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Where Does Apache Geode Fit in CQRS Architectures?

Eitan Suez@eitan_suez

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About Me• 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

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Relationship with Apache Geode• 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

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

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Don’t immediately realize what you’ve got• 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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Example: Queries against partitioned regions

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Can go further with server-side functions

Client

Geode Distributed System

Server

Query ExecutorPartitioned

Region

Server

Partitioned Region

Server

Partitioned Region

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

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Impressive feature set• Briefly reviewed the traits of Apache Geode• It takes time to “wrap one’s head around” the whole of this product

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So, what can you do with it?

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

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Use Cases “in the Small”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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Canonical Architecture

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Geode Distributed System

RegionsFunctionsLocator

Backing Store

Client Client

Events, Continuous Queries

RegionsFunctions

CacheLoader AsyncEventListener

Server

RegionsFunctions

Client

Queries, Transactions, Function Executions

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Switching Gears• 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

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What is CQRS?• 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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CQRS..

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..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..

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..can optimize reads and writes• 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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Read-Optimized Write-Optimized3rd normal formdenormalized views

Data-Representation Spectrum

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Reading when your data is normalized

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Controller

Services Repositories

Relational Database

Queries w/Joins

Request

transformations

compositions

Result Sets

viewsConstantly reassembling views

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With a denormalized schema..• no joins necessary• no transformations• no need to reconstruct a view model for each request

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Apache Geode

Region: Customers

Region: Orders

Region: Products . . .

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..more benefits• 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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Command Side• 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

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See: Udi DahanClarified CQRS

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Event Sourcing• 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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See: Martin KleppmannStream processing, Event sourcing, Reactive, CEP … and making sense of it all

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The Log / Table Duality• 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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See: Jay KrepsThe Log: What every software engineer should know about real-time data's unifying abstraction

See: Martin KleppmannStream processing, Event sourcing, Reactive, CEP … and making sense of it all

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Replaying the Log..• ..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.

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See: Greg YoungCQRS 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” objectsEvents

Projection Updates Views in Regions

Geode Distributed SystemRegions

containingView Models

Read Side - relay view models to ui - little to no transformations

Events, Continuous Queries

Queries

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SummaryApache 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

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References & Attributions• 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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