Manual Kafka... for our time - Journeys of discovery

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We all know Kafka is designed to allow applications to produce and consume data with high throughput and low latency, right? In practice, achieving this goal requires some tuning. Finally a word on some gotchas of performance analysis, that apply to Kafka too! Serverless also known as function-as-a-service is fast emerging as an effective architecture for event-driven applications.

Apache OpenWhisk is one of the more popular open-source cloud serverless platforms, and has first-class support for Kafka as a source of events. Come to this session for an introduction to building microservices without servers using OpenWhisk. Ill describe the challenges to building applications using serverless stacks, and the serverless design patterns to help you get started.

Ill give a demonstration of how you can use Kafka Connect to invoke serverless actions, and how serverless can be an effective way to host event-processing logic. I will demonstrate how the new generation of lightweight and highly-scalable state machines ease the implementation of long running services. Based on my real-life experiences, I will share how to handle complex logic and flows which require proper reactions on failures, timeouts and compensating actions and provide guidance backed by code examples to illustrate alternative approaches.

With CQRS rising and more formal event-sourcing solutions increasing in adoption, event-storming is demonstrated as a powerful technique for event-driven development of microservices in the enterprise. With such an approach, the elusive, but powerful promise of the ubiquitous language from DDD finally emerges from EDD event driven development. This journey embarks with a specific emphasis on the stream first mindset.

Problems with current solutions are introduced, a high level overview of event storming is presented, then the talk transitions through an opinionated version of event storming through a classic, simple project e. The project is a simple UI built on a stream-based coffee service. Eventing and streaming open a world of compelling new possibilities to our software and platform designs. They can reduce time to decision and action while lowering total platform cost. But they are not a panacea.

Understanding the edges and limits of these architectures can help you avoid painful missteps. This talk will focus on event driven and streaming architectures and how Apache Kafka can help you implement these. It will also discuss key tradeoffs you will face along the way from partitioning schemes to the impact of availability vs. This talk assumes a basic understanding of Kafka and distributed computing, but will include brief refresher sections. There are only two hard things in Computer Science: cache invalidation and naming things. Unfortunately, these caches can be tricky to get right.

Who places the data in the cache? When should the cached data expire? What happens when the cache or its datastore fail? We found that by using an event-based push model, we could avoid most of the pitfalls associated with traditional caches. This talk will cover the basic concept of push-based caches and their implications. It will delve into how you might build such a cache and what to do when your dataset is large, as well as look at our experience using these kinds of caches in production at Bloomberg.

Have you ever imagined what it would be like to build a massively scalable streaming application on Kafka, the challenges, the patterns and the thought process involved? How much of the application can be reused? What patterns will you discover? How does it all fit together? Depending upon your use case and business, this can mean many things.

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Starting out with a data pipeline is one thing, but evolving into a company-wide real-time application that is business critical and entirely dependent upon an event streaming platform is a giant leap. Large-scale streaming applications are also called event streaming applications. They are classically different from other data systems; event streaming applications are viewed as a series of interconnected streams that are topologically defined using stream processors; they hold state that models your use case as events. Almost like a deconstructed real-time database. In this talk, I step through the origins of event streaming systems, understanding how they are developed from raw events to evolve into something that can be adopted at an organizational scale.

Building upon this, I explain how to build common business functionality by stepping through the patterns for: — Scalable payment processing — Run it on rails: Instrumentation and monitoring — Control flow patterns Finally, all of these concepts are combined in a solution architecture that can be used at an enterprise scale. I will introduce enterprise patterns such as events-as-a-backbone, events as APIs and methods for governance and self-service.

The Kafka Project

You will leave talk with an understanding of how to model events with event-first thinking, how to work towards reusable streaming patterns and most importantly, how it all fits together at scale. With increasing data volumes typically comes a corresponding increase in non-windowed batch processing times, and many companies have looked to streaming as a way of delivering data processing results faster and more reliably. Event Driven Architectures further enhance these offerings by breaking centralised Data Platforms into loosely coupled and distributed solutions, supported by linearly scalable technologies such as Apache Kafka TM and Apache Kafka Streams TM.

However, there remains a problem of how to handle changes to operational systems: if a record is the result of business logic, and that business logic changes, what do we do? Do we recalculate everything on the fly, adding in additional latencies for all data requests and potentially breaching non-functional requirements? Or do we run a batch job, risking that incorrect data will be served whilst the job is running? This talk covers how 6point6 leveraged Kafka and Kafka Streams to transition a customer from a traditional business flow onto an Event Driven Architecture, with business logic triggered directly by real-time events across over loosely coupled business services, whilst ensuring that the active development of these services and their containing logic and models would not affect components which relied on data served by the platform.

The goal of this talk is to show that Kafka can be the unique source of truth, not only for what happened the events but also for what it is now the current state of an aggregate.


The solution shown is completely Kafka-based, and this allows us to be consistent and avoid the well known problem we face when we have to update a repository and publish an event on the bus, two operations that, in most of the cases, cannot be considered as atomic. Implementing long-running, asynchronous, and complex collaboration of distributed microservices is challenging.

How can we ensure visibility of cross-microservice flows and provide status and error monitoring? How do we guarantee that overall flows always complete, even if single services fail? Or how do we at least recognize stuck flows so that we can fix them?

Zeebe can connect to Kafka to coordinate workflows that span many microservices, providing end-to-end process visibility without violating the principles of loose coupling and service independence. Once an orchestration flow starts, Zeebe ensures that it is eventually carried out, retrying steps upon failure. In a Kafka architecture, Zeebe can easily produce events or commands and subscribe to events that will be correlated to workflows. Along the way, Zeebe provides monitoring and visibility into the progress and status of orchestration flows.

Internally, Zeebe works as a distributed, event-driven, and event-sourced system, making it not only very fast but horizontally scalable and fault tolerant—and able to handle the throughput required to operate alongside Kafka in a microservices architecture. Expect not only slides but also live hacking sessions and user stories.

SolarWinds MSP collects and aggregates information from millions of agents via hundreds of intermediate services deployed across the globe.

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It provides business intelligence, reporting and analytical capabilities to both internal and external clients. Having gone through a massive expansion in the past few years the traditional Extract Transform Load ETL pipelines cannot cope with the agility the business demands in order to deliver world class features with minimal engineering friction.

The fabric of our data has evolved from cold storage independent silos into distributed interconnected continuous flows of information that demand high resilience and configurable delivery semantics at near real-time. Built for scalability, it connects millions of agents through hundreds of micro-services that exchange tens of billions of messages per day deployed in four geographical regions. It exposes a unified gRPC interface that allows clients in different programming languages to seamlessly interact with topics across multiple Kafka clusters.

DaVinci EventBus uses Akka to implement self-service topic management, provide high-throughput batch publication, coordinate consumption groups and replicate data while guaranteeing sequential consistency across multiple Kafka clusters. We dive deep into the design of the DaVinci EventBus and show how Akka can be used to implement an external coordination mechanism that federates multiple Kafka clusters. We discuss our journey of breaking monolithic legacy systems into a set of resilient event-driven micro-services.

We show how our event-driven approach massively reduced the data propagation network traffic and simplified the data manipulation and analysis in order to drive new features such as automated anomaly detection to our end users. Further, we expand on our future plans to provide multiple consumption mechanisms on a single event firehose, on-demand automated Kafka cluster deployment, and asynchronous workflow management across multiple micro-service boundaries. Key takeaways include: The different flavors of event sourcing and where their value lies.

The difference between stream processing at application- and infrastructure-levels. The relationship between stream processors and serverless functions. Developers have long employed message queues to decouple subsystems and provide an approximation of asynchronous processing.

The events are carrying both notification and state. This allows for developers and data engineers to event-driven systems. Developers benefit from the asynchronous communication that events enable between services, and data engineers benefit from the integration capabilities.

In this talk, Viktor will discuss the concepts of events, their relevance to software and data engineers, as well as its power for effectively unifying architectures.

You learn how stream processing makes sense in microservices and data integration projects. The talk concludes with a hands-on demonstration of these concepts in practice, using modern toolchain — Kotlin, Spring Boot and Apache Kafka! Do you wonder how to cope with the right to be forgotten? Do you wonder how to only process the events of individuals who have given their consent for processing their data?

Do you wonder how to protect PII data of your users? Or do you wonder how to implement these across all your heterogeneous languages, clients and processing frameworks without having to re-implement all your streaming services? This talk is for you! Microservices are seen as the way to simplify complex systems, until you need to coordinate a transaction across services, and in that instant, the dream ends. Transactions involving multiple services can lead to a spaghetti web of interactions.

Protocols such as two-phase commit come with complexity and performance bottlenecks. The Saga pattern involves a simplified transactional model. In sagas, a sequence of actions are executed, and if any action fails, a compensating action is executed for each of the actions that have already succeeded. This is particularly well suited to long-running and cross-microservice transactions. Built using Kafka streams, it provides a scalable fault tolerance event-based transaction processing engine.

KSQL- Kafka for Data Processing

We walk through a use case of coordinating a sequence of complex financial transactions. We demonstrate the easy to use DSL, show how the system copes with failure, and discuss this overall approach to building scalable transactional systems in an event-driven streaming context. Apache Kafka vs. MQ, ETL and ESB middleware are often used as integration backbone between legacy applications, modern microservices and cloud services. This introduces several challenges and complexities like point-to-point integration or non-scalable architectures.

Learn the differences between a event-driven event streaming platform leveraging Apache Kafka and middleware like MQ, ETL and ESBs — including best practices and anti-patterns, but also how these concepts and tools complement each other in an enterprise architecture. However, there are a few challenges w.

We can now elastically scale to any number of partitions and any number of nodes. Cryptocurrency exchanges like Coinbase, Binance, and Kraken enable investors to buy, sell and trade cryptocurrencies, including Bitcoin, Litecoin, Ethereum and many more. Depending on the exchange, trades can be made using fiat currencies legal government tender like U.

Most exchanges also allow investors to purchase one type of cryptocurrency with another for example, buy Bitcoin with Ethereum. Given the high velocity and high volatility of cryptocurrency valuations, monitoring and analyzing trading activity and the performance of trading algorithms is daunting. Kafka Streams provides a perfect infrastructure to support visibility into the market and participant behavior with a very high degree of temporal accuracy, which is critical when trading such volatile instruments. In particular, cryptocurrency traders need several ways to visualize trading activity and rebuild and view their order books at full depth.

They need tools that can make large numbers of complex real-time calculations, including:. All calculations must be done for multiple pairs of fiat currencies and cryptocurrencies in real time throughout the trading day. Traders must have visibility into all aspects of every order through to execution. New tools leverage the power of Kafka Streams that enable traders themselves to build directed graphs on screen, without writing any code. A directed graphs control data flows, calculations, and statistical analysis of cryptocurrency trading data and can output it the screen for in depth monitoring and analysis of real time data as well as historical trading data stored in in-memory time series databases.

This paper describes practical approaches to building and deploying Kafka Streams to support cryptocurrency trading. Do you think that writing simple, expressive code to react to event streams in real-time can sometimes look just a little too easy? Either way, come prepared to have some fun and guess the answers to our streaming brain-teasers as we highlight some misconceptions and things that may surprise you in the brave new world of continuous stream processing!

Kafka Streams performance monitoring and tuning is important for many reasons, including identifying bottlenecks, achieving greater throughput, and capacity planning. Performance tuning of Kafka and Kafka Streams configuration and properties. A biographical sketch and a commented list of all works are available for a quick consultation. Through the manuscript page you can experience the concreteness of Kafka's writing in a chapter of The Trial. With the general bibliography under construction you enter the commentary part of the site; new articles and essays are announced in the home page, and collected in a dedicated part of the site.

Newly published books about Kafka are presented in a separate section. Our huge archive includes all past articles and recommended books, and all essays grouped according to the work they refer to. You can contact the team of the Kafka Project through the contact page , or simply drop a line in the guestbook ; a search engine helps you to retrive a word or a quote from Kafka's work or from the entire site. And last but not least, do not miss the help page if you are only looking for a hint in order to get that Kafka paper written!

The result is an electric and poignant performance of intense musical and visual collaboration. You can find more information about the event here. Thanks to Jeff Nowak for the wonderful effort! Give your contribution to its completing! Read here a press release!

If you have translations or essays of your own, please contact us for submission! Which was the family name of Kafka's mother before she got married to Hermann Kafka? Give here your answer! You win nothing, but you will be cited in the next update if you do not answer anonymously. Visit the Hall of Fame! On Writing. Stephen King. Pride and Prejudice.

How to Create a Kafka Topic

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