Partitioned clusters tell no lies!

The problem

You are happily running a 10-node cluster. You want failover and speed and are using distributed mode with 2 copies of data for each key (numOwners=2). But disaster strikes: a switch in your network crashes and 5 of your nodes can't reach the other 5 anymore ! Now there are two independent clusters, each containing 5 nodes, which we are smartly going to name P1 and P2. Both P1 and P2 continue to serve user requests (puts and gets) as usual.

This cluster split in two or more parts is called partitioning or split brain. And it's bad for business, as in really bad ! Bob and Alice share a bank account stored in the cache. Bob updates his account on P1, then Alice reads it from P2: she sees a stale value of Bob's account (or even no value for Bob's account, depending on how the split looks like). This is a consistency issue, as there's an inconsistent view of the data between the two partitions.

Our solution

In Infinispan 7.0.0.Beta1 we added support for reacting to split brains: if nodes leave, Infinispan acknowledges that data might have been lost and denies user access to such data. We won't deny access to all the data, but just the data that might have been affected by the partitioning. Or, more formally: Infinispan sacrifices data availability in order to offer consistency (PC in Brewer's CAP theorem). For now partition handling is disabled by default, however we do intend to make it the default in an upcoming release: running with partition handling off is like running with scissors: do it at your own risk and only if you (don't) know what you're doing.

How we do it

A partition is assumed to happen when numOwners or more nodes disappear at the same time. When this happen two (or more) partitions form which are not aware of each other. Each such partition does not start a rebalance, but enters in degraded mode:

• request (read and writes) for entries that have all the copies on nodes within this partition are honored
• requests for entries that are partially or totally owned by nodes that disappeared are rejected through an AvailabilityException

To exemplify, consider the initial cluster C0={A,B,C,D}, A,B,C,D - nodes, configured in distributed mode with numOwners=2. Further on, the cluster contains k1, k2 and k3 keys such that owners(k1) = {A,B}, owners(k2) = {B,C} and owners(k3) = {C,D}. Then a partition happens C1={A,B} and C2={C,D}, the degraded mode exhibits the following behavior:

• on C1, k1 is available for read/write, k2 (partially owned) and k3 (not owned) are not available and accessing them results in an AvailabilityException
• on C2, k1 and k2  are not available for read/write, k3 is available

A relevant aspect of the partition handling process is the fact that when a split brain happens, the resulting partitions rely on the original consistent hash function (the one that existed before the split brain) in order to calculate key ownership. So it doesn't matter if k1, k2 or k3 already exists in the cluster or not, as the availability is strictly determined by the consistent hash and not by the key existence.

If at a further point in time the initial partition C0 forms again as a result of the network healing and C1 and C2 partitions being merged back together, then C0 exists the degraded mode becoming fully available again.

Configuration for partition handling functionality

In order to enable partition handling within the XML configuration:

The same can be achieved programmatically:

The actual implementation is work in progress and Beta2 will contain further improvements which we will publish here!

Cheers,
Mircea Markus

Hot Rod Remote Events #2: Filtering events

This blog post is the second in a series that looks at the forthcoming Hot Rod Remote Events functionality included in Infinispan 7.0. In the first blog post we looked at how to get started receiving remote events from Hot Rod servers. This time we are going to focus on how to filter events directly in the server.

Sending events to remote clients has a cost which increases as the number of clients. The more clients register remote listeners, the more events the server has to send. This cost also goes up as the number of modifications are executed against the cache. The more cache modifications, the more events that need to be sent.

A way to reduce this cost is by filtering the events to send server-side. If at the server level custom code decides that clients are not interested in a particular event, the event does not even need to leave the server, improving the overall performance of the system.

Remote event filters are created by implementing a org.infinispan.notifications.cachelistener.filter.CacheEventFilterFactory class. Each factory must have a name associated to it via the org.infinispan.notifications.cachelistener.filter.NamedFactory annotation.

When a listener is added, we can provide the name of a key value filter factory to use with this listener, and when the listener is added, the server will look up the factory and invoke getFilter method to get a org.infinispan.notifications.cachelistener.filter.CacheEventFilterFactory class instance to filter events server side.

Filtering can be done based on key or value information, and even based on cached entry metadata. Here's a sample implementation which will filter key "2" out of the events sent to clients:

Plugging the server with this key value filter requires deploying this filter factory (and associated filter class) within a jar file including a service definition inside the META-INF/services/org.infinispan.notifications.cachelistener.filter.CacheEventFilterFactory file:

With the server plugged with the filter, the next step is adding a remote client listener that will use this filter. For this example, we'll extend the EventLogListener implementation provided in the first blog post in the series and we override the @ClientListener annotation to indicate the filter factory to use with this listener:

Next, we add the listener via the RemoteCache API and we execute some operations against the remote cache:

If we checkout the system output we'll see that the client receives events for all keys except those that have been filtered:

Finally, with Hot Rod remote events we have tried to provide additional flexibility at the client side, which is why when adding client listeners, users can provide parameters to the filter factory so that filter instances with different behaviours can be generated out of a single filter factory based on client side information. To show this in action, we are going to enhance the filter factory above so that instead of filtering on a statically given key, it can filter dynamically based on the key provided when adding the listener. Here's the revised version:

Finally, here's how we can now filter by "3" instead of "2":

And the output:


To summarise, we've seen how Hot Rod remote events can be filtered providing key/value filter factories that can create instances that filter which events are sent to clients, and how these filters can act on client provided information.

In the next blog post, we'll look at how to customize remote events in order to reduce the amount of information sent to the clients, or on the contrary, provide even more information to our clients.

Cheers,
Galder

Hot Rod Remote Events #1: Getting started

Shortly after the first Hot Rod server implementation was released in 2010, ISPN-374 was created requesting cache events to be forwarded back to connected clients. Even though embedded caches have had access to these events since Infinispan's first release, propagating them to remote clients has taken a while, due to the increased complexity involved.

For Infinispan 7.0, we've finally addressed this. This is the first post in a series that looks at Hot Rod Remote Events and the different functionality we've implemented for this release. On this first post, we show you how to get started with Hot Rod Remote Events with the most basic of examples:

Start by downloading the Server distribution for the latest 7.0 (or higher) release from Infinispan's download page. The server contains the Hot Rod server with which the client will communicate. Once downloaded, start it up running the following from the root of the server:

./bin/standalone.sh

Next up, we need to write a little application that interacts with the Hot Rod server. If you're using Maven, create an application with this dependency, changing version to 7.0.0.Beta1 or higher:

If not using Maven, adjust according to your chosen build tool or download the all distribution with all Infinispan jars.

With the application dependencies in place, we need to start to write the client application. We'll start with a simple remote event listener that simply logs all events received:
Now it's time to write a simple main java class which adds the remote event listener and executes some operations against the remote cache:

Once executed, we should see a console console output similar to this:

As you can see from the output, by default events come with the key and the internal data version associated with the current value. The actual value is not shipped back to the client for performance reasons. Clearly, receiving remote events has a cost, and as the cache size increases and more operations are executed, more events will be generated. To avoid inundating Hot Rod clients, remote events can either be filtered server side, or the event contents can be customised. In the next blog post in this series, we will see this functionality in action.

Cheers,
Galder

Infinispan 7.0.0.Beta1 is out!

Dear Community,

It is our pleasure to announce the first Beta release of Infinispan 7.0.0!

The release highlights are:

* Initial support for Cluster partitions (aka Split Brain) ISPN-263
* Added JGroups SASL support to Infinispan Server ISPN-4303
* Upgraded internal JGroups version to 3.5.0.CR2 ISPN-4609
* Map/Reduce tasks no longer have a timeout by default ISPN-4618
* Merge batching configuration with transaction modes to prevent ambiguity ISPN-4197
* Improve server test suite run times ISPN-4317
* Multiple improvements and bug fixes!

For a complete list of features and bug fixes included in this release please refer to the release notes.  Visit our downloads section to find the latest release.

If you have any questions please check our forums, our mailing lists or ping us directly on IRC.

Cheers,

The Infinispan team.