1. In Memory Grids break problems into parts that can be solved using multiple resources on a network, using main memory instead of disk for faster file I/O. 2. In Memory Compute Grids allow computation tasks to be split and executed in parallel across grid nodes, while In Memory Data Grids provide applications with the ability to keep frequently accessed data in memory across multiple JVMs for high availability and low latency access. 3. Reference architectures show how In Memory Grids distribute data, computation tasks, and resources across a cluster for real-time processing of large datasets.

1. 100K times faster apps.
In Memory Grids
Prateek Jain

2. Agenda
• In Memory Grids
– 10,000 foot view.
– Present scenario
– Why
– Why now
• Use Cases
• Types of In Memory Grids
– Compute Grid
– Data Grid
• Reference Architecture
• Sample application demo
• Further Resources
• Questions & Feedback

3. In Memory Grids
• 10,000 foot view
Breaking your problem to solve it using multiple resources on network.
Using main memory instead of Disk to do file I/O.

4. BigData landscape
Traditional App Associated Challenges
RDBMS -- Used to run many analytics
systems
Performance ( Not Real time), Scaling,
Cost++
CEP -- Designed to correlate data in real
time
Scaling (often necessary to aggregate
events into a centralized source ), not
designed for historical data.
Hadoop -- Designed for batch analytics and
complex correlation
Not designed for Real time.
NoSQL -- Designed to handle large data
volumes at low cost
Processing capability: Sheer amount of
data can be challenging.
IMDG -- Fast for storing and processing
data
Storing vast amounts of information in-
memory doesn’t scale, in terms of both
system scaling and cost
Different problems, so are the solutions.

5. Why ?
• Speed matters
– Citi : 100ms == $1 M
– Google : 500ms == 20% traffic drop
• Disk up to 107 times slower than RAM.

6. In Memory Grids• Why now?
– Hardware, ability++ and cost--
• 1TB RAM & 48 core cluster (can hold full week tweets) ~ $40K
Data Growth, PB DRAM Cost, $
BigData tech. plannedData is growing exponentially 30% drop each 12-18 months

7. Use Cases
• Trading Systems
– Handle large volume of transactions
• Real time risk analytics
– Analysis of trading positions and risk
• Online gaming
– Online real-time backbone for gaming
• Geo Mapping
– Real-time geographical route and traffic information
• Bio Informatics
– Real-time DNA sequencing and matching

8. In Memory Compute Grid
(IMCG)

9. In Memory Grids
1. In Memory -- Compute Grid.
Compute Grids allow you to take a computation, optionally split it into multiple parts, and
execute them on different grid nodes in parallel.

10. Functionality
• Distributed Execution Models - map-reduce, Streaming
Processing & CEP, MPP, MPI style
• Distributed Execution Management Services – task
distribution, failover, load balancing, collision resolution,
job stealing, redundant mapping support, task scheduling,
asynchronous reduction, task checkpoints
• Distributed Deployment & Provisioning.
• Distributed Resources Management - Automatic discovery

11. In Memory Data Grid
(IMDG)

12. In Memory Grids
2. In Memory -- Data Grid. (aka, Distributed data caching )
Provides applications with ability to keep data in memory for high availability rather than
constantly fetching it from slower storage elsewhere, like RDBMS or shared file systems.

13. IMDG ?
• Several JVMs sharing in-memory partitioned data.
• Provides extremely low latency access to,
and high availability of, application data by keeping it in
memory and to do so in a highly parallelized way.
• Support most of the Big Data processing requirements.

14. Common Features
• Distributed maps
• Caching , Evictions
• Code execution (executor service, map-reduce)
• Listeners
• Queries (SQL like)
• Pluggable indexing
• Hibernate L 2 cache (optional)
• ACID Transactions
• MapStore (write-behind, write-through, read-through)
• Optimized Serialization

15. Common Features
• The same object your business logic is using can be kept in the data grid.
• No extra step of marshaling and un-marshaling.
• Embeddable (optional)

16. Reference Architecture

17. IMDG is not a
• NoSQL database
• In Memory Database (IMDB)
• How?
• Support for true distributed ACID transactions with highly optimized 2PC protocol implementation.
• Scalable Data Partitioning across a cluster including both partitioned or fully replicated scenarios
• Ability to work directly with application domain objects rather than with primitive types or “documents”
• Tight integration with In-Memory Compute Grid (IMCG)
• Pluggable segmentation (a.k.a. "brain split" problem) resolution
• Pluggable expiration policies
• Pluggable indexing support

18. Further Reading
• http://www.ventanaresearch.com/uploadedFiles/Content/Landing_Pages/Ventana_Research_Big_
Data_Benchmark_Research_Presentation.pdf
• http://wikibon.org/wiki/v/Data_in_DRAM_is_a_Flash_in_the_Pan#Data_in_Memory_Solutions_for
_Real-Time_High-Performance_Transaction_Analytics
• http://www.gridgain.com/book/book.html
• http://java.dzone.com/articles/compute-grids-vs-data-grids
• http://www.infoq.com/articles/in-memory-data-grids
• http://natishalom.typepad.com/nati_shaloms_blog/2011/07/real-time-analytics-for-big-data-an-
alternative-approach-to-facebooks-new-realtime-analytics-system.html
• https://del.sapient.resultspace.com/scm/gmtechip/POCs/gridgain_risk_analytics