SpringOne Platform 2019 Session Title: Turnkey Multi-Region, Active-Active Session Stores with Steeltoe, Redis Enterprise, and PAS Speaker: Adi Foulger, Principal Solution Architect, Redis Labs Youtube: https://youtu.be/8IkZRzyRc1c

1. Global Multi-Model Databases
with Steeltoe and PCF
ADI FOULGER | October 2019

2. $ whoami
E-mail: adi.foulger@redislabs.com
LinkedIn: whois.redis.ninja

3. An In-memory open source database, supporting a variety
high performance operational, analytics or hybrid use case
Our Roots Are in Open Source

4. Redis Enterprise
An in-memory multi-model database
built on top of open source Redis

5. Real Time
Analytics
User Session
Store
Real Time Data
Ingest
High Speed
Transactions
Job & Queue
Management
Time Series Data Complex
Statistical Analysis
Notifications Distributed Lock Content Caching
Geospatial Data Streaming Data Machine Learning Search
Uniquely Suited to Modern Use Cases
A full range of capabilities that simplify and accelerate next generation applications

6. Data Structures - Redis’ Building Blocks
6
Lists
[ A → B → C → D → E ]
Hashes
{ A: “foo”, B: “bar”, C: “baz” }
Bitmaps
0011010101100111001010
Strings
"I'm a Plain Text String!”
Bit field
{23334}{112345569}{766538}
Key
”Retrieve the e-mail address of the user with the highest
bid in an auction that started on July 24th at 11:00pm PST” ZREVRANGE 07242015_2300 0 0=
Streams
🡪{id1=time1.seq1(A:“xyz”, B:“cdf”),
d2=time2.seq2(D:“abc”, )}🡪
Hyperloglog
00110101 11001110
Sorted Sets
{ A: 0.1, B: 0.3, C: 100 }
Sets
{ A , B , C , D , E }
Geospatial Indexes
{ A: (51.5, 0.12), B: (32.1, 34.7) }

7. Multi-Model Functionality at Any Scale
• Dedicated engine for each data
model (vs. API only)
• Models engines can be
selectively loaded, according
to use case
• All model engines access the
same data, eliminating the
need for transferring data
between them

8. Redis Enterprise Technology
8
Redis Enterprise Node Redis Enterprise Cluster
• Shared nothing cluster architecture
• Fully compatible with open source
commands & data structures

9. High Performance
Read and write with low local
sub-millisecond latency
Guaranteed data consistency
CRDT based: The datatypes are conflict-free
by design. All databases eventually converge
automatically to the same state with strong
eventual consistency
Supports causal consistency executing read
and write operations in an order that reflects
causality
Simplifies the app design
Develop as if it’s a single app in a single geo,
we take care of all the rest
Redis Enterprise Delivers Strong Eventual Consistency
and Causal Consistency
9

10. Multi-Cloud and Hybrid Cloud-OnPrem Support
App
App
App
App
Active-Active or Active-Passive

11. Multi-Cloud and Hybrid Cloud-OnPrem Support
Causal Consistency

12. CRDT Example: Counter
12
Applies Commutative and Associative Properties
K = 30 + 40 + 50 = 120
K = 40 + 30 + 50 = 120
K = 50 + 30 + 40 = 120
INCR K 50
INCR K 30INCR K 40
A
B
C
Time: t1

13. CRDT Example: Set
13
Applies idempotence
Star Wars
Coco
Wonder Women
Star Wars
A
B
C
Time: t1

14. Causal Consistency
14
Based on a Directed Acyclic Graph
model.
DAGs can only be traversed in one
direction, no matter their shape.

15. Redis CRDTs : Per Key Causal Consistency
Hot
Replica A
Apple
Replica B
Replica C
Apple
Apple
Pie Hot
Pie Peach?
Source FIFO
15
Replica A
Apple
Replica B
Replica C
TastyHot
Apple Pie Hot
Pie
Causal consistency
Apple

16. Reference Architecture

17. Thank you!