ScaleOut Software Product Demo

Welcome to the ScaleOut Demo!

Let’s look at some key capabilities of ScaleOut Software’s in-memory data grid with integrated in-memory computing and stream processing. This demo will let you try out some typical operations on an in-memory data grid running on four standard D2as v5 instances in the Microsoft Windows Azure cloud.

ScaleOut StateServer®

ScaleOut StateServer provides a highly scalable, battle-tested in-memory data grid with a rich set of features and industry-leading ease of use. It stores data objects in memory for fast access and transparently scales across a cluster of grid servers. Learn more...

Load objects into the grid

This operation will demonstrate loading a large set of 1 KB objects into the grid in parallel. Using four servers speeds up the load time and demonstrates the grid’s scalability. Be sure to track the operation's progress using the performance charts at the right.

Set the object count (1,000 to 100,000):

The load operation completed in 76 milliseconds, 1,000 objects were successfully added and then deleted from the store (13,091 objects/sec)

Perform access operations

The buttons below let you create an object in the grid and then read it, update it, and delete it using C#, Java, and C/C++ APIs. This demonstrates how fast applications can store and access data objects within an in-memory grid.

Set the object size (10 bytes to 10 KB):

Object data (arbitrary text)

ScaleOut StateServer® Pro

ScaleOut StateServer Pro adds an in-memory compute engine to an in-memory data grid so that you can track and analyze live data in parallel. This lets your applications detect patterns and trends within fast-changing data sets in real time and provide immediate feedback — operational intelligence. Learn more...

MapReduce

This operation demonstrates a widely used, data-parallel analysis called MapReduce. The English text of Leo Tolstoy’s War and Peace (594,179 words) has been loaded into the in-memory data grid. When you press the button below, the MapReduce operation will count and record all distinct words in the book and store a report within the grid. See how fast this operation completes using ScaleOut’s integrated, in-memory computing engine.

The MapReduce operation finished in 4,627 milliseconds. The total number of words is 594,179. The number of unique words is 16,940

ScaleOut StreamServer®

ScaleOut StreamServer lets applications perform stateful stream processing on incoming events for much deeper introspection and more effective real time feedback than previously possible. Its integrated in-memory data grid and stream-processing engine enables applications to build "digital twins" for millions of data-sources. Learn more...

Process a stream of incoming events

This operation sends a stream of events to ScaleOut StreamServer for 100,000 data sources and processes them within the in-memory data grid. The state of each data source is tracked by a "digital twin" object within the grid.

Set the number of events per data source to process (1 to 10):

100,000 events were successfully posted in 17,430 milliseconds (5,737 events/sec)

Ready?

If you're ready to deploy ScaleOut's in-memory data grid for your application, check out our Azure deployment web page.

Learn more

Statistics

The following statistics show key metrics for the four-server in-memory data grid running in the Azure cloud. They display aggregate statistics for all grid users.

Name	Status	Object count	Memory used	Details
10.20.2.4	Active	40,945	1.000 MB	In config, In state, Unused, Failover, Up
10.20.2.5	Active	40,946	1.000 MB	In config, In state, Connected, Is default, Up
10.20.2.6	Active	41,009	1.000 MB	In config, In state, Unused, Failover, Up
10.20.2.7	Active	40,946	1.000 MB	In config, In state, Unused, Failover, Up
Grid total		163,846	7.000 MB