Twitter’s Apache Kafka Adoption Journey | Ming Liu, Twitter
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Until recently, the Messaging team at Twitter had been running an in-house build Pub/Sub system, namely EventBus (built on top of Apache DistributedLog and Apache Bookkeeper, and similar in architecture to Apache Pulsar) to cater to our pubsub needs. In 2018, we made the decision to move to Apache Kafka by migrating existing use cases as well as onboarding new use cases directly onto Apache Kafka. Fast forward to today, Kafka is now an essential piece of Twitter Infrastructure and processes over 200M messages per second. In this talk, we will share the learning and challenges in our journey moving to Apache Kafka.
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In our payments platform at Goldman Sachs Transaction Banking, Apache Kafka plays a critical role as the messaging bus in our micro-services architecture. Being a part of the financial service industry we need to ensure high-availability of our platform and quick response time during failures.
In this talk we will explore how we monitor and alert on the health of our Kafka clusters using our heartbeat application and clients using DataDog dashboards. We will see how we consolidate JMX metrics such as error-rates, connection-rates, latencies and consumer lag from all producers and consumers using JMX agent sidecar to provide a live view of the health of our entire infrastructure. We will also discuss our culture of game days where we regularly test the resiliency of all the clients in our infrastructure by simulating various failure scenarios to improve the overall availability of our infrastructure.
Apache Pulsar: A Foundation Backbone for Clever Cloud - Pulsar Virtual Summit...
As a Cloud Service Provider, Clever Cloud makes heavy use of messaging in its core architecture. Initially using other messaging solutions, we faced a number of different challenges. Then we discovered Apache Pulsar, which held the promise to solve these challenges. This session will dive into the adoption of Apache Pulsar at Clever Cloud, including how we worked with StreamNative to develop KoP (Kafka on Pulsar), and how we're using Apache Pulsar as the foundation for our cloud backbone.
Look how easy it is to go from events to blazing-fast analytics! | Neha Pawar...
Apache Kafka is the de facto standard for real-time event streaming, but what do you do if you want to perform user-facing, ad-hoc, real-time analytics too? That's a hard problem. Apache Pinot solves it, and the two together are like chocolate and peanut butter, peaches and cream, and Steve Rogers and Peggy Carter.
Come to this talk for an introduction to Pinot and an overview of how the Pinot Kafka Connector works. Hear the challenges unique to a user-facing realtime analytics system, and how Pinot and Kafka work harmoniously to solve them. Witness an action-packed demo, showing just how easy it is to go from events to blazing-fast analytics, and how to use powerful features of both systems that help you do this at scale.
Give Your Confluent Platform Superpowers! (Sandeep Togrika, Intel and Bert Ha...
Whether you are a die-hard DC comic enthusiast, mad for Marvel, or completely clueless when it comes to comic books, at the end of the day each of us would love to possess the superpower to transform data in seconds versus minutes or days. But architects and developers are challenged with designing and managing platforms that scale elastically and combine event streams with stored data, to enable more contextually rich data analytics. This made even more complex with data coming from hundreds of sources, and in hundreds of terabytes, or even petabytes, per day.
Now, with Apache Kafka and Intel hardware technology advances, organizations can turn massive volumes of disparate data into actionable insights with the ability to filter, enrich, join and process data instream. Let's consider Information Security. IT leaders need to ensure all company data and IP is secured against threats and vulnerabilities. A combination of real-time event streaming with Confluent Platform and Intel Architecture has enabled threat detection efforts that once took hours to be completed in seconds, while simultaneously reducing technical debt and data processing and storage costs.
In this session, Confluent and Intel architects will share detailed performance benchmarking results and new joint reference architecture. We’ll detail ways to remove Kafka performance bottlenecks, and improve platform resiliency and ensure high availability using Confluent Control Center and Multi-Region Clusters. And we’ll offer up tips for addressing challenges that you may be facing in your own super heroic efforts to design, deploy, and manage your organization’s data platforms.
Kafka on Kubernetes: Does it really have to be "The Hard Way"? (Viktor Gamov,...
When it comes to choosing a distributed streaming platform for real-time data pipelines, everyone knows the answer - Apache Kafka! And when it comes to deploying applications at scale without needing to integrate different pieces of infrastructure yourself, the answer nowadays is increasingly Kubernetes. However, with all great things, the devil is truly in the details. While Kubernetes does provide all the building blocks that are needed, a lot of thought is required to truly create an enterprise-grade Kafka platform that can be used in production. In this technical deep dive, Michael and Viktor will go through challenges and pitfalls of managing Kafka on Kubernetes as well as the goals and lessons learned from the development of the Confluent Operator for Kubernetes.
NOTE: This talk together with Michael Ng from Confluent
Securing Kafka At Zendesk (Joy Nag, Zendesk) Kafka Summit 2020
Kafka is one of the most important foundation services at Zendesk. It became even more crucial with the introduction of Global Event Bus which my team built to propagate events between Kafka clusters hosted at different parts of the world and between different products. As part of its rollout, we had to add mTLS support in all of our Kafka Clusters (we have quite a few of them), this was to make propagation of events between clusters hosted at different parts of the world secure. It was quite a journey, but we eventually built a solution that is working well for us.
Things I will be sharing as part of the talk:
1. Establishing the use case/problem we were trying to solve (why we needed mTLS)
2. Building a Certificate Authority with open source tools (with self-signed Root CA)
3. Building helper components to generate certificates automatically and regenerate them before they expire (helps using a shorter TTL (Time To Live) which is good security practice) for both Kafka Clients and Brokers
4. Hot reloading regenerated certificates on Kafka brokers without downtime
5. What we built to rotate the self-signed root CA without downtime as well across the board
6. Monitoring and alerts on TTL of certificates
7. Performance impact of using TLS (along with why TLS affects kafka’s performance)
8. What we are doing to drive adoption of mTLS for existing Kafka clients using PLAINTEXT protocol by making onboarding easier
9. How this will become a base for other features we want, eg ACL, Rate Limiting (by using the principal from the TLS certificate as Identity of clients)
Asynchronous Transaction Processing With Kafka as a Single Source of Truth - ...
This document discusses using Kafka as a central backbone for building an asynchronous mobile ticketing application. It describes moving from a monolithic backend to a microservices architecture with separate databases for each service. It then outlines using Kafka to facilitate asynchronous communication between services, allowing for easy scaling, distribution, and evolution. Transaction management is achieved through message ordering in Kafka and a SAGA pattern. Leveraging Kafka Streams enables materialized views without a separate database. Overall this approach provides scalability, distribution, ease of evolution, and an improved user experience.
How did we move the mountain? - Migrating 1 trillion+ messages per day across...
Have you ever migrated Kafka clusters from one data center to another being completely transparent to client applications?
At PayPal, as part of a massive datacenter migration initiative, Kafka team successfully moved all PayPal Kafka traffic across data centers. This initiative involved migrating 20+ Kafka clusters (1000+ broker and zookeeper nodes), as well as 60+ mirrormaker groups which seamlessly handle Kafka traffic volumes as high as 1 trillion messages per day. Throughout the course of this migration, applications required no modification, encountered 0% service outage, 0% message loss and duplicated messages. The whole migration process was fully transparent to Kafka applications.
In this session, you will learn the strategies, techniques and tools the PayPal Kafka team has utilized for managing the migration process. You will also learn the lessons and pitfalls they experienced during this exercise, as well as the secret sauce of making the migration successful.
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...
In this talk, we'll discuss how VillageMD is able to use Kafka topic compaction for rapidly scaling our reprocessing pipelines to encompass hundreds of feeds. Within healthcare data ecosystems, privacy and data minimalism are key design priorities. Being able to handle data deletion in a reliable, timely manner within event-driven architectures is becoming more and more necessary with key governance frameworks like the GDPR and HIPAA.
We'll be giving an overview of the building and governance of dead-letter queues for streaming data processing.
We'll discuss:
1. How to architect a data sink for failed records.
2. How topic compaction can reduce duplicate data and enable idempotency.
3. Building a tombstoning system for removing successfully reprocessed records from the queues.
4. Considerations for monitoring a reprocessing system in production -- what metrics, dataops, and SLAs are useful?
Confluent Developer Training
This three-day course teaches developers how to build applications that can publish and subscribe to data from an Apache Kafka cluster. Students will learn Kafka concepts and components, how to use Kafka and Confluent APIs, and how to develop Kafka producers, consumers, and streams applications. The hands-on course covers using Kafka tools, writing producers and consumers, ingesting data with Kafka Connect, and more. It is designed for developers who need to interact with Kafka as a data source or destination.
From Newbie to Highly Available, a Successful Kafka Adoption Tale (Jonathan S...
The document discusses running Apache Kafka across multiple datacenters and provides guidance on how to configure Kafka for disaster recovery in a multi-datacenter setup. It recommends having separate Kafka clusters in each datacenter rather than a single cluster spanning datacenters, due to high replication latency over long-distance links. It also outlines three ways to prepare for disaster recovery: running in separate clusters per datacenter, using Kafka MirrorMaker to replicate topics between clusters, and using a shared storage system.
Exposing and Controlling Kafka Event Streaming with Kong Konnect Enterprise |...
Event streaming allows companies to build more scalable and loosely coupled real-time applications supporting massive concurrency demands and simplifying the construction of services.
At the same time, API management provides capabilities to securely control the upstream services consumption, including the event processing infrastructure.
This session shows how Kong Konnect Enterprise can complement Kafka Event Streaming, exposing it to new and external consumers while applying specific and critical policies to control its consumption, including API key, OAuth/OIDC and others for authentication, rate limiting, caching, log processing, etc.
Cross the streams thanks to Kafka and Flink (Christophe Philemotte, Digazu) K...
The last 5 years, Kafka and Flink have become mature technologies that have allowed us to embrace the streaming paradigm. You can bet on them to build reliable and efficient applications. They are active projects backed by companies using them in production. They have a good community contributing, and sharing experience and knowledge. Kafka and Flink are solid choices if you want to build a data platform that your data scientists or developers can use to collect, process, and distribute data. You can put together Kafka Connect, Kafka, Schema Registry, and Flink. First, you will take care of their deployment. Then, for each case, you will setup each part, and of course develop the Flink job so it can integrate easily with the rest. Looks like a challenging but exciting project, isn't it? In this session, you will learn how you can build such data platform, what are the nitty-gritty of each part, how you can plug them together, in particular how to plug Flink in the Kafka ecosystem, what are the common pitfalls to avoid, and what it requires to be deployed on kubernetes. Even if you are not familiar with all the technologies, there will be enough introduction so you can follow. Come and learn how we can actually cross the streams!
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications
In this talk by Sean Glover, Principal Engineer at Lightbend, we will review how the Strimzi Kafka Operator, a supported technology in Lightbend Platform, makes many operational tasks in Kafka easy, such as the initial deployment and updates of a Kafka and ZooKeeper cluster.
See the blog post containing the YouTube video here: https://www.lightbend.com/blog/running-kafka-on-kubernetes-with-strimzi-for-real-time-streaming-applications
Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
Apache Kafka is the most popular open-source stream-processing software for collecting, processing, storing, and analyzing data at scale. Most known for its excellent performance, low latency, fault tolerance, and high throughput, it's capable of handling thousands of messages per second. For mission-critical applications, how do you ensure that the performance delivered is the performance required? This is especially important as Kafka is written in Java and Scala and runs on the JVM. The JVM is a fantastic platform that delivers on an internet scale. In this session, we'll explore how making changes to the JVM design can eliminate the problems of garbage collection pauses and raise the throughput of applications. For cloud-based Kafka applications, this can deliver both lower latency and reduced infrastructure costs. All without changing a line of code!
Improving Logging Ingestion Quality At Pinterest: Fighting Data Corruption An...
This document summarizes Heng Zhang's presentation on improving logging ingestion quality at Pinterest. It discusses how Pinterest ingests large volumes of logging data at scale through a pipeline that favors scalability over consistency. This can lead to data corruption and loss issues. The presentation proposes a logging auditing framework to address these problems. It would add CRC checksums, audit headers and events at various stages to detect corrupted messages, track data loss metrics, and process audit events to remove bad data and provide alerts. The framework was tested and rolled out across Pinterest's ingestion pipelines with no downtime, improving data quality.
What's hot (20)
Connect at Twitter-scale | Jordan Bull and Ryanne Dolan, Twitter
Connect at Twitter-scale | Jordan Bull and Ryanne Dolan, Twitter
Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
Tradeoffs in Distributed Systems Design: Is Kafka The Best? (Ben Stopford and...
Tradeoffs in Distributed Systems Design: Is Kafka The Best? (Ben Stopford and...
Monitoring and Resiliency Testing our Apache Kafka Clusters at Goldman Sachs ...
Monitoring and Resiliency Testing our Apache Kafka Clusters at Goldman Sachs ...
Apache Pulsar: A Foundation Backbone for Clever Cloud - Pulsar Virtual Summit...
Apache Pulsar: A Foundation Backbone for Clever Cloud - Pulsar Virtual Summit...
Look how easy it is to go from events to blazing-fast analytics! | Neha Pawar...
Look how easy it is to go from events to blazing-fast analytics! | Neha Pawar...
Give Your Confluent Platform Superpowers! (Sandeep Togrika, Intel and Bert Ha...
Give Your Confluent Platform Superpowers! (Sandeep Togrika, Intel and Bert Ha...
Kafka on Kubernetes: Does it really have to be "The Hard Way"? (Viktor Gamov,...
Kafka on Kubernetes: Does it really have to be "The Hard Way"? (Viktor Gamov,...
Securing Kafka At Zendesk (Joy Nag, Zendesk) Kafka Summit 2020
Securing Kafka At Zendesk (Joy Nag, Zendesk) Kafka Summit 2020
Asynchronous Transaction Processing With Kafka as a Single Source of Truth - ...
Asynchronous Transaction Processing With Kafka as a Single Source of Truth - ...
How did we move the mountain? - Migrating 1 trillion+ messages per day across...
How did we move the mountain? - Migrating 1 trillion+ messages per day across...
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...
Building Retry Architectures in Kafka with Compacted Topics | Matthew Zhou, V...
From Newbie to Highly Available, a Successful Kafka Adoption Tale (Jonathan S...
From Newbie to Highly Available, a Successful Kafka Adoption Tale (Jonathan S...
Exposing and Controlling Kafka Event Streaming with Kong Konnect Enterprise |...
Exposing and Controlling Kafka Event Streaming with Kong Konnect Enterprise |...
Cross the streams thanks to Kafka and Flink (Christophe Philemotte, Digazu) K...
Cross the streams thanks to Kafka and Flink (Christophe Philemotte, Digazu) K...
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming Applications
Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
Better Kafka Performance Without Changing Any Code | Simon Ritter, Azul
Improving Logging Ingestion Quality At Pinterest: Fighting Data Corruption An...
Improving Logging Ingestion Quality At Pinterest: Fighting Data Corruption An...
Similar to Twitter’s Apache Kafka Adoption Journey | Ming Liu, Twitter
Netflix Keystone Pipeline at Big Data Bootcamp, Santa Clara, Nov 2015
Keystone - Processing over Half a Trillion events per day with 8 million events & 17 GB per second peaks, and at-least once processing semantics. We will explore in detail how we employ Kafka, Samza, and Docker at scale to implement a multi-tenant pipeline. We will also look at the evolution to its current state and where the pipeline is headed next in offering a self-service stream processing infrastructure atop the Kafka based pipeline and support Spark Streaming.
Distributed Kafka Architecture Taboola Scale
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Meetup#7 | Session 2 | 21/03/2018 | Taboola
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In this talk, we will present our multi-DC Kafka architecture, and discuss how we tackle sending and handling 10B+ messages per day, with maximum availability and no tolerance for data loss.
Our architecture includes technologies such as Cassandra, Spark, HDFS, and Vertica - with Kafka as the backbone that feeds them all.
Insta clustr seattle kafka meetup presentation bb
This document summarizes Instaclustr's lessons learned from building a managed Apache Kafka service. It provides an overview of Kafka and how it works, details Instaclustr's offering and development process, and discusses choices around hardware, security configuration, monitoring, backups and restores. Key topics covered include benchmarking storage types, enabling SSL, managing topics and users, and exposing metrics for monitoring brokers and topics.
Netflix Data Pipeline With Kafka
This document summarizes Netflix's use of Kafka in their data pipeline. It discusses how Netflix evolved from using S3 and EMR to introducing Kafka and Kafka producers and consumers to handle 400 billion events per day. It covers challenges of scaling Kafka clusters and tuning Kafka clients and brokers. Finally, it outlines Netflix's roadmap which includes contributing to open source projects like Kafka and testing failure resilience.
Netflix Data Pipeline With Kafka
This document summarizes Netflix's use of Kafka in their data pipeline. It discusses the evolution of Netflix's data pipeline to incorporate Kafka to handle 400 billion events per day. It describes how Netflix uses Kafka clusters with different priorities and configurations. It also outlines some of the challenges of using Kafka at Netflix's scale, such as Zookeeper client issues and cluster scaling, and the solutions Netflix developed to address these challenges.
An Introduction to Apache Kafka
Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala. The project aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Kafka meetup seattle 2019 mirus reliable, high performance replication for ap...
Mirus is a tool developed by Salesforce to replicate data between Apache Kafka clusters at scale. It is based on Kafka Connect and provides dynamic configuration, monitoring of topics and partitions, and improved resilience over the default Mirror Maker tool. Mirus handles reliable replication of data between multiple global data centers with minimal latency and data duplication.
Kafka Practices @ Uber - Seattle Apache Kafka meetup
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.
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...
Title: Apache Kafka Entering The Streaming World Via Lenses
Speaker: Marios Andreopoulos (https://linkedin.com/in/marios-andreopoulos-53910a167//) & Giannis Polyzos (https://linkedin.com/in/john-polyzos-04a60688//)
Date: Saturday, June 23, 2018
Event: https://meetup.com/Athens-Big-Data/events/251550256/
Capital One Delivers Risk Insights in Real Time with Stream Processing
Speakers: Ravi Dubey, Senior Manager, Software Engineering, Capital One + Jeff Sharpe, Software Engineer, Capital One
Capital One supports interactions with real-time streaming transactional data using Apache Kafka®. Kafka helps deliver information to internal operation teams and bank tellers to assist with assessing risk and protect customers in a myriad of ways.
Inside the bank, Kafka allows Capital One to build a real-time system that takes advantage of modern data and cloud technologies without exposing customers to unnecessary data breaches, or violating privacy regulations. These examples demonstrate how a streaming platform enables Capital One to act on their visions faster and in a more scalable way through the Kafka solution, helping establish Capital One as an innovator in the banking space.
Join us for this online talk on lessons learned, best practices and technical patterns of Capital One’s deployment of Apache Kafka.
-Find out how Kafka delivers on a 5-second service-level agreement (SLA) for inside branch tellers.
-Learn how to combine and host data in-memory and prevent personally identifiable information (PII) violations of in-flight transactions.
-Understand how Capital One manages Kafka Docker containers using Kubernetes.
Watch the recording: https://videos.confluent.io/watch/6e6ukQNnmASwkf9Gkdhh69?.
Uber Real Time Data Analytics
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 Samza) and in-house technologies have helped Uber scale.
Introduction to Apache Kafka
This document provides an overview of Apache Kafka including its main components, architecture, and ecosystem. It describes how LinkedIn used Kafka to solve their data pipeline problem by decoupling systems and allowing for horizontal scaling. The key elements of Kafka are producers that publish data to topics, the Kafka cluster that stores streams of records in a distributed, replicated commit log, and consumers that subscribe to topics. Kafka Connect and the Schema Registry are also introduced as part of the Kafka ecosystem.
Netflix keystone streaming data pipeline @scale in the cloud-dbtb-2016
Keystone processes over 700 billion events per day (1 peta byte) with at-least once processing semantics in the cloud. We will explore in detail how we leverage Kafka, Samza, Docker, and Linux at scale to implement a multi-tenant pipeline in AWS cloud within a year. We will also share our plans on offering a Stream Processing as a Service for all of Netflix use.
Fundamentals of Apache Kafka
Kafka's basic terminologies, its architecture, its protocol and how it works.
Kafka at scale, its caveats, guarantees and use cases offered by it.
How we use it @ZaprMediaLabs.
How Apache Kafka® Works
Watch this talk here: https://www.confluent.io/online-talks/how-apache-kafka-works-on-demand
Pick up best practices for developing applications that use Apache Kafka, beginning with a high level code overview for a basic producer and consumer. From there we’ll cover strategies for building powerful stream processing applications, including high availability through replication, data retention policies, producer design and producer guarantees.
We’ll delve into the details of delivery guarantees, including exactly-once semantics, partition strategies and consumer group rebalances. The talk will finish with a discussion of compacted topics, troubleshooting strategies and a security overview.
This session is part 3 of 4 in our Fundamentals for Apache Kafka series.
kafka
This document discusses the evolution of Kafka clusters at AppsFlyer over time. The initial cluster had 4 brokers and handled hundreds of millions of messages with low partitioning and replication. A new cluster was designed with more brokers, replication across availability zones, and higher partitioning to support billions of messages. However, this led to issues like uneven leader distribution and failures. Various solutions were implemented like increasing brokers, splitting topics, and hardware upgrades. Ongoing testing and monitoring helped identify more problems and improvements around replication, partitioning, and automation. Key lessons learned included balancing replication and leaders, supporting dynamic changes, and thorough testing of failure scenarios.
Hadoop summit - Scaling Uber’s Real-Time Infra for Trillion Events per Day
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 Samza) and in-house technologies have helped Uber scale.
How Uber scaled its Real Time Infrastructure to Trillion events per day
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 Samza) and in-house technologies have helped Uber scale.
Kafka Cluster Federation at Uber (Yupeng Fui & Xiaoman Dong, Uber) Kafka Summ...
Uber has one of the largest Kafka deployment in the industry. To improve the scalability and availability, we developed and deployed a novel federated Kafka cluster setup which hides the cluster details from producers/consumers. Users do not need to know which cluster a topic resides and the clients view a "logical cluster". The federation layer will map the clients to the actual physical clusters, and keep the location of the physical cluster transparent from the user. Cluster federation brings us several benefits to support our business growth and ease our daily operation. In particular, Client control. Inside Uber there are a large of applications and clients on Kafka, and it's challenging to migrate a topic with live consumers between clusters. Coordinations with the users are usually needed to shift their traffic to the migrated cluster. Cluster federation enables much control of the clients from the server side by enabling consumer traffic redirection to another physical cluster without restarting the application. Scalability: With federation, the Kafka service can horizontally scale by adding more clusters when a cluster is full. The topics can freely migrate to a new cluster without notifying the users or restarting the clients. Moreover, no matter how many physical clusters we manage per topic type, from the user perspective, they view only one logical cluster. Availability: With a topic replicated to at least two clusters we can tolerate a single cluster failure by redirecting the clients to the secondary cluster without performing a region-failover. This also provides much freedom and alleviates the risks for us to carry out important maintenance on a critical cluster. Before the maintenance, we mark the cluster as a secondary and migrate off the live traffic and consumers. We will present the details of the architecture and several interesting technical challenges we overcame.
Westpac Bank Tech Talk 1: Dive into Apache Kafka
Watch the webcast here: https://videos.confluent.io/watch/bfDAKeLTiq751YyiZaCgHG
Speaker: Brett Randall
Similar to Twitter’s Apache Kafka Adoption Journey | Ming Liu, Twitter (20)
Netflix Keystone Pipeline at Big Data Bootcamp, Santa Clara, Nov 2015
Netflix Keystone Pipeline at Big Data Bootcamp, Santa Clara, Nov 2015
Kafka meetup seattle 2019 mirus reliable, high performance replication for ap...
Kafka meetup seattle 2019 mirus reliable, high performance replication for ap...
Kafka Practices @ Uber - Seattle Apache Kafka meetup
Kafka Practices @ Uber - Seattle Apache Kafka meetup
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...
14th Athens Big Data Meetup - Landoop Workshop - Apache Kafka Entering The St...
Capital One Delivers Risk Insights in Real Time with Stream Processing
Capital One Delivers Risk Insights in Real Time with Stream Processing
Netflix keystone streaming data pipeline @scale in the cloud-dbtb-2016
Netflix keystone streaming data pipeline @scale in the cloud-dbtb-2016
Hadoop summit - Scaling Uber’s Real-Time Infra for Trillion Events per Day
Hadoop summit - Scaling Uber’s Real-Time Infra for Trillion Events per Day
How Uber scaled its Real Time Infrastructure to Trillion events per day
How Uber scaled its Real Time Infrastructure to Trillion events per day
Kafka Cluster Federation at Uber (Yupeng Fui & Xiaoman Dong, Uber) Kafka Summ...
Kafka Cluster Federation at Uber (Yupeng Fui & Xiaoman Dong, Uber) Kafka Summ...
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.
Additionally, I will highlight specific business use cases that would not have been implemented without our platform.0 characters0 characters"
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.
Large language models for your streaming application - explained with a little maths and a lot of pub stories"
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."
A Window Into Your Kafka Streams Tasks | KSL
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."
Debezium vs. the World: An Overview of the CDC Ecosystem
"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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Twitter’s Apache Kafka Adoption Journey | Ming Liu, Twitter
- 1. Twitter’s Apache Kafka Adoption Journey Ming Liu Yiming Zang 2021 May
- 2. Overview Challenges & Experience 03 ● Challenges & Success Stories sharing Kafka Ecosystem 02 ● Ecosystem we have built around Kafka at Twitter Kafka Adoption 01 ● In-house PubSub solution at a glance ● Why we adopted Kafka ● Kafka migration story
- 3. EventBus
- 5. Self Serve Migration Client Eventbus Provisioning Service Kafka Provisioning Service Replicate Dynamic Switch Migration tool
- 6. Migration Challenges Migration Scale ● Thousands of topics ● Tens of thousands of consumers Mission Critical ● Revenue impact ● Product impact ● Hundreds of teams ● Customer support after migration Cooperation
- 7. Kafka Ecosystem at Twitter Kafka Kafka Kafka Kafka Kafka Kafka Heron Hadoop Kafka Streams Kafka Consumer HDFS Replicator Cross DC/Cluster Replicator Filtering Service Kafka Self Serve & Provisioning Service Kafka Producer Scribe/Flume Kafka Auto Balancer Observer Customer Onboarding Produce Consume Realtime Processing Batch Processing External Monitoring Kick rebalance after cluster expansion/shrink Google PubSub Connect
- 8. Challenges and Learning ● Overall, very successful experience migrating to Kafka with the diverse and high throughput workloads
- 9. Kafka today at Twitter (March 2021) ● Cluster: ○ On-prem, bare-metal powered by Aurora Mesos ○ Moving to Kubernetes on cloud and on-Prem ● Broker Config: ○ 4TB/8TB SSD, will also support 24TB SSD soon ○ JVM heap 5GB to 15GB, pagecache around 45GB currently ● Scale: (in one of three data center) ○ 50 clusters, up to 300 broker per cluster ○ > 2000 topics, > 40k subscribers ○ 120M EPS, 120G BPS Ingress, 900G BPS Egress ○ Some topics has > 3G BPS ingress ● Version: ○ Broker: 2.5, Client/Stream: 2.4.1
- 10. Latency sensitivity and tuning ● Architecture: ○ Shared storage and serving layer ○ Durable by Replication ● 1. Deployment and upgrade ○ Automatically config topic throttle rate before each broker. ● 2. Noisy neighbor and Traffic fluctuations ○ Quota management ● 3. Operations: Rebalancing and replica movement ● 4. Read/write interference ● 5. Network thread round robin scheduling
- 11. Producer and Consumption ● Batch efficiency for high throughput: ○ KIP-480: Use Sticky Partition ● Idempotent producer and Transactional producer: ○ Problems: GC on broker due to producerId cache, not purging aborted transaction. ○ More stable in last two year ● Lag from Rebalance ○ With KIP-429 (incremental cooperative), less disruptive ○ The rebalance latency is still at least 200 ms, looking to improve that.
- 12. Kafka Stream ● Open Source Finatra: native Kafka Stream integration ○ Allow fast creation of a fully functional KafkaStream service ○ Support custom DSL and async processor and transformer ○ Rich unit testing functionality ○ RocksDB integration supporting efficient range scan ● One Example Stateful Workload: 4.5G BPS, 2.5M EPS ingress, Confluent Blog ● Limitation ○ Scalability: The StreamAssignor group message size limit the maximum size of group member (fixed in 2.5) ○ Stateful: Ensure the streamThread on the same machine is assigned the same partitions as before. (KIP 345 help partially)
- 13. Disk failure and unclean leader election ● Disk failure, sometimes, can cause the offline partition and data loss ● In Kafka 2.5, it happens with less frequency (suspect due to replica.lag.time.max.ms = 30 ) ● KIP 501: Avoid offline partitions
- 14. Replica Rebalance ● Bare metal Machine: ○ Cluster expansion and disk rebalance is very slow process, need to plan ahead ○ Rebalance to improve disk utilization ○ Rebalance include inter-broker and intra-broker (multi-disks) ● Implement Kafka Auto Rebalance Service ● Future: Separate compute and storage ○ Network attached volumes. ○ Tiered storage
- 15. Thank you !