Kafka for JUnit enables developers to start and stop a complete Kafka cluster comprised of Kafka brokers and distributed Kafka Connect workers from within a JUnit test. It also provides a rich set of convenient accessors to interact with such an embedded or external Kafka cluster in a lean and non-obtrusive way.
Kafka for JUnit can be used to both whitebox-test individual Kafka-based components of your application or to blackbox-test applications that offer an incoming and/or outgoing Kafka-based interface.
This presentation gives a brief introduction into Kafka for JUnit, discussing its design principles and code examples to get developers quickly up to speed using the library.
This document provides an overview of Kafka, a distributed streaming platform. It can publish and subscribe to streams of records, store streams durably across clusters, and process streams as they occur. The Kafka cluster stores streams of records in topics. It has four main APIs: Producer API to publish data, Consumer API to subscribe to topics, Streams API to transform streams, and Connector API to connect Kafka and other systems. Records in Kafka topics are partitioned and ordered with offsets for scalability and fault tolerance. Consumers subscribe to topics in consumer groups to process partitions in parallel.
Apache Kafka is a fast, scalable, durable and distributed messaging system. It is designed for high throughput systems and can replace traditional message brokers. Kafka has better throughput, partitioning, replication and fault tolerance compared to other messaging systems, making it suitable for large-scale applications. Kafka persists all data to disk for reliability and uses distributed commit logs for durability.
Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming apps. It provides a unified, scalable, and durable platform for handling real-time data feeds. Kafka works by accepting streams of records from one or more producers and organizing them into topics. It allows both storing and forwarding of these streams to consumers. Producers write data to topics which are replicated across clusters for fault tolerance. Consumers can then read the data from the topics in the order it was produced. Major companies like LinkedIn, Yahoo, Twitter, and Netflix use Kafka for applications like metrics, logging, stream processing and more.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
An Introduction to Apache Kafka with Timothy Spann and Carolyn Duby Cloudera Principal engineers.
We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Apache Kafka is a distributed streaming platform and distributed publish-subscribe messaging system. It uses a log abstraction to order events and replicate data across clusters. Kafka allows developers to publish and subscribe to streams of records known as topics. Producers publish data to topics and consumers subscribe to topics to process streams of records. The Kafka ecosystem includes tools like KStreams for stream processing and KSQL for querying streams of data.
Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics. Producers write data to topics and consumers read from topics. The data is partitioned and replicated across clusters of machines called brokers for reliability and scalability. A common data format like Avro can be used to serialize the data.
Apache Kafka is a distributed publish-subscribe messaging system that can handle high volumes of data and enable messages to be passed from one endpoint to another. It uses a distributed commit log that allows messages to be persisted on disk for durability. Kafka is fast, scalable, fault-tolerant, and guarantees zero data loss. It is used by companies like LinkedIn, Twitter, and Netflix to handle high volumes of real-time data and streaming workloads.
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
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.
This document provides an overview of Kafka, a distributed streaming platform. It can publish and subscribe to streams of records, store streams durably across clusters, and process streams as they occur. The Kafka cluster stores streams of records in topics. It has four main APIs: Producer API to publish data, Consumer API to subscribe to topics, Streams API to transform streams, and Connector API to connect Kafka and other systems. Records in Kafka topics are partitioned and ordered with offsets for scalability and fault tolerance. Consumers subscribe to topics in consumer groups to process partitions in parallel.
Apache Kafka is a fast, scalable, durable and distributed messaging system. It is designed for high throughput systems and can replace traditional message brokers. Kafka has better throughput, partitioning, replication and fault tolerance compared to other messaging systems, making it suitable for large-scale applications. Kafka persists all data to disk for reliability and uses distributed commit logs for durability.
Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming apps. It provides a unified, scalable, and durable platform for handling real-time data feeds. Kafka works by accepting streams of records from one or more producers and organizing them into topics. It allows both storing and forwarding of these streams to consumers. Producers write data to topics which are replicated across clusters for fault tolerance. Consumers can then read the data from the topics in the order it was produced. Major companies like LinkedIn, Yahoo, Twitter, and Netflix use Kafka for applications like metrics, logging, stream processing and more.
Hello, kafka! (an introduction to apache kafka)Timothy Spann
Hello ApacheKafka
An Introduction to Apache Kafka with Timothy Spann and Carolyn Duby Cloudera Principal engineers.
We also demo Flink SQL, SMM, SSB, Schema Registry, Apache Kafka, Apache NiFi and Public Cloud - AWS.
Apache Kafka is a distributed streaming platform and distributed publish-subscribe messaging system. It uses a log abstraction to order events and replicate data across clusters. Kafka allows developers to publish and subscribe to streams of records known as topics. Producers publish data to topics and consumers subscribe to topics to process streams of records. The Kafka ecosystem includes tools like KStreams for stream processing and KSQL for querying streams of data.
Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics. Producers write data to topics and consumers read from topics. The data is partitioned and replicated across clusters of machines called brokers for reliability and scalability. A common data format like Avro can be used to serialize the data.
Apache Kafka is a distributed publish-subscribe messaging system that can handle high volumes of data and enable messages to be passed from one endpoint to another. It uses a distributed commit log that allows messages to be persisted on disk for durability. Kafka is fast, scalable, fault-tolerant, and guarantees zero data loss. It is used by companies like LinkedIn, Twitter, and Netflix to handle high volumes of real-time data and streaming workloads.
Kafka Tutorial - Introduction to Apache Kafka (Part 1)Jean-Paul Azar
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.
What is Apache Kafka and What is an Event Streaming Platform?confluent
Speaker: Gabriel Schenker, Lead Curriculum Developer, Confluent
Streaming platforms have emerged as a popular, new trend, but what exactly is a streaming platform? Part messaging system, part Hadoop made fast, part fast ETL and scalable data integration. With Apache Kafka® at the core, event streaming platforms offer an entirely new perspective on managing the flow of data. This talk will explain what an event streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use—including several examples of where it is solving real business problems. New developments in this area such as KSQL will also be discussed.
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.
Apache Kafka Fundamentals for Architects, Admins and Developersconfluent
This document summarizes a presentation about Apache Kafka. It introduces Apache Kafka as a modern, distributed platform for data streams made up of distributed, immutable, append-only commit logs. It describes Kafka's scalability similar to a filesystem and guarantees similar to a database, with the ability to rewind and replay data. The document discusses Kafka topics and partitions, partition leadership and replication, and provides resources for further information.
Kafka is a distributed publish-subscribe messaging system that allows both streaming and storage of data feeds. It is designed to be fast, scalable, durable, and fault-tolerant. Kafka maintains feeds of messages called topics that can be published to by producers and subscribed to by consumers. A Kafka cluster typically runs on multiple servers called brokers that store topics which may be partitioned and replicated for fault tolerance. Producers publish messages to topics which are distributed to consumers through consumer groups that balance load.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
Apache Kafka is a distributed publish-subscribe messaging system that allows for high-throughput, persistent storage of messages. It provides decoupling of data pipelines by allowing producers to write messages to topics that can then be read from by multiple consumer applications in a scalable, fault-tolerant way. Key aspects of Kafka include topics for categorizing messages, partitions for scaling and parallelism, replication for redundancy, and producers and consumers for writing and reading messages.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
Performance Tuning RocksDB for Kafka Streams’ State Storesconfluent
Performance Tuning RocksDB for Kafka Streams’ State Stores, Bruno Cadonna, Contributor to Apache Kafka & Software Developer at Confluent and Dhruba Borthakur, CTO & Co-founder Rockset
Meetup link: https://www.meetup.com/Berlin-Apache-Kafka-Meetup-by-Confluent/events/273823025/
Kafka and Avro with Confluent Schema RegistryJean-Paul Azar
The document discusses Confluent Schema Registry, which stores and manages Avro schemas for Kafka clients. It allows producers and consumers to serialize and deserialize Kafka records to and from Avro format. The Schema Registry performs compatibility checks between the schema used by producers and consumers, and handles schema evolution if needed to allow schemas to change over time in a backwards compatible manner. It provides APIs for registering, retrieving, and checking compatibility of schemas.
Managing multiple event types in a single topic with Schema Registry | Bill B...HostedbyConfluent
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.
Apache Kafka is a fast, scalable, and distributed messaging system that uses a publish-subscribe messaging protocol. It is designed for high throughput systems and can replace traditional message brokers due to its higher throughput and built-in partitioning, replication, and fault tolerance. Kafka uses topics to organize streams of messages and partitions to allow horizontal scaling and parallel processing of data. Producers publish messages to topics and consumers subscribe to topics to receive messages.
Apache Kafka is a distributed publish subscribe messaging system which was originally developed at LinkedIn and later on became a part of the Apache project.
Kafka is fast, agile, scalable and distributed by design.
The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
Apache Kafka is a high-throughput distributed messaging system that allows for both streaming and offline log processing. It uses Apache Zookeeper for coordination and supports activity stream processing and real-time pub/sub messaging. Kafka bridges the gaps between pure offline log processing and traditional messaging systems by providing features like batching, transactions, persistence, and support for multiple consumers.
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit and when it is not.
The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage and ksqlDB as event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Key takeaways:
- Kafka can store data forever in a durable and high available manner
- Kafka has different options to query historical data
- Kafka-native add-ons like ksqlDB or Tiered Storage make Kafka more powerful than ever before to store and process data
- Kafka does not provide transactions, but exactly-once semantics
- Kafka is not a replacement for existing databases like MySQL, MongoDB or Elasticsearch
- Kafka and other databases complement each other; the right solution has to be selected for a problem
- Different options are available for bi-directional pull and push-based integration between Kafka and databases to complement each other
Video Recording:
https://youtu.be/7KEkWbwefqQ
Blog post:
https://www.kai-waehner.de/blog/2020/03/12/can-apache-kafka-replace-database-acid-storage-transactions-sql-nosql-data-lake/
Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It was developed by LinkedIn in 2011 to solve problems with data integration and processing. Kafka uses a publish-subscribe messaging model and is designed to be fast, scalable, and durable. It allows both streaming and storage of data and acts as a central data backbone for large organizations.
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.
Exactly-once Stream Processing with Kafka StreamsGuozhang Wang
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.
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streamsconfluent
Kafka Streams provides exactly-once stream processing by coordinating transactions across Kafka topics. Records are processed and written to output topics, offset commits and state updates occur atomically using transactions. This ensures each record is processed once even if failures occur. Connectors will extend exactly-once processing to data from outside Kafka.
What is Apache Kafka and What is an Event Streaming Platform?confluent
Speaker: Gabriel Schenker, Lead Curriculum Developer, Confluent
Streaming platforms have emerged as a popular, new trend, but what exactly is a streaming platform? Part messaging system, part Hadoop made fast, part fast ETL and scalable data integration. With Apache Kafka® at the core, event streaming platforms offer an entirely new perspective on managing the flow of data. This talk will explain what an event streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use—including several examples of where it is solving real business problems. New developments in this area such as KSQL will also be discussed.
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.
Apache Kafka Fundamentals for Architects, Admins and Developersconfluent
This document summarizes a presentation about Apache Kafka. It introduces Apache Kafka as a modern, distributed platform for data streams made up of distributed, immutable, append-only commit logs. It describes Kafka's scalability similar to a filesystem and guarantees similar to a database, with the ability to rewind and replay data. The document discusses Kafka topics and partitions, partition leadership and replication, and provides resources for further information.
Kafka is a distributed publish-subscribe messaging system that allows both streaming and storage of data feeds. It is designed to be fast, scalable, durable, and fault-tolerant. Kafka maintains feeds of messages called topics that can be published to by producers and subscribed to by consumers. A Kafka cluster typically runs on multiple servers called brokers that store topics which may be partitioned and replicated for fault tolerance. Producers publish messages to topics which are distributed to consumers through consumer groups that balance load.
Fundamentals and Architecture of Apache KafkaAngelo Cesaro
Fundamentals and Architecture of Apache Kafka.
This presentation explains Apache Kafka's architecture and internal design giving an overview of Kafka internal functions, including:
Brokers, Replication, Partitions, Producers, Consumers, Commit log, comparison over traditional message queues.
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
Apache Kafka is a distributed publish-subscribe messaging system that allows for high-throughput, persistent storage of messages. It provides decoupling of data pipelines by allowing producers to write messages to topics that can then be read from by multiple consumer applications in a scalable, fault-tolerant way. Key aspects of Kafka include topics for categorizing messages, partitions for scaling and parallelism, replication for redundancy, and producers and consumers for writing and reading messages.
Kafka is an open source messaging system that can handle massive streams of data in real-time. It is fast, scalable, durable, and fault-tolerant. Kafka is commonly used for stream processing, website activity tracking, metrics collection, and log aggregation. It supports high throughput, reliable delivery, and horizontal scalability. Some examples of real-time use cases for Kafka include website monitoring, network monitoring, fraud detection, and IoT applications.
Performance Tuning RocksDB for Kafka Streams’ State Storesconfluent
Performance Tuning RocksDB for Kafka Streams’ State Stores, Bruno Cadonna, Contributor to Apache Kafka & Software Developer at Confluent and Dhruba Borthakur, CTO & Co-founder Rockset
Meetup link: https://www.meetup.com/Berlin-Apache-Kafka-Meetup-by-Confluent/events/273823025/
Kafka and Avro with Confluent Schema RegistryJean-Paul Azar
The document discusses Confluent Schema Registry, which stores and manages Avro schemas for Kafka clients. It allows producers and consumers to serialize and deserialize Kafka records to and from Avro format. The Schema Registry performs compatibility checks between the schema used by producers and consumers, and handles schema evolution if needed to allow schemas to change over time in a backwards compatible manner. It provides APIs for registering, retrieving, and checking compatibility of schemas.
Managing multiple event types in a single topic with Schema Registry | Bill B...HostedbyConfluent
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.
Apache Kafka is a fast, scalable, and distributed messaging system that uses a publish-subscribe messaging protocol. It is designed for high throughput systems and can replace traditional message brokers due to its higher throughput and built-in partitioning, replication, and fault tolerance. Kafka uses topics to organize streams of messages and partitions to allow horizontal scaling and parallel processing of data. Producers publish messages to topics and consumers subscribe to topics to receive messages.
Apache Kafka is a distributed publish subscribe messaging system which was originally developed at LinkedIn and later on became a part of the Apache project.
Kafka is fast, agile, scalable and distributed by design.
The document provides an introduction and overview of Apache Kafka presented by Jeff Holoman. It begins with an agenda and background on the presenter. It then covers basic Kafka concepts like topics, partitions, producers, consumers and consumer groups. It discusses efficiency and delivery guarantees. Finally, it presents some use cases for Kafka and positioning around when it may or may not be a good fit compared to other technologies.
Apache Kafka is a high-throughput distributed messaging system that allows for both streaming and offline log processing. It uses Apache Zookeeper for coordination and supports activity stream processing and real-time pub/sub messaging. Kafka bridges the gaps between pure offline log processing and traditional messaging systems by providing features like batching, transactions, persistence, and support for multiple consumers.
Can and should Apache Kafka replace a database? How long can and should I store data in Kafka? How can I query and process data in Kafka? These are common questions that come up more and more. This session explains the idea behind databases and different features like storage, queries, transactions, and processing to evaluate when Kafka is a good fit and when it is not.
The discussion includes different Kafka-native add-ons like Tiered Storage for long-term, cost-efficient storage and ksqlDB as event streaming database. The relation and trade-offs between Kafka and other databases are explored to complement each other instead of thinking about a replacement. This includes different options for pull and push-based bi-directional integration.
Key takeaways:
- Kafka can store data forever in a durable and high available manner
- Kafka has different options to query historical data
- Kafka-native add-ons like ksqlDB or Tiered Storage make Kafka more powerful than ever before to store and process data
- Kafka does not provide transactions, but exactly-once semantics
- Kafka is not a replacement for existing databases like MySQL, MongoDB or Elasticsearch
- Kafka and other databases complement each other; the right solution has to be selected for a problem
- Different options are available for bi-directional pull and push-based integration between Kafka and databases to complement each other
Video Recording:
https://youtu.be/7KEkWbwefqQ
Blog post:
https://www.kai-waehner.de/blog/2020/03/12/can-apache-kafka-replace-database-acid-storage-transactions-sql-nosql-data-lake/
Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It was developed by LinkedIn in 2011 to solve problems with data integration and processing. Kafka uses a publish-subscribe messaging model and is designed to be fast, scalable, and durable. It allows both streaming and storage of data and acts as a central data backbone for large organizations.
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.
Exactly-once Stream Processing with Kafka StreamsGuozhang Wang
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.
Kafka Summit SF 2017 - Exactly-once Stream Processing with Kafka Streamsconfluent
Kafka Streams provides exactly-once stream processing by coordinating transactions across Kafka topics. Records are processed and written to output topics, offset commits and state updates occur atomically using transactions. This ensures each record is processed once even if failures occur. Connectors will extend exactly-once processing to data from outside Kafka.
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.
This is the first part presentation of the Multimodal Application Framework (MAF) held in CINECA few months ago. Its development is still ongoing and is aimed to create an instrument for rapidly develop scientific visualization applications.
Spark Streaming provides an easier API for streaming data than Storm, replacing Storm's spouts and bolts with Akka actors. It integrates better with Hadoop and makes time a core part of its API. This document provides instructions for setting up Spark Streaming projects using sbt or Maven and includes a demo reading from Kafka and processing a Twitter stream.
Exactly-once Data Processing with Kafka Streams - July 27, 2017confluent
This document discusses exactly-once processing in stream processing systems. It begins by defining exactly-once processing and describing some of the challenges in achieving it. It then outlines three options for achieving exactly-once processing with Kafka: at-least-once processing with deduplication, using Kafka's idempotent producer and transactions, and using Kafka Streams. The document focuses on Kafka Streams, describing how it provides exactly-once guarantees through transactional processing of data in batches across the processing topology.
Kafka is a distributed publish-subscribe messaging system that allows processes called producers to publish messages to topics, and processes called consumers to subscribe to topics and receive the stream of published messages. It maintains feeds of messages in categories called topics, and messages are distributed across partitions in a fault-tolerant way. Kafka is run as a cluster of servers called brokers that together form a scalable and durable messaging backbone.
Scala promotes test-driven development (TDD) through features like immutability, case classes, traits, and better type safety that allow writing more testable code. It supports various testing frameworks including Specs2, ScalaTest, and JUnit. Scala's functional capabilities, immutable data structures, and support for asynchronous programming further enable effective TDD practices.
Kafka Streams is a lightweight stream processing library included in Apache Kafka since version 0.10. It provides a simple yet powerful API for building stream processing applications. The API uses a domain-specific language that allows developers to define stream processing topologies where data from Kafka topics acts as input streams and can be transformed before writing the results to output topics. The library handles common stream processing tasks like state management, windowing, and fault tolerance using Kafka's distributed and fault-tolerant architecture.
Developing a custom Kafka connector? Make it shine! | Igor Buzatović, Porsche...HostedbyConfluent
Some people see their cars just as a means to get them from point A to point B without breaking down halfway, but most of us want it also to be comfortable, performant, easy to drive, and of course - to look good.
We can think of Kafka Connect connectors in a similar way. While the main focus is on getting data from or writing data to the external target system, it’s also relevant how easy it is to configure, does it scale well, does it provide the best possible data consistency, is it resilient to both the external system and Kafka cluster failures, and so on. This talk focuses on aspects of connector plugin development important for achieving these goals. More specifically - we‘ll cover configuration definition and validation, external source partitions and offsets handling, achieving desired delivery semantics, and more."
These slides were used during a technical session for the Cloud-Native El Salvador community. It covers the basic Kubernetes components, some installers and main Kubernetes resources. For the demo, it was used the capabilites provided by the Horizontal Pod Autoscaler.
I can't believe it's not a queue: Kafka and SpringJoe Kutner
Kafka is a distributed streaming platform that allows for publishing and subscribing to streams of records. It provides functionality for building real-time data pipelines and streaming apps. The document discusses Kafka concepts like producers, consumers, topics, partitions and offsets. It also provides examples of using Kafka with Java and Spring, and describes how Heroku uses Kafka for logging and metrics aggregation.
Secure Kafka at scale in true multi-tenant environment ( Vishnu Balusu & Asho...confluent
Application teams in JPMC have started shifting towards building event driven architectures and real time steaming pipelines and Kafka has been at core in this journey. As application teams have started adopting Kafka rapidly, need for a centrally managed Kafka as a service has emerged. We have started delivering Kafka as a service in early 2018 and running in production for more than an year now operating 80+ clusters (and growing) in all environments together. One of the key requirements is to provide truly segregated, secured multi-tenant environment with RBAC model while satisfying financial regulations and controls at the same time. Operating clusters at large scale requires scalable self-service capabilities and cluster management orchestration. In this talk we will present - Our experiences in delivering and operating secured, multi-tenant and resilient Kafka clusters at scale. - Internals of our service framework/control plane which enables self-service capabilities for application teams, cluster build/patch orchestration and capacity management capabilities for TSE/admin teams. - Our approach in enabling automated Cross Datacenter failover for application teams using service framework and confluent replicator.
Declarative benchmarking of cassandra and it's data modelsMonal Daxini
Monal Daxini presented on the declarative benchmarking tool NDBench and its Cassandra plugin. The tool allows users to define performance test profiles that specify the Cassandra schema, queries, load patterns, and other parameters. It executes the queries against Cassandra clusters and collects metrics to analyze performance. The plugin supports all Cassandra data types and allows testing different versions. Netflix uses it to validate data models and certify Cassandra upgrades. Future enhancements include adding more data generators and supporting other data stores.
TDC2016POA | Trilha Arquitetura - Apache Kafka: uma introdução a logs distrib...tdc-globalcode
The document introduces Apache Kafka, an open-source distributed event streaming platform. It was created by the architecture team at LinkedIn to provide a high-throughput, scalable messaging infrastructure. Kafka uses a distributed commit log to store streams of records in a fault-tolerant way and allows consumers to read data from the commit logs. It also uses Zookeeper for coordination between nodes in the Kafka cluster.
Apache Kafka® is the technology behind event streaming which is fast becoming the central nervous system of flexible, scalable, modern data architectures. Customers want to connect their databases, data warehouses, applications, microservices and more, to power the event streaming platform. To connect to Apache Kafka, you need a connector!
This online talk dives into the new Verified Integrations Program and the integration requirements, the Connect API and sources and sinks that use Kafka Connect. We cover the verification steps and provide code samples created by popular application and database companies. We will discuss the resources available to support you through the connector development process.
This is Part 2 of 2 in Building Kafka Connectors - The Why and How
Streaming Design Patterns Using Alpakka Kafka Connector (Sean Glover, Lightbe...confluent
Do you ever feel that your stream processor gets in the way of expressing business requirements? Most processors are frameworks, which are highly opinionated in the design and implementation of apps. Performing Complex Event Processing invariably leads to calling out to other technologies, but what if that integration didn’t require an RPC call or could be modeled into your stream itself? This talk will explore how to build rich domain, low latency, back-pressured, and stateful streaming applications that require very little infrastructure, using Akka Streams and the Alpakka Kafka connector.
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2. 2
There are several approaches to testing Kafka-enabled
components or services.
Problem
Solution ▪ Spring Kafka provides an embeddable cluster
▪ Lacks the possiblity to work with external clusters
▪ Leads to boilerplate code in your test
▪ There are (costly) testing tools that are able to inject records into topics
▪ Lacks the possibility to work with embedded clusters
▪ Suitable for system testing, not integration testing
▪ Testing Kafka-based components or services is not trivial
▪ Need to have a working cluster
▪ Must be able to instrument the cluster
▪ Mocking is not an option for integration or system tests
3. 3
Kafka for JUnit is suitable for integration testing as well as
system testing.
Solution ▪ Kafka for JUnit
▪ Works with embedded Kafka clusters – suitable for integration testing
▪ Works with external Kafka clusters – suitable for system testing
▪ Features a concise and readable API
▪ Features fault injection to trigger error handling
▪ Integrates well with Spring Kafka
4. 4
Kafka for JUnit allows you to write integration tests that work
against an embedded Kafka cluster.
Integration Test
JUNIT JUPITER
Consumer / Producer
KAFKA-BASED COMPONENT
my.test.topic
EMBEDDED APACHE KAFKA CLUSTER
VERTICALS
instruments
provisions
interacts
with
5. 6
Kafka for JUnit allows you to write system tests that work against
an external Kafka cluster.
VERTICALS
Event Producer
SERVICE A
Event Consumer
SERVICE B
topic.for.events
APACHE KAFKA CLUSTER
System Test
JUNIT JUPITER
trigger use case
observes
topic
for
expected
records
publishes
events
consumes
events
6. 7
Kafka for JUnit provides abstractions for interacting with a Kafka cluster
through EmbeddedKafkaCluster and ExternalKafkaCluster.
my.test.topic
APACHE KAFKA CLUSTER
RecordProducer
EmbeddedKafkaCluster
RecordConsumer
TopicManager
FaultInjection
my.test.topic
APACHE KAFKA CLUSTER
RecordProducer
ExternalKafkaCluster
RecordConsumer
TopicManager
7. 8
A RecordProducer provides the means to send key-value pairs
or non-keyed values to a Kafka topic.
public interface RecordProducer {
<V> List<RecordMetadata> send(SendValues<V> sendRequest) throws ...;
<V> List<RecordMetadata> send(SendValuesTransactional<V> sendRequest) throws ...;
<K,V> List<RecordMetadata> send(SendKeyValues<K,V> sendRequest) throws ...;
<K,V> List<RecordMetadata> send(SendKeyValuesTransactional<K,V> sendRequest) ...;
/* overloaded methods that accept builder instances
* for the resp. type have been omitted for brevity
*/
}
Kafka for JUnit provides builders
for these requests!
Interface definition of RecordProducer
8. 9
Publishing data to a Kafka topic is as simple as contributing a
one-liner in the default case.
kafka.send(SendValues.to(“my.test.topic“, “a“, “b“, “c“));
Sending non-keyed values using defaults
kafka.send(SendValuesTransactional.inTransaction(
“my.test.topic“,
Arrays.asList(“a“, “b“, “c“)));
kafka.send(SendValues.to(“my.test.topic“, “a“, “b“, “c“))
.with(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG, “true“)
.with(ProducerConfig.MAX_IN_FLIGHT_REQUEST_PER_CONNECTION, “1“));
Sending non-keyed values using overrides
Sending non-keyed values transactionally
9. 1
A RecordConsumer provides the means to read data from a topic or
observe a topic until some criteria is met or a timeout elapses.
public interface RecordConsumer {
<V> List<V> readValues(ReadKeyValues<String,V> readRequest) throws ...;
<K,V> List<KeyValue<K,V>> read(ReadKeyValues<K,V> readRequest) throws ...;
<V> List<V> observeValues(ObserveKeyValues<String,V> observeRequest) throws ...;
<K,V> List<KeyValue<K,V>> observe(ObserveKeyValues<K,V> observeRequest) throws ...;
/* overloaded methods that accept builder instances
* for the resp. type have been omitted for brevity
*/
}
Kafka for JUnit provides builders
for these requests!
Interface definition of RecordConsumer
10. 1
Consuming records is just as easy as producing them.
val values = kafka.readValues(ReadKeyValues.from(“my.test.topic“));
Consuming only values using defaults
List<KeyValue<String, Long>> records = kafka.read(ReadKeyValues
.from(“my.test.topic“, Long.class)
.with(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, ...));
val records = kafka.read(ReadKeyValues.from(“my.test.topic“));
Consuming key-value pairs using defaults
Consuming key-value pairs using overrides
11. 1
Observations can be used to let a test fail unless given criteria
are met.
kafka.observeValues(ObserveKeyValues.on(“my.test.topic“, 3));
Observing a topic until n values have been consumed
val keyFilter = Integer.parseInt(k) % 2 == 0;
kafka.observe(ObserveKeyValues.on(“my.test.topic“, 3, Integer.class)
.with(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, ...)
.filterOnKeys(keyFilter));
List<KeyValue<String,String>> observedRecords = kafka
.observe(ObserveKeyValues.on(“my.test.topic“, 3));
Observing a topic until n records have been consumed
Using filters when consuming or observing a topic
12. 1
A TopicManager provides the means to manage Kafka
topics.
public interface TopicManager {
void createTopic(TopicConfig config);
void deleteTopic(String topic);
boolean exists(String topic);
Map<Integer, LeaderAndIsr> fetchLeaderAndIsr(String topic);
Properties fetchTopicConfig(String topic);
/* overloaded methods that accept builder instances for the resp. type have been
omitted for brevity */
}
Kafka for JUnit provides builders
for these requests!
Interface definition of TopicManager
13. 1
The default TopicManager implementation leverages the
AdminClient implementation of the Kafka Client library.
kafka.createTopic(TopicConfig.withName(“my.test.topic“));
Creating a topic using defaults
kafka.deleteTopic(“my.test.topic“);
Deleting a topic
kafka.createTopic(TopicConfig.withName(“my.test.topic“)
.withNumberOfPartitions(5)
.withNumberOfReplicas(1));
Creating a topic with specific properties
15. 1
Let’s write a test that exercises the write-path of Kafka-based
component.
TurbineEventPublisherTest
JUNIT JUPITER
TurbineEventPublisher
TURBINE REGISTRY
turbine.lifecycle.events
EMBEDDED APACHE KAFKA CLUSTER
instruments
provisions
interacts
with
17. 1
Let’s write a simple test that verifies that TurbineEventPublisher
is able to write events to the designated topic.
public class TurbineEventPublisherTest {
private EmbeddedKafkaCluster kafka;
@BeforeEach
void setupKafka() {
kafka = provisionWith(defaultClusterConfig());
kafka.start();
}
@AfterEach
void tearDownKafka() {
kafka.stop();
}
}
Provide the skeleton for the component test incl. a workable Kafka cluster.
static import from
ExternalKafkaCluster
static import from
ExternalKafkaClusterConfig
18. 1
The observe method throws an AssertionError once a
certain configurable amount of time has elapsed.
var config = Map.<String, Object>of(
ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, kafka.getBrokerList());
var publisher = new TurbineEventPublisher(“turbine.lifecycle.events“,
config);
var event = new TurbineRegisteredEvent(“1a5c6012“, 49.875114, 8.978702);
publisher.log(event);
Create an instance of the subject-under-test and publish test data
19. 2
The observe method throws an AssertionError once a
certain configurable amount of time has elapsed. (cont.)
kafka.observe(ObserveKeyValues
.on(“turbine.lifecycle.events“, 1, TurbineEvent.class)
.observeFor(15, TimeUnit.SECONDS)
.filterOnKeys(key -> key.equals(“1a5c6012“))
.with(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,
TurbineEventDeserializer.class));
Observe the designated topic for new data
override arbitrary
consumer properties
the topic we want to
observe
the number of
records we expect
the value type of the
expected records
use filters to add
observation criterias
20. 2
The observe method returns all records that it obtained from
watching the topic.
var record = kafka.observe(
on(“turbine.lifecycle.events“, 1, TurbineEvent.class)
.with(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,
TurbineEventDeserializer.class))
.stream()
.findFirst()
.orElseThrow(AssertionError::new);
assertThat(record.getKey()).isEqualTo(“1a5c6012“);
assertThat(record.getValue()).isInstanceOf(TurbineRegisteredEvent.class);
Fetch observed records and assert that their data is what you expect it to be
23. 2
We’ll use a simple client interface to provide the means to exert an
external stimulus on the system. (cont.)
Listing 1: Example for Feign-based HTTP client that interacts with the system
public interface GettingThingsDone {
@RequestLine(“POST /items“)
@Headers(“Content-Type: application/json“)
Response createItem(CreateItem payload);
@RequestLine(“GET /items/{itemId}“)
@Headers(“Accept: application/json“)
Item getItem(@Param(“itemId“) String itemId);
/* additional methods omitted for brevity */
}
Example for Feign-based HTTP client that interacts with the system
24. 2
We’ll use a simple client interface to provide the means to exert an
external stimulus on the system. (cont.)
Listing 1: Example for Feign-based HTTP client that interacts with the system
val kafka = ExternalKafkaCluster.at(“http://localhost:9092“);
Gain programmatic access to the cluster
val itemId = extractItemId(response);
Extract the ID of the newly created item from the response
val gtd = createGettingThingsDoneClient();
val payload = new CreateItem(“Buy groceries!“);
val response = gtd.createItem(payload)
Trigger a use case using the client interface
25. 2
Leverage Kafka for JUnit to observe the designated topic and apply
assertions on the returned records.
Listing 1: Example for Feign-based HTTP client that interacts with the system
List<AvroItemEvent> publishedEvents = kafka
.observeValues(on(“item.events“, 1, AvroItemEvent.class)
.observeFor(10, TimeUnit.SECONDS)
.with(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG,
ItemEventDeserializer.class)
.filterOnKeys(aggregateId -> aggregateId.equals(itemId)));
Observe the designated topic for the
throws an AssertionError
if the timeout elapses
26. 2
Want to know more?
GitHub
Blog ▪ Günther, M., Writing component tests for Kafka producers,
https://bit.ly/39NpoCU
▪ Günther, M., Writing component tests for Kafka consumers,
https://bit.ly/36KrXoV
▪ Günther, M., Writing system tests for a Kafka-enabled microservice,
https://bit.ly/2OUeEMs
▪ Günther, M., Using Kafka for JUnit with Spring Kafka,
https://bit.ly/3c61WSx
▪ Kafka for JUnit on GitHub,
https://mguenther.github.io/kafka-junit/
▪ User Guide to Kafka for JUnit,
https://mguenther.github.io/kafka-junit/