Slides from the Meetup Monday March 7, 2016 just before the beginning of #GeodeSummit, where we cover an introduction of the technology and community that is Apache Geode, the in-memory data grid.

1. Building Apps with
Distributed In-Memory Computing
using Apache Geode
Nitin Lamba
@nlamba9
(incubating)
William Markito
@william_markito

2. Introduction (Nitin)
• WHAT? Overview & history
• WHY? Relevance & Differentiators
• HOW? Features & Basic Concepts
• SEE! Quick start
Hands-on (William)
• LEARN: Advanced Concepts - Persistence, f(x), PDX, …
• SHOW: Demos (Docker, PDX)
Resources
Q & A
Agenda
2

3. Introduction
Nitin
3

4. From GEM to GEODE…
4

5. A distributed, memory-based data
management platform for data
oriented apps that need:
• high performance, scalability,
resiliency and continuous
availability
• fast access to critical data sets
• location-aware distributed data
processing
• event-driven data architecture
What is GEODE?
5

6. High-level Architecture
6
Powerful app development kit
• APIs: Java & REST
• Adapters: Redis, Lucene*, Spark*, …
Multiple persistence options
• Filesystem, RDBMS or HDFS*
• Sync: read-through, write-through
• Async: write-behind
Durable <K,V> cache/ store
• Data replicated or partitioned
• Redundant storage in-memory/ disk
• Flexible data retention policiesÎ
!
Locator
Server
Server
Server
Server
+""""
" 
$
%
%
%
&& &
% % % % % % % %
&&
A Peer-2-Peer
Distributed System
REST
!
* Experimental and waiting community feedback

7. • 1000+ systems in production (real customers)
• Cutting edge use cases
Incubating but ROCK solid…
7
<2000 2004 2008 2012 2016
Early drivers
• Data Volumes
• Margins/ transactions
• IT maintenance costs
• Elasticity needs
Real-time needs
• Real-time response
• Time to market needs
• Flexible Data Models
• Persistent+In-memory
Global Data
• Visibility across DC
• Fast Ingest
• Device to enterprise
• Uptime (always on)
Open Source!
• Apache Incubation
• Gemfire > Geode
• M1 release
• 1st Geode Summit
Financial
Services
US DoD
Trade Clearing
Travel Portal
Online
Gambling
Telcos
Manufacturing
Auto Insurance
Payroll processing
Rail systems

8. …with both SCALE and SPEED, …
8
40K
Transactions
per second
3TB
Data
in-memory
17B
Records
in-memory
120K
Concurrent
users

9. … and impacting a LOT of people!
9
China Railway
Corporation
Indian
Railways
19%
17%
36%
of the world population

10. Built for PERFORMANCE…
10
Operationspersecond
0
200,000
400,000
600,000
800,000
YCSB Workloads
AReads
AUpdates
BReads
BUpdates
CReads
DInserts
DReads
FReads
FUpdates
Cassandra
Geode

11. …and horizontal, consistent SCALABILITY!
11
Horizontal scaling for reads, consistent latency and CPU
0
4.5
9
13.5
18
Speedup
0
1.25
2.5
3.75
5
Server Hosts
2 4 6 8 10
speedup latency (ms) CPU %
• Scaled from 256 clients and 2 servers to 1280 clients and 10 servers
• Partitioned region with redundancy and 1K data size

12. • Minimize copying
• Minimize contention points
• Run user code in-process
• Partitioning & parallelism
• Avoid disk seeks
• Automated benchmarks
What makes it go FAST?
12

13. • Cache
• Region
• Member
• Client Cache
• Persistence
• Functions
• Events & Listeners
• High Availability
• Serialization
Let’s talk about a few (basic) CONCEPTS…
13

14. • In-memory storage and
management for your data
• Configurable through XML,
Java API or CLI
• Collection of Region
What is a CACHE?
14
Region
Region
Region
Cache
JVM

15. • Distributed java.util.Map on
steroids (Key/Value)
• Consistent API regardless of where
or how data is stored
• Observable (reactive)
• Highly available, redundant on
cache Member (s).
What is a REGION?
15
Region
Cache
java.util.Map
JVM
Key Value
K01 May
K02 Tim

16. • Local, Replicated or Partitioned
• In-memory or persistent
• Redundant
• LRU
• Overflow
Region: Types & Options
16
Region
Cache
java.util.Map
JVM
Key Value
K01 May
K02 Tim
Region
Cache
java.util.Map
JVM
Key Value
K01 May
K02 Tim
LOCAL
LOCAL_HEAP_LRU
LOCAL_OVERFLOW
LOCAL_PERSISTENT
LOCAL_PERSISTENT_OVERFLOW
PARTITION
PARTITION_HEAP_LRU
PARTITION_OVERFLOW
PARTITION_PERSISTENT
PARTITION_PERSISTENT_OVERFLOW
PARTITION_PROXY
PARTITION_PROXY_REDUNDANT
PARTITION_REDUNDANT
PARTITION_REDUNDANT_HEAP_LRU
PARTITION_REDUNDANT_OVERFLOW
PARTITION_REDUNDANT_PERSISTENT
PARTITION_REDUNDANT_PERSISTENT_OVERFLOW
REPLICATE
REPLICATE_HEAP_LRU
REPLICATE_OVERFLOW
REPLICATE_PERSISTENT
REPLICATE_PERSISTENT_OVERFLOW
REPLICATE_PROXY

17. • Durability
• WAL for efficient writing
• Consistent recovery
• Compaction
Persistent Regions
17
Modify
k1->v5
Create
k6->v6
Create
k2->v2
Create
k4->v4
Oplog2.crf
Member
1
Modify
k4->v7Oplog3.crf
Put k4->v7
Region
Cache
java.util.Map
JVM
Key Value
K01 May
K02 Tim
Region
Cache
java.util.Map
JVM
Key Value
K01 May
K02 Tim
Server 1 Server N

18. • A process that has a connection to
the system
• A process that has created a cache
• Embeddable within your
application
What is a MEMBER?
18
Client
Locator
Server

19. • A process connected to the
Geode server(s)
• Can have a local copy of the data
• Run OQL queries on local data
• Can be notified about events on
the servers
What is a CLIENT CACHE?
19
Application
GemFire Server
Region
Region
RegionClient Cache

20. • Clone & Build
•
• Start Services
• Create & Monitor Region
How to START? Easy as !!
20
git clone https://github.com/apache/incubator-geode
cd incubator-geode
./gradlew build -Dskip.tests=true
cd gemfire-assembly/build/install/apache-geode
./bin/gfsh
gfsh> start locator --name=locator
gfsh> start server --name=server
gfsh> create region --name=myRegion —type=REPLICATE
gfsh> start [pulse | jconsole]
1
2
3
'
1 2 3

21. Hands On
William
21

22. • Cache
• Region
• Member
• Client Cache
• Persistence
• Functions
• Events & Listeners
• High Availability
• Serialization
More (advanced) CONCEPTS…
22

23. Persistence - Shared Nothing
23
Server 3Server 2Server 1

24. Persistence - Shared Nothing
24
Server 3Server 2Server 1
B1
B3
B2
B1
B3
B2
Primary
Secondary

25. Persistence - Shared Nothing
25
Server 3Server 2Server 1
B1
B3
B2
B1
B3
B2
Primary
Secondary

26. Persistence - Shared Nothing
26
Server 3Server 2Server 1
B1
B3
B2
B1
B3
B2
Primary
Secondary

27. Persistence - Shared Nothing
27
Server 3Server 2Server 1
B1
B3
B2
B1
B3
B2
Primary
Secondary

28. Persistence - Shared Nothing
28
Server 3Server 2Server 1
B1
B3
B2
B1
B3
B2
Primary
Secondary
B3
B2
Server 1 waits for others when it starts

29. Persistence - Shared Nothing
29
Server 3Server 2Server 1
B1
B3
B2
B1
B3
B2
Primary
Secondary
Fetches missed operations on restart

30. Persistence - Operational Logs
30
Create
k1->v1
Create
k2->v2
Modify
k1->v3
Create
k4->v4
Modify
k1->v5
Create
k6->v6
Member 1
Put k6->v6
Oplog2.crf
Oplog1.crf
Append to
operation log

31. Persistence - Operational Logs: Compaction
31
Create
k1->v1
Create
k2->v2
Modify
k1->v3
Create
k4->v4
Modify
k1->v5
Create
k6->v6
Member 1
Put k6->v6
Oplog2.crf
Oplog1.crf
Append to
operation log
Copy live
data forward

32. • Used for distributed concurrent
processing
(Map/Reduce, stored procedure)
• Highly available
• Data oriented
• Member oriented
Functions
32
Submit (f1)
f1 , f2 , … fn
Execute
Functions

33. Functions
33
Server Server
FunctionService.onRegion.withFilter.execute
ResultCollector.getResult
Server Distributed System
execute
Server
Server
6
1
result
execute
execute
result
result
2
5
3
4
3 4
Server
Partitioned Region
Data Store - X
Partitioned Region
Data Store - Y
Partitioned Region
Data Store - Z
Partitioned Region
Data Accessor
Partitioned Region
Data Accessor
filter = Keys X, Y
Client Region

34. • Register Interest
• Individual Keys OR RegEx for Keys
• Updates Local Copy
• Examples:
• region.registerInterest(“key-1”);
• region1.registerInterestRegex(“[a-z]+“);
• Continuous Query
• Receive Notification when Query condition met on server
• Example:
• SELECT * FROM /tradeOrder t WHERE t.price > 100.00
Can be DURABLE
Events & Notifications
34

35. • CacheWriter / CacheListener
• AsyncEventListener (queue / batch)
• Parallel or Serial
• Conflation
Listeners
35

36. High Availability
36

37. Fixed or Flexible schema?
37
id name age pet_id
or
{
id : 1,
name : “Fred”,
age : 42,
pet : {
name : “Barney”,
type : “dino”
}
}

38. Portable Data eXchange (PDX)
38
C#, C++, Java, JSON
No IDL, no schemas, no hand-coding
Schema evolution (Forward and Backward Compatible)
* domain object classes not required
| header | data |
| pdx | length | dsid | typeid | fields | offsets |

39. Efficient for queries
39
{
id : 1,
name : “Fred”,
age : 42,
pet : {
name : “Barney”,
type : “dino”
}
}
SELECT p.name FROM /Person p WHERE p.pet.type = “dino”
single field
deserialization

40. But HOW to serialize data?
40
Benchmark: https://github.com/eishay/jvm-serializers

41. Schema Evolution
41
Member A Member B
Distributed Type Definitions
v2v1
Application #1
Application #2
v2 objects preserve data
from missing fields
v1 objects use default values to
fill in new fields
PDX provides forwards and backwards
compatibility, no code required

42. Demo
(Docker, PDX, …)
42

43. Code
• New features
• Bug fixes
• Writing tests
Documentation
• Wiki
• Web site
• User guide
How to CONTRIBUTE?
43
Community
• Join the mailing list
• Ask or answer
• Join our HipChat
• Become a speaker
• Finding bugs
• Testing an RC/Beta

44. Website
http://geode.incubator.apache.org/
JIRA
https://issues.apache.org/jira/browse/GEODE
Wiki
cwiki.apache.org/confluence/display/GEODE
GitHub
https://github.com/apache/incubator-geode
Mailing lists
mail-archives.apache.org/mod_mbox/incubator-geode-dev/
Where to BEGIN?
44

45. 45
Thank you!
http://geode.incubator.apache.org
https://github.com/Pivotal-Open-Source-Hub