Liferay Portal is designed to serve everything from the smallest to the largest web sites. Out of the box, it’s configured optimally for a single server environment. If one server isn’t sufficient to serve the high traffic needs of your site, Liferay scales to the size you need.
Liferay works well in clusters of multiple machines (horizontal cluster) or in clusters of multiple VMs on a single machine (vertical cluster), or any mixture of the two. Once you have Liferay installed in more than one application server node, there are several optimizations that need to be made. At a minimum, Liferay should be configured in the following way for a clustered environment:
All nodes should be pointing to the same Liferay database or database cluster.
Documents and Media repositories should be accessible to all nodes of the cluster.
Search should be configured for replication or should use a separate search server.
The cache should be replicating across all nodes of the cluster.
Hot deploy folders should be configured for each node if you’re not using server farms.
If you haven’t configured your application server to use farms for deployment, the hot deploy folder should be a separate folder for all the nodes, and plugins will have to be deployed to all of the nodes individually. This can be done via a script. If you do have farms configured, you can deploy normally to any node’s deploy folder, and your farm configuration should take care of syncing the deployment to all nodes.
Many of these configuration changes can be made by adding or modifying properties in your
portal-ext.properties file. Remember that this file overrides the defaults in the
portal.properties file. The original version of this file can be found in the Liferay source code or can be extracted from the
portal-impl.jar file in your Liferay installation. It is a best practice to copy the relevant section you want to modify from
portal.properties into your
portal-ext.properties file, and then modify the values there.
Note: This chapter documents a Liferay-specific cluster configuration, without getting into specific implementations of third party software, such as Java EE application servers, HTTP servers, and load balancers. Please consult your documentation for those components of your cluster for specific details of those components. Before configuring Liferay in a cluster configuration, make sure your OS is not defining the hostname of your box to the local network at 127.0.0.1.
We’ll take each of the points above one by one to present a clear picture of how to cluster Liferay.
All nodes should be pointing to the same Liferay database
This is pretty self-explanatory. Each node should be configured with a data source that points to one Liferay database (or a database cluster) that all the nodes will share. This ensures all the nodes operate from the same basic data set. This means, of course, Liferay cannot (and should not) use the embedded HSQL database that is shipped with the bundles (but you already knew that, right?). And, of course, it goes without saying the database server is a separate physical box from the server which is running Liferay.
Beyond a database cluster, there are two more advanced options you can use to optimize your database configuration: a read-writer database configuration, and sharding.
Read-Writer database configuration
Liferay allows you to use two different data sources for reading and writing. This enables you to split your database infrastructure into two sets: one that is optimized for reading and one that is optimized for writing. Since all major databases support replication in one form or another, you can then use your database vendor’s replication mechanism to keep the databases in sync in a much faster manner than if you had a single data source which handled everything.
Enabling a read-writer database is simple. In your
portal-ext.properties file, configure two different data sources for Liferay to use, one for reading, and one for writing:
jdbc.read.url=jdbc:mysql://dbread.com/lportal?useUnicode=true&characterEncoding=UTF-8&useFastDateParsing=false jdbc.read.username=**your user name**
jdbc.write.url=jdbc:mysql://dbwrite.com/lportal?useUnicode=true&characterEncoding=UTF-8&useFastDateParsing=false jdbc.write.username=**your user name**
Of course, specify the user name and password to your database in the above configuration.
After this, enable the read-writer database configuration by uncommenting the Spring configuration file which enables it in your
spring.configs property (line to uncomment is in bold):
The next time you restart Liferay, it will now use the two data sources you have defined. Be sure you have correctly set up your two databases for replication before starting Liferay.
Next, we’ll look at database sharding.
Liferay starting with version 5.2.3 supports database sharding for different portal instances. Sharding is a term used to describe an extremely high scalability configuration for systems with massive amounts of users. In diagrams, a database is normally pictured as a cylinder. Instead, picture it as a glass bottle full of data. Now take that bottle and smash it onto a concrete sidewalk. There will be shards of glass everywhere. If that bottle were a database, each shard now is a database, with a subset of the data in each shard.
This allows you to split up your database by various types of data that might be in it. For example, some implementations of sharding a database split up the users: those with last names beginning with A to D go in one database; E to I go in another; etc. When users log in, they are directed to the instance of the application that is connected to the database that corresponds to their last names. In this manner, processing is split up evenly, and the amount of data the application needs to sort through is reduced.
By default, Liferay allows you to support sharding through different portal instances, using the round robin shard selector. This is a class which serves as the default algorithm for sharding in Liferay. Using this algorithm, Liferay selects from several different portal instances and evenly distributes the data across them. Alternatively, you can use the manual shard selector. In this case, you’d need to use the UI provided in the control panel to configure your shards.
Of course, if you wish to have your developers implement your own sharding algorithm, you can do that. This is a great use of the Ext plugin. You can select which algorithm is active via the
#shard.selector=[your implementation here]
Enabling sharding is easy. You’ll need to make sure you are using Liferay’s data source implementation instead of your application server’s. Set your various database shards in your
portal-ext.properties file this way:
Once you do this, you can set up your DNS so several domain names point to your Liferay installation (e.g., abc1.com, abc2.com, abc3.com). Next, go to the control panel and click Portal Instances in the Server category. Create two to three instances bound to the DNS names you have configured.
If you’re using the RoundRobinShardSelector class, Liferay automatically enters data into each instance one by one. If you’re using the
ManualShardSelector class, you’ll have to specify a shard for each instance using the UI.
Figure 19.19: When creating a shard using the manual shard selector, specify the shard you want to use for that instance.
The last thing you need to do is modify the
spring.configs section of your
portal-ext.properties file to enable the sharding configuration, which by default is commented out. To do this, your
spring.configs should look like this (modified section is in bold):
That’s all there is to it. Your system is now set up for sharding. Now that you’ve got your database set up and optimized for a large installation, let’s turn to clustering the Documents and Media Library.
Documents and Media Library clustering
Liferay 6.1 introduces a new Documents and Media Library which is capable of mounting several repositories at a time and presenting a unified interface to the user. By default, users can make use of the Liferay repository, which is already mounted. This repository is built into Liferay Portal and can use as its back-end one of several different store implementations. In addition to this, many different kinds of third party repositories can be mounted. If you have a separate repository you’ve mounted, all nodes of the cluster will point to this repository. Your avenue for improving performance at that point is to cluster your third party repository, using the documentation for the repository you have chosen. If you don’t have a third party repository, there are ways you can configure the Liferay repository to perform well in a clustered configuration.
The main thing to keep in mind is you need to make sure every node of the cluster has the same access to the file store as every other node. For this reason, you’ll need to take a look at your store configuration.
There are several options available for configuring how Liferay’s Documents and Media library stores files. Each option is a store which can be configured through the
portal-ext.properties file by setting the
dl.store.impl= property. Let’s consider the ramifications of the various store options.
Using the File System store
This is the default store. It’s a simple file storage implementation that uses a local folder to store files. You can use the file system for your clustered configuration, but you’d have to make sure the folder to which you point the store can handle things like concurrent requests and file locking. For this reason, you need to use a Storage Area Network or a clustered file system.
The file system store was the first store created for Liferay and is heavily bound to the Liferay database. By default, documents are stored in a
document_library subfolder of the
data folder in a Liferay bundle. Of course, you can change this path to anything you want by using the
This store creates a folder structure based on primary keys in the Liferay database. If, for example, you upload a presentation with the file name
workflow.odp into a folder called stuff, the file system store creates a folder structure which looks like figure 19.3.
Figure 19.20: Liferay’s file system store creates a folder structure based on primary keys in Liferay’s database.
The first folder is the company ID to which the site belongs. The second folder is the group ID of the site where the document resides. The third is the ID of the document itself, and finally the file name of the document is renamed to a version number for storing multiple versions of the document.
As you can see, this binds your documents very closely to Liferay, and may not be exactly what you want. But if you’ve been using the default settings for a while and need to migrate your documents, Liferay provides a migration utility in the control panel in Server Administration → Data Migration. Using this utility, you can move your documents very easily from one store implementation to another.
Speaking of other store implementations, let’s look at some others Liferay provides.
Using the Advanced File System store
Liferay’s advanced file system store is similar to the default file system store. Like that store, it saves files to the local file system–which, of course, could be a remote file system mount. It uses a slightly different folder structure to store files, which is pictured below.
Figure 19.21: The advanced file system store creates a more nested folder structure than the file system store.
So what makes the advanced file system store advanced? Several operating systems have limitations on the number of files which can be stored in a particular folder. The advanced file system store overcomes this limitation by programmatically creating a structure that can expand to millions of files, by alphabetically nesting the files in folders. This not only allows for more files to be stored, but also improves performance as there are less files stored per folder.
The same rules apply to the advanced file system store as apply to the default file system store. To cluster this, you’ll need to point the store to a network mounted file system that all the nodes can access, and that networked file system needs to support concurrent requests and file locking. Otherwise, you may experience data corruption issues if two users attempt from two different nodes to write to the same file at the same time.
You may decide the advanced file system store for whatever reason doesn’t serve your needs. If this is the case, you can of course mount other file systems into the documents and media library. In addition to this, you can also redefine the Liferay store to use one of three other supported protocols. We’ll look at these next.
Using the CMIS store
Though you can mount as many different CMIS (Content Management Interoperability Services) repositories as you like in the documents and media library, you may wish also to redefine the Liferay repository to point to a CMIS repository as well. Why? Because, as you know, users are users, and it’s possible they may find a way to create a folder or upload content to the Liferay repository. It would be nice if that Liferay repository was connected to a clustered CMIS repository by the administrator without having to mount it through the UI. The CMIS store allows you to do just that.
If you wish to use the CMIS store, all you need to do is set the following four directives in your
Now the Liferay repository is connected to CMIS via the CMIS store. As long as all nodes are pointing to your CMIS repository, everything in your Liferay cluster should be fine, as the CMIS protocol prevents multiple simultaneous file access from causing data corruption.
From here, we’ll move on to the JCR store.
Using the JCR store
Liferay Portal supports as a store the Java Content Repository standard. Under the hood, Liferay uses Jackrabbit—-which is a project from Apache-—as its JSR-170 compliant document repository. By default, Jackrabbit is configured to store the documents on the local file system upon which Liferay is installed, in the
[Liferay Home]/liferay/jackrabbit folder. Inside this folder is Jackrabbit’s configuration file, called
Using the default settings, the JCR store is not very different from the file system stores, except you can use any JCR client to access the files. You can, however, modify Jackrabbit’s configuration so it stores files in a database that can be accessed by all nodes, and so that it operates as a cluster within Liferay’s cluster.
To move the default repository location to a shared folder, you do not need to edit Jackrabbit’s configuration file. Instead, find the section in
portal.properties labeled JCR and copy/paste that section into your
portal-ext.properties file. One of the properties, by default, is the following:
Change this property to point to a shared folder that all the nodes can see. A new Jackrabbit configuration file is then generated in that location, and you’ll have to edit that file to modify Jackrabbit’s configuration.
Note that because of file locking issues, this isn’t the best way to share Jackrabbit resources, unless you’re using a networked file system that can handle concurrency and file locking. If you have two people logged in at the same time uploading content, you could encounter data corruption using this method, and because of this, we don’t recommend it for a production system. Instead, if you want to use the Java Content Repository in a cluster, you should redirect Jackrabbit into your database of choice. You can use the Liferay database or another database for this purpose. This requires editing Jackrabbit’s configuration file.
The default Jackrabbit configuration file has sections commented out for moving the Jackrabbit configuration into the database. This has been done to make it as easy as possible to enable this configuration. To move the Jackrabbit configuration into the database, simply comment out the sections relating to the file system and comment in the sections relating to the database. These by default are configured for a MySQL database. If you are using another database, you will likely need to modify the configuration, as there are changes to the configuration file that are necessary for specific databases. For example, the default configuration uses Jackrabbit’s
DbFileSystem class to mimic a file system in the database. While this works well in MySQL, it doesn’t work for all databases. For example, if you’re using an Oracle database, you’ll need to modify this to use
Modify the JDBC database URLs so they point to your database. This, of course, must be done on all nodes of the cluster. Don’t forget to create the database first, and grant the user ID you are specifying in the configuration file access to create, modify, and drop tables. After this, be sure to uncomment the
<Cluster/> section at the bottom of the file. For further information, it’s best to check out the Jackrabbit documentation. Please see the Jackrabbit documentation at
http://jackrabbit.apache.org for further information.
Once you’ve configured Jackrabbit to store its repository in a database, the next time you bring up Liferay, the necessary database tables are created automatically. Jackrabbit, however, does not create indexes on these tables, and so over time this can be a performance penalty. To fix this, you’ll need to manually go into your database and index the primary key columns for all the Jackrabbit tables.
Note that this configuration doesn’t perform as well as the advanced file system store, because you’re storing documents in a database instead of in the file system. But it does have the benefit of clustering well. Next, we’ll look at Amazon’s S3 store.
Using Amazon Simple Storage Service
Amazon’s simple storage service (S3) is a cloud-based storage solution which you can use with Liferay. All you need is an account, and you can store your documents to the cloud from all nodes, seamlessly.
This is easy to set up. When you sign up for the service, Amazon assigns you some unique keys which link you to your account. In Amazon’s interface, you can create “buckets” of data optimized by region. Once you’ve created these to your specifications, all you need to do is declare them in
Once you have these configured, set your store implementation to the
Consult the Amazon Simple Storage documentation for additional details on using Amazon’s service.
We have one more store to go over: the Documentum store.
Using the Documentum store
EE Only Feature
If you have a Liferay Portal EE license, you have access to the Documentum hook which adds support for Documentum to Liferay’s Documents and Media library. Install this hook by using the Liferay Marketplace.
This hook doesn’t add an option to make the Liferay repository into a Documentum repository, as the other store implementations do. Instead, it gives you the ability to mount Documentum repositories via the Documents and Media library UI.
There’s not really a lot to this; it’s incredibly easy. Click Add → Repository, and in the form that appears, choose Documentum as the repository type. After that, give it a name and specify the Documentum repository and cabinet, and Liferay mounts the repository for you. That’s really all there is to it. If all your nodes are pointing to a Documentum repository, you can cluster Documentum to achieve higher performance.
Now that we’ve covered the available ways you can configure documents and media for clustering, we can move on to configuring search.
You can configure search for clustering in one of two ways: use pluggable enterprise search (recommended), or configure Lucene so indexes replicate across the individual file systems of the nodes in the cluster. We’ll look at both ways to do this.
Using Pluggable Enterprise Search
As an alternative to using Lucene, Liferay supports pluggable search engines. The first implementation of this uses the open source search engine Solr, but in the future there will be many such plugins for your search engine of choice. This allows you to use a completely separate product for search, and this product can be installed on another application server or cluster of servers. Your search engine then operates completely independently of your Liferay Portal nodes in a clustered environment, acting as a search service for all the nodes simultaneously.
This makes it much easier to deal with search indexes. You no longer have to maintain indexes on every node in your cluster, and you get to offload indexing activity to a separate server, so your nodes can concentrate their CPU power on serving pages. Each Liferay node sends requests to the search engine to update the search index when needed, and these updates are then queued and handled automatically by the search engine, independently. It’s kind of like having an army of robots ready and willing to do your bidding.
First, you’ll need to configure your Solr server, and then you need to install Liferay’s Solr plugin to redirect searches over to it.
Configuring the Solr Search Server
Since Solr is a standalone search engine, you’ll need to download it and install it first according to the instructions on the Solr web site (
http://lucene.apache.org/solr). Of course, it’s best to use a server that is separate from your Liferay installation, as your Solr server becomes responsible for all indexing and searching for your entire cluster. You definitely don’t want both Solr and Liferay on the same box. Solr is distributed as a
.war file with several
.jar files which need to be available on your application server’s classpath. Once you have Solr up and running, integrating it with Liferay is easy, but it requires a restart of your application server.
The first thing you need to define on the Solr box is the location of your search index. Assuming you’re running a Linux server and you’ve mounted a file system for the index at
/solr, create an environment variable that points to this folder. This environment variable needs to be called
$SOLR_HOME. So for our example, we would define:
This environment variable can be defined anywhere you need: in your operating system’s start up sequence, in the environment for the user who is logged in, or in the start up script for your application server. If you’re using Tomcat to host Solr, modify
setenv.bat and add the environment variable there.
Once you’ve created the environment variable, you then can use it in your application server’s start up configuration as a parameter to your JVM. This is configured differently per application server, but again, if you’re using Tomcat, edit
catalina.bat and append the following to the
This takes care of telling Solr where to store its search index. Go ahead and install Solr to this box according to the instructions on the Solr web site (
http://lucene.apache.org/solr). Once it’s installed, shut it down, as there is some more configuration to do.
Installing the Solr Liferay Plugin
Next, you have a choice. If you have installed Solr on the same system upon which Liferay is running (not recommended), you can simply go to the Liferay Marketplace and install the solr-web plugin. This, however, defeats much of the purpose of using Solr, because the goal is to offload search indexing to another box to free up processing for your installation of Liferay. For this reason, you really shouldn’t run Liferay and your search engine on the same box. Unfortunately, the configuration in the plugin is set exactly that way, presumably to allow you to experiment with different search configurations. To run them separately–as you would in a production environment–, you’ll have to make a change to a configuration file in the plugin before you install it so you can tell Liferay where to send indexing requests. In this case, go to the Liferay Marketplace and download the plugin to your system.
Open or extract the plugin. Inside the plugin, you’ll find a file called
solr-spring.xml in the
WEB-INF/classes/META-INF folder. Open this file in a text editor and you will see the entry which defines where the Solr server can be found by Liferay:
<bean class="com.liferay.portal.spring.context.PortletBeanFactoryPostProcessor" />
<!-- Solr search engine -->
<bean id="com.liferay.portal.search.solr.server.BasicAuthSolrServer" class="com.liferay.portal.search.solr.server.BasicAuthSolrServer">
<constructor-arg type="java.lang.String" value="http://localhost:8080/solr" />
Modify this value so it points to the server where Solr is running. Then save the file and put it back into the plugin archive in the same place it was before.
Next, extract the file
schema.xml from the plugin. It should be in the
docroot/WEB-INF/conf folder. This file tells Solr how to index the data coming from Liferay, and can be customized for your installation. Copy this file to
$SOLR_HOME/conf on your Solr box (you may have to create the
Before you start Solr, you should provide Solr with a list of synonyms and stop words. Synonyms are words that should be equivalent in search. For example, if a user searches for important information, you may want to show results for required information or critical information. You can define these in
synonyms.txt. Stop words are defined in
stopwords.txt and are words that should not be indexed: articles, pronouns, and other words that have little value in a search. Place these files in your
$SOLR_HOME/conf folder. Examples for both of these files are found in the Solr archive in the
solr-4.1.0/example/solr/collection1/conf folder. Additional Solr configuration options, most importantly
elevate.xml, are in the
$SOLR_HOME/conf folder. Now you can start Solr. After Solr has started, hot deploy the
solr-web plugin to all your nodes. See the next section for instructions on hot deploying to a cluster.
Once the plugin is hot deployed, your Liferay server’s search is automatically upgraded to use Solr. It’s likely, however, that initial searches will come up with nothing: this is because you need to reindex everything using Solr.
Go to the control panel. In the Server section, click Server Administration. Click the Execute button next to Reindex all search indexes at the bottom of the page. Liferay will begin sending indexing requests to Solr for execution. Once Solr has indexed all your data, you’ll have a search server running independently of all your Liferay nodes.
Installing the plugin to your nodes has the effect of overriding any calls to Lucene for searching. All Liferay’s search boxes will now use Solr as the search index. This is ideal for a clustered environment, as it allows all your nodes to share one search server and one search index, and this search server operates independently of all your nodes. If, however, you don’t have the server hardware upon which to install a separate search server, you can sync the search indexes between all your nodes, as is described next.
Clustering Lucene indexes on all nodes
Lucene, the search indexer which Liferay uses, can be configured to sync indexes across each cluster node. This is the easiest configuration to implement, though of course, it’s not as “clean” a configuration as using pluggable enterprise search. Sometimes, however, you just don’t have another server to use for search indexing, so you need a way to keep all your nodes in sync. By default, Liferay provides a method called Cluster Link which can send indexing requests to all nodes in the cluster to keep them in sync. This configuration doesn’t require any additional hardware, and it performs very well. It may increase network traffic when an individual server reboots, since a full reindex will be needed. But this should rarely happen, making it a good tradeoff if you don’t have the extra hardware to implement a Solr search server.
You can enable Cluster Link by setting the following property in your
To cluster your search indexes, you also need to set the following property:
If you have
lucene.replicate.write=false, you’ll enable cache replication but not index replication.
lucene.replicate.write=true need to be set on all your nodes. That’s all you need to do to sync your indexes. Pretty easy, right? Of course, if you have existing indexes, you’ll want to reindex as described in the previous section once you have Cluster Link enabled on all your nodes.
Enabling Cluster Link automatically activates distributed caching. Distributed caching enables some RMI (Remote Method Invocation) cache listeners that are designed to replicate the cache across a cluster.
Liferay uses Ehcache, which has robust distributed caching support. This means that the cache can be distributed across multiple Liferay nodes running concurrently. Enabling this cache can increase performance dramatically. For example, say that two users are browsing the message boards. The first user clicks a thread to read it. Liferay must look up that thread from the database and format it for display in the browser. With a distributed Ehcache running, this thread is stored in a cache for quick retrieval, and that cache is then replicated to the other nodes in the cluster. Say then that the second user who is being served by another node in the cluster wants to read the same forum thread and clicks on it. This time, the data is retrieved more quickly. Because the thread is in the cache, no trip to the database is necessary.
This is much more powerful than having a cache running separately on each node. The power of distributed caching allows for common portal destinations to be cached for multiple users. The first user can post a message to the thread he or she was reading, and the cache is updated across all the nodes, making the new post available immediately from the local cache. Without that, the second user would need to wait until the cache was invalidated on the node he or she connected to before he or she could see the updated forum post.
Once you enable distributed caching, of course, you should do some due diligence and test your system under a load that best simulates the kind of traffic your system needs to handle. If you’ll be serving up a lot of message board messages, your script should reflect that. If web content is the core of your site, your script should reflect that too.
As a result of a load test, you may find that the default distributed cache settings aren’t optimized for your site. In this case, you’ll need to tweak the settings yourself. You can modify the Liferay installation directly or you can use a plugin to do it. Either way, the settings you change are the same. Let’s see how to do this with a plugin first.
The next thing we’ll cover about caching is a special EE-only optimization that can be made to the cache.
Enhanced distributed cache algorithm
EE Only Feature
By default, Liferay’s distributed cache uses the RMI replication mechanism, which uses a point to point communication topology. As you can guess, this kind of structure doesn’t scale well for a large cluster with many nodes. Because each node has to send the same event to other nodes
N - 1 times, network traffic becomes a bottleneck when
N is too large. Ehcache also has a performance issue of its own, in that it creates a replication thread for each cache entity. In a large system like Liferay Portal, it’s very common to have more than 100 cached entities. This translates to 100+ cache replication threads. Threads are expensive, because they take resources (memory and CPU power). Most of the time, these threads are sleeping, because they only need to work when a cached entity has to talk to remote peers.
Figure 19.22: The default algorithm requires each node to create massive amounts of dispatch threads to update the cache for each node in the cluster.
Putting heap memory aside (because the amount of memory on the heap depends on the application(s) running), consider the stack memory footprint of those 100+ threads. By default on most platforms, the thread stack size is 2 MB; for 100 threads, that’s more than 200 MB. If you include the heap memory size, this number can become as high as 500 MB for just one node. And that massive amount of threads can also cause frequent context switch overhead, which translates to increased CPU cycles.
For large installations containing many nodes, Liferay has developed an enhanced algorithm for handling cache replication that can can fix both the
1 network communication bottleneck, as well as the massive threads bottleneck. The default implementation uses JGroups’ UDP multicast to communicate.
Figure 19.23: Liferay’s algorithm uses a single UDP multicast channel, so that nodes don’t have to create a thread for each other node in the cluster.
To reduce the number of replication threads, we provide a small pool of dispatching threads. These deliver cache cluster events to remote peers. Since all cache entities’ cluster events must go through our pool of dispatching threads to communicate, this gives us a chance to coalesce events: if two modifications to the same cache object happen at almost the same time, we can combine the changes into one, and then we only need to notify remote peers once. This reduces traffic on the network. We should also note that newer versions of Ehcache support the JGroups replicator and can also fix the
N - 1 network communication; however, they cannot fix the massive threads issue and they cannot coalesce cache events.
For EE customers who are interested in this feature, all you have to do to enable the enhanced algorithm is to install a plugin from the Liferay Marketplace and set the following property in the
portal-ext.properties files of each of your nodes:
Search Liferay Marketplace for the Ehcache Cluster EE plugin, which is free to all EE customers, and install it on each of your nodes. The new algorithm is immediately activated and you can reap the benefits right away.
Next, let’s discuss how to modify your Ehache settings. As we’ve seen, it’s easy to use the default Ehcache settings just by enabling Cluster Link. If you need to tweak the cache for your site, you have two options: you can modify Ehcache settings with a plugin or you can modify them directly.
Modifying the Ehcache settings with a plugin
A benefit of working with plugins is that you can quickly install a plugin on each node of your cluster without taking down the cluster. We’ll cover this first. If you’re not a developer, don’t worry–even though you’ll create a plugin, you won’t have to write any code.
Since we’re assuming you’re an administrator and not a developer, we’ll take the easiest route, and use Liferay’s graphical development tools, rather than the command line Plugins SDK by itself. If you’re a Liferay EE customer, download Liferay Developer Studio from the Customer Portal. Set it up with all the defaults from the first start wizard, and you’re good to go (skip the next paragraph).
If you’re not a Liferay EE customer, download Eclipse and install Liferay IDE from the Eclipse Marketplace. Download the Plugins SDK for your edition of Liferay from either the Customer Portal (EE) or the Downloads page on liferay.com. Connect Liferay IDE to your Plugins SDK using the instructions found in the Liferay Developer’s Guide.
Next, create a hook plugin by selecting File → New → Liferay Project. Select Hook as the project type and give your project a name. Click Finish and your project is created.
In your project, create a text file called
portlet.properties in the
docroot/WEB-INF/src folder. This file can override properties in your portal just like
portal-ext.properties. Into this file place the following three properties:
Liferay’s configuration files are, of course, used by default. If you’re overriding these properties, it’s because you want to customize the configuration for your own site. A good way to start with this is to extract Liferay’s configuration files and then customize them. If you’re running an application server (such as Tomcat) that allows you to browse to the running instance of Liferay, you can extract Liferay’s configuration files from Liferay itself. If you’re not, you can extract them from Liferay’s
.war file or Liferay’s source code. In either place, you’ll find the files in the
portal-impl.jar file, which is in Liferay’s
WEB-INF/lib folder. The files you want are
liferay-multi-vm-clustered.xml, and they’ll be in the
/ehcache folder in this
.jar. Once you have these, make a subfolder of the
docroot folder in your project. Place the files you extracted into this folder and then specify this folder in the properties above.
For example, if you created a folder called
custom_cache in your project’s
docroot folder, you’d copy the three XML configuration files (
liferay-multi-vm-clustered.xml) there. Then you’d edit your
portlet.properties and specify your configuration files in the three properties above:
Save the file and deploy the plugin (deploying plugins is covered in the Liferay Developer’s Guide), and the settings you’ve placed in those files will override the default Liferay settings. In this way, you can tweak your cache settings so that your cache performs optimally for the type of traffic generated by your site. The strength of doing it this way is that you don’t have restart your server to change the cache settings. This is a great benefit, but beware: since Ehcache doesn’t allow for changes to cache settings while the cache is alive, reconfiguring a cache while the server is running will flush the cache.
There is, of course, another way to do this if you don’t want to create a plugin. It requires you to restart the server to enable the new cache settings, but you don’t have to work with any developer tools to do it.
Modifying the Ehcache settings directly
This method is pretty similar to the plugin method, except that you have to modify the Liferay installation directly. You’ll still need to extract Liferay’s configuration files as described in the previous section. Next, shut down your server and find the location in the server where Liferay is installed (this may not be possible on all application servers, and if this is the case, you’ll need to use the plugin method described above). For example, say you’re running Liferay on Tomcat. Tomcat stores the deployed version of Liferay in
Home]/webapps/ROOT. Inside this folder is the folder structure
WEB-INF/classes. You can create a new folder in here called
custom_cache to store the custom versions of the cache configuration files. Copy the files you extracted from Liferay into this folder.
You then need to modify the properties in
portal-ext.properties that point to these files. Copy/paste the Hibernate section of
portal.properties into your
portal-ext.properties file and then modify the
net.sf.ehcache.configurationResourceName property to point to the clustered version of the configuration file that is now in your custom folder:
Now that Liferay is pointing to your custom file, you can modify the settings in this file to change the cache configuration for Hibernate.
Next, copy/paste the Ehcache section from the
portal.properties file into your
portal-ext.properties file. Modify the properties so they point to the files in your custom folder. For example:
You can now take a look at the settings in these files and tune them to fit your environment and application. Let’s examine how to do that next.
Customizing Hibernate cache settings
By default, Hibernate (Liferay’s database persistence layer) is configured to use Ehcache as its cache provider. This is the recommended setting. If you’re using the default settings using Cluster Link, you already have this enabled. If, however, you need to customize the settings, you’ll have to customize it in one of the ways described above: either in a plugin or in the deployed instance of Liferay. The first thing, of course, is to start off with the clustered version of the file. Copy the
hibernate-clustered.xml configuration file to your plugin or to a place in Liferay’s classpath (as described above) where you can refer to it. Then change the following property to point to the file:
Next, open this file in a text editor. You’ll notice that the configuration is already set up to perform distributed caching through a multi-cast connection. The configuration, however, might not be set up optimally for your particular application. Notice that by default, the only object cached in the Hibernate cache is the User object (
com.liferay.``portal``.model.impl.UserImpl). This means that when a user logs in, his or her User object will go in the cache so that any portal operation that requires access to it (such as permission checking) can retrieve that object very quickly from the cache.
You may wish to add other objects to the cache. For example, a large part of your application may be document management using the Documents and Media portlet. In this case, you may want to cache media objects, such as
DLFileEntryImpl to improve performance as users access documents. To do that, add another block to the configuration file with the class you want to cache:
<bootstrapCacheLoaderFactory class="com.liferay.portal.cache.ehcache.LiferayBootstrapCacheLoaderFactory" />
Your site may use the message boards portlet, and those message boards may get a lot of traffic. To cache the threads on the message boards, configure a block with the
<bootstrapCacheLoaderFactory class="com.liferay.portal.cache.ehcache.LiferayBootstrapCacheLoaderFactory" />
Note that if your developers have overridden any of these classes in an Ext plugin, you’ll have to specify the overridden versions rather than the stock ones that come with Liferay Portal. You can customize the other ehcache configuration files in exactly the same way. Refer to Ehcache’s documentation for information on how to do this.
As you can see, it’s easy to add specific data to be cached. Be careful, however, as too much caching can actually reduce performance if the JVM runs out of memory and starts garbage collecting too frequently. You’ll likely need to experiment with the memory settings on your JVM as well as the cache settings above. You can find the specifics about these settings in the documentation for Ehcache.
Next, we’ll show how to share indexes in a database. This is actually not a recommended configuration, as it’s slow (databases are always slower than file systems), but for completeness, we’ll go ahead and tell you how to do it anyway. But you’ve been forewarned: it’s far better to use one of the other methods of clustering your search index.
Sharing a search index (not recommended unless you have a file locking-aware SAN)
If you wish to have a shared index (and we really hope you don’t), you’ll need to either share the index on the file system or in the database. This requires changing your Lucene configuration.
The Lucene configuration can be changed by modifying values in your
portal-ext.properties file. Open your
portal.properties file and search for the text Lucene. Copy that section and then paste it into your
If you wish to store the Lucene search index on a file system that is shared by all of the Liferay nodes (not recommended: you’ve been warned), you can modify the location of the search index by changing the
lucene.dir property. By default, this property points to the
lucene folder inside the Liferay home folder:
Change this to the folder of your choice. You’ll need to restart Liferay for the changes to take effect. You can point all of the nodes to this folder and they will use the same index.
Like Jackrabbit, however, this is not the best way to share the search index, as it could result in file corruption if different nodes try reindexing at the same time. We do not recommend this for a production system. A better way (though still not great) is to share the index via a database, where the database can enforce data integrity on the index. This is very easy to do; it is a simple change to your
portal-ext.properties file. Of course, we also don’t recommend this for a production system, as accessing the index from a database will be slower than from a file system. If, however, you have no other option and want to do this anyway, keep reading.
There is a single property called
lucene.store.type. By default this is set to go to the file system. You can change this so that the index is stored in the database by making it the following:
The next time Liferay is started, new tables are created in the Liferay database, and the index is stored there. If all the Liferay nodes point to the same database tables, they will be able to share the index. Again, performance on this is not very good. Your DBAs may be able to tweak the database indexes a bit to improve performance. For better performance, you should consider using a separate search server or syncing the indexes on the nodes’ file systems.
Note: MySQL users need to modify their JDBC connection string for this to work. Add the following parameter to your connection string:
Alternatively, you can leave the configuration alone, and each node will have its own index. This ensures against collisions when multiple nodes update the index. However, the indices will quickly get out of sync since they don’t replicate. For this reason, this is not a recommended configuration either. Again, for a better configuration, replicate the indexes with Cluster Link or use a separate search server (see the section on Solr above).
Now we can look at the last consideration when clustering Liferay: hot deploy.
Plugins which are hot deployed will need to be deployed separately to all the Liferay nodes. The best way to do this is to configure your application server to support farms. This is a feature that enables you to deploy an application on one node and then it replicates automatically to each of the other nodes. This, of course, is configured differently for each application server, so you’ll need to consult your application server’s documentation to learn how to do this. It’s by far the best way to handle hot deploy, and is the recommended configuration. If you have this working, great! You can skip the rest of this section completely.
If for some reason your application server doesn’t support this feature or you can’t use it, you’ll need to come up with a way to deploy applications across your cluster. Each node needs to have its own hot deploy folder. This folder needs to be writable by the user under which Liferay is running, because plugins are moved from this folder to a temporary folder when they are deployed. This is to prevent the system from entering an endless loop, because the presence of a plugin in the folder is what triggers the hot deploy process.
When you want to deploy a plugin to the entire cluster, copy that plugin to the hot deploy folders of all of the Liferay nodes. Depending on the number of nodes, it may be best to create a script to do this. Once the plugin has been deployed to all of the nodes, you can then make use of it (by adding the portlet to a page or choosing the theme as the look and feel for a page or page hierarchy).
All of the above will get basic Liferay clustering working; however, the configuration can be further optimized. We will see how to do this next.