Kafka Summit NYC 2017 - The Best Thing Since Partitioned Bread
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The document discusses Apache Kafka's new Streams API for stream processing. It introduces the Streams API and provides examples of stateless and stateful computations that can be performed on streams of data. It also notes that the Streams API is now in production use at LINE Corp, processing millions of messages per second, and provides additional resources for learning more about the Streams API.
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There are many stream processing frameworks available nowadays, but the cost of provisioning infrastructure and maintaining distributed computations is usually very high. Sometimes you just have to satisfy some specific requirements, like using HDFS or YARN.
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We've been developing a data pipeline in Demonware/Activision for a while. We learned how to scale it not only in terms of messages/sec it can handle, but also in terms of supporting more games and more use-cases.
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The talk will be a technical deep dive covering topics like:
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-How to meet production SLAs by minimizing failover and recovery times: configuring standby tasks and the pros and cons of having standby replicas for local state
-How to improve resiliency and 24×7 operability: the use of different configurable error handlers, callbacks and how they can be used to see what’s going on inside the application
-How to achieve efficient scalability: a thorough review of the relationship between the number of instances, threads and state stores and how they relate to each other
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This document discusses Kafka indexing in Druid, including:
1. Kafka indexing service handles creating Kafka indexing tasks to ingest data from Kafka in a guaranteed exactly-once manner by leveraging Kafka's partitioning and offsets.
2. The supervisor spec is submitted to the overlord and defines the data schema, tuning configuration, and IO configuration for reading from Kafka topics.
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Real-Time Stream Processing with KSQL and Apache Kafka
Real Time Stream Processing with KSQL and Kafka
David Peterson, Confluent APAC
APIdays Melbourne 2018
Unordered, unbounded and massive datasets are increasingly common in day-to-day business. Using this to your advantage is incredibly difficult with current system designs. We are stuck in a model where we can only take advantage of this *after* it has happened. Many times, this is too late to be useful in the enterprise.
KSQL is a streaming SQL engine for Apache Kafka. KSQL lowers the entry bar to the world of stream processing, providing a simple and completely interactive SQL interface for processing data in Kafka. KSQL (like Kafka) is open-source, distributed, scalable, and reliable.
A real time Kafka platform moves your data up the stack, closer to the heart of your business, allowing you to build scalable, mission-critical services by quickly deploying SQL-like queries in a severless pattern.
This talk will highlight key use cases for real time data, and stream processing with KSQL: Real time analytics, security and anomaly detection, real time ETL / data integration, Internet of Things, application development, and deploying Machine Learning models with KSQ.
Real time data and stream processing means that Kafka is just as important to the disrupted as it is to the disruptors.
The Wonderful World of Apache Kafka
"Apache Kafka is the core of an amazing ecosystem of tools and frameworks that enable us to get more value from our data. Let's take a tour through this wonderful world and see what we can learn.
In this session, we'll have a gentle introduction to Apache Kafka, and then a survey of some of the more popular components in the Kafka ecosystem. We'll look at the Kafka Producer and Consumer libraries, Kafka Connect, Kafka Streams, the Confluent Schema Registry, and more. You will leave loaded with ideas of ways to put Kafka to use in your organization, and a list of resources to help you on your journey."
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To address those issues, we improved on the existing EOS model by integrating static Producer transaction semantics with dynamic Consumer group semantics. We will have a deep-dive into the newly added features (KIP-447), from which the audience will have more insight into the scalability v.s. semantics guarantees tradeoffs and how Kafka Streams specifically leveraged them to help scale EOS streaming applications written in this library. We would also present how the EOS code can be simplified with plain Producer and Consumer. Come to learn more if you wish to adopt this improved EOS feature and get started on building your own EOS application today!
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Yet another presentation about Event Sourcing? Yes and no. Event Sourcing is a really great concept. Some could say it’s a Holy Grail of the software architecture. I might agree with that, while remembering that everything comes with a price. This session is a summary of my experience with ES gathered while working on 3 different commercial products. Instead of theoretical aspects, I will focus on possible challenges with ES implementation. What could explode (very often with delayed ignition)? How and where to store events effectively? What are possible schema evolution solutions? How to achieve the highest level of scalability and live with eventual consistency? And many other interesting topics that you might face when experimenting with ES.
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In this presentation, I discuss two approaches to an asynchronous event-based architecture. The first is a "classic" style protocol (Python services driven by callbacks with decorators communicating using a messaging layer) that we've been implementing at Demonware (Activision) for Call of Duty back-end services. The second is an actor-based approach (Scala/Akka based microservices communicating using a messaging layer and a centralized router) in place at Bench Accounting.
Both systems, while event based, take different approaches to building asynchronous, reactive applications. This talk explores the benefits, challenges, and lessons learned architecting both Actor and Non-Actor systems.
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High-speed and low footprint data stream processing is high in demand for Kafka Streams applications. However, how to write an efficient streaming application using the Streams DSL has been asked by many users in the past since it requires some deep knowledge about Kafka Streams internals. In this talk, I will talk about how to analyze your Kafka Streams applications, target performance bottlenecks and unnecessary storage costs, and optimize your application code accordingly using the Streams DSL.
In addition, I will talk about the new optimization framework that we have been developed inside Kafka Streams since the 2.1 release which replaced the in-place translation of the Streams DSL into a comprehensive process composed of streams topology compilation and rewriting phases, with a focus on reducing various storage footprints of Streams applications, such as state stores, internal topics etc.
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What can be easier than building a data pipeline nowadays? You add a few Apache Kafka clusters, some way to ingest data (probably over HTTP), design a way to route your data streams, add a few stream processors and consumers, integrate with a data warehouse... wait, it does start to look like A LOT of things, doesn't it? And you probably want to make it highly scalable and available in the end, correct?
We've been developing a data pipeline in Demonware/Activision for a while. We learned how to scale it not only in terms of messages/sec it can handle, but also in terms of supporting more games and more use-cases.
In this presentation you'll hear about the lessons we learned, including (but not limited to):
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Apache Kafka added a powerful stream processing library in mid-2016, Kafka Streams, which runs on top of Apache Kafka. The community has embraced Kafka Streams with many early adopters, and the adoption rate continues to grow. Large to mid-size organizations have come to rely on Kafka Streams in their production environments. Kafka Streams has many advanced features to make applications more robust.
The point of this presentation is to show users of Kafka Streams some of the latest and greatest features, as well as some that may be advanced, that can make streams applications more resilient. The target audience for this talk are those users already comfortable writing Kafka Streams applications and want to go from writing their first proof-of-concept applications to writing robust applications that can withstand the rigor that running in a production environment demands.
The talk will be a technical deep dive covering topics like:
-Best practices on configuring a Kafka Streams application
-How to meet production SLAs by minimizing failover and recovery times: configuring standby tasks and the pros and cons of having standby replicas for local state
-How to improve resiliency and 24×7 operability: the use of different configurable error handlers, callbacks and how they can be used to see what’s going on inside the application
-How to achieve efficient scalability: a thorough review of the relationship between the number of instances, threads and state stores and how they relate to each other
While this is a technical deep dive, the talk will also present sample code so that attendees can view the concepts discussed in practice. Attendees of this talk will walk away with a deeper understanding of how Kafka Streams works, and how to make their Kafka Streams applications more robust and efficient. There will be a mix of discussion.
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Apache Kafka’s rise in popularity as a streaming platform has demanded a revisit of its traditional at least once message delivery semantics. In this talk, we present the recent additions to Apache Kafka to achieve exactly once semantics. We shall discuss the newly introduced transactional APIs and use Kafka Streams as an example to show how these APIs are leveraged for streams tasks.
Streaming ETL - from RDBMS to Dashboard with KSQL
Apache Kafka is a massively scalable message queue that is being used at more and more places connecting more and more data sources. This presentation will introduce Kafka from the perspective of a mere mortal DBA and share the experience of (and challenges with) getting events from the database to Kafka using Kafka connect including poor-man’s CDC using flashback query and traditional logical replication tools. To demonstrate how and why this is a good idea, we will build an end-to-end data processing pipeline. We will discuss how to turn changes in database state into events and stream them into Apache Kafka. We will explore the basic concepts of streaming transformations using windows and KSQL before ingesting the transformed stream in a dashboard application.
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Real-Time Stream Processing with KSQL and Apache Kafka
Real Time Stream Processing with KSQL and Kafka
David Peterson, Confluent APAC
APIdays Melbourne 2018
Unordered, unbounded and massive datasets are increasingly common in day-to-day business. Using this to your advantage is incredibly difficult with current system designs. We are stuck in a model where we can only take advantage of this *after* it has happened. Many times, this is too late to be useful in the enterprise.
KSQL is a streaming SQL engine for Apache Kafka. KSQL lowers the entry bar to the world of stream processing, providing a simple and completely interactive SQL interface for processing data in Kafka. KSQL (like Kafka) is open-source, distributed, scalable, and reliable.
A real time Kafka platform moves your data up the stack, closer to the heart of your business, allowing you to build scalable, mission-critical services by quickly deploying SQL-like queries in a severless pattern.
This talk will highlight key use cases for real time data, and stream processing with KSQL: Real time analytics, security and anomaly detection, real time ETL / data integration, Internet of Things, application development, and deploying Machine Learning models with KSQ.
Real time data and stream processing means that Kafka is just as important to the disrupted as it is to the disruptors.
The Wonderful World of Apache Kafka
"Apache Kafka is the core of an amazing ecosystem of tools and frameworks that enable us to get more value from our data. Let's take a tour through this wonderful world and see what we can learn.
In this session, we'll have a gentle introduction to Apache Kafka, and then a survey of some of the more popular components in the Kafka ecosystem. We'll look at the Kafka Producer and Consumer libraries, Kafka Connect, Kafka Streams, the Confluent Schema Registry, and more. You will leave loaded with ideas of ways to put Kafka to use in your organization, and a list of resources to help you on your journey."
Asynchronous stream processing with Akka Streams
The document discusses Akka streams, which provide a toolkit for building highly concurrent and distributed applications on the JVM. It describes key components of Akka like actors for concurrency, clustering for location transparency, and streams for asynchronous stream processing. Reactive Streams is introduced as a standard for asynchronous stream processing with back pressure. Examples are provided to demonstrate how to create stream processing graphs with Akka Streams in just a few lines of code.
Streams Don't Fail Me Now - Robustness Features in Kafka Streams
"Stream processing applications can experience downtime due to a variety of reasons, such as a Kafka broker or another part of the infrastructure breaking down, an unexpected record (known as a poison pill) that causes the processing logic to get stuck, or a poorly performed upgrade of the application that yields unintended consequences.
Apache Kafka's native stream processing solution, Kafka Streams, has been successfully used with little or no downtime in many companies. This has been made possible by several robustness features built into Streams over the years and best practices that have evolved from many years of experience with production-level workloads.
In this talk, I will present the unique solutions the community has found for making Streams robust, explain how to apply them to your workloads and discuss the remaining challenges. Specifically, I will talk about standby tasks and rack-aware assignments that can help with losing a single node or a whole data center. I will also demonstrate how custom exception handlers and dead letter queues can make a pipeline more resistant to bad data. Finally, I will discuss options to evolve stream topologies safely."
Real Time Stream Processing with KSQL and Kafka
Unordered, unbounded and massive datasets are increasingly common in day-to-day business. Using this to your advantage is incredibly difficult with current system designs. We are stuck in a model where we can only take advantage of this *after* it has happened. Many times, this is too late to be useful in the enterprise.
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A real time Kafka platform moves your data up the stack, closer to the heart of your business, allowing you to build scalable, mission-critical services by quickly deploying SQL-like queries in a severless pattern.
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Real time data and stream processing means that Kafka is just as important to the disrupted as it is to the disruptors.
APAC ksqlDB Workshop
Replay available here: https://videos.confluent.io/watch/en9ukgZLWVEMXVpsUAAkAx?
Speaker Name: Kaijun Xu
Event streaming webinar feb 2020
Event streaming applications unlock new benefits by combining various data feeds. However, getting actionable insights in a timely fashion has remained a challenge, as the data has been siloed in disparate systems. ksqlDB solves this by providing an interactive SQL interface that can seamlessly combine and transform data from various sources.
In this webinar, we will show how streaming queries of high throughput NoSQL systems can derive insights from various push/pull queries via ksqlDB's User-Defined Functions, Aggregate Functions and Table Functions.
Watch this to learn:
Real-world examples of the benefits of using a streaming database like ksqlDB and seamlessly combining data from Kafka & Cassandra/Scylla (NoSQL).
The functionality of ksqlDB via push/pull queries and UDFs/UDAFs/UDTFs.
The ease with which data stored in a NoSQL database can be transformed using ksqlDB and then persisted back for long-term storage.
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Event streaming applications unlock new benefits by combining various data feeds. However, getting actionable insights in a timely fashion has remained a challenge, as the data has been siloed in disparate systems. ksqlDB solves this by providing an interactive SQL interface that can seamlessly combine and transform data from various sources.
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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."
KSQL – The Open Source SQL Streaming Engine for Apache Kafka (Big Data Spain ...
KSQL – The Open Source SQL Streaming Engine for Apache Kafka (Talk at Big Data Spain 2018 in Madrid).
- KSQL includes access to the rich Apache Kafka ecosystem and is suitable for various use cases, including Streaming ETL, Real Time Stream Monitoring and Anomaly Detection
- KSQL allows to realize stream processing without coding and without additional analytics cluster
Description:
The rapidly expanding world of stream processing can be daunting, with new concepts such as various types of time semantics, windowed aggregates, changelogs, and programming frameworks to master.
KSQL is an open-source, Apache 2.0 licensed streaming SQL engine on top of Apache Kafka which aims to simplify all this and make stream processing available to everyone. Even though it is simple to use, KSQL is built for mission-critical and scalable production deployments (using Kafka Streams under the hood).
Benefits of using KSQL include: No coding required; no additional analytics cluster needed; streams and tables as first-class constructs; access to the rich Kafka ecosystem. This session introduces the concepts and architecture of KSQL. Use cases such as Streaming ETL, Real Time Stream Monitoring or Anomaly Detection are discussed. A live demo shows how to setup and use KSQL quickly and easily on top of your Kafka ecosystem.
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1) Rxjs provides a paradigm for dealing with asynchronous operations in a synchronous way using observables. Observables allow representing push-based data streams and provide operators to compose and transform these streams.
2) Operators like map, filter, and switchMap allow manipulating observable streams and their values in various ways. Common patterns include cascading asynchronous calls, parallelizing multiple requests, and retrying or error handling of streams.
3) Schedulers allow bending time and virtual clocks, enabling testing asynchronous code by controlling when asynchronous operations complete. Marble testing uses descriptive patterns to visually test observable streams and operator behavior.
Kick Your Database to the Curb
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My talk at Google DevFest Switzerland, Fribourg, Oct 2017.
https://devfest.ch/schedule/day1?sessionId=118
Abstract:
Modern businesses have data at their core, and this data is changing continuously. How can we harness this torrent of information in real-time? The answer is stream processing, and the technology that has since become the core platform for streaming data is Apache Kafka.
Among the thousands of companies that use Kafka to transform and reshape their industries are the likes of Netflix, Uber, PayPal, and AirBnB, but also established players such as Goldman Sachs, Cisco, and Oracle. Unfortunately, today’s common architectures for real-time data processing at scale suffer from complexity: there are many technologies that need to be stitched and operated together, and each individual technology is often complex by itself. This has led to a strong discrepancy between how we, as engineers, would like to work vs. how we actually end up working in practice.
In this session we talk about how Apache Kafka helps you to radically simplify your data architectures. We cover how you can now build normal applications to serve your real-time processing needs — rather than building clusters or similar special-purpose infrastructure — and still benefit from properties such as high scalability, distributed computing, and fault-tolerance, which are typically associated exclusively with cluster technologies. We discuss common use cases to realize that stream processing in practice often requires database-like functionality, and how Kafka allows you to bridge the worlds of streams and databases when implementing your own core business applications (inventory management for large retailers, patient monitoring in healthcare, fleet tracking in logistics, etc), for example in the form of event-driven, containerized microservices. We will also give a brief shout-out to the recently launched KSQL, a streaming SQL engine for Apache Kafka.
Angular2 rxjs
1. Rxjs provides a better way of handling asynchronous code through observables which are streams of values over time. Observables allow for cancellable, retryable operations and easy composition of different asynchronous sources.
2. Common Rxjs operators like map, filter, and flatMap allow transforming and combining observable streams. Operators make observables quite powerful for tasks like async logic, event handling, and API requests.
3. In Angular, observables are used extensively for tasks like HTTP requests, routing, and component communication. Key aspects are using async pipes for subscriptions and unsubscribing during lifecycle hooks. Rxjs greatly simplifies many common asynchronous patterns in Angular applications.
RxJS - The Reactive extensions for JavaScript
RxJS is a library for composing asynchronous and event-based programs using observable sequences and functional style operators. It provides developers with Observables to represent asynchronous data streams, operators to query those streams, and Schedulers to parameterize concurrency. Some key operators include map to project values, filter to select values, reduce to aggregate values, and flatMap to flatten nested observables. RxJS supports both hot and cold observables, and is used for tasks like DOM event handling, API communication, and state management in applications.
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KSQL - Stream Processing simplified!
KSQL is a stream processing SQL engine, which allows stream processing on top of Apache Kafka. KSQL 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.
KSQL 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 KSQL for most part. This will be done in a live demo on a fictitious IoT sample.
Unlocking the world of stream processing with KSQL, the streaming SQL engine ...
Slides of my Strata London 2018 talk:
https://conferences.oreilly.com/strata/strata-eu/public/schedule/detail/65325
Abstract:
Modern businesses have data at their core, and this data is changing continuously. Stream processing is what allows you harness this torrent of information in real time, and thousands of companies use Apache Kafka as the core platform for streaming data to transform and reshape their industries. However, the world of stream processing still has a very high barrier to entry. Today’s most popular stream processing technologies require the user to write code in programming languages such as Java or Scala. This hard requirement on coding skills is preventing many companies to unlock the benefits of stream processing to their full effect.
However, imagine that instead of having to write a lot of code in a programming language like Java or Scala for your favorite stream processing technology, all you’d need to get started with stream processing is a simple SQL statement, such as: SELECT * FROM payments-kafka-stream WHERE fraudProbability > 0.8.
Michael Noll offers an overview of KSQL, the open source streaming SQL engine for Apache Kafka, which makes it easy to get started with a wide range of real-time use cases, such as monitoring application behavior and infrastructure, detecting anomalies and fraudulent activities in data feeds, and real-time ETL. With KSQL, there’s no need to write any code in a programming language. KSQL brings together the worlds of streams and databases by allowing you to work with your data in a stream and in a table format. Built on top of Kafka’s Streams API, KSQL supports many powerful operations, including filtering, transformations, aggregations, joins, windowing, sessionization, and much more. It is open source (Apache 2.0 licensed), distributed, scalable, fault tolerant, and real time. You’ll learn how KSQL makes it easy to get started with a wide range of stream processing use cases and how to get up and running as you explore how it all works under the hood.
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On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
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Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
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在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样
原版一模一样【微信：741003700 】【加拿大英属哥伦比亚大学毕业证本科学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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Odoo ERP Vs. Traditional ERP Systems – A Comparative Analysis
When it is all about ERP solutions, companies typically meet their needs with common ERP solutions like SAP, Oracle, and Microsoft Dynamics. These big players have demonstrated that ERP systems can be either simple or highly comprehensive. This remains true today, but there are new factors to consider, including a promising new contender in the market that’s Odoo. This blog compares Odoo ERP with traditional ERP systems and explains why many companies now see Odoo ERP as the best choice.
What are ERP Systems?
An ERP, or Enterprise Resource Planning, system provides your company with valuable information to help you make better decisions and boost your ROI. You should choose an ERP system based on your company’s specific needs. For instance, if you run a manufacturing or retail business, you will need an ERP system that efficiently manages inventory. A consulting firm, on the other hand, would benefit from an ERP system that enhances daily operations. Similarly, eCommerce stores would select an ERP system tailored to their needs.
Because different businesses have different requirements, ERP system functionalities can vary. Among the various ERP systems available, Odoo ERP is considered one of the best in the ERp market with more than 12 million global users today.
Odoo is an open-source ERP system initially designed for small to medium-sized businesses but now suitable for a wide range of companies. Odoo offers a scalable and configurable point-of-sale management solution and allows you to create customised modules for specific industries. Odoo is gaining more popularity because it is built in a way that allows easy customisation, has a user-friendly interface, and is affordable. Here, you will cover the main differences and get to know why Odoo is gaining attention despite the many other ERP systems available in the market.
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国外毕业证学位证成绩单办理方法：
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如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
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Mobile App Development Company In Noida | Drona Infotech
Drona Infotech is a premier mobile app development company in Noida, providing cutting-edge solutions for businesses.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
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Kafka Summit NYC 2017 - The Best Thing Since Partitioned Bread
- 1. 1 The Best Thing Since Partitioned Bread: Rethinking Stream Processing with Apache Kafka’s new Streams API Michael G. Noll Confluent
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- 18. 18 KStream<Integer, Integer> input = builder.stream("numbers-‐topic"); // Stateless computation KStream<Integer, Integer> doubled = input.mapValues(v -‐> v * 2); // Stateful computation KTable<Integer, Integer> sumOfOdds = input .filter((k,v) -‐> v % 2 != 0) .selectKey((k, v) -‐> 1) .groupByKey() .reduce((v1, v2) -‐> v1 + v2, "sum-‐of-‐odds"); class PrintToConsoleProcessor implements Processor<K, V> { @Override public void init(ProcessorContext context) {} @Override void process(K key, V value) { System.out.println("Got value " + value); } @Override void punctuate(long timestamp) {} @Override void close() {} }
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- 45. 45 2016 2017 First release of Kafka’s Streams API (0.10.0.0) today In production at LINE Corp. 220+ million active users, processing millions of msg/s “Applying Kafka Streams for internal message delivery pipeline” https://engineering.linecorp.com/en/blog/detail/80 Example in the wild
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