Implementing Storage and Backing Maps

Cache Layers

The Partitioned (Distributed) cache service in Coherence has three distinct layers that are used for data storage.

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 13-1 shows a conceptual view of backing maps.

Figure 13-1 Backing Map Storage

Description of "Figure 13-1 Backing Map Storage"

Parent topic: Implementing Storage and Backing Maps

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.

Caution:

Be careful when using any backing map that does not store data on heap, especially if storing more data than can actually fit on heap. Certain cache operations (for example, unindexed queries) can potentially traverse a large number of entries that force the backing map to bring those entries onto the heap. Also, partition transfers (for example, restoring from backup or transferring partition ownership when a new member joins) force the backing map to bring lots of entries onto the heap. This can cause GC problems and potentially lead to OutOfMemory errors.

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. 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 backing store (such as 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 outside of the Java heap 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. 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.
Journal: This is a backing map implementation that stores data to either RAM, disk, or both RAM and disk. Journaling use the com.tangosol.io.journal.JournalBinaryStore class. See Using the Elastic Data Feature to Store Data.
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.
Custom Map: This is a backing map implementation that conforms to the java.util.Map interface.
For example:
```
<backing-map-scheme>
   <class-scheme>
      <class-name>com.tangosol.util.SafeHashMap</class-name>
   </class-scheme>
</backing-map-scheme>
```
Note:
Irrespective of the backing map implementation you use, the map must be thread safe.

Parent topic: Implementing Storage and Backing Maps

Operations

There are number of operation types performed against a backing map. The operations include:

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.

Parent topic: Implementing Storage and Backing Maps

Capacity Planning

The total amount of data placed into the data grid must not exceed some predetermined amount of memory. Applications can directly manage the total amount of data through application tier logic or can rely on automatic management using size- or expiry-based eviction. A backing map can store cache data in several ways depending on the backing map implementation (on or off heap, disk, and solid state). Keeping data in memory naturally provides dramatically smaller access and update latencies and is most commonly used.

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 the 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. The following example configures size constraints on the backing map:

<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. If no unit is specified in <high-units>, the default is byte. See <high-units>. You can also specify a <unit-factor>, as shown in the following example:

<backing-map-scheme>
  <local-scheme>
    <eviction-policy>LRU</eviction-policy>
    <high-units>100</high-units>
    <unit-calculator>BINARY</unit-calculator>
    <unit-factor>1000000</unit-factor>
  </local-scheme>
</backing-map-scheme>

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>). If the value exceeds Integer.MAX_VALUE, then a unit factor is automatically used and the value for <high-units> and <low-units> are adjusted accordingly. 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 LRU and LFU).

The following backing map automatically evicts any entries that have not been updated for more than an hour. Entries that exceed one hour are not returned to a caller and are lazily removed from the cache when the next cache operation is performed or the next scheduled daemon EvictionTask is run. The EvictionTask daemon schedule depends on the cache expiry delay. The minimum interval is 250ms.

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

A backing map within a distributed scheme also supports sliding expiry. If enabled:

Read operations extend the expiry of the accessed cache entries. The read operations include get, getAll, invoke and invokeAll without mutating the entries (for example, only entry.getValue in an entry processor).
Any enlisted entries that are not mutated (for example, from interceptors or triggers) are also expiry extended.
The backup (for expiry change) is done asynchronously if the operation is read access only. If a mutating operation is involved (for example, an eviction occurred during a put or a putAll operation), then the backup is done synchronously.

Note:

Sliding expiry is not performed for entries that are accessed based on query requests like aggregate and query operations.

To enable sliding expiry, set the <sliding-expiry> element, within a <backing-map-scheme> element to true and ensure that the <expiry-delay> element is set to a value greater than zero. For example,

<distributed-scheme>
   <scheme-name>dist-expiry</scheme-name>
   <service-name>DistributedExpiry</service-name>
   <backing-map-scheme>
      <sliding-expiry>true</sliding-expiry>
      <local-scheme>
         <expiry-delay>3s</expiry-delay>
      </local-scheme>
   </backing-map-scheme>
</distributed-scheme>

Parent topic: Implementing Storage and Backing Maps

Using Partitioned Backing Maps

Coherence provides a partitioned backing map implementation that differs from the default backing map implementation.The conventional backing map implementation stores entries for all partitions owned by the corresponding node. During partition transfer, it could also hold in-flight entries that, from the client perspective, are temporarily not owned by anyone.

Figure 13-2 shows a conceptual view of the conventional backing map implementation.

Figure 13-2 Conventional Backing Map Implementation

Description of "Figure 13-2 Conventional Backing Map Implementation"

A partitioned backing map is a multiplexer of actual Map implementations, each of which contains only entries that belong to the same partition. Partitioned backing maps raise the storage limit (induced by the java.util.Map API) from 2G for a backing map to 2G for each partition. Partitioned backing maps are typically used whenever a solution may reach the 2G backing map limit, which is often possible when using the elastic data feature. See Using the Elastic Data Feature to Store Data.

Figure 13-3 shows a conceptual view of the partitioned backing map implementation.

Figure 13-3 Partitioned Backing Map Implementation

Description of "Figure 13-3 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>
  </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).

Parent topic: Implementing Storage and Backing Maps

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 can work 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:

Parent topic: Implementing Storage and Backing Maps

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 can automatically accept the overflow from the RAM Journal, allowing for caches to expand far beyond the size of RAM.

Note:

Elastic data is ideal when performing key-based operations and typically not recommend for large filter-based operations. 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 General Guidelines in Administering Oracle Coherence.

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).

Parent topic: Using the Elastic Data Feature to Store Data

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 ramjournal-scheme and flashjournal-scheme.

This section includes the following topics:

Parent topic: Using the Elastic Data Feature to Store Data

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.

<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>

Parent topic: Defining Journal Schemes

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>

Parent topic: Defining Journal Schemes

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>

Parent topic: Defining Journal Schemes

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. 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>
         <expiry-delay>0</expiry-delay>
      </ramjournal-scheme>
   </backing-map-scheme>
   <autostart>true</autostart>
</distributed-scheme>

Parent topic: Defining Journal Schemes

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>

Parent topic: Defining Journal Schemes

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.

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>

Parent topic: Defining Journal Schemes

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:

Parent topic: Using the Elastic Data Feature to Store Data

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.

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 system-property="coherence.ramjournal.size">
               2G</maximum-size>
         </ramjournal-manager>
      </journaling-config>
   </cluster-config>
</coherence>

Parent topic: Changing Journaling Behavior

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.

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 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.
A flash journal is automatically used as overflow when the capacity of the RAM journal is reached. The flash journal can be disabled by setting the maximum size of the flash journal to 0, which means journaling exclusively uses a RAM journal.

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
               system-property="coherence.flashjournal.highjournalsize">
               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.

Parent topic: Changing Journaling Behavior

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. Consider using asynchronous backup if an application does not require backups (that is, data can be restored from a system of record if lost) but the application still wants to offer fast recovery in the event of a node failure.

Note:

The use of asynchronous backups together with rolling restarts requires the use of the shutdown method to perform an orderly shut down of cluster members instead of the stop method or kill -9. Otherwise, a member may shutdown before asynchronous backups are complete. The shutdown method guarantees that all updates are complete.

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="coherence.distributed.asyncbackup">
                     false
                  </param-value>
               </init-param>
            </init-params>
         </service>
      </services>
   </cluster-config>
</coherence>

The 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:

-Dcoherence.distributed.asyncbackup=true

Parent topic: Implementing Storage and Backing Maps

Using Asynchronous Persistence

Asynchronous persistence mode allows the storage servers to persist data asynchronously, thus a mutating request is successful once the primary stores the data and (if there is a synchronous backup) once the backup receives the update.

This allows writes to not be blocked on latency of writing to the underlying device, however does introduce a potential to lose writes. This mode is primarily viable when data can be replenished and the persisted data provides an optimized means to recover data.

You can enable asynchronous persistence by specifying the <persistence-mode> of the <persistence-environment> as active-async.

There is an out-of-the-box persistence environment that specifies this mode and can be referred to by customer applications by either:

Specifying the JVM argument:
```
-Dcoherence.distributed.persistence.mode=active-async
```
Note:
This is used by all distributed services.

Referring to the default-active-async persistence environment in the cache configuration:

See persistence.

<distributed-scheme>
         ….….….
         <persistence>   
             <environment>default-active-async</environment>
         </persistence>
         ….….….
</distributed-scheme>

Note:

This allows you to selectively choose which services have persistence enabled.

You can also define a new <persistence-environment> element in the operational configuration file and specify the mode to be active-async to enable asynchronous persistence. See persistence-environment.

For example:

<persistence-environment id="async-environment">
         <persistence-mode>active-async</persistence-mode>
         <active-directory>/tmp/store-bdb-active</active-directory>   
         <snapshot-directory>/tmp/store-bdb-snapshot</snapshot-directory>
         <trash-directory>/tmp/store-bdb-trash</trash-directory>
</persistence-environment>

To use this environment in the nominated distributed-schemes, it must be referenced in the cache configuration.

For example:

<distributed-scheme>
         ….….….
         <persistence>   
             <environment>async-environment</environment>
         </persistence>
         ….….….
</distributed-scheme>

Note:

With asynchronous persistence, it is possible to have data loss if the cluster is shutdown before the persistent transaction is complete.

To ensure that there is no data loss during a controlled shutdown, customers can leverage the service suspend feature. A service is only considered suspended once all data is fully written, including asynchronous persistence tasks, entries in the write-behind queue of a readwrite-backing-map, and so on.

Parent topic: Implementing Storage and Backing Maps

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. However, entries that change by more than 50% typically demonstrate little or no performance gain. In this case, the use of delta backup should only be used if no adverse effect on performance is observed.

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.

This section includes the following topics:

Parent topic: Implementing Storage and Backing Maps

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="coherence.distributed.compressor">
                     standard</param-value>
               </init-param>
            </init-params>
         </service>
      </services>
   </cluster-config>
</coherence>

The 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:

-Dcoherence.distributed.compressor=standard

Parent topic: Using Delta Backup

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. 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>

Parent topic: Using Delta Backup