How in-memory data grids enable a real-time microservices architecture while diminishing the accidental complexity of persistence, orchestration, and fragmentation of scale.

1. Real-time Microservices and In-Memory
Data Grids

2. Meet Your Host
Ali Hodroj, VP of Products and Strategy at GigaSpaces @ahodroj

3. Meet Our Guest
Praveen Rawat, Technology Evangelist at Microsoft @praveensrawat

4. Meet Our Moderator
Kyle Sparzo, Business Development Manager at GigaSpaces

5. Overview
• The Microservices trend
• 3 Microservices challenges
• How Real-time Microservices can
help your architecture
• Benchmarks
• Comparing architectures
• Q&A

6. We’re recording.

7. Have a
question?

8. About GigaSpaces IMC
GigaSpaces IMC provides Cloud native In-
Memory Compute middleware for mission-
critical applications.
GigaSpaces IMC serves more than 500 large
enterprises & ISVs, over 50 of which are
Fortune-listed.
Direct customers
300+
Fortune / Organizations
50+ / 500+
Large installations in
production (OEM)
5,000+
ISVs
25+

9. The
Microservices
Trend

10. * From the The Art of Scalability
An attribute of the
request used to
route the request to
a particular server
Running multiple identical copies of the
application behind a load balancer
Microservices Architecture
pattern
What exactly is “microservices” architecture?

11. Decompose
services by
granular business
functions
Autonomous
component
development
Functional decomposition

12. Pitfalls

13. Accidental Complexity
Accidental complexity is that
which arises in programs, or
during their development process,
which is non-essential to the
problem to be solved

14. Operational
Complexity
Eventual
Consistency
Distribution
Source: https://martinfowler.com/articles/microservice-trade-offs.html

15. All these should maintain state that
should be synchronized continuously –
complex and expensive
Challenges
• Granularity control
• Inter-process communication
mechanism
• Handle partial failure
• Update multiple data store owned
by different services
• Eventual consistency is hard
Distribution and Fragmentation of Scale

16. Data Inconsistency

17. Can you Really Avoid 2PC?
Source: https://www.slideshare.net/chris.e.richardson/building-and-deploying-microservices-with-event-sourcing-cqrs-and-docker-microxchg-munich-microservices-meetup-2015

18. Command Query Responsibility Segregation (CQRS)
Let’s imagine that you
want to display an
account and its recent
transactions
Source: https://www.slideshare.net/chris.e.richardson/building-and-deploying-microservices-with-event-sourcing-cqrs-and-docker-microxchg-munich-microservices-meetup-2015

19. Event Store should
support event driven
fabric
Canonical CQRS Implementation
Source: https://www.slideshare.net/chris.e.richardson/building-and-deploying-microservices-with-event-sourcing-cqrs-and-docker-microxchg-munich-microservices-meetup-2015

20. Operational
Complexity
Eventual
Consistency
Distribution
Source: https://martinfowler.com/articles/microservice-trade-offs.html

21. Pick the right architectural choice
System-Level
Architecture
Service-level
Architecture
All services share a
common inter-service
communication glue
Each service uses a
different technology
stack
Single
technology
stack
Stack
sprawl

22. System-Level
Architecture
Service-level
Architecture
All services share a
common inter-service
communication glue
Each service uses a
different technology
stack
Pick the right architectural choice
Diminish accidental complexity
by deferring it to an in-memory
data grid, while maintaining
upstream API diversity
Single
technology
stack
Stack
sprawl

23. Polyglot Data Models
GigaSpaces XAP In-Memory Data Grid
http://gigaspaces.com
Data Locality and Sharding
Event-Driven / Reactive /
Continuous Queries
ACID / Transactionality

25. Rest
API +
State +
logicRest
API +
State +
logic
Rest
API +
State +
logic
StateState
Deployment Unit per Microservice
Stateless services with related data managed as
a single unit
Statefull services with
collocated data in-
memory
XAP Microservices Approach

26. Stateless
Services
Stateful
Services

27. Multi-Dimensional Scaling

28. Real-Time Data Access Interoperability
REST/HTTP
Microservices
RPC
Low Latency Services

29. Geo-Spatial Full Text
Polyglot Data Modeling and Access
(Diminish data tier complexity)
Transactional
Services
REST/JSON
Services
Location-Based
Services
Search
Services
Lookup/Caching
Services

30. Data Grid
Grid REST API
Benefits:
• Seamless integration with DevOps tool chains
• Microservices/App Registry for large-scale deployments
• Roll your own client/UI with any language (Swagger codegen)
RESTful Orchestration of Services
(Diminish orchestration complexity)

31. Unified Grid Manager and Resource Balancer
• Quorum-based cluster discovery
• Fault-tolerance against any split brain scenario
• Consistent partition balancing (1 active master
at a time)
• Leader election is done through ZooKeeper /
robust for large-scale deployments (100s of
nodes)
Built-in ZooKeeper service discovery
(Diminish orchestration complexity)

32. Co-location of events and data
(CQRS event data store)

33. Container Packaged Dynamically/REST Managed Microservices-Oriented
• XAP 12.1 Docker Images
• Kubernetes integration
• XAP 12.1 REST API
• Open API (Swagger), promotes
seamless integration with DevOps
toolchains
• Web PU’s
• Web PU + 1 RocksDB instance
• Remoting services (gRPC-like)

37. Upcoming
Webinars
Wed, Jul 12 | 2:00 PM EST
HTAP: Beyond the Big Database
Hype
Wed, Aug 2 | 2:00 PM EST
From Data to Services at the
Speed of Business
Wed, Aug 23 | 2:00 PM EST
Machine Learning and Insight
Platforms
Visit our site to sign up:
www.gigaspaces.com

38. Q&A

39. Use the Q&A box to
submit your questions
(Questions are not posted to the public.)

40. www.gigaspaces.com
Thank you for attending!