Speaker: Cedrick Lunven, Developer Advocate, DataStax Speaker Bio: Cedrick is a Developer Advocate at DataStax where he finds opportunities to share his passions by speaking about developing distributed architectures and implementing reference applications for developers. In 2013, he created FF4j, an open source framework for Feature Toggle which he still actively maintains. He is now contributor in JHipster team. Talk Synopsis: We have all introduced more or less functional programming and asynchronous operations into our applications in order to speed up and distribute treatments (e.g., multi-threading, future, completableFuture, etc.). To build truly non-blocking components, optimize resource usage, and avoid "callback hell" you have to think reactive—everything is an event. From the frontend UI to database communications, it’s now possible to develop Java applications as fully reactive with frameworks like Spring WebFlux and Reactor. With high throughput and tunable consistency, applications built on top of Apache Cassandra™ fit perfectly within this pattern. DataStax has been developing Apache Cassandra drivers for years, and in the latest version of the enterprise driver we introduced reactive programming. During this session we will migrate, step by step, a vanilla CRUD Java service (SpringBoot / SpringMVC) into reactive with both code review and live coding. Bring home a working project! Filmed at Skills Matter/Code Node London on 9th May 2019 as part of the Big Data LDN Meetup Blueprint Series. Meetup sponsored by DataStax.

1. Speed up your Apache
Cassandra™ Applications
A Practical Guide to Reactive Programming
London

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About me
Cedrick Lunven
Developer Advocate, DataStax
Creator Contributor

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Agenda
2
3
4 BACK TO SANITY WITH REACTIVE
LET’S GO ASYNCHRONOUS (AND CRAZY)!
SIMPLE & SYNCHRONOUS
5 TAKE AWAYS
1 REMINDERS APACHE CASSANDRA™

4. APACHE CASSANDRA
..reminders

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This is a DISTRIBUTED Database
Node
• Up to 1TB
• 3000 Tx/s/core
Datacenter (Ring)

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…which scale linearly

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…anywhere
On-Premise
Data Center
Cluster

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Data is distributed
0
13
25
38
50
63
75
88 59 (data)
RF=2
59 (data)
RF=3
replicated

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Tuneable Consistency
RF=3
Client CL=ONECL=QUORUMCL=ALL

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Sweet spots
1. High Throughput (because we can keep up)
2. High Volume (because we scale linearly and still OLTP)
3. Data distribution (read/write around the globe)
4. High Availability (replication, masterless)

11. SIMPLE AND SYNCHRONOUS
(kiss)

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Sample Application Overview
● Time-based events series
CREATE TABLE stock (
symbol text,
value decimal,
date timestamp,
PRIMARY KEY ((symbol), date)
)
WITH CLUSTERING
ORDER BY (date DESC)
AND default_time_to_live = 86400
AND compaction = {
'class' : 'TimeWindowCompactionStrategy'
'compaction_window_unit’ : MINUTES'
'compaction_window_size’ : 60
};
v
v

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DRIVERAPI
Synchronous Stack
CLIENT
12+

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Demo #1: Sync

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CLIENT API DRIVER
DseSession.builder()
MetaData
DseSession
SimpleStatement
stmt.prepare()
PreparedStatement
PreparedStatement
(preparedId)
Initializing
Statements
Connection
API

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CLIENT API DRIVER
Parameters
SynchronousQueries
PreparedStatement
& Parameters
Bind
Parameters
BoundStatement
ResultSet
ResultSet
Results
Blocked
😴
API

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Synchronous - SWOT Analysis
Strengths Weaknesses
ThreatsOpportunities
Simple
Easy to Test & Maintain Scalability ⚠️
Blocking 😴

18. LET’S GO ASYNCHRONOUS
(and crazy !)

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DRIVERAPI
Asynchronous Stack (Yes,….no changes)
CLIENT
12+

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Demo #2: Async

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CLIENT API DRIVER
v
Parameters
AsyncQueries
PreparedStatement
& Parameters Bind
Parameters
BoundStatement
AsyncResultSet
AsyncResultSet
Result
API
CompletionStage
Callback Hell 🔥

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Asynchronous - SWOT Analysis
Strengths Weaknesses
ThreatsOpportunities
Non Blocking
Scalability Maintainability 🤯
Callback Hell 🔥

23. BACK TO SANITY
(with reactive driver)

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Reactive Manifesto
https://www.reactivemanifesto.org/

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Reactive Streams API
SUBSCRIBER PUBLISHER
subscribe(this)
onSubscribe(subscription)
onNext(...)
onComplete()
subscription.request(...)
http://www.reactive-streams.org/

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DRIVERAPI
Reactive Stack
CLIENT
12+

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Demo #3: Reactive

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CLIENT API DRIVER
v
Parameters
ReactiveQueries
PreparedStatement
& Parameters Bind
Parameters
Row
ReactiveRow
Flux
API
ReactiveResultSet
Subscribe
BoundStatement
Subscriber.onNext
Query execution

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Backpressure
● When Reading:
−Driver will slow down
if client slows down
● When Writing:
−Driver cannot slow down
if server slows down
−Risk of
OverloadedException
CLIENT DRIVER
Backpressure
❌
CLIENTDRIVER
Backpressure
✅

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Reactive - SWOT Analysis
Strengths Weaknesses
ThreatsOpportunities
Simple & Non Blocking
Scalable & Maintainable OverloadedException
Hard to debug?

31. TAKE AWAYS

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Take Aways
● Asynchronous
−Less control
−Segregates requests and results processing
−Complex : callback hell
● Reactive
−Async capabilities but no callback hell
−Backpressure (for reads)
−Not a new way of querying DSE

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References & Questions
https://github.com/datastax/cassandra-
reactive-demo
https://www.youtube.com/channel/UCAIQ
Y251avaMv7bBv5PCo-A
https://www.twitch.tv/datastaxacademy/vid
eos

34. THANK YOU