Mule Runtime High Availability (HA) Cluster Overview
|Clustering is not available in CloudHub, see CloudHub Fabric for details on how workers can be shared or doubled to scale your application and provide high availability.|
Mule Enterprise Edition supports scalable clustering to provide high availability (HA) for applications.
A cluster is a set of Mule runtime engines that acts as a unit. In other words, a cluster is a virtual server composed of multiple nodes (Mule runtime engines). The nodes in a cluster communicate and share information through a distributed shared memory grid. This means that the data is replicated across memory in different machines.
|Contact your customer service representative about pricing for this feature.|
By default, clustering Mule runtime engines ensures high system availability. If a Mule runtime engine node becomes unavailable due to failure or planned downtime, another node in the cluster can assume the workload and continue to process existing events and messages. The following figure illustrates the processing of incoming messages by a cluster of two nodes. Notice that the processing load is balanced across nodes: Node 1 processes message 1 while Node 2 simultaneously processes message 2.
If one node fails, the other available nodes pick up the work of the failing node. As shown in the following figure, if Node 2 fails, Node 1 processes both message 1 and message 2.
Because all nodes in a cluster of Mule runtime engines process messages simultaneously, clusters can also improve performance and scalability. Compared to a single node instance, clusters can support more users or improve application performance by sharing the workload across multiple nodes or by adding nodes to the cluster.
The following figure illustrates workload sharing in more detail. Both nodes process messages related to order fulfillment. However, when one node is heavily loaded, it can move the processing for one or more steps in the process to another node. Here, processing of the Process order discount step is moved to Node 1, and processing of the Fulfill order step is moved to Node 2.
Beyond benefits such as high availability through automatic failover, improved performance, and enhanced scalability, clustering Mule runtime engines offers the following benefits:
Automatic coordination of access to resources, such as files, databases, and FTP sources.
The Mule runtime engine cluster automatically manages which node (Mule runtime engine) will handle communication from a data source.
Automatic load balancing of processing within a cluster.
If you divide your flows into a series of steps and connect these steps with a connector such as VM, each step is put in a queue, making it cluster-enabled. The cluster of Mule runtime engines can then process each step in any node to better balance the load across nodes.
You can set up an alert to appear when a node goes down and when a node comes back up.
|All Mule runtime engines in a cluster actively process messages. Note that each Mule node is also internally scalable – a single node can scale by taking advantage of multiple cores. Mule operates as a single node in a cluster, even when it uses multiple cores.|
The following problems might exist when you have a server group composed of multiple servers that aren’t bound as a cluster. You don’t have to worry about any of them if you group your servers as a cluster:
File based connectors.
All Mule instances access the same Mule file folders concurrently, which can lead to duplicate file processing and possible failures if a file is deleted or modified by the Mule application.
All Mule instances get the same TCP requests and then process duplicate messages.
All Mule instances connect to the same JMS topic, which might lead to the repeated processing of messages when scaling the non-clustered Mule instance horizontally.
All Mule instances are listening for messages in the same response queue. This implies that a Mule instance might obtain a response that isn’t correlated to the request it sent and might result in incorrect responses or make a flow fail with timeout.
Idempotent Redelivery Policy.
Idempotency doesn’t work if the same request is received by different Mule instances. Duplicated messages aren’t possible.
Salesforce streaming API.
If multiple instances of the same application are deployed, they will fail because the API only supports a single consumer. There is no failover support if the instance connected is stopped or crashes.
You must have at least two Mule runtime engines in a cluster, each one running on different machines.
Mule high availability (HA) requires a reliable network connection between servers to maintain synchronization between the nodes in the cluster.
You must keep the following ports open to set up a Mule cluster: port
If you configure your cluster to use multicast, you need to enable the multicast IP address:
18.104.22.168. Multicast is used to discover new cluster members.
High Availability is a method of designing a computer system to prevent any downtime for the applications that run on it. Some systems use multiple servers so that if one server experiences downtime, the application can continue to run smoothly on the others, without interrupting service for the application’s end users.
The Mule Management Console and Runtime Manager are two interfaces that enable you to set up a cluster of Mule instances, then deploy an application to run on the cluster. You can also monitor the status information for clusters and individual nodes. When clustered, you can easily manage several servers as one.
|For more detailed information about cluster management, see Managing Servers in Runtime Manager.|
A Mule Cluster consists of two or more Mule runtime engines, or nodes, grouped together and treated as a single unit. With the initial configuration, MuleSoft recommends that you scale a cluster to no more than eight Mule runtime engines.
With Anypoint Runtime Manager, you can deploy, monitor, or stop all the Mule runtime engines in a cluster as if they were a single Mule runtime engine.
All the nodes in a cluster share memory as illustrated below:
Mule uses an active-active model to cluster Mule runtime engines. The benefit of this model over an active-passive approach is that your application runs in all nodes, splitting message processing with the other nodes in your cluster, which expedites processing.
In an active-active model, there is no primary node. However, one of the nodes acts as the primary polling node. This means that sources can be configured to only be used by the primary polling node so that no other node reads messages from that source.
This feature works differently depending on the source type:
Scheduler source: only runs in the primary polling node.
Any other source: defined by the
primaryNodeOnlyattribute. Check each connector’s documentation to know which is the default value for
primaryNodeOnlyin that connector.Example configuration for JMS:
<flow name="jmsListener"> <jms:listener config-ref="config" destination="listen-queue" primaryNodeOnly="true"/> <logger message="#[payload]"/> </flow>
You can set up a VM queue explicitly to load balance across Mule runtime engines (nodes). Thus, if your entire application flow contains a sequence of child flows, the cluster can assign each successive child flow to whichever Mule runtime engine happens to be available at the time. The cluster can potentially process a single message on multiple nodes as it passes through the VM endpoints in the application flow, as illustrated below:
A high-reliability application must meet the following requirements:
Zero tolerance for message loss
A reliable integration engine (Mule)
Highly reliable individual connections
The feature known as transactionality tracks application event sequences to ensure that each message-processing step completes successfully, and therefore, no messages get lost or processed incorrectly. If a step fails for some reason, the transactional mechanism rolls back all previous processing events, then restarts the message-processing sequence.
Connectors such as JMS, VM, and JDBC provide built-in transactional support, thus ensuring that messages get processed reliably. For example, you can configure a transaction on an inbound JMS connection endpoint to remove messages from the JMS server only after the transaction has been committed. This practice ensures that the original message remains available for reprocessing if an error occurs during the processing flow.
You must use XA transactions to move messages between dissimilar connectors that support transactions. This ensures that the Mule runtime engine commits associated transactions from all the dissimilar connectors as a single unit.
All Mule connectors are supported within a cluster. Because of differences in the way different connectors access inbound traffic, the details of this support vary. In general, outbound traffic acts the same way inside and outside a cluster. Mule runtimes support three basic types of connectors:
Socket-based connectors read input sent to network sockets that Mule owns. Examples include TCP, UDP, and HTTP[S].
Socket-based connectors are supported in clusters as described below:
Since each clustered Mule runtime runs on a different network node, each instance receives only the socket-based traffic sent to its node. Incoming socket-based traffic should be Clustering and Load Balancing to distribute it among the clustered instances.
Output to socket-based connectors is written to a specific host/port combination. If the host/port combination is an external host, no special considerations apply. If it is a port on the local host, consider using that port on the load balancer instead to better distribute traffic among the cluster.
Listener-based connectors read data using a protocol that fully supports concurrent multiple accessors. Examples include JMS and VM.
Listener-based connectors are supported in clusters as described below:
Listener-based connectors fully support multiple readers and writers. No special considerations apply either to input or to output.
Note that, in a cluster, VM connector queues are a shared, cluster-wide resource. The cluster will automatically synchronize access to the VM connector queues. Because of this, a message written to a VM queue can be processed by any cluster node. This makes VM ideal for sharing work among cluster nodes.
Resource-based connectors read data from a resource that allows multiple concurrent accessors, but does not natively coordinate their use of the resource. Examples of resource-based connectors include File, FTP, SFTP, E-mail, and JDBC.
Resource-based connectors are supported in clusters as described below:
Mule HA Clustering automatically coordinates access to each resource only for nodes within the cluster, ensuring that only one clustered instance accesses each resource at a time. Because of this, it is generally a good idea to immediately write messages read from a resource-based connector to VM queues. This allows the other cluster nodes to take part in processing the messages.
If you run other applications (either Mule applications outside the cluster or non-Mule applications) that access the same resources as the nodes in the cluster, you must implement logic to lock those resources.
For example, if multiple programs are processing files in the same shared directory by reading, modifying, and then deleting the files. These programs must use an explicit, application-level locking strategy to prevent the same file from being processed more than once.
There are no special considerations in writing to resource-based clustered connectors:
When writing to file-based connectors (File, FTP, SFTP), Mule will generate unique file names.
When writing to JDBC, Mule can generate unique keys.
Writing e-mail is effectively listener-based rather than resource-based.
High-reliability applications must have zero tolerance for message loss. This means that both the underlying Mule and its individual connections need to be reliable. You can find tools to build fully reliable applications in your clusters in Reliability Patterns.
If your application uses a non-transactional connector, follow the reliability pattern. These patterns ensure that a message is accepted and successfully processed or that it generates an "unsuccessful" response, allowing the client to retry.
If your application uses transactional connectors, such as JMS, VM, and JDBC, use transactions. Mule’s built-in support for transactional connectors enables reliable messaging for applications that use these connectors.
These actions can also apply to non-clustered applications.
To ensure reliable connectivity between cluster nodes, all nodes of a cluster must be located on the same LAN. Implementing a cluster with nodes across geographically separated locations, such as different data centers that are connected through a VPN, is possible but not recommended.
Ensuring that all cluster nodes reside on the same LAN is the best practice to lower the possibility of network interruptions and unintended consequences, such as duplicated messages.
Linking cluster nodes through a WAN network introduces many possible points of failure, such as external routers and firewalls, which can prevent proper synchronization between cluster nodes. This not only affects performance but also requires you to plan for possible side effects in your app. For example, when two cluster nodes reconnect after getting cut off by a failed network link, the ensuing synchronization process can cause messages to be processed twice, which creates duplicates that must be handled in your application logic.
Note that it is possible to use nodes of a cluster located in different data centers and not necessarily located on the same LAN, but some restrictions apply.
To prevent this behavior, it is necessary to enable the Quorum Protocol. This protocol is used to allow one set of nodes to continue processing data while other sets do nothing with the shared data until they reconnect. Basically, when a disconnection occurs, only the portion with the most nodes will continue to function. For instance, assume two data centers, one with three nodes and another with two nodes. If a connectivity problem occurs in the data center with two nodes, then the data center with three nodes will continue to function, and the second data center will not. If the three-node data center goes offline, none of your nodes will function. To prevent this outage, you must create the cluster in at least three data centers with the same number of nodes. It is unlikely for two data centers to crash, so if just one data center goes offline, the cluster will always be functional.
|A cluster partition that does not have enough nodes to function will continue reacting to external system calls, but all operations over the object stores will fail, preventing data generation.|
Quorum is only supported in Object Store-related operations.
Distributed locking is not supported, which affects:
File/FTP connector polling for files concurrent
Idempotent Redelivery Policy component
Idempotent Message Filter component
In-memory messaging is not supported, which affects:
The Quorum feature can only be configured manually.
When Mule clusters are used to serve TCP requests (where TCP includes SSL/TLS, UDP, Multicast, HTTP, and HTTPS), some load balancing is needed to distribute the requests among the clustered instances. There are various software load balancers available, two of them are:
NGINX, an open-source HTTP server and reverse proxy. You can use NGINX’s
HttpUpstreamModulefor HTTP(S) load balancing.
The Apache web server, which can also be used as an HTTP(S) load balancer.
There are also many hardware load balancers that can route both TCP and HTTP(S) traffic.
This section applies only for on-premises deployments. High performance is implemented differently on CloudHub and Pivotal Cloud Foundry.
See Deployment Strategies for more information about each of these deployments.
If high performance is your primary goal (rather than reliability), you can configure a Mule cluster or an individual application for maximum performance using a performance profile. By implementing the performance profile for specific applications within a cluster, you can maximize the scalability of your deployments while deploying applications with different performance and reliability requirements in the same cluster. Performance profiles that you configure at the container level apply to all applications within the container. Application-level configuration overrides container-level configuration.
Setting the performance profile has two effects:
It disables distributed queues, using local queues instead to prevent data serialization/deserialization and distribution in the shared data grid.
It implements the object store without backups, to avoid replication.
To configure the performance profile at the container level, add the following to
mule-cluster.properties, to the system properties (from the command line), or to
To configure the performance profile at the individual application level, add the profile inside a configuration wrapper, as shown below.
Performance Store Profile
<mule> <configuration> <cluster:cluster-config> <cluster:performance-store-profile/> </cluster:cluster-config> </configuration> </mule>
Remember that an application-level configuration overrides a container-level configuration. If you want to configure the container for high performance but make one or more individual applications within that container prioritize reliability, include the following code in those applications:
Reliable Store Profile
<mule> <configuration> <cluster:cluster-config> <cluster:reliable-store-profile/> </cluster:cluster-config> </configuration> </mule>
In cases of high load with endpoints that do not support load balancing, applying the performance profile might degrade performance. If you are using a File-based connector with an asynchronous processing strategy, JMS topics, multicasting, or HTTP connectors without a load balancer, the high volume of messages entering a single node can cause bottlenecks, and thus it can be better for performance to turn off the performance profile for these applications.
You can also choose to define the minimum number of machines that a cluster requires to remain in an operational state. This configuration grants you a consistency improvement in the overall performance.
There are a number of recommended practices related to clustering. These include:
As much as possible, organize your application into a series of steps where each step moves the message from one transactional store to another.
If your application processes messages from a non-transactional connector, use a reliability pattern to move them to a transactional store such as a VM or JMS store.
Use transactions to process messages from a transactional connector. This ensures that if an error is encountered, the message reprocesses.
Use distributed stores such as those used with the VM or JMS connector – these stores are available to an entire cluster. This is preferable to the non-distributed stores used with connectors such as File, FTP, and JDBC – these stores are read by a single node at a time.
Use the VM connector to get optimal performance. Use the JMS connector for applications where data needs to be saved after the entire cluster exits.
Implement reliability patterns to create high reliability applications.