have an exception in its signature, so an implementation would typically look like this:

public void getState(OutputStream output) {
    try {
        marshalStateToStream(output);
    }
    catch(Exception ex) {
         log.error(ex);
    }
}

In 3.x, getState() is allowed to throw an exception, so the code looks like this now:

public void getState(OutputStream output) throws Exception {
    marshalStateToStream(output);
}

First of all, we don't need to catch (and swallow !) the exception. Secondly, a possible exception will now actually be passed to the state requester, so that we know *why* a state transfer failed when we call JChannel.getState().

There are many small (or bigger) changes like this, which I hope will make using JGroups simpler. A list of all API changes can be found at [2].

The stability of 3 beta1 is about the same as 2.12.1 (very high), because there were mainly API changes, and only a few bug fixes or optimizations.

I've also created a new 3.x specific set of documentation (manual, tutorial, javadocs), for example see the 3.x manual at [3].

JGroups 3 beta1 can be downloaded from [1]. Please try it out and send me your feedback (mailing lists preferred) !

Enjoy !



[1] https://sourceforge.net/projects/javagroups/files/JGroups/3.0.0.Beta1

[2] https://github.com/belaban/JGroups/blob/master/doc/API_Changes.txt

[3] http://www.jgroups.org/manual-3.x/html/index.html

Friday, April 29, 2011

Largest JGroups cluster ever: 536 nodes !

I just returned from a trip to a customer who's working on creating a large scale JGroups cluster. The largest cluster I've ever created is 32 nodes, due to the fact that I don't have access to a larger lab...

I've heard of a customer who's running a 420 node cluster, but I haven't seen it with my own eyes.

However, this record was surpassed on Thursday April 28 2011: we managed to run a 536 node cluster !

The setup was 130 celeron based blades with 1GB of memory, each running 4 JVMs with 96MB of heap, plus 4 embedded devices with 4 JVMs running on each. Each blade had 2 1GB NICs setup with IP Bonding. Note that the 4 processes are competing for CPU time and network IO, so with more blades or more physical memory available, I'm convinced we could go to 1000+ nodes !

The configuration used was udp-largecluster.xml (with some modifications), recently created and shipped with JGroups 2.12.

We started the processes in batches of 130, then waited for 20 seconds, then launched the second batch and so on. The reason we staggered the startup was to reduce the number of merges, which would have increased the startup time.

Running this a couple of times (plus 50+ times over night), the cluster always formed fine, and most of the time we didn't have any merges at all.

It took around 150-200 seconds (including the 5 sleeps of 20 seconds each) to start the cluster; in the picture at the bottom we see a run that took 176 seconds.

Changes to JGroups

This large scale setup revealed that certain protocols need slight modifications to optimally support large clusters, a few of these changes are:
  • Discovery: the current view is sent back with every discovery response. This is not normally an issue, but if you have a 500+ view, then the size of a discovery response becomes huge. We'll fix this by returning only the coordinator's address and not the view. For discovery requests triggered by MERGE2, we'll return the ViewId instead of the entire view.
  • We're thinking about canonicalizing UUIDs with IDs, so nodes will be assigned unique (short) IDs instead of UUIDs. This means reducing the size for having 17 bytes (UUID) in memory in favor of 2 bytes (short).
  • STABLE messages: here, we return an array of members plus a digest (containing 3 longs) for *each* member. This also generates large messages (11K for 260 nodes).
  • The fix in general for these problems is to reduce the data sent, e.g. by compressing the view, or not sending it at all, if possible. For digests, we can also reduce the data sent by sending only diffs, by sending only 1 long and using shorts for diffs, by using bitsets representing offsets to a previously sent value, and so on. 
Ideas are abundant, we now need to see which one is the most efficient.

For now, 536 nodes is an excellent number and - remember - we got to this number *without* the changes discussed above ! I'm convinced we can easily go higher, e.g. to 1000 nodes, without any changes. However, to reach 2000 nodes, the above changes will probably be required.

Anyway, I'm very happy to see this new record !

If anyone has created an even larger cluster, I'd be very interested in hearing about it !
Cheers, and happy clustering,



Friday, April 01, 2011

JBossWorld 2011 around the corner

Wanted to let you know that I've got 2 talks at JBW (Boston, May 3-6).

The first talk [1] is about geographic failover of JBoss clusters. I'll show 2 clusters, one in NYC, the other one in ZRH. Both are completely independent and don't know about each other. However, they're bridged with a JGroups RELAY and therefore appear as if they were one big virtual cluster.

This can be used for geographic failover, but it could also be used for example to extend a private cloud with an external, public cloud without having to use a hardware VPN device.

As always with my talks, this will be demo'ed, so you know this isn't just vapor ware !

The second talk [2] discusses 5 different ways of running a JBoss cluster on EC2. I'll show 2 demos, one of which works only on EC2, the other works on all clouds.

This will be a fun week, followed by a week of biking in the Bay Area ! YEAH !!

Hope to see and meet many of you in Boston !
Cheers,


[1] http://www.redhat.com/summit/sessions/best-of.html#66

[2] http://www.redhat.com/summit/sessions/jboss.html#43

Friday, March 11, 2011

A quick update on performance of JGroups 2.12.0.Final

I forgot to add performance data to the release announcement of 2.1.0.Final, so here it is.

Caveat: this is a quick check to see if we have a performance regression, which I run routinely before a release, and my no means a comprehensive performance test !

I ran this both on my home cluster and our internal lab.


org.jgroups.tests.perf.Test

This test is described in detail in [1]. It forms a cluster of 4 nodes, and every node sends 1 million messages of varying size (1K, 5K, 20K). We measure how long it takes for every node to receive the 4 million messages, and compute the message rate and throughput, per second, per node.

This is my home cluster and consists of 4 HP ProLiant DL380G5 quad core servers (ca 3700 bogomips), connected to a GB switch, and running Linux 2.6. The JDK is 1.6 and the heap size is 600M. I ran 1 process on every box. The configuration used was udp.xml (using IP multicasting) shipped with JGroups.

Results
  •   1K message size: 140 MBytes / sec / node
  •   5K message size: 153 MBytes / sec / node
  • 20K message size: 154 MBytes / sec / node
 This shows that GB ethernet is saturated. The reason that every node receives more than the limit of GB ethernet (~ 125 MBytes/sec) is that every node loops back its own traffic, and therefore doesn't have to share it with other incoming packets. In theory, the max throughput should therefore be 4/3 * 125 ~= 166 MBytes/sec. We see that the numbers above are not too far away from this.


org.jgroups.tests.UnicastTestRpcDist

This test mimicks the way Infinispan's DIST mode works.

Again, we form a cluster of between 1 and 9 nodes. Every node is on a separate machine. The test then has every node invoke 2 unicast RPCs in randomly selected nodes. With a chance of 80% the RPCs are reads, and with a chance of 20% they're writes. The writes carry a payload of 1K, and the reads return a payload of 1K. Every node makes 20'000 RPCs.

The hardware is a bit more powerful than my home cluster; every machine has 5300 bogomips, and all machines are connected with GB ethernet.

Results
  • 1 node:   50'000 requests / sec /node
  • 2 nodes: 23'000 requests / sec / node
  • 3 nodes: 20'000 requests / sec / node
  • 4 nodes: 20'000 requests / sec / node
  • 5 nodes: 20'000 requests / sec / node
  • 6 nodes: 20'000 requests / sec / node
  • 7 nodes: 20'000 requests / sec / node
  • 8 nodes: 20'000 requests / sec / node
  • 9 nodes: 20'000 requests / sec / node
As can be seen, the number of requests per node is the same after 2-3 nodes. The 1 node scenario is somewhat contrived as there is no network communication involved.

This is actually good news, as it shows that performance grows linearly. As a matter of fact, with increasing cluster size, the chances of more than 2 nodes picking the same target decreases, therefore performance degradation due to (write) access conflicts are likely to decrease.

Caveat: I haven't tested this on a larger cluster yet, but the current performance is already very promising.

[1] http://community.jboss.org/docs/DOC-11594

Wednesday, March 09, 2011

It took me 9 years to go from JGroups 2.0.0 to 2.12.0

Yes, you heard right: I released JGroups 2.0.0, new, shiny and refactored, in Feb 2002.

I just released JGroups 2.12.0.Final, which will be the last minor release on the 2.x branch. (There won't be a 2.13; bug fixes will go into 2.12.x).

Time difference: 9 years and change...:-)

I'm still investigating why it took me so long !

Anyway, 2.12.0.Final is here and it is an important release, as it will be shipped in Infinispan 4.2.1 and JBoss 6.


Below are the major features and bug fixes.

On to 3.0 !
Cheers,




Release Notes JGroups 2.12


JGroups 2.12 is API-backwards compatible with previous versions (down to 2.2.7).



New features



RELAY: connecting local (autonomous) clusters into a large virtual cluster


[https://issues.jboss.org/browse/JGRP-747]

A new protocol to connect 2 geographically separate sites into 1 large virtual cluster. The local clusters are
completely autonomous, but RELAY makes them appear as if they were one.

This can for example be used to implement geographic failover

Blog: http://belaban.blogspot.com/2010/11/clustering-between-different-sites.html



LockService: a new distributed locking service

[https://issues.jboss.org/browse/JGRP-1249]
[https://issues.jboss.org/browse/JGRP-1298]
[https://issues.jboss.org/browse/JGRP-1278]

New distributed lock service, offering a java.util.concurrent.lock.Lock implementation (including conditions)
providing cluster wide locks.

Blog: http://belaban.blogspot.com/2011/01/new-distributed-locking-service-in.html



Distributed ExecutorService

[https://issues.jboss.org/browse/JGRP-1300]

New implementation of java.util.concurrent.ExecutorService over JGroups (contributed by William Burns).
Read the documentation at www.jgroups.org for details.



BPING (Broadcast Ping): new discovery protocol based on broadcasting

[https://issues.jboss.org/browse/JGRP-1269]

This is mainly used for discovery of JGroups on Android based phones. Apparently, IP multicasting is not correctly implemented / supported on Android (2.1), and so we have to resort to UPD broadcasting.

Blog: http://belaban.blogspot.com/2011/01/jgroups-on-android-phones.html



JDBC_PING: new discovery protocol using a shared database


[https://issues.jboss.org/browse/JGRP-1231]

All nodes use a shared DB (e.g. RDS on EC2) to place their location information into, and to read information from.
Thanks to Sanne for coming up with the idea and for implementing this !
Additional infos are on the wiki: community.jboss.org/wiki/JDBCPING


FD_SOCK: ability to pick the bind address and port for the client socket

[https://issues.jboss.org/browse/JGRP-1262]



Pluggable address generation


[https://issues.jboss.org/browse/JGRP-1297]

Address generation is now pluggable; JChannel.setAddressGenerator(AddressGenerator) allows for generation of specific implementations of Address. This can for example be used to pass additional information along with every address. Currently used by RELAY to pass the name of the sub cluster around with a UUID.





Optimizations



NAKACK: retransmitted messages don't need to be wrapped


[https://issues.jboss.org/browse/JGRP-1266]

Not serializing retransmitted messages at the retransmitter and deserializing them at the requester saves
1 serialization and 1 deserialization per retransmitted message.


Faster NakReceiverWindow

[https://issues.jboss.org/browse/JGRP-1133]

Various optimizations to reduce locking in NakReceiverWindow:
  • Use of RetransmitTable (array-based matrix) rather than HashMap (reduced memory need, reduced locking, compaction)
  • Removal of double locking






Bug fixes



NAKACK: incorrect digest on merge and state transfer

[https://issues.jboss.org/browse/JGRP-1251]

When calling JChannel.getState() on a merge, the fetched state would overwrite the digest incorrectly.


AUTH: merge can bypass authorization

[https://issues.jboss.org/browse/JGRP-1255]

AUTH would not check creds of other members in case of a merge. This allowed an unauthorized node to join a cluster by triggering a merge.


Custom SocketFactory ignored

[https://issues.jboss.org/browse/JGRP-1276]

Despite setting a custom SocketFactory, it was ignored.


UFC: crash of depleted member could hang node

[https://issues.jboss.org/browse/JGRP-1274]

Causing it to wait forever for credits from the crashed member.


Flow control: crash of member doesn't unblock sender


[https://issues.jboss.org/browse/JGRP-1283]
[https://issues.jboss.org/browse/JGRP-1287]
[https://issues.jboss.org/browse/JGRP-1274]

When a sender block on P sending credits, and P crashes before being able to send credits,
the sender blocks indefinitely.


UNICAST2: incorrect delivery order under stress

[https://issues.jboss.org/browse/JGRP-1267]

UNICAST2 could (in rare cases) deliver messages in incorrect order. Fixed by using the same (proven)
algorithm as NAKACK.


Incorrect conversion of TimeUnit if MILLISECONDS were not used

[https://issues.jboss.org/browse/JGRP-1277]


Check if bind_addr is correct

[https://issues.jboss.org/browse/JGRP-1280]

JGroups now verifies that the bind address is indeed a valid IP address: it has to be either the wildcard
address (0.0.0.0) or an address of a network interface that is up.


ENCRYPT: sym_provider ignored

[https://issues.jboss.org/browse/JGRP-1279]

Property sym_provider is ignored



Manual


The manual is online at http://www.jgroups.org/manual/html/index.html



The complete list of features and bug fixes can be found at http://jira.jboss.com/jira/browse/JGRP.

Download the new release at https://sourceforge.net/projects/javagroups/files/JGroups/2.12.0.Final.

Bela Ban, Kreuzlingen, Switzerland
Vladimir Blagojevic, Toronto, Canada
Richard Achmatowicz, Toronto, Canada
Sanne Grinovero, Newcastle, Great Britain

March 2011

Saturday, January 22, 2011

JGroups on Android phones

Yann Sionneau recently completed a port of JGroups to Android (2.1+). He took the 2.11 version of JGroups and removed classes which weren't available on Android, and changed some code to make JGroups run on Android.

The QR code for a demo app (based on Draw) is available at [1]. Point a QR code scanner to it, download the app and run it on your Android based phone (I ran it on my HTC Desire). Then start Draw on your local computer, connected to the same wifi network as the phone. The instances, whether run on the phone or computers, should find each other and form a cluster.

It was cool to draw some lines on my HTC and see them getting drawn on all cluster instances as well !

[1] http://sionneau.net/index.php?option=com_content&view=article&id=12%3Atouchsurface-android-app-now-pc-compatible-&catid=3%3Adivers&Itemid=2&lang=en

Friday, January 21, 2011

New distributed locking service in JGroups

I just uploaded JGroups 2.12.0.Beta1, which contains a first version of the new distributed locking service (LockService), which replaces DistributedLockManager.

LockService provides a distributed implementation of java.util.concurrent.lock.Lock. A lock is named and locking granularity is per thread. Here's an example of how to use it:

// lock.xml has to have a locking protocol in it
JChannel ch=new JChannel("/home/bela/lock.xml");
LockService lock_service=new LockService(ch);
Lock lock=lock_service.getLock("mylock");
if(lock.tryLock(2000, TimeUnit.MILLISECONDS)) {
    try {
        // access the resource protected by "mylock"
    }
    finally {
        lock.unlock();
    }
}

If "mylock" is locked by a different thread, it doesn't matter whether inside the same JVM, on the same box, or somewhere in the same cluster, then tryLock() will return false after 2 seconds, else it'll return true.

Lock.newCondition() is currently not implemented - if there's a need for this, let us know on one of the JGroups mailing lists and we'll tackle this. If you have a chance to play with LockService, we're also grateful for feedback.

The new locking service is part of 2.12.0.Beta1, which can be downloaded at [1]. Documentation is at [2].
Cheers,


[1] http://sourceforge.net/projects/javagroups/files/JGroups/2.12.0.Beta1
[2] http://www.jgroups.org/manual/html/index.html, section 4.6

Tuesday, November 30, 2010

Clustering between different sites / geopgraphic failover

I just completed a new feature in JGroups which allows for transparent bridging of separate clusters, e.g. at different sites.

Let's say we have a (local) cluster in New York (NYC) and another cluster in San Francisco (SFO). They're completely autonomous, and can even have completely different configurations.

RELAY [1] essentially has the coordinators of the local clusters relay local traffic to the remote cluster, and vice versa. The relaying (or bridging) is done via a separate cluster, usually based on TCP, as IP multicasting is typically not allowed between sites.

SFO could be a backup of NYC, or both could be active, or we could think of a follow-the-sun model where each cluster is active during working hours at its site.

If we have nodes {A,B,C} in NYC and {D,E,F} in SFO, then there would be a global view, e.g. {D,E,F,A,B,C}, which is the same across all the nodes of both clusters.

One use of RELAY could be to provide geographic failover in case of site failures. Because all of the data in NYC is also available in SFO, clients can simply fail over from NYC to SFO if the entire NYC site goes down, and continue to work.

Another use case is to have SFO act as a read-only copy of NYC, and run data analysis functions on SFO, without disturbing NYC, and with access to almost real-time data.

As you can guess, this feature is going to be used by Infinispan, and since Infinispan serves as the data replication / distribution layer in JBoss, we hope to be able to provide replication / distribution between sites in JBoss as well...

Exciting times ... stay tuned for more interesting news from the Infinispan team !

Read more on RELAY at [1] and provide feedback !
Cheers,


[1] http://www.jgroups.org/manual/html/user-advanced.html#RelayAdvanced

Tuesday, November 23, 2010

JGroups finally has a logo

After conducting a vote on the logos designed by James Cobb, the vast majority voted for logo #1. So I'm happy to say that, after 12 years, JGroups finally has a logo !

I added the logo and favicon to jgroups.org. Let me know what you think !


There's also swag available on cafepress, check it out !

Friday, October 29, 2010

JGroups 2.11 final released

FYI,

2.11.0.final can be downloaded here. Its main features, optimizations and bug fixes are listed below.

I hope that 2.12 will be the last release before finally going to 3.0 !

2.12 should be very small, currently it contains only 8 issues (mainly optimizations).

However, I also moved RELAY from 3.x to 2.12.

RELAY allows for connecting geographically separate clusters into a large virtual cluster. This will be interesting to apps which need to provide geographic failover. More on this in the next couple of weeks...

Meanwhile ... enjoy 2.11 !

Bela, Vladimir & Richard



Release Notes JGroups 2.11
==========================


Version: $Id: ReleaseNotes-2.11.txt,v 1.2 2010/10/29 11:45:35 belaban Exp $
Author: Bela Ban

JGroups 2.11 is API-backwards compatible with previous versions (down to 2.2.7).

Below is a summary (with links to the detailed description) of the major new features.


New features
============



AUTH: pattern matching to prevent unauthorized joiners
------------------------------------------------------
[https://jira.jboss.org/browse/JGRP-996]

New plugin for AUTH which can use pattern matching against regular expressions to prevent unauthorized
IP addresses to join a cluster.

Blog: http://belaban.blogspot.com/2010/09/cluster-authentication-with-pattern.html



DAISYCHAIN: implementation of daisy chaining
--------------------------------------------
[https://jira.jboss.org/browse/JGRP-1021]

Daisy chaining sends messages around in a ring, improving throughput for non IP multicast networks.

Blog: http://belaban.blogspot.com/2010/08/daisychaining-in-clouds.html



New flow control protocols for unicast (UFC) and multicast (MFC) messages
-------------------------------------------------------------------------
[https://jira.jboss.org/browse/JGRP-1154]

MFC and UFC replace FC. They can be used independently, and performance is faster than that of FC only.


API for programmatic creation of channel
----------------------------------------
[https://jira.jboss.org/browse/JGRP-1245]

Allows for programmatic creation of a JChannel, no need for XML config file.

Blog: http://belaban.blogspot.com/2010/10/programmatic-creation-of-channel.html


S3: new features
----------------
[https://jira.jboss.org/browse/JGRP-1234] Allow use of public buckets (no credentials need to be sent)
[https://jira.jboss.org/browse/JGRP-1235] Pre-signed URLs



STOMP: new protocol to allows STOMP clients to talk to a JGroups node
---------------------------------------------------------------------
[https://jira.jboss.org/browse/JGRP-1248]

Blog: http://belaban.blogspot.com/2010/10/stomp-for-jgroups.html







Optimizations
=============


NAKACK: simplify and optimize handling of OOB messages
------------------------------------------------------
[https://jira.jboss.org/browse/JGRP-1104]


Discovery: reduce number of discovery responses sent in a large cluster
-----------------------------------------------------------------------
[https://jira.jboss.org/browse/JGRP-1181]

A new propery (max_rank) determines who will and who won't send discovery responses.


New timer implementations
-------------------------
[https://jira.jboss.org/browse/JGRP-1051]

Way more effecient implementations of the timer (TimeScheduler).




Bug fixes
=========

ENCRYPT: encrypt entire message when length=0
---------------------------------------------
[https://jira.jboss.org/browse/JGRP-1242]

ENCRYPT would not encrypt messages whose length = 0


FD_ALL: reduce number of messages sent on suspicion
---------------------------------------------------
[https://jira.jboss.org/browse/JGRP-1241]


FILE_PING: empty files stop discovery
-------------------------------------
[https://jira.jboss.org/browse/JGRP-1246]




Manual
======

The manual is online at http://www.jgroups.org/manual/html/index.html



The complete list of features and bug fixes can be found at http://jira.jboss.com/jira/browse/JGRP.


Bela Ban, Kreuzlingen, Switzerland
Vladimir Blagojevic, Toronto, Canada
Richard Achmatowicz, Toronto, Canada

Nov 2010

Wednesday, October 27, 2010

STOMP for JGroups

FYI,

I've written a new JGroups protocol STOMP, which implements the STOMP protocol. This allows for STOMP clients to connect to any JGroups server node (which has the JGroups STOMP protocol in its configuration).

The benefits of this are:
  •  Clients can be written in any language. For example, I've used stomppy, a Python client, to connect to JGroups server nodes, and successfully subscribed to destinations, and sent and received messages.
  • Sometimes, clients don't want to be peers, ie. they don't want to join a cluster and become full members. These (light-weight) clients could also be in a different geographic location, and not be able to use IP multicasting.
  • Clients are started and stopped frequently, and there might be many of them. Frequently starting and stopping a full-blown JGroups server node has a cost, and is not recommended. Besides, a high churn rate might move the cluster coordinator around quite a lot, preventing it from doing real work.
  • We can easily scale to a large number of clients. Although every client requires 1 thread on the server side, we can easily support hundreds of clients. Note though that I wouldn't use the current JGroups STOMP protocol to connect thousands of clients...
Let's take a quick look: I started an instance of JGroups with STOMP on the top of the protocol stack (on 192.168.1.5). Then I connected to it with the JGroups client:

JGroups STOMP client

As can be seen, the first response the client received was an INFO with information about the available endpoints (STOMP instances) in the cluster. This is actually used by the StompConnection client to failover to a different server node should the currently connected to server fail.
Next, we subscribe to destination /a using the simplified syntax of the JGroups STOMP client.

Then, a telnet session to 192.168.1.5:8787 was started:

Telnet STOMP client



We get the INFO response with the list of endpoints too here. Then we subscribe to the /a destination. Note that the syntax used here is compliant with the STOMP protocol spec: first is the verb (SUBSCRIBE), then an optional bunch of headers (here just one, defining the destination to subscribe to), a newline and finally the body, terminated with a 0 byte. (SUBSCRIBE does not have a body).

Next, we send a message to all clients subscribed to /a. This is the telnet session itself, as evidenced by the reception of MESSAGE. If you look at the JGroups STOMP client, the message is also received there.

Next the JGroups client also sends a message to destination /a, which is received by itself and the telnet client.

JGroups 2.11.0.Beta2 also ships with a 'stompified' Draw demo, org.jgroups.demos.StompDraw, which is a stripped down version of Draw, using the STOMP protocol to send updates to the cluster.

Let me know what you think of this; feature requests, feedback etc appreciated (preferably on one of the JGroups mailing lists) !



The new protocol is part of JGroups 2.11.0.Beta2, which can be downloaded here.

Documentation is here.

Enjoy !

Wednesday, October 20, 2010

Programmatic creation of a channel

I've committed code which provides programmatic creation of channels. This is a way of creating a channel without XML config files. So instead of writing

JChannel ch=new JChannel("udp.xml");

, I can construct the channel programmatically:


JChannel ch=new JChannel(false);                 // 1
ProtocolStack stack=new ProtocolStack(); // 2
ch.setProtocolStack(stack);              // 3
stack.addProtocol(new UDP().setValue("ip_ttl", 8));
     .addProtocol(new PING())
     .addProtocol(new MERGE2())
     .addProtocol(new FD_SOCK())
     .addProtocol(new FD_ALL().setValue("timeout", 12000));
     .addProtocol(new VERIFY_SUSPECT())
     .addProtocol(new BARRIER())
     .addProtocol(new NAKACK())
     .addProtocol(new UNICAST2())
     .addProtocol(new STABLE())
     .addProtocol(new GMS())
     .addProtocol(new UFC())
     .addProtocol(new MFC())
     .addProtocol(new FRAG2());       // 4
stack.init();                         // 5


First, a JChannel is created (1). The 'false' argument means that the channel must not create its own protocol stack, because we create it (2) and stick it into the channel (3).

Next, all protocols are created and added to the stack (4). This needs to happen in the order in which we want the protocols to be, so the first protocol added is the transport protocol (UDP in the example).

Note that we can use Protocol.setValue(String attr_name, Object attr_value) to configure each protocol instance. We can also use regular setters if available.

Finally, we call init() (5), which connects the protocol list correctly and calls init() on every instance. This also handles shared transports correctly. For an example of how to create a shared transport with 2 channels on top see ProgrammaticApiTest.

I see mainly 3 use cases where programmatic creation of a channel is preferred over declarative creation:
  1. Someone hates XML (I'm not one of them) :-)
  2. Unit tests
  3. Projects consuming JGroups might have their own configuration mechanism (e.g. GUI, properties file, different XML configuration  etc) and don't want to use the XML cofiguration mechanism shipped with JGroups.
Let me know what you think about this API ! I deliberately kept it simple and stupid, and maybe there are things people like to see changed. I'm open to suggestions !


Cheers,

Friday, October 01, 2010

Confessions of a serial protocol designer

I have a confession to make.

I'm utterly disgusted by my implementation of FD_ALL, and thanks to David Forget for pointing this out !

What's bad about FD_ALL ? It will not scale at all ! After having written several dozen protocols, I thought an amateurish mistake like the one I'm about to show would certainly not happen to me anymore. Boy, was I wrong !

FD_ALL is about detecting crashed nodes in a cluster, and the protocol then lets GMS know so that the crashed node(s) can be excluded from the view.

Let's take a look at the design.
  • Every node periodically multicasts a HEARTBEAT
  • This message is received by everyone in the cluster and a hashmap of nodes and timestamps is updated; for a node P, P's timestamp is set to the current time
  • Another task run at every node periodcially iterates through the timestamps and checks if any timestamps haven't been updated for a given time. If that's the case, the members with outdated timestamps are suspected
  • A suspicion of P results in a SUSPECT(P) multicast
  • On reception of SUSPECT(P), every node generates a SUSPECT(P) event and passes it up the stack
  • VERIFY_SUSPECT catches SUSPECT(P) and sends an ARE_YOU_DEAD message to P
  • If P is still alive, it'll respond with a I_AM_NOT_DEAD message
  • If the sender doesn't get this message for a certain time, it'll pass the SUSPECT(P) event further up the stack (otherwise it'll drop it), and GMS will exclude P from the view, but if and only if that given node is the coordinator (first in the view)
Can anyone see the flaw in this design ? Hint: it has to do with the number of messages generated...

OK, so let's see what happens if we have a cluster of 100 nodes:
  • Say node P is temporarily slow; it doesn't send HEARTBEATs because a big garbage collection is going on, or the CPU is crunching at 90%
  • 99 nodes multicast a SUSPECT(P) message
  • Every node Q therefore receives 99 SUSPECT(P) messages
    • Q (via VERIFY_SUSPECT) sends a ARE_YOU_DEAD message to P
    • P (if it can) responds with an I_AM_NOT_DEAD back to Q
    • So the total number of messages generated by a single node is 99 * 2
  • This is done on every node, so the total number of messages is 99 * 99 * 2 = 19'602 messages !

Can you imagine what happens to P, which is a bit overloaded and cannot send out HEARTBEATs in time when it receives 19'602 messages ?

It it aint dead yet, it will die !

Isn't it ironic: by asking a node if it is still alive, we actually kill it !

This is an example of where the effects of using IP multicasts were not taken into account: if we multicast M, and everybody who receives M sends 2 messages, I neglected to see that the number of messages sent is a function of the cluster size !

So what's the solution ? Simple, elegant and outlined in [1].
  • Everybody sends a HEARTBEAT multicast periodically
  • Every member maintains a suspect list 
  • This list is adjusted on view changes 
  • Reception of a SUSPECT(P) message adds P to the list 
  • When we suspect P because we haven't received a HEARTBEAT (or traffic if enabled): 
    • The set of eligible members is computed as: members - suspected members 
    • If we are the coordinator (first in the list): 
      • Pass a SUSPECT(P) event up the stack, this runs the VERIFY_SUSPECT protocol and eventually passes the SUSPECT(P) up to GMS, which will exclude P from the view

The cost of running the suspicion protocol is (excluding the periodic heartbeat multicasts):
  • 1 ARE_YOU_DEAD unicast to P
  • A potential response (I_AM_NOT_DEAD) from P to the coordinator
TOTAL COST in a cluster of 100: 2 messages (this is always constant), compared to 19'602 messages before !

This is way better than the previous implementation !


[1] https://jira.jboss.org/browse/JGRP-1241

Wednesday, September 22, 2010

JUDCon 2010 Berlin

I'll be giving a talk at JUDCon 2010 (Oct 7 and 8, Berlin) on how to configure JBoss clusters to run optimally in a cloud (EC2).

It would be cool to see some of you, we can discuss JGroups and other topics over a beer !

The agenda is here.

Cheers,