[Meetup ms] Kafka Streams

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Cristiano Altmann

Uma apresentação sobre o processamento e patterns relacionados a streams e o framework Kafka Streams.

Technology

Kafka Streams
Quando streams encontram tabelas

Cristiano Altmann
Arquiteto Software
https://www.linkedin.com/in/crisaltmann/

Matheus Alagia
Eng. de Computação
https://www.linkedin.com/in/matheusalagia/
https://ubots.com.br

Microservices
Independent deploy
Low coupling
Horizontal Scalability
Technological choices
Business time
Independent teams
Technological
evolution
Resilient

Microservices is really just distributed
systems!

The Hardest Part About Microservices:
Your Data

Pessoa Conta Corrente
CPF,
Nome,
Endereço,
Renda, etc.
Número conta,
Tipo,
Lançamentos,
etc
Services with different database

Pessoa Conta Corrente
CPF,
Nome,
Endereço,
Renda, etc.
Número conta,
Tipo,
Lançamentos,
etc
Relatório
CPF, Nome,
Número Conta,
Lançamentos
Services with different database

Pessoa Conta Corrente
CPF,
Nome,
Endereço,
Renda, etc.
Número conta,
Tipo,
Lançamentos,
etc
Relatório
CPF, Nome,
Número
Conta,
Lançamentos
REST

Pessoa Conta Corrente
CPF,
Nome,
Endereço,
Renda, etc.
Número conta,
Tipo,
Lançamentos,
etc
Relatório
CPF, Nome,
Número
Conta,
Lançamentos
Database integration

Pessoa
Relatório
Services with different database
Conta Cartão Previdência Título Serasa Crédito

Streaming processing can be the
answer...

What is Streaming Processing?
“Is some kind of computation over a Data Stream. First and foremost, a data
stream is an abstraction representing an unbounded dataset. Unbounded means
infinite and ever growing.”
Kafka: The Definitive Guide

Streaming processing examples
FILTER
MAP

Stream processing is a programming paradigm...
Request-Response Batch ProcessingStreaming
Processing
Throughput
Latency

The world always changes, and sometimes we are interested
in the events that caused those changes, whereas other
times we are interested in the current state of the world….
Stream-Table Duality

Stream-Table Duality Example
LANÇAMENTOS CONTA CORRENTE
Conta: 1
Valor: 100
Conta: 1
Valor: 200
Conta: 5
Valor: 200
Conta: 1
Valor: -50

Stream-Table Duality Example
LANÇAMENTOS CONTA CORRENTE
Conta: 1
Valor: 100
Conta: 1
Valor: 200
Conta: 5
Valor: 200
Conta: 1
Valor: -50
1: 250
5: 200

Systems that allow you to transition back and forth between
the two ways of looking at data are more powerful than
systems that support just one.
- Neha Narkhede (Kafka: The definitive guide)

Kafka vs Kafka Stream
● Distributed log
● High available
● ⅓ Fortune 500
● APIS:
○ Producer
○ Consumer
○ Connect
○ Streams
● Part of Kafka ecosystem
● Just a lib
● Simple API
● DSL

Mantra: Stateless
Service
Client
STATELESS
STATE STORAGE
??
?

Stateless is good!!
● Services start instantly
● Thread safe
● Scaled out linearly
● Not shared state

But sometimes we need state….
Moving the state to a database just push the
problem to another layer.

Statefull can be good too...
● Services start rapidly
● Thread safe
● Scaled out linearly
● Not shared state

KAFKA STREAMS
● Services start rapidly V
● Thread safe V
● Scaled out linearly V
● Not shared state V
● HIGH THROUGHPUT

Stream-Processing Concepts
Time
Event time
Local state
Log append time
State
Processing time
External state
Time Windows
Slide window
Tumbling window
Hopping Window

Join
● Stream - Stream
● Stream - Table
● Table - Table

Conclusion
● Stream processing is a powerful tool
● Kafka Streams is simple
● Designed for microservices

What's hot

SQL is the lingua franca of data processing, and everybody working with data knows SQL. Apache Flink provides SQL support for querying and processing batch and streaming data. Flink's SQL support powers large-scale production systems at Alibaba, Huawei, and Uber. Based on Flink SQL, these companies have built systems for their internal users as well as publicly offered services for paying customers.In my talk I will show how to leverage the simplicity and power of SQL on Flink. I’ll explain why unified batch and stream processing is important and what it means to run SQL queries on streams of data. Once we’ve covered the basics, I will spend the remainder of the talk demonstrating the capabilities of Flink SQL. We will explore different use cases that Flink SQL was designed for by running queries on Flink’s SQL shell. In particular, I will demonstrate the unified batch and streaming engine by running the same query on batch and streaming data and show how to build a real-time dashboard that is powered by a streaming SQL query, which continuously updates an external result table.

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Since Pac-Man was originally released in the '80s, it has been a beacon of fun and joy for people of all ages. What few people know is that this game can also be used to inspire developers on how to build event streaming applications. In this near-zero-slides talk, attendees will get to play the game to generate events. As they play, the presenter will write from scratch a scoreboard using ksqlDB -- an open-source event streaming database built for Apache Kafka. After building the scoreboard, it will be discussed the different strategies to make the data available elsewhere so any interested service could leverage it with ease. Examples of these services will be provided to monitor in near real-time the scoreboard, revealing whoever is the most proficient Pac-Man player in the room.

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In this workshop we will set up a streaming framework which will process realtime data of traffic sensors installed within the Belgian road system. Starting with the intake of the data, you will learn best practices and the recommended approach to split the information into events in a way that won't come back to haunt you. With some basic stream operations (count, filter, ... ) you will get to know the data and experience how easy it is to get things done with Spring Boot & Spring Cloud Stream. But since simple data processing is not enough to fulfill all your streaming needs, we will also let you experience the power of windows. After this workshop, tumbling, sliding and session windows hold no more mysteries and you will be a true streaming wizard.

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Presented at Stream Processing Meetup (7/19/2018)(https://www.meetup.com/Stream-Processing-Meetup-LinkedIn/events/251481797/). At Uber, we operate 20+ Kafka clusters to collect system and application logs as well as event data from rider and driver apps. We need a Kafka replication solution to replicate data between Kafka clusters across multiple data centers for different purposes. This talk will introduce the history behind uReplicator and the high level architecture. As the original uReplicator ran into scalability challenges and operational overhead as the scale of Kafka clusters increased, we built the Federated uReplicator which addressed above issues and provide an extensible architecture for further scaling.

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Moving RDF Stream Processing to the Client

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Watch this talk here: https://www.confluent.io/online-talks/building-a-streaming-etl-solution-with-apache-kafka-rail-data-on-demand As data engineers, we frequently need to build scalable systems working with data from a variety of sources and with various ingest rates, sizes, and formats. This talk takes an in-depth look at how Apache Kafka can be used to provide a common platform on which to build data infrastructure driving both real-time analytics as well as event-driven applications. Using a public feed of railway data it will show how to ingest data from message queues such as ActiveMQ with Kafka Connect, as well as from static sources such as S3 and REST endpoints. We'll then see how to use stream processing to transform the data into a form useful for streaming to analytics in tools such as Elasticsearch and Neo4j. The same data will be used to drive a real-time notifications service through Telegram. If you're wondering how to build your next scalable data platform, how to reconcile the impedance mismatch between stream and batch, and how to wrangle streams of data—this talk is for you!

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With 60+ products and over 24% of the US GDP flowing through it, system integration is a tough problem for Intuit. Seasonality, scale, and massive peaks in products like TurboTax, QuickBooks, and Mint.com add extra layers of difficulty when building shared data services around transaction and user graphs, clickstream processing, a/b testing, and personalization. To reduce complexity and latency, we’ve implemented Kafka as the backbone across these data services. This allows us to asynchronously trigger relevant processing, elegantly scaling up and down as needed around peaks, all without the need for point-to-point integrations. In this talk, we share what we’ve learned about Kafka at Intuit and describe our data services architecture. We found that Kafka is invaluable in achieving a scalable, clean architecture, allowing engineering teams to focus less on integration and more on product development.

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(Krunal Vora, Tinder) Kafka Summit San Francisco 2018 At Tinder, we have been using Kafka for streaming and processing events, data science processes and many other integral jobs. Forming the core of the pipeline at Tinder, Kafka has been accepted as the pragmatic solution to match the ever increasing scale of users, events and backend jobs. We, at Tinder, are investing time and effort to optimize the usage of Kafka solving the problems we face in the dating apps context. Kafka forms the backbone for the plans of the company to sustain performance through envisioned scale as the company starts to grow in unexplored markets. Come, learn about the implementation of Kafka at Tinder and how Kafka has helped solve the use cases for dating apps. Engage in the success story behind the business case of Kafka at Tinder.

Matching the Scale at Tinder with Kafka

confluent

Navigate Data Service using AWS

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Stream processing affects a wide range of industries today: capturing sensor data, connecting microservices, processing the workloads of internet giants and giving us a real-time alternative to batch analytics. While these use cases are exciting and valuable they are only a taste of what is to come. In this talk we look at three areas that are likely to become more prominent: Global Apps, Event Stores and Serverless Stream Processing

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Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder. Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Flink) and in-house technologies have helped Uber scale.

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Realtime stream processing with kafka

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Have you ever tried to debug a production outage, when your system comprises apps your team has written, third-party apps your team runs, with logs going into some system, application performance metrics going into another system, and cloud platform metrics going somewhere else? Did you find yourself switching tabs, trying to correlate metrics with logs and alerts and finding yourself in a huge tangle? It is a nightmare. In the data world, we talk about aggregating all our data so we can derive new insights quickly, but what about our operational data? Observability is your ability to be able to ask questions of your system without having to write new code, or grab new data. When you've got an observable system, it feels like you have debugging superpowers, but can be challenging to even know where to start. If you can even convince your colleagues to start, finding the right tools can be challenging. In this talk Inny and Andrew will talk about what monitoring and logging are not sufficient anymore (if they ever were), observability basics, and demo an observability platform that you can use to start your observability journey today.

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From Event to Action: Accelerate Your Decision Making with Real-Time Automation

Safe Software

What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.

04-2024-HHUG-Sales-and-Marketing-Alignment.pptx

HampshireHUG

Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance

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Axa Assurance Maroc - Insurer Innovation Award 2024

The Digital Insurer

MySQL Webinar, presented on the 25th of April, 2024. Summary: MySQL solutions enable the deployment of diverse Database Architectures tailored to specific needs, including High Availability, Disaster Recovery, and Read Scale-Out. With MySQL Shell's AdminAPI, administrators can seamlessly set up, manage, and monitor these solutions, ensuring efficiency and ease of use in their administration. MySQL Router, on the other hand, provides transparent routing from the application traffic to the backend servers in the architectures, requiring minimal configuration. Completely built in-house and supported by Oracle, these solutions have been adopted by enterprises of all sizes for their business-critical applications. In this presentation, we'll delve into various database architecture solutions to help you choose the right one based on your business requirements. Focusing on technical details and the latest features to maximize the potential of these solutions.

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[Meetup ms] Kafka Streams

1. Kafka Streams Quando streams encontram tabelas

2. Cristiano Altmann Arquiteto Software https://www.linkedin.com/in/crisaltmann/

3. Matheus Alagia Eng. de Computação https://www.linkedin.com/in/matheusalagia/ https://ubots.com.br

4. Microservices Independent deploy Low coupling Horizontal Scalability Technological choices Business time Independent teams Technological evolution Resilient

5. Microservices is really just distributed systems!

6. Increasingly we build ecosystem

7. The Hardest Part About Microservices: Your Data

8. Pessoa Conta Corrente CPF, Nome, Endereço, Renda, etc. Número conta, Tipo, Lançamentos, etc Services with different database

9. Pessoa Conta Corrente CPF, Nome, Endereço, Renda, etc. Número conta, Tipo, Lançamentos, etc Relatório CPF, Nome, Número Conta, Lançamentos Services with different database

10. Pessoa Conta Corrente CPF, Nome, Endereço, Renda, etc. Número conta, Tipo, Lançamentos, etc Relatório CPF, Nome, Número Conta, Lançamentos REST

11. Pessoa Conta Corrente CPF, Nome, Endereço, Renda, etc. Número conta, Tipo, Lançamentos, etc Relatório CPF, Nome, Número Conta, Lançamentos Database integration

12. Pessoa Relatório Services with different database Conta Cartão Previdência Título Serasa Crédito

13. Streaming processing can be the answer...

14. What is Streaming Processing? “Is some kind of computation over a Data Stream. First and foremost, a data stream is an abstraction representing an unbounded dataset. Unbounded means infinite and ever growing.” Kafka: The Definitive Guide

15. Streaming processing examples FILTER MAP

16. Stream processing is a programming paradigm... Request-Response Batch ProcessingStreaming Processing Throughput Latency

17. The world always changes, and sometimes we are interested in the events that caused those changes, whereas other times we are interested in the current state of the world…. Stream-Table Duality

18. Stream-Table Duality Example LANÇAMENTOS CONTA CORRENTE Conta: 1 Valor: 100 Conta: 1 Valor: 200 Conta: 5 Valor: 200 Conta: 1 Valor: -50

19. Stream-Table Duality Example LANÇAMENTOS CONTA CORRENTE Conta: 1 Valor: 100 Conta: 1 Valor: 200 Conta: 5 Valor: 200 Conta: 1 Valor: -50 1: 250 5: 200

20. Systems that allow you to transition back and forth between the two ways of looking at data are more powerful than systems that support just one. - Neha Narkhede (Kafka: The definitive guide)

21. Kafka vs Kafka Stream ● Distributed log ● High available ● ⅓ Fortune 500 ● APIS: ○ Producer ○ Consumer ○ Connect ○ Streams ● Part of Kafka ecosystem ● Just a lib ● Simple API ● DSL

22. Stream-Processing Design Patterns

23. Single-Event Processing FILTER

24. Code

25. External Lookup: Stream-Table Join

26. Processing with Local State

27. Mantra: Stateless Service Client STATELESS STATE STORAGE ?? ?

28. Stateless is good!! ● Services start instantly ● Thread safe ● Scaled out linearly ● Not shared state

29. But sometimes we need state…. Moving the state to a database just push the problem to another layer.

30.

31. Statefull can be good too... ● Services start rapidly ● Thread safe ● Scaled out linearly ● Not shared state

32. KAFKA STREAMS ● Services start rapidly V ● Thread safe V ● Scaled out linearly V ● Not shared state V ● HIGH THROUGHPUT

33.

34. Stream

35. Table

36. Stream

37.

38. Stream-Processing Concepts Time Event time Local state Log append time State Processing time External state Time Windows Slide window Tumbling window Hopping Window

39. Stream-Processing Concepts Time Event time Local state Log append time State Processing time External state Time Windows Slide window Tumbling window Hopping Window

40. Stream-Processing Concepts Time Event time Local state Log append time State Processing time External state Time Windows Slide window Tumbling window Hopping Window

41. Join ● Stream - Stream ● Stream - Table ● Table - Table

42. Stream - Stream (Windowed-join)

43. Stream - Table

44. Under the wood

45. Under the wood

46. Under the wood

47. Stream-Processing Landscape

48.

49. Conclusion ● Stream processing is a powerful tool ● Kafka Streams is simple ● Designed for microservices

50. Obrigado!

51. Perguntas?