The document discusses GemFire, a memory-oriented key-value data store from Pivotal. It provides three use cases where GemFire was used to scale online ticket sales, global electronic trading systems, and the largest railway in China. GemFire enabled significant performance improvements like 50-100x faster queries and the ability to scale elastically with data growth. The document also summarizes GemFire's features like data partitioning, replication, off-heap memory, and integration with Apache Geode.

1. © Copyright 2017 Pivotal Software, Inc. All rights Reserved. Version 1.0
Mike Stolz, Pivotal Gemfire Product Manager:
March 2018
GemFire Use Cases

2. Safe Harbor
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3. © Copyright 2017 Pivotal Software, Inc. All rights Reserved. Version 1.0
March 2018
Scaling Online Sales for the
Largest Railway in the World
Case Study

4. Introduction
China Railway Corporation (CRC) is the national
railway operator in China.
! website books 4.5 million tickets per day,
based on 20 million daily users.
! Holiday travel periods create peaks of 15,000
tickets sold per minute
! 1.4 billion page views per day and 40,000
visits per second.
! Demand has far exceeded expectations and
the future shows as much as 50% growth per
year as mobile phone access is added.
Other Data Sources
Social Media
TV, Movies Usage

5. Challenges
Inability to Scale Traditional RDBMS
! Fundamental bottlenecks in the old system(72 UNIX)
- The relational database was overloaded and could not
handle either the scale of incoming requests
- The computational power of the UNIX servers was
inadequate for the capacity requirements
- Traditional RDBMS and mainframe computing models do
not support to use memory across multiple nodes

6. Solution
Gemfire through PoC
• Improving the speed of ticket calculation
performance by 50 to 100 times
• Low latency, Fast query response times on a
consistent basis
• As load increased and excellent, near-linear
scalability, high availability and elasticity

7. Solution : As-Is Architecture
Realtime
data
streaming
Realtime
data
streaming
18 railway
branches
Main DB
.
.
。
。
DB
replication
64 Unix for
Parallel Computing ATN
8 Unix for pre-
process
DB replication
Available Ticket Number(ATN) computing/query
cluster
.
.
second level
cache cluster CDN
cluster
ATN computing
result
ATN computing
result
ATN
update
query
ATN
update
queryDB
replication
mobile
users
Internet
Users
DATA Center
.
.

8. Solution : Redesigned Architecture w/ Gemfire
records
Web & App
Servers
N =120
Webserver
ApplicationServer
.
.
.
distributed data
stream parallel
computing
real-time
streaming
原有IT 系统结构seperate the data stream
real-time Data
Replication
real-time Data
Replication
Center DB
DB
N =8 M =64
DB(x86)
DB
Rabbit MQ (x86) cluster
data sync
GemFire
(x86)clusters > 5

9. Benefits
High Performance and Continuous Uptime
GemFire easily handles thousands of transactions per second and, while it
can act as a cache to mission-critical databases and mainframes
On-Demand Scale for Data
GemFire allows member nodes to be added to the system as needed and
can scale from ten to thousands of commodity computers at near-linear
response rates
Increased Developer Productivity
GemFire offers access through C++, C#, Java and REST (via a familiar
hash map type of interface, Spring Data)

10. © Copyright 2017 Pivotal Software, Inc. All rights Reserved. Version 1.0
March 2018
Global Electronic Trading
System
Case Study

11. Project Achievements
! Low-latency trade insertion
! Permanent Archival of every trade
! Keeps pace with fast ticking market data
! Rapid, Event Based Position Calculation
! Distribution of Position Updates Globally
! Consistent Global Views of Positions
! Pass the Book
! Regional Close-of-day / High Availability / Disaster Recovery / Regional
Autonomy

12. Global Clustering
Legacy Archival DB
Local Exchange
Local Exchange
Local Exchange
GemFire can keep clusters that are distributed around the world
“eventually consistent” in near real-time and can operate reliably in
Disconnected, Intermittent and Low-Bandwidth network environments.

13. Simplifying The Architecture
In this application, GemFire REPLACED…
• Sybase Database In Every Region
Still need 1 instance for archival purposes
• TIBCO Rendezvous for Local Area Messaging
• IBM MQ Series for WAN Distribution
• Veritas N+1 Clustering for H/A
In fact, we save the physical +1 node itself

14. © Copyright 2017 Pivotal Software, Inc. All rights Reserved. Version 1.0
March 2018
Pivotal GemFire
Memory Oriented, Key/Value Object Store

15. Apache Geode: Now a Top-Level Project
• Pivotal announced plans to donate GemFire core
to ASF in February 2015 - Incubation began May
2015
• Graduated to Apache Top-Level Project in
November 2016
• Released Geode 1.3.0 in Feb 2018
• Huge attendance in Dec 2017 Geode Summit

16. GemFire 9
• GemFire’s core is 100% based on Apache Geode Top-Level
Project
• Pivotal Cloud Cache
• AWS and Azure Marketplace
• Six important new features in GemFire 9.x
- Off-heap memory
- GemFire/GPDB connector
- Integrated Security
- Lucene Search
- Parallel Snapshot export/import
- Built-in Partitioning and Co-location

17. Pivotal Cloud Cache
• PCF Developers create their own Pivotal Cloud Cache
instances on-demand in seconds.
• PCF Administrators manage the type of VMs available.
• Look-aside and in-line Cache.
• Session State Cache
• WAN replication
• All instances are BOSH managed for high availability and
horizontal scale-out
• PCF Developers dynamically bind PCC user credentials
and host information to their Elastic Runtime pushed
applications.
• Backed by Pivotal's global, 24x7 support infrastructure.

18. Pivotal GemFire in AWS Marketplace
● Bring Your Own License (BYOL)
● Or hourly billing
● Continuous Development and Improvement
● Eases evaluation
● Eases proof of concepts
● Eases moving to the Cloud

19. Pivotal GemFire in Azure Marketplace
Bring Your Own License (BYOL)

20. What is Pivotal GemFire
© Copyright 2017 Pivotal Software, Inc. All rights Reserved.

21. ● Key/Value Object Store
● Horizontally Scalable and
Elastic
Key Features
● Memory Oriented
● Active Everywhere – Multi-
Site

22. The Enemy of Performance is Latency
Memory offers the lowest latency
Read 1 MB from memory:
Disk seek:
Send 1MB over LAN:
Read 1 MB from disk:
Accessing 1MB, relative to memory:
Disk takes 100x longer (seek+read)
Transferring data over a LAN adds another 40x
250,000 ns
6,000,000 ns
10,000,000 ns
20,000,000 ns

23. Memory-based Performance
10X to 100X faster than Traditional DBMS
Optionally write updates to disk,
or to a data warehouse, asynchronously and reliably.

24. Cloud Ready
Elastic
Add/remove data servers dynamically
Grow or shrink dynamically with no interruption of service or data loss.

25. Distributed Events
Real-time, Active Architecture
Pub/Sub and
Continuous Query

26. Partitioned
Regions model
One-to-Many and
Many-to-One
Many-to-Many, Many-to-One and One-to-Many relationships can be modeled
Co-location of related data eliminates distributed transactions
Partitioning and Co-location
Example - Partitioned Regions
Customers
Orders
Shipments
Payments
Partitioned Data
NEW: Partitioning by convention instead of custom code:
CustomerXYZ|Order1004

27. Partitioning and Co-location
Example - Replicated Regions - Many to Many Relationships
Many-to-Many, Many-to-One and One-to-Many relationships can be modeled
Co-location of related data eliminates distributed transactions
Product Descriptions
Pricing Data
Inventory
Replicated Regions
model Many-to-
Many
Relationships
Replicated Data

28. Data Aware Function Execution
Data-Aware Function Routing
Leverage Data Locality for
Enhanced Performance
Data Aware
Function

29. Parallel Function Execution and Queries
Scatter-Gather (Map-Reduce Style) Queries and Functions

30. Synchronous Consistency within a Cluster
Eventual Consistency with Archival Database
Eventual Consistency with other GemFire Clusters
Consistency Model
Archival, OLAP &
Regulatory RDBMS Storage Device

31. Availability Zones
Active Everywhere
Stretched Cluster Across Availability Zones.
Synchronous Replication For High Availability
AZ 1 AZ 2 AZ 3

32. Multi-Site Multi-Geography Capability
Active Everywhere
Asynchronous, Fault Tolerant, Bi-Directional WAN Gateway
New York
London Tokyo

33. Off-Heap Memory
Using Memory That is Separate From the Java Heap
Guest OS
Memory
Java Stack
Perm Gen
Java
Heap
Guest OS
Memory
Java Stack
Perm Gen
Java Heap
Other
Memory
(Off
Heap)
VMMemory
JVMMemoryforGemFire
VMMemory
JVMMemoryforGemFire
254 GB
Heap
30 GB
Heap
224 GB
Off-Heap
Before After
Long GC
pauses
because of
very large heap
size
256 GB 256 GB
Nearly pause-less

34. Tight integration of Lucene with GemFire Partitioned
Regions
● Lucene indexes are actually stored in a GemFire region that is
colocated with the region in which the data is being stored
● Index region is persisted, if data region is persisted
● Horizontally scalable indexing
Use Cases
● JSON document search is the number one use-case
● Look-ups by partial name, partial SSN, other attributes
● Can be used for type-ahead usage patterns as well
Lucene Search

35. Integrated Security
Configurable Authentication and Authorization
• Role-based, configurable, authorization for
administrative activities
• Configuration of access levels from single place or
repository
• Uniformity across clients that perform cache/region
operations and those that perform management
operations
• Consistent mechanism for authenticating and
authorizing actions
• Every administrative function now can require
authorization
• Some users can read/write data
• Others can start/stop servers
• Still others can configure the cluster

36. DataTemperatureWarmHot
GemFire/Greenplum
Connector
Transactional
data
Write behind
Analytical
parameters
to cache
GemFire and GPDB - Big Data meets Fast Data
Seamlessly share data
between GemFire and
Greenplum
Bi-directional direct
connection between
GemFire CacheServers
and Greenplum
Segment Servers

37. New capability in gfsh to snapshot in parallel
● GemFire has had snapshot capability since version 6.0
● Snapshots are simple point-in-time view of the data
● Unlike GemFire backups, snapshots can be used to
transfer data from one cluster to another of a different
size and shape
● This feature will make Snapshots MUCH faster and
more usable
Use Cases
● Populate a new cluster with data from an existing
cluster
● QA/dev environments
● Saving point in time to re-hydrate cache again later
Parallel Snapshot Import/Export

38. Backups are for recovery in case of loss due to:
● Human error - deleted all data
● Programmatic error - application corrupted data
● Loss of entire disk subsystem - ie. if using shared SAN
instead of shared-nothing storage
Full and Incremental Backup
This feature is required to go beyond “Caching” use-cases
to “System-of-Record”.