Improving fault tolerance and scaling out in Kafka Streams with Bill Bejeck | Kafka Summit London 2022
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Kafka Streams is the popular stream processing component of Apache Kafka®. One of its best features is stateful operations. Kafka Streams works hard to ensure stateful operations can scale horizontally and survive failures, but doing so takes time. Kafka Streams offers the concept of ""standby-tasks,"" allowing for near-zero downtime failover, but surprisingly this feature still isn't widely used. The could be for various reasons, from lack of awareness to needing additional resources. This presentation will cover how standby tasks work and how they're enabled. Additionally, I'll cover the work done in KIP-441 that enables faster scaling out for stateful tasks and provides more balanced stateful assignments. I'll also dive into the consumer rebalance protocol improvements that enable KIP-441 to be effective. Attendees of this presentation will walk away understanding how and when to use standby tasks, leverage the improvements from KIP-441, and have a deeper understanding of how Kafka Streams works with state.
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If you were to ask any developer, ""what's a schema and where is it used?"" Most likely, you'd get an answer involving a relational database. The truth is the domain objects used in applications represent a contract, an implied schema, whether developers choose to acknowledge them or not. But even if you recognize the need for a formal schema, what's the best way to manage them?
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Watch this talk here: https://www.confluent.io/online-talks/from-zero-to-hero-with-kafka-connect-on-demand
Integrating Apache Kafka® with other systems in a reliable and scalable way is often a key part of a streaming platform. Fortunately, Apache Kafka includes the Connect API that enables streaming integration both in and out of Kafka. Like any technology, understanding its architecture and deployment patterns is key to successful use, as is knowing where to go looking when things aren't working.
This talk will discuss the key design concepts within Apache Kafka Connect and the pros and cons of standalone vs distributed deployment modes. We'll do a live demo of building pipelines with Apache Kafka Connect for streaming data in from databases, and out to targets including Elasticsearch. With some gremlins along the way, we'll go hands-on in methodically diagnosing and resolving common issues encountered with Apache Kafka Connect. The talk will finish off by discussing more advanced topics including Single Message Transforms, and deployment of Apache Kafka Connect in containers.
Kafka Tutorial - Introduction to Apache Kafka (Part 1)
Why is Kafka so fast? Why is Kafka so popular? Why Kafka? This slide deck is a tutorial for the Kafka streaming platform. This slide deck covers Kafka Architecture with some small examples from the command line. Then we expand on this with a multi-server example to demonstrate failover of brokers as well as consumers. Then it goes through some simple Java client examples for a Kafka Producer and a Kafka Consumer. We have also expanded on the Kafka design section and added references. The tutorial covers Avro and the Schema Registry as well as advance Kafka Producers.
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...
Flink Forward San Francisco 2022.
Being in the payments space, Stripe requires strict correctness and freshness guarantees. We rely on Flink as the natural solution for delivering on this in support of our Change Data Capture (CDC) infrastructure. We heavily rely on CDC as a tool for capturing data change streams from our databases without critically impacting database reliability, scalability, and maintainability. Data derived from these streams is used broadly across the business and powers many of our critical financial reporting systems totalling over $640 Billion in payment volume annually. We use many components of Flink’s flexible DataStream API to perform aggregations and abstract away the complexities of stream processing from our downstreams. In this talk, we’ll walk through our experience from the very beginning to what we have in production today. We’ll share stories around the technical details and trade-offs we encountered along the way.
by
Jeff Chao
KSQL Performance Tuning for Fun and Profit ( Nick Dearden, Confluent) Kafka S...
Ever wondered just how many CPU cores of KSQL Server you need to provision to handle your planned stream processing workload ? Or how many GBits of aggregate network bandwidth, spread across some number of processing threads, you'll need to deal with combined peak throughput of multiple queries ? In this talk we'll first explore the basic drivers of KSQL throughput and hardware requirements, building up to more advanced query plan analysis and capacity-planning techniques, and review some real-world testing results along the way. Finally we will recap how and what to monitor to know you got it right!
ksqlDB - Stream Processing simplified!
ksqlDB is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. ksqlDB is based on Kafka Stream and provides capabilities for consuming messages from Kafka, analysing these messages in near-realtime with a SQL like language and produce results again to a Kafka topic. By that, no single line of Java code has to be written and you can reuse your SQL knowhow. This lowers the bar for starting with stream processing significantly.
ksqlDB offers powerful capabilities of stream processing, such as joins, aggregations, time windows and support for event time. In this talk I will present how KSQL integrates with the Kafka ecosystem and demonstrate how easy it is to implement a solution using ksqlDB for most part. This will be done in a live demo on a fictitious IoT sample.
Kafka Streams vs. KSQL for Stream Processing on top of Apache Kafka
Spoilt for Choice – Kafka Streams vs. KSQL for Stream Processing on top of Apache Kafka:
Apache Kafka is a de facto standard streaming data processing platform. It is widely deployed as event streaming platform. Part of Kafka is its stream processing API “Kafka Streams”. In addition, the Kafka ecosystem now offers KSQL, a declarative, SQL-like stream processing language that lets you define powerful stream-processing applications easily. What once took some moderately sophisticated Java code can now be done at the command line with a familiar and eminently approachable syntax.
This session discusses and demos the pros and cons of Kafka Streams and KSQL to understand when to use which stream processing alternative for continuous stream processing natively on Apache Kafka infrastructures. The end of the session compares the trade-offs of Kafka Streams and KSQL to separate stream processing frameworks such as Apache Flink or Spark Streaming.
Apache Spark Data Source V2 with Wenchen Fan and Gengliang Wang
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
Deploying Flink on Kubernetes - David Anderson
Kubernetes has rapidly established itself as the de facto standard for orchestrating containerized infrastructures. And with the recent completion of the refactoring of Flink's deployment and process model known as FLIP-6, Kubernetes has become a natural choice for Flink deployments. In this talk we will walk through how to get Flink running on Kubernetes
ksqlDB: A Stream-Relational Database System
Speaker: Matthias J. Sax, Software Engineer, Confluent
ksqlDB is a distributed event streaming database system that allows users to express SQL queries over relational tables and event streams. The project was released by Confluent in 2017 and is hosted on Github and developed with an open-source spirit. ksqlDB is built on top of Apache Kafka®, a distributed event streaming platform. In this talk, we discuss ksqlDB’s architecture that is influenced by Apache Kafka and its stream processing library, Kafka Streams. We explain how ksqlDB executes continuous queries while achieving fault tolerance and high vailability. Furthermore, we explore ksqlDB’s streaming SQL dialect and the different types of supported queries.
Matthias J. Sax is a software engineer at Confluent working on ksqlDB. He mainly contributes to Kafka Streams, Apache Kafka's stream processing library, which serves as ksqlDB's execution engine. Furthermore, he helps evolve ksqlDB's "streaming SQL" language. In the past, Matthias also contributed to Apache Flink and Apache Storm and he is an Apache committer and PMC member. Matthias holds a Ph.D. from Humboldt University of Berlin, where he studied distributed data stream processing systems.
https://db.cs.cmu.edu/events/quarantine-db-talk-2020-confluent-ksqldb-a-stream-relational-database-system/
Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka
Streaming all over the World: Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka.
Learn about various case studies for event streaming with Apache Kafka across industries. The talk explores architectures for real-world deployments from Audi, BMW, Disney, Generali, Paypal, Tesla, Unity, Walmart, William Hill, and more. Use cases include fraud detection, mainframe offloading, predictive maintenance, cybersecurity, edge computing, track&trace, live betting, and much more.
Stream processing with Apache Flink (Timo Walther - Ververica)
Speaker: Timo Walther (https://www.linkedin.com/in/twalthr/)
Video: https://www.youtube.com/watch?v=Ych5bbmDIoA
Talk presented during Bangalore Kafka group's stream processing meetup at Hotstar https://www.meetup.com/Bangalore-Apache-Kafka-Group/events/265285812/
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
Flink Forward San Francisco 2022.
With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi.
by
Ethan Guo & Kyle Weller
Restoring Restoration's Reputation in Kafka Streams with Bruno Cadonna & Luca...
"Restoring local state in Kafka Streams applications is indispensable for recovering after a failure or for moving stream processors between Kafka Streams clients. However, restoration has a reputation for being operationally problematic, because a Streams client occupied with restoration of some stream processors blocks other stream processors that are ready from processing new records. When the state is large this can have a considerable impact on the overall throughput of the Streams application. Additionally, when failures interrupt restoration, restoration restarts from the beginning, thus negatively impacting throughput further.
In this talk, we will explain how Kafka Streams currently restores local state and processes records. We will show how we decouple processing from restoring by moving restoration to a dedicated thread and how throughput profits from this decoupling. We will present how we avoid restarting restoration from the beginning after a failure. Finally, we will talk about the concurrency and performance problems that we had to overcome and we will present benchmarks that show the effects of our improvements."
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Building a fully managed stream processing platform on Flink at scale for Lin...
Building a fully managed stream processing platform on Flink at scale for Lin...
Performance Analysis and Optimizations for Kafka Streams Applications (Guozha...
Performance Analysis and Optimizations for Kafka Streams Applications (Guozha...
Temporal-Joins in Kafka Streams and ksqlDB | Matthias Sax, Confluent
Temporal-Joins in Kafka Streams and ksqlDB | Matthias Sax, Confluent
Kafka Tutorial - Introduction to Apache Kafka (Part 1)
Kafka Tutorial - Introduction to Apache Kafka (Part 1)
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...
Squirreling Away $640 Billion: How Stripe Leverages Flink for Change Data Cap...
KSQL Performance Tuning for Fun and Profit ( Nick Dearden, Confluent) Kafka S...
KSQL Performance Tuning for Fun and Profit ( Nick Dearden, Confluent) Kafka S...
Kafka Streams vs. KSQL for Stream Processing on top of Apache Kafka
Kafka Streams vs. KSQL for Stream Processing on top of Apache Kafka
Apache Spark Data Source V2 with Wenchen Fan and Gengliang Wang
Apache Spark Data Source V2 with Wenchen Fan and Gengliang Wang
Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka
Real-Life Use Cases & Architectures for Event Streaming with Apache Kafka
Stream processing with Apache Flink (Timo Walther - Ververica)
Stream processing with Apache Flink (Timo Walther - Ververica)
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
Restoring Restoration's Reputation in Kafka Streams with Bruno Cadonna & Luca...
Restoring Restoration's Reputation in Kafka Streams with Bruno Cadonna & Luca...
Similar to Improving fault tolerance and scaling out in Kafka Streams with Bill Bejeck | Kafka Summit London 2022
What's in store? Part Deux; Creating Custom Queries with Kafka Streams IQv2
"Kafka Streams is a powerful stream-processing library that offers stateful operations. Along with the stateful operations is a unique feature of Kafka Streams, Interactive Queries, or IQ. IQ allows you to leverage the application's state from the outside by directly querying the state stores.
IQ has always been a part of Kafka Streams, but the original version lacked some key features. Namely, the ability to query the state of a custom state store. With the addition of IQ v2, that has now changed. Additionally, IQ v2 brings other benefits like pushing queries down to the store level making querying more efficient.
In this talk, I will cover:
* Building an Interactive Query service including routing queries between app instances
* Creating custom queries using Interactive Query v2
* Testing your IQ Application
Attending this talk will teach you how to use IQ v2 to create queries more aligned with your unique business needs."
Schema Registry 101 with Bill Bejeck | Kafka Summit London 2022
If you were to ask any developer, ""what's a schema and where is it used?"" Most likely, you'd get an answer involving a relational database. The truth is the domain objects used in applications represent a contract, an implied schema, whether developers choose to acknowledge them or not. But even if you recognize the need for a formal schema, what's the best way to manage them?
This presentation will contain some theory and primarily practical application for schemas with Schema Registry. I'll briefly explain what a schema is and how it's very relevant to any application working with Kafka today. It will go into the practical, introducing Schema Registry, describing how it works and how developers can leverage it to provide schemas across an organization. The discussion will cover working with Schema Registry from the command line, how to leverage it with Kafka clients, and the supported serialization formats. Some established build tools that make life easier for the Kafka developer will also be covered.
Attendees will walk away with knowledge of Schema Registry and a solid understanding of how it works, how to integrate them into Kafka clients. They'll also learn enough about the supported serialization frameworks to start implementing schemas right away in their Kafka development efforts.
Managing multiple event types in a single topic with Schema Registry | Bill B...
With Apache Kafka, it's typical to place different events in their own topic. But different event types can be related. Consider customer interactions with an online retailer. The customer searches through the site and clicks on various items before deciding on a final purchase. But businesses can gain insight by processing these events in sequence. Using the event type per topic leaves a lot of work for developers. Is there a better way?
Fortunately, there is. Schema Registry now supports having multiple event types in the same topic. By placing various event types in a single topic, you can now handle different related events in-order. In this presentation, I'll introduce Schema Registry then we'll dive into how it handles multiple event types in a single topic, including examples.
You will learn how and when to apply the multiple event types per topic pattern. Additionally, you'll learn how schema references work in Schema Registry.
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
Presented at Kafka Summit SF 2017 by Guozhang Wang, Confluent
Exactly-once Stream Processing with Kafka Streams
I will present the recent additions to Kafka to achieve exactly-once semantics (0.11.0) within its Streams API for stream processing use cases. This is achieved by leveraging the underlying idempotent and transactional client features. The main focus will be the specific semantics that Kafka distributed transactions enable in Streams and the underlying mechanics to let Streams scale efficiently.
Exactly-once Data Processing with Kafka Streams - July 27, 2017
Presentation by Guozhang Wang, Confluent at the Bay Area Apache Kafka July Meetup
Tarabica 2019 (Belgrade, Serbia) - SQL Server performance troubleshooting
Finding and fixing performance issues in SQL Server and the Azure SQL database requires understanding how database engine works and what can affect performance. People sometime make changes without finding the exact cause of the problem, which causes additional issues in the future. In this presentation, we will see some techniques you can apply to identify problems and solutions using Query Store technology, DMVs, SQL plan analysis, etc.
https://www.tarabica.org/Session/Details/78
Windowing in Kafka Streams and Flink SQL
"Shakespeare once said that the ""Eyes are the window to your soul"". Had Shakespeare been a developer today, he may have changed that to ""Windows are the eyes into your data.""
Stream processing has become the de facto standard of working with data, with Kafka Streams and Flink being the top choices to implement an event streaming application. Responding quickly to any event is only possible when you can access those events as they happen. But in many cases, you're not concerned with one single event. Instead, it's a series of events within a given period that commands attention. In other words, it's essential to analyze events within discrete windows of time. Yet, with the different options available and the time semantics around them, windowing can be tricky to get right.
In this talk, I will cover the following topics for windowing in Kafka Streams and Flink SQL:
* Different window types (hopping, tumbling, sliding, etc.) and use-case application
* The semantics of time advancement, window closing, and the emitting of results
* Analyzing windowed results
* Testing strategies
Developers attending this presentation will gain an understanding of what windowing is in stream processing, the different types available to them, and some guidelines on when to apply which window type."
Consistency and Completeness: Rethinking Distributed Stream Processing in Apa...
We present Apache Kafka’s core design for stream processing, which relies on its persistent log architecture as the storage and inter-processor communication layers to achieve correctness guarantees. Kafka Streams, a scalable stream processing client library in Apache Kafka, defines the processing logic as read process-write cycles in which all processing state updates and result outputs are captured as log appends. Idempotent and transactional write protocols are utilized to guarantee exactly once semantics. Furthermore, revision-based speculative processing is employed to emit results as soon as possible while handling out-of-order data. We also demonstrate how Kafka Streams behaves in practice with large-scale deployments and performance insights exhibiting its flexible and low-overhead trade-offs.
Understanding Query Plans and Spark UIs
"The common use cases of Spark SQL include ad hoc analysis, logical warehouse, query federation, and ETL processing. Spark SQL also powers the other Spark libraries, including structured streaming for stream processing, MLlib for machine learning, and GraphFrame for graph-parallel computation. For boosting the speed of your Spark applications, you can perform the optimization efforts on the queries prior employing to the production systems. Spark query plans and Spark UIs provide you insight on the performance of your queries. This talk discloses how to read and tune the query plans for enhanced performance. It will also cover the major related features in the recent and upcoming releases of Apache Spark.
"
Apache Kafka, and the Rise of Stream Processing
For a long time, a substantial portion of data processing that companies did ran as big batch jobs. But businesses operate in real-time and the software they run is catching up. Today, processing data in a streaming fashion becomes more and more popular in many companies over the more "traditional" way of batch-processing big data sets available as a whole.
SFBigAnalytics_20190724: Monitor kafka like a Pro
Kafka operators need to provide guarantees to the business that Kafka is working properly and delivering data in real time, and they need to identify and triage problems so they can solve them before end users notice them. This elevates the importance of Kafka monitoring from a nice-to-have to an operational necessity. In this talk, Kafka operations experts Xavier Léauté and Gwen Shapira share their best practices for monitoring Kafka and the streams of events flowing through it. How to detect duplicates, catch buggy clients, and triage performance issues – in short, how to keep the business’s central nervous system healthy and humming along, all like a Kafka pro.
Speakers: Gwen Shapira, Xavier Leaute (Confluence)
Gwen is a software engineer at Confluent working on core Apache Kafka. She has 15 years of experience working with code and customers to build scalable data architectures. She currently specializes in building real-time reliable data processing pipelines using Apache Kafka. Gwen is an author of “Kafka - the Definitive Guide”, "Hadoop Application Architectures", and a frequent presenter at industry conferences. Gwen is also a committer on the Apache Kafka and Apache Sqoop projects.
Xavier Leaute is One of the first engineers to Confluent team, Xavier is responsible for analytics infrastructure, including real-time analytics in KafkaStreams. He was previously a quantitative researcher at BlackRock. Prior to that, he held various research and analytics roles at Barclays Global Investors and MSCI.
Kubernetes Problem-Solving
Presented at All Thing Open RTP Meetup
Presented by Brent Laster
Abstract: Kubernetes is the leading way to run and manage your containerized workloads across any cloud or on-premises environment. It provides an automated, reliable way to execute the services, deployments, etc. that make up your application. But what happens when running those doesn’t go as you’d expect, or the system isn’t happy with what you’re trying to get to run? How do you figure out what’s going wrong, track down the root causes, figure out a solution, and get things working again?
In this hands-on three-hour workshop, we’ll look at some basic and advanced ways to debug problems that you may run into with Kubernetes. You’ll learn techniques from basic ways to zero in on root cause to log analysis to using advanced tools such as creating your own debug containers. Armed with these skills, you’ll be in a position to deal with day-to-day issues with running workloads in Kubernetes and keep them from becoming disruptions and/or show-stoppers.
Jakarta EE 8 on JDK17
JDK 17, the next LTS version of Java, is available and it contains not only new language constructs but also there are many operational improvements like higher performance. We have a look at what a Jakarta EE developer will find interesting, even if you are using Jakarta EE 8.
Learn about these features and improvements including Records, Text blocks, Garbage collection improvements, and monitoring through Flight Recorder in several live demos with Payara Micro. After this session you will be able to use all new shiny features of JDK 17 in your next Java Enterprise application.
Richmond kafka streams intro
Introduction to Kafka Streams, Bill Bejeck, Integration Architect, Confluent
https://www.meetup.com/Kafka-ATL/events/271187768/
Deploying Kafka Streams Applications with Docker and Kubernetes
(Gwen Shapira + Matthias J. Sax, Confluent) Kafka Summit SF 2018
Kafka Streams, Apache Kafka’s stream processing library, allows developers to build sophisticated stateful stream processing applications which you can deploy in an environment of your choice. Kafka Streams is not only scalable, but fully elastic allowing for dynamic scale-in and scale-out as the library handles state migration transparently in the background. By running Kafka Streams applications on Kubernetes, you will be able to use Kubernetes powerful control plane to standardize and simplify the application management—from deployment to dynamic scaling.
In this technical deep dive, we’ll explain the internals of dynamic scaling and state migration in Kafka Streams. We’ll then show, with a live demo, how a Kafka Streams application can run in a Docker container on Kubernetes and the dynamic scaling of an application running in Kubernetes.
Databus - Abhishek Bhargava & Maheswaran Veluchamy - DevOps Bangalore Meetup...
Databus - Abhishek Bhargava & Maheswaran Veluchamy - DevOps Bangalore Meetup March 28th 2015
Introduction to Kafka Streams
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Sebastien goasguen cloud stack the next year
The VP of the Apache CloudStack project talks about what's coming in the next year.
Similar to Improving fault tolerance and scaling out in Kafka Streams with Bill Bejeck | Kafka Summit London 2022 (20)
What's in store? Part Deux; Creating Custom Queries with Kafka Streams IQv2
What's in store? Part Deux; Creating Custom Queries with Kafka Streams IQv2
Schema Registry 101 with Bill Bejeck | Kafka Summit London 2022
Schema Registry 101 with Bill Bejeck | Kafka Summit London 2022
Managing multiple event types in a single topic with Schema Registry | Bill B...
Managing multiple event types in a single topic with Schema Registry | Bill B...
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streams
Exactly-once Data Processing with Kafka Streams - July 27, 2017
Exactly-once Data Processing with Kafka Streams - July 27, 2017
Tarabica 2019 (Belgrade, Serbia) - SQL Server performance troubleshooting
Tarabica 2019 (Belgrade, Serbia) - SQL Server performance troubleshooting
Consistency and Completeness: Rethinking Distributed Stream Processing in Apa...
Consistency and Completeness: Rethinking Distributed Stream Processing in Apa...
Deploying Kafka Streams Applications with Docker and Kubernetes
Deploying Kafka Streams Applications with Docker and Kubernetes
Databus - Abhishek Bhargava & Maheswaran Veluchamy - DevOps Bangalore Meetup...
Databus - Abhishek Bhargava & Maheswaran Veluchamy - DevOps Bangalore Meetup...
More from HostedbyConfluent
Transforming Data Streams with Kafka Connect: An Introduction to Single Messa...
"In this talk, attendees will be provided with an introduction to Kafka Connect and the basics of Single Message Transforms (SMTs) and how they can be used to transform data streams in a simple and efficient way. SMTs are a powerful feature of Kafka Connect that allow custom logic to be applied to individual messages as they pass through the data pipeline. The session will explain how SMTs work, the types of transformations they can be used for, and how they can be applied in a modular and composable way.
Further, the session will discuss where SMTs fit in with Kafka Connect and when they should be used. Examples will be provided of how SMTs can be used to solve common data integration challenges, such as data enrichment, filtering, and restructuring. Attendees will also learn about the limitations of SMTs and when it might be more appropriate to use other tools or frameworks.
Additionally, an overview of the alternatives to SMTs, such as Kafka Streams and KSQL, will be provided. This will help attendees make an informed decision about which approach is best for their specific use case.
Whether attendees are developers, data engineers, or data scientists, this talk will provide valuable insights into how Kafka Connect and SMTs can help streamline data processing workflows. Attendees will come away with a better understanding of how these tools work and how they can be used to solve common data integration challenges."
Renaming a Kafka Topic | Kafka Summit London
"While Apache Kafka lacks native support for topic renaming, there are scenarios where renaming topics becomes necessary. This presentation will delve into the utilization of MirrorMaker 2.0 as a solution for renaming Kafka topics. It will illustrate how MirrorMaker 2.0 can efficiently facilitate the migration of messages from the old topic to the new one and how Kafka Connect Metrics can be employed to monitor the mirroring progress. The discussion will encompass the complexity of renaming Kafka topics, addressing certain limitations, and exploring potential workarounds when using MirrorMaker 2.0 for this purpose. Despite not being originally designed for topic renaming, MirrorMaker 2.0 has a suitable solution for renaming Kafka topics.
Blog Post : https://engineering.hellofresh.com/renaming-a-kafka-topic-d6ff3aaf3f03"
Evolution of NRT Data Ingestion Pipeline at Trendyol
"Trendyol, Turkey's leading e-commerce company, is committed to positively impacting the lives of millions of customers. Our decision-making processes are entirely driven by data. As a data warehouse team, our primary goal is to provide accurate and up-to-date data, enabling the extraction of valuable business insights.
We utilize the benefits provided by Kafka and Kafka Connect to facilitate the transfer of data from the source to our analytical environment. We recently transitioned our Kafka Connect clusters from on-premise VMs to Kubernetes. This shift was driven by our desire to effectively manage rapid growth(marked by a growing number of producers, consumers, and daily messages), ensuring proper monitoring and consistency. Consistency is crucial, especially in instances where we employ Single Message Transforms to manipulate records like filtering based on their keys or converting a JSON Object into a JSON string.
Monitoring our cluster's health is key and we achieve this through Grafana dashboards and alerts generated through kube-state-metrics. Additionally, Kafka Connect's JMX metrics, coupled with NewRelic, are employed for comprehensive monitoring.
The session will aim to explain our approach to NRT data ingestion, outlining the role of Kafka and Kafka Connect, our transition journey to K8s, and methods employed to monitor the health of our clusters."
Ensuring Kafka Service Resilience: A Dive into Health-Checking Techniques
"Join our lightning talk to delve into the strategies vital for maintaining a resilient Kafka service.
While proactive monitoring is key for issue prevention, failures will still occur. Rapid detection tools will enable you to identify and resolve problems before they impact end-users. This session explores the techniques employed by Kafka cloud providers for this detection, many of which are also applicable if you are managing independent Kafka clusters or applications.
The talk focuses on health-checking, a powerful tool that encompasses an application and its monitoring to validate Kafka environment availability. The session navigates through Kafka health-check methods, sharing best practices, identifying common pitfalls, and highlighting the monitoring of critical performance metrics like throughput and latency for early issue detection.
Attendees will gain valuable insights into the art of health-checking their Kafka environment, equipping them with the tools to identify and address issues before they escalate into critical problems. We invite all Kafka enthusiasts to join us in this talk to foster a deeper understanding of Kafka health-checking and ensure the continued smooth operation of your Kafka environment."
Exactly-once Stream Processing with Arroyo and Kafka
"Stream processing systems traditionally gave their users the choice between at least once processing and at most once processing: accepting duplicate data or missing data. But ideally we would provide exactly-once processing, where every event in the input data is represented exactly once in the output.
Kafka provides a transaction API that enables exactly-once when using Kafka as your source and sink. But this API has turned out to not be well suited for use by high level streaming systems, requiring various work arounds to still provide transactional processing.
In this talk, I’ll cover how the transaction API works, and how systems like Arroyo and Flink have used it to build exactly-once support, and how improvements to the transactional API will enable better end-to-end support for consistent stream processing."
Fish Plays Pokemon | Kafka Summit London
"In this talk, we will explore the exciting world of IoT and computer vision by presenting a unique project: Fish Plays Pokemon. Using an ESP Eye camera connected to an ESP32 and other IoT devices, to monitor fish's movements in an aquarium.
This project showcases the power of IoT and computer vision, demonstrating how even a fish can play a popular video game. We will discuss the challenges we faced during development, including real-time processing, IoT device integration, and Kafka message consumption.
By the end of the talk, attendees will have a better understanding of how to combine IoT, computer vision, and the usage of a serverless cloud to create innovative projects. They will also learn how to integrate IoT devices with Kafka to simulate keyboard behavior, opening up endless possibilities for real-time interactions between the physical and digital worlds."
Tiered Storage 101 | Kafla Summit London
What is tiered storage and what is it good for? After this session you will know how to leverage the tiered storage feature to enable longer retention than the storage attached to brokers allows. You will get acquainted with the different configuration options and know what to expect when you enable the feature, like for example when will the first upload to the remote object storage take place.
Building a Self-Service Stream Processing Portal: How And Why
"Real-time 24/7 monitoring and verification of massive data is challenging – even more so for the world’s second largest manufacturer of memory chips and semiconductors. Tolerance levels are incredibly small, any small defect needs to be identified and dealt with immediately. The goal of semiconductor manufacturing is to improve yield and minimize unnecessary work.
However, even with real-time data collection, the data was not easy to manipulate by users and it took many days to enable stream processing requests – limiting its usefulness and value to the business.
You’ll hear why SK hynix switched to Confluent and how we developed a self-service stream process portal on top of it. Now users have an easy-to-use service to manipulate the data they want.
Results have been impressive, stream processing requests are available the same day – previously taking 5 days! We were also able to drive down costs by 10% as stream processing requests no longer require additional hardware.
What you’ll take away from our talk:
- What were the pain points in the previous environment
- How we transitioned to Confluent without service downtime
- Creating a self-service stream processing portal built on top of Connect and ksqlDB
- Use case of stream process portal"
From the Trenches: Improving Kafka Connect Source Connector Ingestion from 7 ...
"Discover how default configurations might impact ingestion times, especially when dealing with large files. We'll explore a real-world scenario with a 20,000,000+ line file, assessing metrics and exploring the bottleneck in the default setup. Understand the intricacies of batch size calculations and how to optimize them based on your unique data characteristics.
Walk away with actionable insights as we showcase a practical example, turning a 7-hour ingestion process into a mere 30 minutes for over 30,000,000 records in a Kafka topic. Uncover metrics, configurations, and best practices to elevate the performance of your Kafka Connect CSV source connectors. Don't miss this opportunity to optimize your data pipeline and ensure smooth, efficient data flow."
Future with Zero Down-Time: End-to-end Resiliency with Chaos Engineering and ...
"In order to meet the current and ever-increasing demand for near-zero RPO/RTO systems, a focus on resiliency is critical. While Kafka offers built-in resiliency features, a perfect blend of client and cluster resiliency is necessary in order to achieve a highly resilient Kafka client application.
At Fidelity Investments, Kafka is used for a variety of event streaming needs such as core brokerage trading platforms, log aggregation, communication platforms, and data migrations. In this lightening talk, we will discuss the governance framework that has enabled producers and consumers to achieve their SLAs during unprecedented failure scenarios. We will highlight how we automated resiliency tests through chaos engineering and tightly integrated observability dashboards for Kafka clients to analyze and optimize client configurations. And finally, we will summarize the chaos test suite and the ""test, test and test"" mantra that are helping Fidelity Investments reach its goal of a future with zero down-time."
Navigating Private Network Connectivity Options for Kafka Clusters
"There are various strategies for securely connecting to Kafka clusters between different networks or over the public internet. Many cloud providers even offer endpoints that privately route traffic between networks and are not exposed to the internet. But, depending on your network setup and how you are running Kafka, these options ... might not be an option!
In this session, we’ll discuss how you can use SSH bastions or a self managed PrivateLink endpoint to establish connectivity to your Kafka clusters without exposing brokers directly to the internet. We explain the required network configuration, and show how we at Materialize have contributed to librdkafka to simplify these scenarios and avoid fragile workarounds."
Apache Flink: Building a Company-wide Self-service Streaming Data Platform
"In my talk, we will examine all the stages of building our self-service Streaming Data Platform based on Apache Flink and Kafka Connect, from the selection of a solution for stateful streaming data processing, right up to the successful design of a robust self-service platform, covering the challenges that we’ve met.
I will share our experience in providing non-Java developers with a company-wide self-service solution, which allows them to quickly and easily develop their streaming data pipelines.
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Explaining How Real-Time GenAI Works in a Noisy Pub
"Almost everyone has heard about large language models, and tens of millions of people have tried out OpenAI ChatGPT and Google Bard. However, the intricate architecture and underlying mathematics driving these remarkable systems remain elusive to many.
LLM's are fascinating - so let's grab a drink and find out how these systems are built and dive deep into their inner workings. In the length of time it to enjoy a round of drinks, you'll understand the inner workings of these models. We'll take our first sip of word vectors, enjoy the refreshing taste of the transformer, and drain a glass understanding how these models are trained on phenomenally large quantities of data.
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TL;DR Kafka Metrics | Kafka Summit London
"Monitoring is a fundamental operation when running Kafka and Kafka applications in production. There are numerous metrics available when using Kafka, however the sheer number is overwhelming, making it challenging to know where to start and how to properly utilise them.
This session will introduce you to some of the key metrics that should be monitored and best practices in fine tuning your monitoring. We will delve into which metrics are the indicators for cluster’s availability and performance and are the most helpful when debugging client applications."
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Kafka Streams relies on state restoration for maintaining standby tasks as failure recovery mechanism as well as for restoring the state after rebalance scenarios. When you are scaling up or down your application instances, it is necessary to know the current state of the restoration process for each active and standby task in order to prevent a long restoration process as much as possible. During this presentation, you will get an understanding of how KIP-869 provides valuable information about the current active task restoration after a rebalance and KIP-988 opens a window to the continuous process of standby restoration. When you encounter a situation in which you need to choose whether or not to scale up or down your application instances, both KIPs will be an invaluable ally for you.
Mastering Kafka Producer Configs: A Guide to Optimizing Performance
"In this talk, we will dive into the world of Kafka producer configs and explore how to understand and optimize them for better performance. We will cover the different types of configs, their impact on performance, and how to tune them to achieve the best results. Whether you're new to Kafka or a seasoned pro, this session will provide valuable insights and practical tips for improving your Kafka producer performance.
- Introduction to Kafka producer internal and workflow
- Understanding the producer configs like linger.ms, batch.size, buffer.memory and their impact on performance
- Learning about producer configs like max.block.ms, delivery.timeout.ms, request.timeout.ms and retries to make producer more resilient.
- Discuss configs like enable.idempotence, max.in.flight.requests.per.connection and transaction related configs to achieve delivery guarantees.
- Q&A session with attendees to address specific questions and concerns."
Data Contracts Management: Schema Registry and Beyond
"Data contracts are one of the hottest topics in the data management community. A data contract is a formal agreement between a data producer and its consumers, aimed at reducing data downtime and improving data quality. Schemas are an important part of data contracts, but they are not the only relevant element.
In this talk, we’ll:
1. see why data contracts are so important but also difficult to implement;
2. identify the characteristics of a well-designed data contract:
discuss the anatomy of a data contract, its main elements and, how to formally describe them;
3. show how to manage the lifecycle of a data contract leveraging Confluent Platform's services."
Code-First Approach: Crafting Efficient Flink Apps
"In the realm of stateful stream processing, Apache Flink has emerged as a powerful and versatile platform. However, the conventional SQL-based approach often limits the full potential of Flink applications.
We will delve into the benefits of adopting a code-first approach, which provides developers with greater control over application logic, facilitates complex transformations, and enables more efficient handling of state and time. We will also discuss how the code-first approach can lead to more maintainable and testable code, ultimately improving the overall quality of your Flink applications.
Whether you're a seasoned Flink developer or just starting your journey, this talk will provide valuable insights into how a code-first approach can revolutionize your stream processing applications."
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"Change Data Capture (CDC) has become a commodity in data engineering, much in part due to the ever-rising success of Debezium [1]. But is that all there is? In this lightning talk, we’ll outline the current state of the CDC ecosystem, and understand why adopting a Debezium alternative is still a hard sell. If you’ve ever wondered what else is out there, but can’t keep up with the sprawling of new tools in the ecosystem; we’ll wrap it up for you!
[1] https://debezium.io/"
Beyond Tiered Storage: Serverless Kafka with No Local Disks
"Separation of compute and storage has become the de-facto standard in the data industry for batch processing.
The addition of tiered storage to open source Apache Kafka is the first step in bringing true separation of compute and storage to the streaming world.
In this talk, we'll discuss in technical detail how to take the concept of tiered storage to its logical extreme by building an Apache Kafka protocol compatible system that has zero local disks.
Eliminating all local disks in the system requires not only separating storage from compute, but also separating data from metadata. This is a monumental task that requires reimagining Kafka's architecture from the ground up, but the benefits are worth it.
This approach enables a stateless, elastic, and serverless deployment model that minimizes operational overhead and also drives inter-zone networking costs to almost zero."
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- 1. Standby Tasks and Smooth Scaling in Kafka Streams Fault tolerance and High Availability Bill Bejeck @bbejeck
- 2. @bbejeck Nice to meet you! • Member of the DevX team • Prior to DevX ~3 years as engineer on Kafka Streams team • Apache Kafka® Committer and PMC member • Author of “Kafka Streams in Action” - 2nd edition underway! 2
- 3. @bbejeck Agenda • Kafka Streams application • Architecture • State Management • Issues with state • Interactive Queries 3
- 5. @bbejeck Kafka Streams – DSL API StreamsBuilder builder = new StreamsBuilder(); KStream<String, String > stockStr = builder.stream(”stocks”); stockStr.groupByKey() .windowedBy(TimeWindows.of(Duration.ofMinutes(5)) .aggregate(()-> new TradeStats(), (k, v, tradeStats) -> tradeStats.add(v), Materialized.as(“trade-stats-store”)) .toStream().mapValues(..) .to(“output”); 5
- 7. Architecture
- 11. State Management
- 13. @bbejeck State Management & Task Assignment 13
- 14. @bbejeck State Management & Task Assignment 14
- 16. @bbejeck Availability – Failure Scenario 16
- 17. @bbejeck Availability – Scaling Out 17
- 19. @bbejeck Solution • Standby tasks • Scaling out -> KIP-441 • * Only applies to stateful topologies 19
- 21. @bbejeck Solution: Standby Task • num.standby.replicas • StreamsConfig.NUM_STANDBY_REPLICAS_CONFIG 21
- 22. @bbejeck Availability with Standby Tasks 22
- 24. @bbejeck Standby Task Considerations • Deploy applications on different hosts • Results in double in storage amount • Tradeoff for quickly resumed processing 24
- 25. @bbejeck What about Scaling Out? 25
- 27. @bbejeck Proposal -Warming up stateful tasks 27
- 28. @bbejeck Warming up issues • Kafka Streams instances don’t directly communicate • At what point is “warmed up”? 28
- 29. @bbejeck Solution • Rebalancing – encode some extra metadata • Lag information • Probing rebalances 29
- 30. @bbejeck Solution – KIP-441 • max.warmup.replicas = 2 (default) • probing.interval.rebalance.ms = 10 minutes (default) 30
- 33. @bbejeck Solution – Problem? • But isn’t rebalancing expensive? 33
- 34. @bbejeck Detour – Consumer Rebalance Issues 34
- 35. @bbejeck Solution • KIP-429 Cooperative Rebalancing • Trade off an extra rebalance for not ”stopping the world” • Enabled by default in Kafka Streams 35
- 36. @bbejeck Detour – Consumer Rebalance Issues 36
- 43. @bbejeck Solution • KIP-535 Serve IQ from standby tasks • Provide metadata about standby hosts • StreamsMetadata - updated • KeyQueryMetadata & LagInfo – new classes • Tradeoff consistency vs. availability 43
- 45. @bbejeck KSQL Options • ksql.streams.num.standby.replicas >= 1 • ksql.query.pull.enable.standby.reads = true 45
- 46. @bbejeck Summary • Standby tasks faster failover for stateful application • Improvements with KIP-441 enable smoother scaling out • You can leverage standbys for higher availability with IQ 46
- 47. @bbejeck For More Details • KIP-441 – Smoother Scaling out • https://cwiki.apache.org/confluence/display/KAFKA/KIP- 441%3A+Smooth+Scaling+Out+for+Kafka+Streams • KIP-429 Details on improved rebalancing • https://cwiki.apache.org/confluence/display/KAFKA/KIP- 429%3A+Kafka+Consumer+Incremental+Rebalance+Protocol • KIP-535 Enable reads during rebalance from standby tasks • https://cwiki.apache.org/confluence/display/KAFKA/KIP- 535%3A+Allow+state+stores+to+serve+stale+reads+during+rebalance 47
- 49. @bbejeck Resources • Kafka Tutorials - https://kafka-tutorials.confluent.io/ • Confluent Developer - developer.confluent.io • Examples - https://github.com/confluentinc/kafka-streams-examples/ • Documentation - https://docs.confluent.io/current/streams 49
- 50. Your Apache Kafka® journey begins here developer.confluent.io 50