and databases boost application performance and solve scalability problems by storing and processing large datasets across a cluster of interconnected machines. This session is for software engineers and architects who build data-intensive applications and want practical experience with in-memory computing. You will be introduced to the fundamental capabilities of distributed, in-memory systems and will learn how to tap into your cluster’s resources and how to negate any negative impact that the network might have on the performance of your applications.

1. In-Memory Computing Essentials
for Java Developers and Architects

2. Your Speaker: Denis Magda
➔ Distributed in-memory system
◆ Apache Ignite Committer and PMC Member
◆ Head of DevRel at GridGain
➔ Java engineering and architecture
◆ Java engineering at Oracle
◆ Technology evangelism at Sun Microsystems

3. Agenda
• Introduction
– Why In-Memory Computing?
– Apache Ignite, Brief Overview
• Essentials
– Data Partitioning
– Affinity Co-location
– Co-located Computations

4. Why In-Memory
Computing?

5. Speed and Scale

6. In-Memory Software Scales Horizontally
Application
Cluster

7. Comparing System-Event Latencies
System Event Actual Latency Scaled Latency
One CPU cycle 0.4 ns 1 s
Level 1 cache access 0.9 ns 2 s
Level 2 cache access 2.8 ns 7 s
Level 3 cache access 28 ns 1 min
Main memory access (DDR DIMM) ~100 ns 4 min
Intel Optane DC persistent memory access ~350 ns 15 min
Intel Optane DC SSD I/O <10 µs 7 hrs
NVMe SSD I/O ~25 µs 17 hrs
SSD I/O 50 – 150 µs 1.5 – 4 days
Rotational disk I/O 1 – 10 ms 1 – 9 months
Internet: SF to NY 65 ms 5 years

8. Memory Versus Disk Latency
System Event Actual Latency Scaled Latency
One CPU cycle 0.4 ns 1 s
Level 1 cache access 0.9 ns 2 s
Level 2 cache access 2.8 ns 7 s
Level 3 cache access 28 ns 1 min
Main memory access (DDR DIMM) ~100 ns 4 min
Intel Optane DC persistent memory access ~350 ns 15 min
Intel Optane DC SSD I/O < 10 µs 7 hrs
NVMe SSD I/O ~25 µs 17 hrs
SSD I/O 50 – 150 µs 1.5 – 4 days
Rotational disk I/O 1 – 10 ms 1 – 9 months
Internet: SF to NY 65 ms 5 years

9. Apache Ignite
Brief Overview

10. Apache Ignite In-Memory Computing Platform
Mainframe NoSQL HadoopDistributed Ignite Persistence
Disk Tier
RDBMS
Machine and Deep Learning
EventsStreamingMessaging
Transaction
s
SQLKey-Value
Service GridCompute Grid
Application Layer
Web SaaS SocialMobile IoT
RollingUpgrades
Security&Auditing
Monitoring&Management
SegmentationProtection
DataCenterReplication
NetworkBackups
Full,Incremental,ContinuousBackups
Point-in-TimeRecovery
HeterogeneousRecovery
Distributed In-Memory Tier

11. Apache Ignite as a Cache or as a Database
Ignite as a Cache and Data Grid
Ignite as a Database

12. Apache Ignite Is In Top 5 Apache Projects
...
8.

13. Data Partitioning
Using all of the cluster’s memory and CPUs

14. Partitioning and Horizontal Scalability
RDBMS
Partitioned Database
record#1
record#2
record#3
record#4
record#1
record#2
record#3
record#4
partitioning

15. The World Database Schema
Country
City CountryLanguage
https://dev.mysql.com/doc/world-setup/en/

16. Partitioning
Country
p0
...
p1 p2
p3 p4 p5
p1022 p1023 p1024
p0
p1024
p4
p2
p1022
p5

17. Record -> Partition -> Node Mapping
Country
p0
...
p1
p3 p4
p1022 p1023 p1024
p0
p1024
p4
p2
p1022
p5
p2
p5
1. primary key to partition
2.partition to node

18. Record -> Partition -> Node Mapping
Country
p0
...
p1
p3 p4
p1022 p1023 p1024
p0
p1024
p4
p2
p1022
p5
p2
p5
1. primary key to partition
Code = ‘USA’
2.partition to node

19. Affinity Co-Location
Reducing network utilization for complex requests

20. Default Data Distribution
Canada
Toronto
Calgary
Paris
France
Marseille
Montreal
Ottawa
Country Table City Table

21. Data is Shuffled During the JOIN phase
Thick Client
Canada
Toronto
Calgary
Paris
France
Marseille
Ottawa
Montreal
Paris
Ottawa
Montreal
1 & 4
2
2
3
1. Initiating Execution
2. Execution on Servers (map phase)
3. Data Shuffling
4. Reduce Phase

22. Disk Versus Network Latency Latency
System Event Actual Latency Scaled Latency
One CPU cycle 0.4 ns 1 s
Level 1 cache access 0.9 ns 2 s
Level 2 cache access 2.8 ns 7 s
Level 3 cache access 28 ns 1 min
Main memory access (DDR DIMM) ~100 ns 4 min
Intel Optane DC persistent memory access ~350 ns 15 min
Intel Optane DC SSD I/O < 10 µs 7 hrs
NVMe SSD I/O ~25 µs 17 hrs
SSD I/O 50 – 150 µs 1.5 – 4 days
Rotational disk I/O 1 – 10 ms 1 – 9 months
Internet: SF to NY 65 ms 5 years

23. Co-Located Data Distribution
Canada
Toronto
Calgary
France
Marseille
Country Table City Table
Montreal
Ottawa Paris

24. How to Group Related Data

25. JOINs with Co-Located Data
Thick Client
Canada
Toronto
Calgary
France
Marseille
1 & 3
2
2
1. Initiating Execution
2. Execution on Servers (map phase)
3. Reduce Phase
Ottawa
Paris

26. Co-Located Computations
Executing data-intensive logic on cluster nodes

27. Executing Custom Logic in Cluster

28. Story About Millions Savings Accounts
All
savings accounts
RDBMS
Application
1. Read all accounts
3. Write changes back
2. Interest
calculation

29. Story About Millions Savings Accounts
Ignite Cluster
Application
1. Send compute task
2 2
2

30. Summary
In-Memory Computing Essentials