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Battle Tested Event-Driven Patterns for your Microservices Architecture - Developer Week

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Natan Silnitsky

During the past couple of years I’ve implemented or have witnessed implementations of several key patterns of event-driven messaging designs on top of Kafka that have facilitated creating a robust distributed microservices system at Wix that can easily handle increasing traffic and storage needs with many different use-cases. In this talk I will share these patterns with you, including: * Consume and Project (data decoupling) * End-to-end Events (Kafka+websockets) * In memory KV stores (consume and query with 0-latency) * Events transactions (Exactly Once Delivery)

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natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil Battle-tested event-driven patterns for your microservices architecture Natan Silnitsky Backend Infra Developer, Wix.com
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>200M registered users (website builders) from 190 countries 5% of all Internet websites run on Wix 1,500 Microservices 1,500M Kafka messages produced / Day @NSilnitsky At Wix * Event driven uses cases
3
Agenda Event-driven microservices Event-driven patterns: • Consume and Project • Event-driven from end to end • 0-latency KV Store • Events in Transactions
4
@NSilnitsky Microservices The Monolith
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@NSilnitsky * Separately Monolith to Distributed Microservices System Microservices
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@NSilnitsky Microservices Decoupled Microservices Mutable Contention Choice
7
@NSilnitsky Microservices Data Encapsulation * flexibility, schema
8
What about microservices communication?
9
@NSilnitsky Request-reply communication Microservices Communication Checkout Service User Service Inventory Service Payments Service * ecom example
10
@NSilnitsky Request-reply communication Microservices Communication Request-Reply Model HTTP RPC HTTP RPC HTTP RPC Checkout Service Payments Service Inventory Service User Service
11
@NSilnitsky Request-reply communication * 1 by 1- slow Microservices Communication Request-Reply Model Checkout Service
12
@NSilnitsky Request-reply communication Microservices Communication Request-Reply Model Checkout Service Inventory Service
13
Broker @NSilnitsky * Reliability Event-driven communications Introduce a message broker in between services Message Consumer Message Producer
14
@NSilnitsky * Not ineteract directly Order created Event-driven communications Introduce a message broker in between services Event-driven model Message Consumer Flow control is moved from the sender Broker Message Producer
15
@NSilnitsky Order created Event-driven communications Introduce a message broker in between services Event-driven model Message Consumer Flow control is moved from the sender to the receiver. Broker Message Producer * more robust
16
Kafka Broker Topic Partition Partition Partition @NSilnitsky * Linear scaling Introduce a message broker in between services Distributed log broker Event-driven communications Message Consumer Message Producer
17
@NSilnitsky Append-only log Introduce a message broker in between services Distributed log broker Event-driven communications Kafka Broker Topic Partition Message Consumer Message Producer 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5
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Order Created Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 @NSilnitsky * Data retained, Immutable Introduce a message broker in between services Distributed log broker Event-driven communications Message Consumer Message Producer Kafka Broker
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@NSilnitsky * eventually consumed Distributed log broker eventually consume Event-driven communications Order Created Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 Message Consumer Message Producer Kafka Broker
20
Checkout Service Event-driven communications Microservices communication & data Event driven model User Service Inventory Service Cart Service Payments Service @NSilnitsky
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Checkout Service Cart Service Inventory Service Payments Service User Service Event-driven communications Microservices communication & data Event driven model Kafka Broker Orders Partition Partition Partition Users Partition Partition Partition Cart Partition Partition Partition Inventory Partition Partition Partition
22
Agenda Event-driven microservices Event-driven patterns: • Consume and Project • Event-driven from end to end • 0-latency KV Store • Events in Transactions * used daily. blog
23
Consume and Project 01
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MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project For Popular Services
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MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Site installed Apps? Site version? Site owner?
26
MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Request overload Site installed Apps? Site version? Site owner? (~1M RPM requests) Read + Writes Large MetaSite Object
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Request overload MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Site installed Apps? Site version? Site owner? Read + Writes Large MetaSite Object (~1M RPM requests)
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Kafka Broker Produce to Kafka Producer MetaSite Site Updated! Consume and Project Entire MetaSite Context
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Kafka Broker Filter Site installed Apps Updated! Installed Apps Context Produce to Kafka Consume and Project MetaSite Consumer Reverse lookup writer Consume and Project Producer
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Kafka Broker Installed Apps Context Produce to Kafka Consume and Project MetaSite Reverse lookup writer Reverse lookup reader RPC RPC RPC Split Read from Write Consume and Project Producer Consumer
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Consume and Project — Produce instead of serve - massive load reduction — Project - client-query-optimized “Materialized View” — Read/write split - only scale read-only DB. more resilient
32
Event-driven from end to end 02
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Kafka Broker Consumer Browser Producer Contacts Importer Service Contacts Jobs Service Web Sockets Service @NSilnitsky Event-driven from end to end Use case: Long-running async business process
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Subscribe for notifications Consumer Producer Browser @NSilnitsky Event-driven from end to end Use case: Long-running async business process Kafka Broker Web Sockets Service
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Import CSVs Consumer Producer Browser @NSilnitsky Event-driven from end to end Use case: Long-running async business process Kafka Broker Web Sockets Service
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Web Sockets Service Consumer * Distributed Producer Browser done! Event-driven from end to end Use case: Long-running async business process Kafka Broker
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Web Sockets Service Consumer * Distributed Producer Browser Cross DC! Event-driven from end to end Use case: Long-running async business process Kafka Broker done!
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Event-Driven from End to End — No polling - notify the user on any status change — Scalable - spin up many import job workers — Replication - cross-DC requests are easy to set up
39
0-latency KV Store 03
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Kafka Broker Configuration Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 @NSilnitsky Producer Key - value Consumer 0-latency KV Store Compacted Kafka Topics
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Kafka Broker Consumer KVStoreReader In-Memory Map Compacted Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 Key Value K1 V1 K2 V1 * Startup 0-latency KV Store @NSilnitsky Compacted Kafka Topics
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0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 @NSilnitsky 0-latency KV Store Kafka Broker Consumer KVStoreReader In-Memory Map Key Value K1 V1 -> V2 K2 V1 K3 V1 Compacted Kafka Topics * Finished Compacted Topic
43
Consumer Consumer Bookings Business Manager KVStoreReader Time Zones Key Value Albania UTC+1 Algeria UTC+1 Andorra UTC+1 Time Zones KVStoreReader Countries Key Value Albania .al Algeria .dz Andorra .ad Countries @NSilnitsky 0-latency KV Store Kafka Broker
44
Producer Kafka Broker Consumer Bookings Business Manager KVStoreReader Countries Countries *New* South Sudan KVStoreWriter Countries @NSilnitsky 0-latency KV Store * Writer producer
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Kafka Broker Producer Bookings KVStoreWriter Time Zones Time Zones Consumer KVStoreReader Time Zones Key Value Albania UTC+1 ... South Sudan UTC+3 *New* Time Zone for South Sudan *New* Country South Sudan @NSilnitsky 0-latency KV Store
46
Kafka Broker Producer Bookings KVStoreWriter Time Zones Time Zones Consumer KVStoreReader Time Zones Key Value Albania UTC+2 Algeria UTC+1 Andorra UTC+1 *New* Time Zone for Albania Consumer Events KVStoreReader Time Zones DST in-effect for Albania @NSilnitsky 0-latency KV Store
47
0-latency KV Store — Loading the compacted topic to memory – a 0-latency kv store for dynamic configuration — Compacted topic is still a topic – other services can consume its update events — Small Datasets - for large datasets, the topic should be streamed to a disk-based DB + In-memory Cache
48
Events in Transactions 04 * idempotent
49
Payment Completed Payments Kafka Broker Producer Events in Transactions Classic Ecommerce Flow
50
Payments Checkout Kafka Broker Producer Consumer Producer Order (Items) Completed Events in Transactions Classic Ecommerce Flow Payment Completed
51
Payments Checkout Inventory Kafka Broker Producer Consumer Producer Consumer Delivery Consumer Consumer Invoices Events in Transactions Classic Ecommerce Flow
52
Producer Consumer Producer Consumer Consumer Consumer Completed Orders Events in Transactions Kafka Broker Classic Ecommerce Flow
53
Producer Events in Transactions Kafka Broker Enable.idempotence = true Attaches offset to message Classic Ecommerce Flow
54
Idempotent Producer Events in Transactions Kafka Broker Classic Ecommerce Flow
55
Events in Transactions Kafka Broker Producer 0 1 2 3 4 5 0 1 duplicate! 1 Payments Idempotent Producer
56
Transaction Scope Consumer Producer Consumer Consumer Consumer Producer Events in Transactions Kafka Broker
57
Events in Transactions — Events transactions are quite complex – More boilerplate, performance tuning via transaction size. — DB updates/3rd party calls are not covered – You can dedup by using Kafka record offset as version — Don’t turn on transactions by default - Use in critical flows like payments processing
58
Battle Tested Event-Driven Patterns for your Microservices Architecture - Developer Week
59
Wix developers have employed these event-driven patterns to make their microservices more decoupled, resilient and scalable.
60
A Scala/Java high-level SDK for Apache Kafka. 0.1.5 is out! github.com/wix/greyhound
61
Thank You natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil
62
Resources 6 Event Driven Architecture Patterns - Part 1 & Part 2 - by Natan Silnitsky The Data Dichotomy: Rethinking the Way We Treat Data and Services - by Ben Stopford Shared Database - by Chris Richardson Exactly once delivery - DevOpsDay TLV 2019 - By Natan Silnitsky @NSilnitsky
63
Slides & More slideshare.net/NatanSilnitsky medium.com/@natansil twitter.com/NSilnitsky natansil.com
64
Q&A natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil
natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil Battle-tested event-driven patterns for your microservices architecture Natan Silnitsky Backend Infra Developer, Wix.com
>200M registered users (website builders) from 190 countries 5% of all Internet websites run on Wix 1,500 Microservices 1,500M Kafka messages produced / Day @NSilnitsky At Wix * Event driven uses cases
Agenda Event-driven microservices Event-driven patterns: • Consume and Project • Event-driven from end to end • 0-latency KV Store • Events in Transactions
@NSilnitsky Microservices The Monolith
@NSilnitsky * Separately Monolith to Distributed Microservices System Microservices
@NSilnitsky Microservices Decoupled Microservices Mutable Contention Choice
@NSilnitsky Microservices Data Encapsulation * flexibility, schema
What about microservices communication?
@NSilnitsky Request-reply communication Microservices Communication Checkout Service User Service Inventory Service Payments Service * ecom example
@NSilnitsky Request-reply communication Microservices Communication Request-Reply Model HTTP RPC HTTP RPC HTTP RPC Checkout Service Payments Service Inventory Service User Service
@NSilnitsky Request-reply communication * 1 by 1- slow Microservices Communication Request-Reply Model Checkout Service
@NSilnitsky Request-reply communication Microservices Communication Request-Reply Model Checkout Service Inventory Service
Broker @NSilnitsky * Reliability Event-driven communications Introduce a message broker in between services Message Consumer Message Producer
@NSilnitsky * Not ineteract directly Order created Event-driven communications Introduce a message broker in between services Event-driven model Message Consumer Flow control is moved from the sender Broker Message Producer
@NSilnitsky Order created Event-driven communications Introduce a message broker in between services Event-driven model Message Consumer Flow control is moved from the sender to the receiver. Broker Message Producer * more robust
Kafka Broker Topic Partition Partition Partition @NSilnitsky * Linear scaling Introduce a message broker in between services Distributed log broker Event-driven communications Message Consumer Message Producer
@NSilnitsky Append-only log Introduce a message broker in between services Distributed log broker Event-driven communications Kafka Broker Topic Partition Message Consumer Message Producer 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5
Order Created Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 @NSilnitsky * Data retained, Immutable Introduce a message broker in between services Distributed log broker Event-driven communications Message Consumer Message Producer Kafka Broker
@NSilnitsky * eventually consumed Distributed log broker eventually consume Event-driven communications Order Created Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 Message Consumer Message Producer Kafka Broker
Checkout Service Event-driven communications Microservices communication & data Event driven model User Service Inventory Service Cart Service Payments Service @NSilnitsky
Checkout Service Cart Service Inventory Service Payments Service User Service Event-driven communications Microservices communication & data Event driven model Kafka Broker Orders Partition Partition Partition Users Partition Partition Partition Cart Partition Partition Partition Inventory Partition Partition Partition
Agenda Event-driven microservices Event-driven patterns: • Consume and Project • Event-driven from end to end • 0-latency KV Store • Events in Transactions * used daily. blog
Consume and Project 01
MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project For Popular Services
MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Site installed Apps? Site version? Site owner?
MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Request overload Site installed Apps? Site version? Site owner? (~1M RPM requests) Read + Writes Large MetaSite Object
Request overload MetaSite RPC Wix Stores Wix Bookings Wix Restaurants RPC RPC @NSilnitsky Consume and Project Site installed Apps? Site version? Site owner? Read + Writes Large MetaSite Object (~1M RPM requests)
Kafka Broker Produce to Kafka Producer MetaSite Site Updated! Consume and Project Entire MetaSite Context
Kafka Broker Filter Site installed Apps Updated! Installed Apps Context Produce to Kafka Consume and Project MetaSite Consumer Reverse lookup writer Consume and Project Producer
Kafka Broker Installed Apps Context Produce to Kafka Consume and Project MetaSite Reverse lookup writer Reverse lookup reader RPC RPC RPC Split Read from Write Consume and Project Producer Consumer
Consume and Project — Produce instead of serve - massive load reduction — Project - client-query-optimized “Materialized View” — Read/write split - only scale read-only DB. more resilient
Event-driven from end to end 02
Kafka Broker Consumer Browser Producer Contacts Importer Service Contacts Jobs Service Web Sockets Service @NSilnitsky Event-driven from end to end Use case: Long-running async business process
Subscribe for notifications Consumer Producer Browser @NSilnitsky Event-driven from end to end Use case: Long-running async business process Kafka Broker Web Sockets Service
Import CSVs Consumer Producer Browser @NSilnitsky Event-driven from end to end Use case: Long-running async business process Kafka Broker Web Sockets Service
Web Sockets Service Consumer * Distributed Producer Browser done! Event-driven from end to end Use case: Long-running async business process Kafka Broker
Web Sockets Service Consumer * Distributed Producer Browser Cross DC! Event-driven from end to end Use case: Long-running async business process Kafka Broker done!
Event-Driven from End to End — No polling - notify the user on any status change — Scalable - spin up many import job workers — Replication - cross-DC requests are easy to set up
0-latency KV Store 03
Kafka Broker Configuration Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 @NSilnitsky Producer Key - value Consumer 0-latency KV Store Compacted Kafka Topics
Kafka Broker Consumer KVStoreReader In-Memory Map Compacted Topic 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 Key Value K1 V1 K2 V1 * Startup 0-latency KV Store @NSilnitsky Compacted Kafka Topics
0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 K1 V1 K2 V1 K1 V2 K3 V1 K4 V1 K5 V1 @NSilnitsky 0-latency KV Store Kafka Broker Consumer KVStoreReader In-Memory Map Key Value K1 V1 -> V2 K2 V1 K3 V1 Compacted Kafka Topics * Finished Compacted Topic
Consumer Consumer Bookings Business Manager KVStoreReader Time Zones Key Value Albania UTC+1 Algeria UTC+1 Andorra UTC+1 Time Zones KVStoreReader Countries Key Value Albania .al Algeria .dz Andorra .ad Countries @NSilnitsky 0-latency KV Store Kafka Broker
Producer Kafka Broker Consumer Bookings Business Manager KVStoreReader Countries Countries *New* South Sudan KVStoreWriter Countries @NSilnitsky 0-latency KV Store * Writer producer
Kafka Broker Producer Bookings KVStoreWriter Time Zones Time Zones Consumer KVStoreReader Time Zones Key Value Albania UTC+1 ... South Sudan UTC+3 *New* Time Zone for South Sudan *New* Country South Sudan @NSilnitsky 0-latency KV Store
Kafka Broker Producer Bookings KVStoreWriter Time Zones Time Zones Consumer KVStoreReader Time Zones Key Value Albania UTC+2 Algeria UTC+1 Andorra UTC+1 *New* Time Zone for Albania Consumer Events KVStoreReader Time Zones DST in-effect for Albania @NSilnitsky 0-latency KV Store
0-latency KV Store — Loading the compacted topic to memory – a 0-latency kv store for dynamic configuration — Compacted topic is still a topic – other services can consume its update events — Small Datasets - for large datasets, the topic should be streamed to a disk-based DB + In-memory Cache
Events in Transactions 04 * idempotent
Payment Completed Payments Kafka Broker Producer Events in Transactions Classic Ecommerce Flow
Payments Checkout Kafka Broker Producer Consumer Producer Order (Items) Completed Events in Transactions Classic Ecommerce Flow Payment Completed
Payments Checkout Inventory Kafka Broker Producer Consumer Producer Consumer Delivery Consumer Consumer Invoices Events in Transactions Classic Ecommerce Flow
Producer Consumer Producer Consumer Consumer Consumer Completed Orders Events in Transactions Kafka Broker Classic Ecommerce Flow
Producer Events in Transactions Kafka Broker Enable.idempotence = true Attaches offset to message Classic Ecommerce Flow
Idempotent Producer Events in Transactions Kafka Broker Classic Ecommerce Flow
Events in Transactions Kafka Broker Producer 0 1 2 3 4 5 0 1 duplicate! 1 Payments Idempotent Producer
Transaction Scope Consumer Producer Consumer Consumer Consumer Producer Events in Transactions Kafka Broker
Events in Transactions — Events transactions are quite complex – More boilerplate, performance tuning via transaction size. — DB updates/3rd party calls are not covered – You can dedup by using Kafka record offset as version — Don’t turn on transactions by default - Use in critical flows like payments processing
Battle Tested Event-Driven Patterns for your Microservices Architecture - Developer Week
Wix developers have employed these event-driven patterns to make their microservices more decoupled, resilient and scalable.
A Scala/Java high-level SDK for Apache Kafka. 0.1.5 is out! github.com/wix/greyhound
Thank You natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil
Resources 6 Event Driven Architecture Patterns - Part 1 & Part 2 - by Natan Silnitsky The Data Dichotomy: Rethinking the Way We Treat Data and Services - by Ben Stopford Shared Database - by Chris Richardson Exactly once delivery - DevOpsDay TLV 2019 - By Natan Silnitsky @NSilnitsky
Slides & More slideshare.net/NatanSilnitsky medium.com/@natansil twitter.com/NSilnitsky natansil.com
Q&A natansil.com twitter@NSilnitsky linkedin/natansilnitsky github.com/natansil

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Battle Tested Event-Driven Patterns for your Microservices Architecture - Developer Week

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