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EASILY	SCALE	ENTERPRISE
APPLICATIONS	USING
DISTRIBUTED	DATA	GRIDS
Ondrej	Mihályi
@omihalyi
SCALABLE	APPLICATIONS
What	it	means?	Another	buzzword?
@omihalyi
HIGHLY	SCALABLE	APPLICATIONS	!?
Just	great.	Buzzword	Bingo	time!
@omihalyi
SERIOUSLY,	WHAT	ARE	WE	AFTER?
The	main	Goal:
add	more	resources	to	do	the	job
faster.
@omihalyi
WHAT	IS	SCALABILITY
Ability	to	improve	with	more	resources
Not	straightforward
More	working	hours	≠	more	done
More	people	on	the	job	≠	faster
@omihalyi
TYPES	OF	SCALING
VERTICAL
easy,	brute	force,	often	works	inefficient,
limited	by	physics
HORIZONTAL
enables	granular	scaling
more	complexity
@omihalyi
CHALLENGES	OF	HORIZONTAL
SCALING
distributed	programming
increased	communication
shared	state
@omihalyi
AVOIDING	BOTTLENECKS
:
Speedup	limited	by	the	parts	that
don't	benefit	from	added	resources
bottlenecks:	sequential	tasks,
synchronization,	communication
@omihalyi
AMDAHL'S	LAW
IN-MEMORY	DATAGRIDS	(1/2)
distributed	shared	state
data	replication	and	recovery
data	evenly	distributed
distributed	communication
locks
queues,	topics,	executors
@omihalyi
DISTRIBUTED	IN-MEMORY
DATAGRIDS	(2/2)
simplified	programming	model
common	structures	(map,	set,	...)
service-discovery,	load-balancing
distributed	execution
code	and	queries	sent	to	the	data
bonus:	fast	persistence	with	no	ORM
@omihalyi
EXAMPLES	OF	DATA-GRIDS
Hazelcast
JBoss	Infinispan
Oracle	Coherence
Terracotta
@omihalyi
HAZELCAST	MEMORY	UTILIZATION
Replicated	up	to	X	nodes
Data	evenly	distributed
Lite	nodes	without	data
Off-heap	data	(enterprise)
@omihalyi
OTHER	HAZELCAST	FEATURES
Auto	discovery	and	recovery
multicast	and	TCP	joiners
data	redistribution	when	nodes	join/leave
Distributed	cache	(JCache	API)
Distributed	queries	and	ExecutorService
@omihalyi
DEMO	TIME
PAYARA	SERVER
derived	from	GlassFish	Java	EE	Server
embedded	Hazelcast
HTTP	session	replication
JCache	CDI	integration
message	bus	over	CDI	events
@omihalyi
PAYARA	MICRO
derived	from	embedded	GlassFish
executable	JAR	or	embedded
web	apps	in	a	separate	file	or	uber	JAR
designed	for	flexible	clustering
Hazelcast	started	by	default
@omihalyi
SCALE	UP!
Once	 an	 app	 is	 designed	 for	 flexible
clustering,	 it	 becomes	 elastic	 -	 easily
adaptable	to	increasing	load.
@omihalyi
#1	MULTIPLICATION
Additional	instances
more	CPUs	and	other	resources
Simple	load	balancer	is	enough
data	is	shared	automatically
@omihalyi
#2	SCALING	MEMORY
Data	access	is	often	a	bottleneck
→	Keep	data	in	memory
Additional	instances	with	no	apps
Dumb	nodes	carrying	data
Increase	available	memory
Increase	resilience	to	outages
@omihalyi
#2	SCALING	MEMORY
@omihalyi
#3	SEPARATING	DATA	FROM	LOGIC
more	data	→	more	heap	and	time	in	GC
run	critical	apps	on	lite	nodes
slower	data	access	but	less	GC	cycles
tweak	GC	&	heap	for	throughput
data	and	app	nodes	in	pair	on	the	same
machine
@omihalyi
#3	SEPARATING	DATA	FROM	LOGIC
@omihalyi
DEMO	TIME
#4	SCALE	APP	PARTS	SEPARATELY
some	parts	of	an	app	are	bottlenecks
split	those	into	separate	services
can	be	scaled	higher
resources	assigned	more	granularly
small	and	lean	services	with	Payara	Micro
or	even	a	standalone	Hazelcast
@omihalyi
#4	SCALE	APP	PARTS	SEPARATELY
@omihalyi
ANYTHING	TO	ASK?
Thank	you
RESOURCES
source	code:	
Hazelcast:	 ,	
Payara:	 ,	
@omihalyi
OndrejM-demonstrations/scaling-
with-datagrids
article	about	flexible	clustering
hazelcast.com hazelcast.org
payara.fish payara.org

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Easily scale enterprise applications using distributed data grids