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Developing Applications with Oracle Coherence
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14 Implementing Storage and Backing Maps

This chapter describes how storage is implemented in Coherence and includes instructions for configuring Coherence backing maps.

This chapter includes the following sections:

14.1 Cache Layers

Partitioned (Distributed) cache service in Coherence has three distinct layers:

  • Client View – The client view represents a virtual layer that provides access to the underlying partitioned data. Access to this tier is provided using the NamedCache interface. In this layer you can also create synthetic data structures such as NearCache or ContinuousQueryCache.

  • Storage Manager – The storage manager is the server-side tier that is responsible for processing cache-related requests from the client tier. It manages the data structures that hold the actual cache data (primary and backup copies) and information about locks, event listeners, map triggers, and so on.

  • Backing Map – The Backing Map is the server-side data structure that holds actual data.

Coherence allows users to configure out-of-the-box and custom backing map implementations. The only constraint for a Map implementation is the understanding that the Storage Manager provides all keys and values in internal (Binary) format. To deal with conversions of that internal data to and from an Object format, the Storage Manager can supply Backing Map implementations with a BackingMapManagerContext reference.

Figure 14-1 shows a conceptual view of backing maps.

Figure 14-1 Backing Map Storage

Description of Figure 14-1 follows
Description of "Figure 14-1 Backing Map Storage"

14.2 Local Storage

Local storage refers to the data structures that actually store or cache the data that is managed by Coherence. For an object to provide local storage, it must support the same standard collections interface, java.util.Map. When a local storage implementation is used by Coherence to store replicated or distributed data, it is called a backing map because Coherence is actually backed by that local storage implementation. The other common uses of local storage is in front of a distributed cache and as a backup behind the distributed cache.

Coherence supports the following local storage implementations:

  • Safe HashMap: This is the default lossless implementation. A lossless implementation is one, like the Java Hashtable class, that is neither size-limited nor auto-expiring. In other words, it is an implementation that never evicts ("loses") cache items on its own. This particular HashMap implementation is optimized for extremely high thread-level concurrency. For the default implementation, use class com.tangosol.util.SafeHashMap; when an implementation is required that provides cache events, use com.tangosol.util.ObservableHashMap. These implementations are thread-safe.

  • Local Cache: This is the default size-limiting and auto-expiring implementation. See "Capacity Planning", for details on configuration. A local cache limits the size of the cache and automatically expires cache items after a certain period. For the default implementation, use com.tangosol.net.cache.LocalCache; this implementation is thread safe and supports cache events, com.tangosol.net.CacheLoader, CacheStore and configurable/pluggable eviction policies.

  • Read/Write Backing Map: This is the default backing map implementation for caches that load from a database on a cache miss. It can be configured as a read-only cache (consumer model) or as either a write-through or a write-behind cache (for the consumer/producer model). The write-through and write-behind modes are intended only for use with the distributed cache service. If used with a near cache and the near cache must be kept synchronous with the distributed cache, it is possible to combine the use of this backing map with a Seppuku-based near cache (for near cache invalidation purposes). For the default implementation, use class com.tangosol.net.cache.ReadWriteBackingMap.

  • Binary Map (Java NIO): This is a backing map implementation that can store its information in memory but outside of the Java heap, or even in memory-mapped files, which means that it does not affect the Java heap size and the related JVM garbage-collection performance that can be responsible for application pauses. This implementation is also available for distributed cache backups, which is particularly useful for read-mostly and read-only caches that require backup for high availability purposes, because it means that the backup does not affect the Java heap size yet it is immediately available in case of failover.

  • Serialization Map: This is a backing map implementation that translates its data to a form that can be stored on disk, referred to as a serialized form. It requires a separate com.tangosol.io.BinaryStore object into which it stores the serialized form of the data; usually, this is the built-in LH disk store implementation, but the Serialization Map supports any custom implementation of BinaryStore. For the default implementation of Serialization Map, use com.tangosol.net.cache.SerializationMap.

  • Serialization Cache: This is an extension of the SerializationMap that supports an LRU eviction policy. For example, a serialization cache can limit the size of disk files. For the default implementation of Serialization Cache, use com.tangosol.net.cache.SerializationCache.

  • Overflow Map: An overflow map does not actually provide storage, but it deserves mention in this section because it can combine two local storage implementations so that when the first one fills up, it overflows into the second. For the default implementation of OverflowMap, use com.tangosol.net.cache.OverflowMap.

14.3 Operations

There are number of operation types performed against the Backing Map:

  • Natural access and update operations caused by the application usage. For example, NamedCache.get() call naturally causes a Map.get() call on a corresponding Backing Map; the NamedCache.invoke() call may cause a sequence of Map.get() followed by the Map.put(); the NamedCache.keySet(filter) call may cause an Map.entrySet().iterator() loop, and so on.

  • Remove operations caused by the time-based expiry or the size-based eviction. For example, a NamedCache.get() or NamedCache.size() call from the client tier could cause a Map.remove() call due to an entry expiry timeout; or NamedCache.put() call causing some Map.remove() calls (for different keys) caused by the total amount data in a backing map reaching the configured high water-mark value.

  • Insert operations caused by a CacheStore.load() operation (for backing maps configured with read-through or read-ahead features)

  • Synthetic access and updates caused by the partition distribution (which in turn could be caused by cluster nodes fail over or fail back). In this case, without any application tier call, some entries could be inserted or removed from the backing map.

14.4 Capacity Planning

Depending on the actual implementation, the Backing Map stores the cache data in the following ways:

  • on-heap memory

  • off-heap memory

  • disk (memory-mapped files or in-process DB)

  • solid state device (journal files)

  • combination of any of the above

Keeping data in memory naturally provides dramatically smaller access and update latencies and is most commonly used.

More often than not, applications must ensure that the total amount of data placed into the data grid does not exceed some predetermined amount of memory. It could be done either directly by the application tier logic or automatically using size- or expiry-based eviction. The total amount of data held in a Coherence cache equals the sum of data volume in all corresponding backing maps (one per each cluster node that runs the corresponding partitioned cache service in a storage enabled mode).

Consider following cache configuration excerpts:

<backing-map-scheme>
  <local-scheme/>
</backing-map-scheme>

The backing map above is an instance of com.tangosol.net.cache.LocalCache and does not have any pre-determined size constraints and has to be controlled explicitly. Failure to do so could cause the JVM to go out-of-memory.

<backing-map-scheme>
  <local-scheme>
    <eviction-policy>LRU</eviction-policy>
    <high-units>100m</high-units>
    <unit-calculator>BINARY</unit-calculator>
  </local-scheme>
</backing-map-scheme>

This backing map above is also a com.tangosol.net.cache.LocalCache and has a capacity limit of 100MB. As the total amount of data held by this backing map exceeds that high watermark, some entries are removed from the backing map, bringing the volume down to the low watermark value (<low-units> configuration element, which defaults to 80% of the <high-units>). The choice of the removed entries is based on the LRU (Least Recently Used) eviction policy. Other options are LFU (Least Frequently Used) and Hybrid (a combination of the LRU and LFU). The value of <high-units> is limited to 2GB. To overcome that limitation (but maintain backward compatibility) Coherence uses the <unit-factor> element. For example, the <high-units> value of 8192 with a <unit-factor> of 1048576 results in a high watermark value of 8GB.

<backing-map-scheme>
  <local-scheme>
    <expiry-delay>1h</expiry-delay>
  </local-scheme>
</backing-map-scheme>

The backing map above automatically evicts any entries that have not been updated for more than an hour. Note, that such an eviction is a "lazy" one and can happen any time after an hour since the last update happens; the only guarantee Coherence provides is that entries that exceed one hour are not returned to a caller.

The following backing map is an instance of com.tangosol.net.cache.SerializationCache which stores values in the extended (nio) memory and has a capacity limit of 100MB (100*1048576).

<backing-map-scheme>
  <external-scheme>
    <nio-memory-manager>
      <initial-size>1MB</initial-size>
      <maximum-size>100MB</maximum-size>
    </nio-memory-manager>
    <high-units>100</high-units>
    <unit-calculator>BINARY</unit-calculator>
    <unit-factor>1048576</unit-factor>
  </external-scheme>
</backing-map-scheme>

Configure a backup storage for this cache being off-heap (or file-mapped):

<backup-storage>
  <type>off-heap</type>
  <initial-size>1MB</initial-size>
  <maximum-size>100MB</maximum-size>
</backup-storage>

14.5 Using Partitioned Backing Maps

The conventional backing map implementation contained entries for all partitions owned by the corresponding node. (During partition transfer, it could also hold "in flight" entries that from the clients' perspective are temporarily not owned by anyone).

Figure 14-2 shows a conceptual view of the conventional backing map implementation.

Figure 14-2 Conventional Backing Map Implementation

Description of Figure 14-2 follows
Description of "Figure 14-2 Conventional Backing Map Implementation"

A partitioned backing map is basically a multiplexer of actual Map implementations, each of which would contain only entries that belong to the same partition.

Figure 14-3 shows a conceptual view of the partitioned backing map implementation.

Figure 14-3 Partitioned Backing Map Implementation

Description of Figure 14-3 follows
Description of "Figure 14-3 Partitioned Backing Map Implementation"

To configure a partitioned backing map, add a <partitioned> element with a value of true. For example:

<backing-map-scheme>
  <partitioned>true</partitioned>
  <external-scheme>
    <nio-memory-manager>
      <initial-size>1MB</initial-size>
      <maximum-size>50MB</maximum-size>
    </nio-memory-manager>
    <high-units>8192</high-units>
    <unit-calculator>BINARY</unit-calculator>
    <unit-factor>1048576</unit-factor>
  </external-scheme>
</backing-map-scheme>

This backing map is an instance of com.tangosol.net.partition.PartitionSplittingBackingMap, with individual partition holding maps being instances of com.tangosol.net.cache.SerializationCache that each store values in the extended (nio) memory. The individual nio buffers have a limit of 50MB, while the backing map as whole has a capacity limit of 8GB (8192*1048576).

14.6 Using the Elastic Data Feature to Store Data

The Elastic Data feature is used to seamlessly store data across memory and disk-based devices. This feature is especially tuned to take advantage of fast disk-based devices such as Solid State Disks (SSD) and enables near memory speed while storing and reading data from SSDs. The Elastic Data feature uses a technique called journaling to optimize the storage across memory and disk.

Elastic data contains two distinct components: the RAM journal for storing data in-memory and the flash journal for storing data to disk-based devices. These can be combined in different combinations and are typically used for backing maps and backup storage but can also be used with composite caches (for example, a near cache). The RAM journal always works with the flash journal to enable seamless overflow to disk.

Caches that use RAM and flash journals are configured as part of a cache scheme definition within a cache configuration file. Journaling behavior is configured, as required, by using an operational override file to override the out-of-box configuration.

This section includes the following topics:

14.6.1 Journaling Overview

Journaling refers to the technique of recording state changes in a sequence of modifications called a journal. As changes occur, the journal records each value for a specific key and a tree structure that is stored in memory keeps track of which journal entry contains the current value for a particular key. To find the value for an entry, you find the key in the tree which includes a pointer to the journal entry that contains the latest value.

As changes in the journal become obsolete due to new values being written for a key, stale values accumulate in the journal. At regular intervals, the stale values are evacuated making room for new values to be written in the journal.

The Elastic Data feature includes a RAM journal implementation and a Flash journal implementation that work seamlessly with each other. If for example the RAM Journal runs out of memory, the Flash Journal automatically accepts the overflow from the RAM Journal, allowing for caches to expand far beyond the size of RAM.

Note:

When journaling is enabled, additional capacity planning is required if you are performing data grid operations (such as queries and aggregations) on large result sets. See Administering Oracle Coherence for details.

A resource manager controls journaling. The resource manager creates and utilizes a binary store to perform operations on the journal. The binary store is implemented by the JournalBinaryStore class. All reads and writes through the binary store are handled by the resource manager. There is a resource manager for RAM journals (RamJournalRM) and one for flash journals (FlashJournalRM). For details about these APIs, see Java API Reference for Oracle Coherence.

14.6.2 Defining Journal Schemes

The <ramjournal-scheme> and <flashjournal-scheme> elements are used to configure RAM and Flash journals (respectively) in a cache configuration file. See the "ramjournal-scheme" and the "flashjournal-scheme" for detailed configuration options for these scheme types.

This section includes the following topics:

14.6.2.1 Configuring a RAM Journal Backing Map

To configure a RAM journal backing map, add the <ramjournal-scheme> element within the <backing-map-scheme> element of a cache definition. The following example creates a distributed cache that uses a RAM journal for the backing map. The RAM journal automatically delegates to a flash journal when the RAM journal exceeds the configured memory size. See "Changing Journaling Behavior" to change memory settings.

<distributed-scheme>
   <scheme-name>distributed-journal</scheme-name>
   <service-name>DistributedCacheRAMJournal</service-name>
   <backing-map-scheme>
      <ramjournal-scheme/>
   </backing-map-scheme>
   <autostart>true</autostart>
</distributed-scheme>

14.6.2.2 Configuring a Flash Journal Backing Map

To configure a flash journal backing map, add the <flashjournal-scheme> element within the <backing-map-scheme> element of a cache definition. The following example creates a distributed scheme that uses a flash journal for the backing map.

<distributed-scheme>
   <scheme-name>distributed-journal</scheme-name>
   <service-name>DistributedCacheFlashJournal</service-name>
   <backing-map-scheme>
      <flashjournal-scheme/>
   </backing-map-scheme>
   <autostart>true</autostart>
</distributed-scheme>

14.6.2.3 Referencing a Journal Scheme

The RAM and flash journal schemes both support the use of scheme references to reuse scheme definitions. The following example creates a distributed cache and configures a RAM journal backing map by referencing the RAM scheme definition called default-ram.

<caching-schemes>
   <distributed-scheme>
      <scheme-name>distributed-journal</scheme-name>
         <service-name>DistributedCacheJournal</service-name>
         <backing-map-scheme>
            <ramjournal-scheme>
               <scheme-ref>default-ram</scheme-ref>
            </ramjournal-scheme>
         </backing-map-scheme>
         <autostart>true</autostart>
   </distributed-scheme>

   <ramjournal-scheme>
      <scheme-name>default-ram</scheme-name>
   </ramjournal-scheme>
</caching-schemes>

14.6.2.4 Using Journal Expiry and Eviction

The RAM and flash journal can be size-limited. They can restrict the number of entries to store and automatically evict entries when the journal becomes full. Furthermore, both the sizing of entries and the eviction policies can be customized. This feature was added as part of a patch release. For information about which updates are included in a patch, see Release Notes for Oracle Coherence.

The following example defines expiry and eviction settings for a RAM journal:

<distributed-scheme>
   <scheme-name>distributed-journal</scheme-name>
   <service-name>DistributedCacheFlashJournal</service-name>
   <backing-map-scheme>
      <ramjournal-scheme>
         <eviction-policy>LFU</eviction-policy>
         <high-units>100</high-units>
         <low-units>80</low-units>
         <unit-calculator>Binary</unit-calculator>
         <unit-factor>1</unit-factor>
         <expiry-delay>0</expiry-delay>
      </ramjournal-scheme>
   </backing-map-scheme>
   <autostart>true</autostart>
</distributed-scheme>

14.6.2.5 Using a Journal Scheme for Backup Storage

Journal schemes are used for backup storage as well as for backing maps. By default, Flash Journal is used as the backup storage. This default behavior can be modified by explicitly specifying the storage type within the <backup-storage> element. The following configuration uses a RAM journal for the backing map and explicitly configures a RAM journal for backup storage:

<caching-schemes>
   <distributed-scheme>
      <scheme-name>default-distributed-journal</scheme-name>
         <service-name>DistributedCacheJournal</service-name>
         <backup-storage>
            <type>scheme</type>
            <scheme-name>example-ram</scheme-name>
         </backup-storage>
         <backing-map-scheme>
            <ramjournal-scheme/>
         </backing-map-scheme>
      <autostart>true</autostart>
   </distributed-scheme>

   <ramjournal-scheme>
      <scheme-name>example-ram</scheme-name>
   </ramjournal-scheme>
</caching-schemes>

14.6.2.6 Enabling a Custom Map Implementation for a Journal Scheme

Journal schemes can be configured to use a custom backing map as required. Custom map implementations must extend the CompactSerializationCache class and declare the exact same set of public constructors. For details about this API, see Java API Reference for Oracle Coherence.

To enable, a custom implementation, add a <class-scheme> element whose value is the fully qualified name of the custom class. Any parameters that are required by the custom class can be defined using the <init-params> element. The following example enables a custom map implementation called MyCompactSerializationCache.

<flashjournal-scheme>
   <scheme-name>example-flash</scheme-name>
   <class-name>package.MyCompactSerializationCache</class-name>
</flashjournal-scheme>

14.6.3 Changing Journaling Behavior

A resource manager controls journaling behavior. There is a resource manager for RAM journals (RamJournalRM) and a resource manager for Flash journals (FlashJournalRM). The resource managers are configured for a cluster in the tangosol-coherence-override.xml operational override file. The resource managers' default out-of-box settings are used if no configuration overrides are set.

This section includes the following topics:

14.6.3.1 Configuring the RAM Journal Resource Manager

The <ramjournal-manager> element is used to configure RAM journal behavior. The following list summarizes the default characteristics of a RAM journal. See "ramjournal-manager" for details on all settings that are available and their defaults.

  • Binary values are limited by default to 64KB (and a maximum of 4MB). A flash journal is automatically used if a binary value exceeds the configured limit.

  • An individual buffer (a journal file) is limited by default to 2MB (and a maximum of 2GB). The maximum file size should not be changed.

  • A journal is composed of up to 512 files. 511 files are usable files and one file is reserved for depleted states.

  • The total memory used by the journal is limited to 1GB by default (and a maximum of 64GB). A flash journal is automatically used if the total memory of the journal exceeds the configured limit.

To configure a RAM journal resource manager, add a <ramjournal-manager> element within a <journaling-config> element and define any subelements that are to be overridden. The following example demonstrates overriding RAM journal subelements:

<?xml version='1.0'?>

<coherence xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
   xmlns="http://xmlns.oracle.com/coherence/coherence-operational-config"
   xsi:schemaLocation="http://xmlns.oracle.com/coherence/
   coherence-operational-config coherence-operational-config.xsd">
   <cluster-config>
      <journaling-config>
         <ramjournal-manager>
            <maximum-value-size>64K</maximum-value-size>
            <maximum-size>2G</maximum-size>
         </ramjournal-manager>
      </journaling-config>
   </cluster-config>
</coherence>

14.6.3.2 Configuring the Flash Journal Resource Manager

The <flashjournal-manager> element is used to configure flash journal behavior. The following list summarizes the default characteristics of a flash journal. See "flashjournal-manager" for details on all settings that are available and their defaults.

  • Binary values are limited by default to 64MB.

  • An individual buffer (a journal file) is limited by default to 2GB (and maximum 4GB).

  • A journal is composed of up to 512 journal files. 511 files are usable files and one file is reserved for depleted states. A journal is limited by default to 1TB, with a theoretical maximum of 2TB.

  • A journal has a high journal size of 11GB by default. The high size determines when to start removing stale values from the journal. This is not a hard limit on the journal size, which can still grow to the maximum file count (512).

  • Keys remain in memory in a compressed format. For values, only the unwritten data (being queued or asynchronously written) remains in memory. When sizing the heap, a reasonable estimate is to allow 50 bytes for each entry to hold key data (this is true for both RAM and Flash journals) and include additional space for the buffers (16MB). The entry size is increased if expiry or eviction is configured.

To configure a flash journal resource manager, add a <flashjournal-manager> element within a <journaling-config> element and define any subelements that are to be overridden. The following example demonstrates overriding flash journal subelements:

<?xml version='1.0'?>

<coherence xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
   xmlns="http://xmlns.oracle.com/coherence/coherence-operational-config"
   xsi:schemaLocation="http://xmlns.oracle.com/coherence/
   coherence-operational-config coherence-operational-config.xsd">
   <cluster-config>
      <journaling-config>
         <flashjournal-manager>
            <maximum-value-size>64K</maximum-value-size>
            <maximum-file-size>8M</maximum-file-size>
            <block-size>512K</block-size>
            <maximum-pool-size>32M</maximum-pool-size>
            <directory>/coherence_storage</directory>
            <async-limit>32M</async-limit>
            <high-journal-size>11GB</high-journal-size>
         </flashjournal-manager>
      </journaling-config>
   </cluster-config>
</coherence>

Note:

The directory specified for storing journal files must exist. If the directory does not exist, a warning is logged and the default temporary file directory, as designated by the JVM, is used.

14.7 Using Asynchronous Backup

Distributed caches support both synchronous and asynchronous backup. With synchronous backup, clients are blocked until a backup operation completes. With asynchronous backup, clients continue to respond to requests during backup operations. Backups are performed synchronously unless asynchronous backup is explicitly enabled.

Asynchronous backup is typically used to increase client performance. However, applications that use asynchronous backup must handle the possible effects on data integrity. Specifically, cache operations may complete before backup operations complete (successfully or unsuccessfully) and backup operations may complete in any order.

To enable asynchronous backup for a distributed cache, add an <async-backup> element, within a <distributed-scheme> element, that is set to true. For example:

<distributed-scheme>
   ...        
   <async-backup>true</async-backup>
   ...
</distributed-scheme>

To enable asynchronous backup for all instances of the distributed cache service type, override the partitioned cache service's async-backup initialization parameter in an operational override file. For example:

<?xml version='1.0'?>

<coherence xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
   xmlns="http://xmlns.oracle.com/coherence/coherence-operational-config"
   xsi:schemaLocation="http://xmlns.oracle.com/coherence/
   coherence-operational-config coherence-operational-config.xsd">
   <cluster-config>
      <services>
         <service id="3">
            <init-params>
               <init-param id="27">
                  <param-name>async-backup</param-name>
                  <param-value
                     system-property="tangosol.coherence.distributed.asyncbackup">
                     false
                  </param-value>
               </init-param>
            </init-params>
         </service>
      </services>
   </cluster-config>
</coherence>

The tangosol.coherence.distributed.asyncbackup system property is used to enable asynchronous backup for all instances of the distributed cache service type instead of using the operational override file. For example:

-Dtangosol.coherence.distributed.asyncbackup=true

14.8 Using Delta Backup

Delta backup is a technique that is used to apply changes to a backup binary entry rather than replacing the whole entry when the primary entry changes. Delta backup is ideal in situations where the entry being updated is large but only small changes are being made. In such cases, the cost for changing only a small portion of the entry is often less than the cost associated with rewriting the whole entry and results in better performance. Entries that change by more than 50% typically demonstrate little or no performance gain by using delta backup.

Delta backup uses a compressor that compares two in-memory buffers containing an old and a new value and produces a result (called a delta) that can be applied to the old value to create the new value. Coherence provides standard delta compressors for POF and non-POF formats. Custom compressors can also be created and configured as required.

14.8.1 Enabling Delta Backup

Delta backup is only available for distributed caches and is disabled by default. Delta backup is enabled either individually for each distributed cache or for all instances of the distributed cache service type.

To enable delta backup for a distributed cache, add a <compressor> element, within a <distributed-scheme> element, that is set to standard. For example:

<distributed-scheme>
   ...        
   <compressor>standard</compressor>
   ...
</distributed-scheme>

To enable delta backup for all instances of the distributed cache service type, override the partitioned cache service's compressor initialization parameter in an operational override file. For example:

<?xml version='1.0'?>

<coherence xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
   xmlns="http://xmlns.oracle.com/coherence/coherence-operational-config"
   xsi:schemaLocation="http://xmlns.oracle.com/coherence/
   coherence-operational-config coherence-operational-config.xsd">
   <cluster-config>
      <services>
         <service id="3">
            <init-params>
               <init-param id="22">
                  <param-name>compressor</param-name>
                  <param-value
                     system-property="tangosol.coherence.distributed.compressor">
                     standard</param-value>
               </init-param>
            </init-params>
         </service>
      </services>
   </cluster-config>
</coherence>

The tangosol.coherence.distributed.compressor system property is used to enable delta backup for all instances of the distributed cache service type instead of using the operational override file. For example:

-Dtangosol.coherence.distributed.compressor=standard

14.8.2 Enabling a Custom Delta Backup Compressor

To use a custom compressor for performing delta backup, include an <instance> subelement and provide a fully qualified class name that implements the DeltaCompressor interface. See "instance" for detailed instructions on using the <instance> element. The following example enables a custom compressor that is implemented in the MyDeltaCompressor class.

<distributed-scheme>
   ...        
   <compressor>
      <instance>
         <class-name>package.MyDeltaCompressor</class-name>
      </instance>
   </compressor>
   ...
</distributed-scheme>

As an alternative, the <instance> element supports the use of a <class-factory-name> element to use a factory class that is responsible for creating DeltaCompressor instances, and a <method-name> element to specify the static factory method on the factory class that performs object instantiation. The following example gets a custom compressor instance using the getCompressor method on the MyCompressorFactory class.

<distributed-scheme>
   ...        
   <compressor>
      <instance>
         <class-factory-name>package.MyCompressorFactory</class-factory-name>
         <method-name>getCompressor</method-name>
      </instance>
   </compressor>
   ...
</distributed-scheme>

Any initialization parameters that are required for an implementation can be specified using the <init-params> element. The following example sets the iMaxTime parameter to 2000.

<distributed-scheme>
   ...        
   <compressor>
      <instance>
         <class-name>package.MyDeltaCompressor</class-name>
         <init-params>
            <init-param>
               <param-name>iMaxTime</param-name>
               <param-value>2000</param-value>
            </init-param>
         </init-params>
      </instance>
   </compressor>
   ...
</distributed-scheme>