- Microservices represent the decomposition of monolithic business systems into independently deployable services that each perform a specific function. - There are three main groups of microservices: stateless services, persistence services, and aggregator services. - Messaging systems, such as message brokers, allow microservices to communicate asynchronously through message passing. Common examples are RabbitMQ, Apache Kafka, and Amazon SQS. - Aggregator microservices depend on other microservices, handle network communication, and may involve disk I/O. An example is a device data collector that collects data from multiple devices and processors and stores it in a database.

1. Microservices
Mykhailo Brodskyi
Lead Software Engineer, Сonsultant

2. Agenda
2
Microservices
Main Groups of Microservices
Messaging Systems
Examples

5. Microservices

6. 6
Service
Components
Service
Components
Service
Components
DB
API Layer
Service
Components
Service
Components
Service
Components
DB DB DB DB DB
Client Requests Client Requests Client Requests

7. Microservices
Microservices represents the decomposition of
monolithic business systems into independently
deployable services that do “one thing well”
[Matt Stine]
7

8. Why Microservice Architectures
8
Speed Safety Scale

9. Tree Groups of Microservices
- Stateless
- Persistence
- Aggregator
9

10. Stateless Microservices
- Simple processors: converters, translators, etc
- No dependence on other services
- No local data storage (disk I/O)
- Easy to replace
- Easy to scale up
10

11. Persistence Microservices
- Simple (local) storage reads/writes
- Disk I/O dependent
- Easy to scale for reads operations (CQRS)
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12. Aggregator Microservices
- Depends on other microservices
- Network dependent
- Disk I/O dependence
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13. Aggregator Microservice
13
Devices Device Data
Collector MS
Device Data
Processor
DB
Alert
Generator
HB/IC Data ~1KB HB/IC Data ~1KB Event
sync request
sync response
microservices
Number devices: 5 000 – 50 000
Request frequency: every 5 minutes
Number req./sec: 17 – 170
Stored data for 2 hours: 35 – 350 MB

14. Aggregator Microservice
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Devices Device Data
Collector MS
Device Data
Processor
DB
Alert
Generator
HB/IC Data ~1KB Event
sync request
sync response
microservices
Number devices: 5 000 – 50 000
Request frequency: every 5 minutes
Number req./sec: 17 – 170
Stored data for 2 hours: 35 – 350 MB

15. Messaging Systems

16. Async message-passing systems
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● Message (HTTP Request)
● Message broker/message queue
○ TIBCO, WebSphere, webMethods, RabbitMQ, ActiveMQ, HornetQ, NATS, Apache
Kafka

17. Async message-passing systems
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● Direct communication
○ TCP, one-to-one
○ Without broker: UDP, ZeroMQ (over TCP), Webhooks (HTTP Callbacks)
● Message brokers
○ JMS, AMQP, RabitMQ, ActiveMQ, HornetQ, Qpid, TIBCO Enterprise Message, IBM
MQ, Azure Service Bus, Google Cloud Pub/Sub, SQS
● Log-based message brokers
○ Aache Kafka, Amazon Kinesis Streams, Twitter DistributedLog

18. HTTP Callbacks with Payload
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[W3C Standards – https://www.w3.org/TR/websub]
Subscriber Publisher Hub
Subscribers discover the hub of a topic URL
makes a POST the HUB to subscribe to updates about topic
HUB verifies the subscription attempt with a GET
Publisher notifies the HUB of a new content
HUB delivers the contents of a topic to each subscriber with a POST

19. Logs Based Message Storage
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Partition 0
Partition 1
Partition 0
Partition 1
Partition 2
Offset for P0 = 6
Offset for P1 = 7
Offset for P2 = 3
Topic 1
Topic 2
Producer
Producer
Consumer
Consumer

20. Release Scope
Graph theory provides a simple formula
n – number of changes
Limit the number of changes in each release to increase the safety of each
release
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( 1)
2
n n
I


[Microservice Architecture – Irakli Nadareishvili, Ronnie Mitra]

21. Thank you!
mykhailo.brodskyi@globallogic.com
Michael Brodskiy