Runtime Data Sharing through a Distributed Cache

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Runtime Data Sharing

through

a Distributed Cache

Iqbal Khan [email protected] Ph: +1 (925) 236-2125

Alachisoft .NET Performance Solutions

mailto:[email protected]

Alachisoft

NCache 2 www.alachisoft.com

What is Runtime Data Sharing?

1. Server Apps Sharing Application Data Changes Connected to a relational database

Data changed in the database and then shared

2. Server Apps Sharing Transient Data This data is never stored in the database

Data is created at runtime, shared, and then destroyed

3. Server Apps Mix of .NET and Java Data sharing may be between .NET and Java apps as well

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Traditional Data Sharing Mechanisms

1. Relational Databases Polling

Database notifications (SQL Server Query Notifications or Oracle Database Change Notifications)

Performance and scalability issues

2. Message Queues Message focused (not data focused)

Not intended for large data sets

Performance and scalability issues

3. Web Services or Remote Procedure Calls Too complicated to develop and manage

Weakness: sender and receiver not decoupled

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Use

Fast & Scalable

Distributed Cache

The Solution

NCache

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What is a Distributed Cache?

1. Cluster of cache servers Pools their memory and CPU

2. Synchronizes cache across all servers Updates immediately visible

3. Linearly scales transaction & memory capacity Add servers to grow capacity

4. Replicates data for reliability Without compromising performance & scalability

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Distributed Cache Deployment Architecture

Filesystem Database Servers Mainframe

Distributed Cache Cluster

Windows 2008/2012 (64-bit)

Scale Horizontally

Memory pooled from all cache servers

Grid Computing Apps

(.NET/Java)Web Services (.NET/Java) Server Apps (.NET/Java)Web Apps (.NET/Java)

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Following Apps use Distributed Cache

1. Web Apps (.NET & Java) To handle millions of users

2. Web Services (.NET & Java) To handle millions of requests

3. Big Data Apps (.NET & Java) To quickly process very large amounts of data thru distribution

4. Grid Computing Apps (.NET & Java) To process very large computations thru distribution

5. Other Server Apps (.NET & Java) To handle millions of requests

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Three Common Uses of Distributed Cache

1. App Data Caching (for Database Bottlenecks) In-memory cache faster than database

Cache linearly scalable which database is not

2. Reliable & Scalable Cache for ASP.NET Specific Data ASP.NET Session State storage (most common)

ASP.NET View State cache

ASP.NET Output Cache provider

3. Scalable Runtime Data Sharing Faster & more scalable than traditional message queues

Async data sharing in producer/consumer model

Powerful event notifications & continuous queries for app coordination

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Scalable Event Notifications for Data Sharing

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Event Notification Types for Data Sharing

1. Event Notifications between .NET and Java Apps .NET and Java apps can send event notifications to each other

2. Data Focused Event Notifications Whenever some data in the cache changes

3. Message Based Event Notifications Publish/subscribe model

Message sent to one or more subscribers

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.NET and Java App Event Notifications

1. Binary Portable Format Data and messages in the cache can use Binary Portable Format

.NET objects can be read back as Java objects and vice versa

No XML based costly transformations needed

2. Events Notifications between .NET & Java Apps .NET and Java apps can send event notifications to each other

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Data Focused Event Notifications

1. Item Level Event Notifications When specific cached items change (update/remove)

2. Group Level Event Notifications When any data in a group changes (add/update/remove)

NCache 4.6 will have this feature

3. Cache Level Event Notifications When any data in the cache changes (add/update/remove)

4. Continuous Query Event Notifications When any data in a dataset in the cache changes (add/update/remove)

Dataset defined through SQL-like criteria

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Item Level Event Notifications

// Define and item level event handler function static void OnCacheDataModification (string key, CacheEventArg args) { switch(args.EventType) { case EventType.ItemRemoved:

break; case EventType.ItemUpdated:

break; } }

// Register item level event handler function with a “key” CacheDataNotificationCallback dataNotificationCallback

= new CacheDataNotificationCallback( OnCacheDataModification ); CacheEventDescriptor EventDescriptor

= cache.RegisterCacheNotification (key, dataNotificationCallback, EventType.ItemRemoved | EventType.ItemUpdated, EventDataFilter.DataWithMetadata);

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Cache Level Event Notifications

// Define the event handler function static void OnCacheDataModification (string key, CacheEventArg args) { switch(args.EventType) { case EventType.ItemAdded:

break; case EventType.ItemRemoved:

break; case EventType.ItemUpdated:

break; } } // Register event handler function CacheDataNotificationCallback dataNotificationCallback

= new CacheDataNotificationCallback ( OnCacheDataModification ); CacheEventDescriptor EventDescriptor = cache.RegisterCacheNotification (

dataNotificationCallback, EventType.ItemAdded | EventType.ItemRemoved | EventType.ItemUpdated, EventDataFilter.DataWithMetadata );

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Continuous Query Event Notifications

string queryStr = "Select ClassLibrary.Product WHERE this.supplier = ?"; Hashtable values = new Hashtable(); values.Add("supplier", "Carlos Diaz"); ContinuousQuery cq = new ContinuousQuery(queryStr, values); cq.RegisterNotification (

new QueryDataNotificationCallback (ItemAddedCallBack), EventType.ItemAdded, EventDataFilter.None );

cq.RegisterNotification (

new QueryDataNotificationCallback(QueryItemCallBack), EventType.ItemUpdated | EventType.ItemRemoved , EventDataFilter.None);

cq.RegisterClearNotification (

new ContinuousQueryClearCallback(CacheClear));

cache.RegisterCQ ( cq );

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Message Based Event Notifications

1. App Generated Custom Event Notifications Publish/subscribe model

App fires a notification into cache cluster, other apps receive it

2. Topic Based Event Notifications Publish/subscribe model

Publisher sends a message against a topic to multiple subscribers


3. Queue Based Event Notifications Publish/subscribe model

Publisher sends a message to any one subscriber through cache cluster


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App Generated Event Notifications

// Define a callback function void CacheCustomEvent(object eventId, object data) { } Product product = new Product(); // Registering custom event callback function cache.CustomEvent += new CustomEventCallback(CacheCustomEvent); //Raise custom event when needed cache.RaiseCustomEvent("custom event id", product);

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Hands on Demo

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NCache Architecture

1. High Availability (100% Uptime) Peer to peer cluster

Dynamic configuration

2. Scalability: Caching Topologies Mirrored, Replicated, Partitioned, and Partition-Replica Cache

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High Availability (100% Uptime) Dynamic Cache Cluster

Cache Cluster

NCache Srv

Add

Server

At

Runtime

Remove

Server

At

Runtime

NCache Srv NCache Srv

Remote Clients (Web/App Servers) TCP based Cache Cluster

Does not use Windows Clustering

Peer to peer architecture

No single point of failure

Add/remove servers at runtime

Without stopping cache or your app

Data adjusted automatically based on caching topology

Hot Apply config changes

Change config properties while cache is running

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High Availability (100% Uptime) Dynamic Configuration

Cluster membership info

Propagate to clients at runtime

Cache topology info

Propagate to clients at runtime

Connection strategy at runtime

Connection failover

Clients auto-connect to other servers

Dynamic Cache Cluster

NCache Srv

Remote Clients (Web/App Servers)

NCache SrvNCache Srv

Cluster

Membership Info

Cache

Topology Info

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Caching Topologies Mirrored Cache (2-node active/passive)

Server 1 Server 2

Mirrored Cache

1 2

3 4

5 6

Active

1 2

3 4

5 6

Passive

(Backup)

Async Backup


All clients connect to active node

Passive node becomes active if active node goes down. Clients also automatically connect to new active node

Mirroring to passive node done asynchronously

Recommended use:

Up to 10 app servers

2 cache server cluster

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Caching Topologies Replicated Cache

Each server has the entire cache

Cache updates done to all servers synchronously. So, they’re slower.

Each clients connects to only one cache server. All servers active.

Good for read-intensive scenarios

Server 1 Server 2

Replicated Cache

1 2

3 4

5 6

Active

Synchronous Updates


1 2

3 4

5 6

Active

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Caching Topologies Partitioned Cache

Extremely scalable

Location transparency

Good for large cache clusters

Distribution map sent to all clients at runtime

No replication available

Server 1 Server 2

Partitioned CacheRemote Clients (Web/App Servers)

21

Partition 1

43

Partition 2

Distribution

Map

Distribution

Map

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Caching Topologies Partition-Replica Cache

Each partition has one replica on a different server. All created automatically.

Async & Sync replication options

Extremely fast & scalable

Reliability due to replicas

Server 1 Server 2

Partition-Replica CacheRemote Clients (Web/App Servers)

21

Partition 1

43

Replica 2

43

Partition 2

21

Replica 1

Distribution

Map

Distribution

Map

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Conclusion

1. Distributed Cache is Better for Runtime Data Sharing Faster and more scalable

Likely to be already available for your app

2. NCache is the leading distributed cache for .NET More feature rich product in the market

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Next Steps

Download NCache 60-Day Trial (Fully Working) http://www.alachisoft.com/download.html

Contact us for Personalized Architectural Demo Email: [email protected]. Phone: +1 (925) 236-3830

http://www.alachisoft.com/download.html

mailto:[email protected]

Runtime Data Sharing through a Distributed Cache

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