2016.03.09

Where does Geode fit in modern system architectures?

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Eitan Suez

• 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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About Me

• 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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Relationship with Geode

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

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My Journey

• 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 ExecutorPartitioned

Region

Server

Partitioned Region

Server

Partitioned Region

Can go further with server-side functions

• 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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Unique Combination of Features

• 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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Impressive feature set

So, what can you do with it?

• 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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Use Cases “in the Small”

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Canonical Architecture

Geode Distributed System

RegionsFunctionsLocator

Backing Store

Client Client

Events, Continuous Queries

RegionsFunctions

CacheLoader AsyncEventListener

Server

RegionsFunctions

Client

Queries, Transactions, Function Executions

• 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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Switching Gears

• 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..

• 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 normalizedRequest

ServicesRepositories

multiple queries, lots of joins

results transformations

views

Relational Database

Controller

compositions

Constantly reassembling views

• no joins necessary • no transformations • no need to reconstruct a view model for each

request

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With a denormalized schema

Apache Geode

Region: Customers

Region: Orders

Region: Products . . .

• 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

• 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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Command Side

See: Udi Dahan Clarified CQRS

• 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

• 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

• ..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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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 SystemRegions

containingView Models

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

Events, Continuous Queries

Queries

• 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

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Summary

• 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 Grid28

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/

Thank you!