SCALE APPLICATIONS FLEXIBLY WITH MICROPROFILE Ondrej Mihlyi - - PowerPoint PPT Presentation

SCALE APPLICATIONS FLEXIBLY WITH MICROPROFILE

Ondrej Mihályi

@omihalyi

SCALABLE APPLICATIONS What it means? Another buzzword?

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HIGHLY SCALABLE APPLICATIONS !? Just great. Buzzword Bingo time!

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SERIOUSLY, WHAT ARE WE AFTER? The main Goal: add more resources to do the job faster.

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WHAT IS SCALABILITY

Ability to improve with more resources Not straightforward

More working hours ≠ more done More people on the job ≠ faster

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TYPES OF SCALING

VERTICAL

easy, brute force, often works inefficient, limited by physics

HORIZONTAL

enables higher scaling more complexity

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CHALLENGES OF HORIZONTAL SCALING distributed programming increased communication shared state

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SCALING WITH MICROSERVICES

Microservices HELP with scaling

granular scaling of critical parts

But... LOT MORE MOVING PARTS AND COMPLEXITY

communication bottlenecks are still a problem

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WHAT IS MICROPROFILE?

• pen-source specification for Java

microservices ( ) microprofile.io

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MICROPROFILE HELPS WITH COMPLEXITY

unified Config API service metrics & health passing security context tracing of distributed requests recovery from network failures

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AVOIDING BOTTLENECKS : Speedup limited by the parts that don't benefit from added resources bottlenecks: sequential tasks, synchronization, communication AMDAHL'S LAW

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IN-MEMORY DATAGRIDS

distributed shared state (memory)

• distrib. communication (locks, messaging, ...)

simple distrib. structures (map, set, queue, ...) sophisticated algorithms behind the scenes

data replication and even distribution auto-discovery of nodes

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PAYARA SERVER

derived from GlassFish Java EE Server MicroProfile 1.3 embedded Hazelcast

JCache API, session replication message bus over CDI events

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PAYARA MICRO

shares codebase with Payara Server can cluster with Payara Server runs WAR from command line embeddable, uber JAR

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SCALE UP! Once an app is designed for flexible clustering, it becomes elastic - easily adaptable to increasing load.

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DEMO TIME

#1 MULTIPLICATION

Additional instances

more CPUs and other resources

Simple load balancer is enough

data is shared automatically

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#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

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#2 SCALING MEMORY

#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

run small devices on LITE NODES

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#2 SCALING MEMORY

#3 SEPARATING DATA FROM LOGIC

#4 SCALE PARTS SEPARATELY

#4 MESSAGING TO BALANCE LOAD

#4 SCALE APP PARTS SEPARATELY

some parts of an app are bottlenecks split those into separate services

can be scaled higher use data grid for communication

small and lean services with Payara Micro

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HAZELCAST MEMORY UTILIZATION Replicated up to X nodes Data evenly distributed Lite nodes without data Off-heap data (enterprise)

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OTHER HAZELCAST FEATURES

Auto discovery and recovery

multicast, TCP and cloud joiners data redistribution when nodes join/leave

Distributed cache (JCache API) Distributed queries and ExecutorService

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ANYTHING TO ASK?

Thank you

RESOURCES

source code: MicroProfile: Payara: , OndrejM-demonstrations/scaling- with-datagrids an article about flexible clustering microprofile.io payara.fish payara.org

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