The document discusses how Apache Geode fits into modern system architectures using the Command Query Responsibility Segregation (CQRS) pattern. CQRS separates reads and writes so that each can be optimized independently. Geode is well-suited as the read store in a CQRS system due to its ability to efficiently handle queries and cache data through regions. The document provides references on CQRS and related patterns to help understand how they can be applied with Geode.

1. 2016.03.09

2. Where does Geode fit in
modern system architectures?
2
Eitan Suez

3. • Eitan Suez
• Pivotal Consultant Instructor
• Teach GemFire, Cloud Native, PCF
• Prior to joining Pivotal, was Principal Consultant with ThoughtWorks
• Long-time software developer, based in Austin, TX
3
About Me

4. • Over the years have worked on many enterprise projects for a number of
customers
• First hands-on experience with Geode when consulting at SouthWest
Airlines..
• ..in the role of technical lead on a multi-team project, where Geode played a
prominent role in the system architecture
4
Relationship with Geode

5. • gfsh
• OQL and the data browser
• PDX serialization
• Spring Data GemFire
• Learn how to do automated functional testing with it
5
My Journey

6. • At first, we were so focused on building features
• Regions were already defined by solutions architects, treated them as
tables
• Didn’t pay too close attention to the fact that we had:
• near-linear scale-out capabilities built-in with partitioned regions
• fault-tolerance with redundant data copies
• locators adding indirection, clients isolated from cluster specifics
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Don’t immediately realize what you’ve got

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Example: Queries against partitioned regions
Client
Geode Distributed System
Query against partitioned region
Server
Query Executor
Partitioned
Region
Server
Partitioned
Region
Server
Partitioned
Region
Can go further with server-side functions

8. • A Database, but in-memory?
• Can also double as a simple cache?
• A key-value store, but supports queries?
• Supports transactions
• Events?
8
Unique Combination of Features

9. • Briefly reviewed the traits of Apache Geode
• It takes time to “wrap one’s head around” the whole of this
product
9
Impressive feature set
So, what can you do with it?

10. • Specific to Java stack: O/RM and Hibernate
• can plug in as Hibernate L2 Cache
• Peer-to-peer configuration
10
Use Cases “in the Small”

11. • Can be an out-of-process cache server, like Redis, or memcached
➡ gemcached
• These are fine, but does not take advantage of the full feature set
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Use Cases “in the Small”

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Canonical Architecture
Geode Distributed System
RegionsFunctions
Locator
Backing Store
Client Client
Events, Continuous Queries
RegionsFunctions
CacheLoader AsyncEventListener
Server
RegionsFunctions
Client
Queries, Transactions, Function Executions

13. • On a couple of projects over the last couple of years, have been
exposed to CQRS
• At first it seemed strange, or overly complex. Didn't get it
• Kept asking myself:
• Why not start out simpler?
• Seems rather complicated
• It’s more work
13
Switching Gears

14. • Stands for Command Query Responsibility Segregation
• A Pattern
• deliberately not prescriptive regarding how you implement this
separation
• Separation all the way down to the database
• Germ of the idea came from Bertrand Mayer (of Eiffel fame), with
concept of CQS
• Introduced, proposed by Greg Young
• Active .NET community, Udi Dahan among others
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What is CQRS?

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CQRS..
..tells you what,
not how,
but to answer why,
we are asked to look at what happens when you “go there”

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When reads and writes are separate..

17. • With a single schema, you’re forced to optimize for one at the
expense of the other
• With two schemas, one can be optimized for reads and the
other for writes (have your cake and eat it too)
• relational model for writes
• denormalized views for reads
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..can optimize reads and writes
Read-Optimized Write-Optimized
Data-Representation Spectrum
3rd normal formdenormalized views

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Reading when your data is normalized
Request
Services
Repositories
multiple queries,
lots of joins
results
transformations
views
Relational
Database
Controller
compositions
Constantly reassembling views

19. • no joins necessary
• no transformations
• no need to reconstruct a view model for each
request
19
With a denormalized schema
Apache Geode
Region:
Customers
Region:
Orders
Region:
Products
. . .

20. • Can scale reads and writes independently
• many systems have a profile where reads outnumber writes at 100:1
ratio
• Read and write sides can be implemented with entirely different tools and
technologies
• Read-side can stay up when write-side is temporarily down
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..more benefits

21. • Commands are semantic, in the language of the business, not
REST CRUD:
AddToCart, AddPaymentMethod, ChangeAddress
• Command handling can be asynchronous
• enqueue commands
• can scale command handling
21
Command Side
See: Udi Dahan
Clarified CQRS

22. • The LOG
• Append-only, no mutation
• Immutable storage, doesn't destroy history
• Activity just a stream of events
• tables are projections, can be derived entirely from log
• views can be recreated at will
• multiple views
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Event Sourcing
See: Martin Kleppmann
Stream processing, Event sourcing, Reactive, CEP … and making sense of it all

23. • Data in Motion vs Data at Rest
• Entire History vs Snapshot in time
• Source of truth vs derived information
• materialized views, caches, indexes,
aggregations
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The Log / Table Duality
See: Jay Kreps
The Log: What every software engineer should know about
real-time data's unifying abstraction
See: Martin Kleppmann
Stream processing, Event sourcing, Reactive, CEP … and making sense of it all

24. • ..in test environments to reproduce bugs
• ..in dev environments to test an upcoming release
• ..in production to “undo” a bug
• ..in production for blue-green type deployments
• Can transition to a new schema/representation of data in your regions
because you've come up with a radically different user interface for navigating
that information.
24
Replaying the Log
See: Greg Young
CQRS and Event Sourcing

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Diagram by
“Exploring CQRS and Event Sourcing”, msdn

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✓ update caches when new events come in
✓ invalidate caches proactively - ensure data
in caches remain fresh
✓ inverts the cache loader concept
✓ serving data from fast, in-memory caches
✓ regions contain “ViewModel” objects
Events
Projection Updates
Views in Regions
Geode Distributed System
Regions
containing
View Models
Read Side
- relay view models to ui
- little to no transformations
Events, Continuous Queries
Queries

27. • Apache Geode as the read store in a CQRS system is a
particularly good fit:
• eager cache invalidation
• scalable and fast reads via..
• regions store denormalized views
• partitioned regions enable linear scale-out
• in-memory data supports low-latency reads
• Curious to learn how Geode is being applied in your work
27
Summary

28. • Martin Kleppmann
Stream processing, Event sourcing, Reactive, CEP … and making sense of it all
• Rx, Erik Meijer
Your Mouse is a Database
• Greg Young
CQRS and Event Sourcing
• Jay Kreps
The Log: What every software engineer should know about real-time data's unifying
abstraction
• Udi Dahan
Clarified CQRS
• Dominic Betts, Julian Dominguez, Grigori Melnik, Fernando Simonazzi, Mani
Subramanian
CQRS Journey
• Dannielle Burrow
Four Real World Use Cases For An In-Memory Data Grid
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References & Attributions

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Join the Apache Geode Community!
• Check out http://geode.incubator.apache.org
• Subscribe: user-subscribe@geode.incubator.apache.org
• Download: http://geode.incubator.apache.org/releases/

30. Thank you!