The document compares the performance of Apache Kafka and RabbitMQ for streaming data. It finds that without fault tolerance, both brokers have similar latency, but with fault tolerance enabled, Kafka has slightly higher latency than RabbitMQ. Latency increases with message size and is improved after an initial warmup period. Overall, RabbitMQ demonstrated the lowest latency for both configurations. The document also describes how each system is deployed and configured for the performance tests.
This document provides an overview of Apache Kafka, including its history, architecture, key concepts, use cases, and demonstrations. Kafka is a distributed streaming platform designed for high throughput and scalability. It can be used for messaging, logging, and stream processing. The document outlines Kafka's origins at LinkedIn, its differences from traditional messaging systems, and key terms like topics, producers, consumers, brokers, and partitions. It also demonstrates how Kafka handles leadership and replication across brokers.
This document discusses messaging queues and compares Kafka and Amazon SQS. It begins by explaining what a messaging queue is and provides examples of software that can be used, including Kafka, SQS, SNS, and RabbitMQ. It then discusses why messaging queues are useful by allowing for asynchronous and failed processing. The document proceeds to provide details on Kafka, including that it is a distributed streaming platform used by companies like LinkedIn, Twitter, and Netflix. It defines Kafka terminology and discusses how producers and consumers work. Finally, it compares features of SQS and Kafka like order of messages, delivery guarantees, retention, security, costs, and throughput.
Kafka is an open-source message broker that provides high-throughput and low-latency data processing. It uses a distributed commit log to store messages in categories called topics. Processes that publish messages are producers, while processes that subscribe to topics are consumers. Consumers can belong to consumer groups for parallel processing. Kafka guarantees order and no lost messages. It uses Zookeeper for metadata and coordination.
Markus Günther provides an overview of Apache Kafka. Kafka is a distributed publish-subscribe messaging system that supports topic access semantics. Producers publish data to topics and consumers subscribe to topics of interest to consume data at their own pace. Kafka uses a persistent commit log to implement messaging, with publishers appending messages and consumers reading sequentially. It supports at-least-once and exactly-once delivery guarantees.
This document compares RabbitMQ and Apache Kafka messaging systems. It provides an overview of core concepts for each including queues/topics, exchanges/partitions, and consumer groups. It also includes example messaging patterns and topologies for handling orders in an e-commerce system, demonstrating how each system could be used to implement request/response and publish-subscribe messaging across services.
The document provides an overview of Kafka including its problem statement, use cases, key terminologies, architecture, and components. It defines topics as streams of data that can be split into partitions with a unique offset. Producers write data to brokers which replicate across partitions for fault tolerance. Consumers read data from partitions in a consumer group. Zookeeper manages the metadata and brokers act as the developers while topics are analogous to modules with partitions as tasks.
This session goes through the understanding of Apache Kafka, its components and working with best practices to achieve fault tolerant system with high availability and consistency by tuning Kafka brokers and producer to achieve the best result.
This document provides an overview of Apache Kafka, including its history, architecture, key concepts, use cases, and demonstrations. Kafka is a distributed streaming platform designed for high throughput and scalability. It can be used for messaging, logging, and stream processing. The document outlines Kafka's origins at LinkedIn, its differences from traditional messaging systems, and key terms like topics, producers, consumers, brokers, and partitions. It also demonstrates how Kafka handles leadership and replication across brokers.
This document discusses messaging queues and compares Kafka and Amazon SQS. It begins by explaining what a messaging queue is and provides examples of software that can be used, including Kafka, SQS, SNS, and RabbitMQ. It then discusses why messaging queues are useful by allowing for asynchronous and failed processing. The document proceeds to provide details on Kafka, including that it is a distributed streaming platform used by companies like LinkedIn, Twitter, and Netflix. It defines Kafka terminology and discusses how producers and consumers work. Finally, it compares features of SQS and Kafka like order of messages, delivery guarantees, retention, security, costs, and throughput.
Kafka is an open-source message broker that provides high-throughput and low-latency data processing. It uses a distributed commit log to store messages in categories called topics. Processes that publish messages are producers, while processes that subscribe to topics are consumers. Consumers can belong to consumer groups for parallel processing. Kafka guarantees order and no lost messages. It uses Zookeeper for metadata and coordination.
Markus Günther provides an overview of Apache Kafka. Kafka is a distributed publish-subscribe messaging system that supports topic access semantics. Producers publish data to topics and consumers subscribe to topics of interest to consume data at their own pace. Kafka uses a persistent commit log to implement messaging, with publishers appending messages and consumers reading sequentially. It supports at-least-once and exactly-once delivery guarantees.
This document compares RabbitMQ and Apache Kafka messaging systems. It provides an overview of core concepts for each including queues/topics, exchanges/partitions, and consumer groups. It also includes example messaging patterns and topologies for handling orders in an e-commerce system, demonstrating how each system could be used to implement request/response and publish-subscribe messaging across services.
The document provides an overview of Kafka including its problem statement, use cases, key terminologies, architecture, and components. It defines topics as streams of data that can be split into partitions with a unique offset. Producers write data to brokers which replicate across partitions for fault tolerance. Consumers read data from partitions in a consumer group. Zookeeper manages the metadata and brokers act as the developers while topics are analogous to modules with partitions as tasks.
This session goes through the understanding of Apache Kafka, its components and working with best practices to achieve fault tolerant system with high availability and consistency by tuning Kafka brokers and producer to achieve the best result.
Testing Kafka components with Kafka for JUnitMarkus Günther
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 discusses RabbitMQ and Apache Kafka. It provides an overview of AMQP and how it defines features like message orientation, queuing, routing, security and reliability. It also describes RabbitMQ concepts like exchanges, queues, routing and plugins. For Apache Kafka, it explains how it is distributed, replicated and uses commit logs and topics to store messages. It then discusses reliability, performance, clustering and high availability aspects of RabbitMQ and Kafka.
Kafka Connect is a framework which connects Kafka with external Systems. It helps to move the data in and out of the Kafka. Connect makes it simple to use existing connector configuration for common source and sink Connectors.
In Apache Pulsar Beijing Meetup, Sijie Guo and Yong Zhang gave a preview of transaction support in Pulsar 2.5.0. Sijie Guo started with the current state of messaging semantics in Pulsar and talked about the implementation of message deduplication introduced by PIP-6. Then he went into the details of why do we need transaction and how do we implement transaction in Pulsar. Finally Yong walked through the whole transaction execution flow.
Deep Dive into the Pulsar Binary Protocol - Pulsar Virtual Summit Europe 2021StreamNative
To achieve maximum performance, some important choices have been made when designing the Pulsar binary protocol.
This session will explain how Pulsar implements all the features of a high quality streaming protocol such as frame multiplexing, session establishment, keep-alive, flow control, authentication and authorisation, encoding, zero-copy capabilities and more.
Data processing use cases, from transformation to analytics, perform tasks that require various combinations of queuing, streaming & lightweight processing steps. Until now, supporting all of those needs has required different systems for each task--stream processing engines, messaging queuing middleware, & streaming messaging systems. That has led to increased complexity for development & operations.
In this session, well discuss the need to unify these capabilities in a single system & how Apache Pulsar was designed to address that. Apache Pulsar is a next generation distributed pub-sub system that was developed & deployed at Yahoo. Streamlios Karthik Ramasamy, will explain how the architecture & design of Pulsar provides the flexibility to support developers & applications needing any combination of queuing, messaging, streaming & lightweight compute.
Data Con LA 2018 - A Serverless Approach to Data Processing using Apache Puls...Data Con LA
This document discusses Apache Pulsar Functions, a lightweight serverless compute framework built on Apache Pulsar. Pulsar Functions allows users to run stateless and stateful functions against data streams in Pulsar. Functions are simple Java functions that process individual messages. The functions integrate seamlessly with Pulsar for scalable, low-latency processing of streaming data at the edge and in cloud environments.
RabbitMQ and Apache Kafka are two popular messaging systems. RabbitMQ uses a push model where consumers register interest in queues and brokers push messages. It offers low latency but requires back pressure. Kafka uses a pull model where consumers pull messages from topics in batches. This improves throughput but can affect processing order. Both systems provide reliability through mechanisms like persistent messages, clustering, and mirrors/replicas. However, RabbitMQ prioritizes low latency while Kafka prioritizes high throughput.
This document provides a preview of new features in Apache Pulsar 2.5.0, including transactional streaming, sticky consumers, batch receiving, and namespace change events. It also discusses messaging semantics like at least once, at most once, and effectively once delivery. Transactional streaming allows atomic multi-topic publishes and acknowledgments. Sticky consumers improve partitioning for key-based topics. Batch receiving allows consuming messages in batches. Namespace change events provide notifications of namespace changes.
How Zhaopin contributes to Pulsar communityStreamNative
This document discusses Apache Pulsar usage in Zhaopin and some key features:
1. It provides an overview of how Pulsar is used in Zhaopin and the increasing message throughput over time.
2. It describes several Pulsar features in detail, including key-shared subscriptions, schema versioning, HDFS offloading, and upcoming topics like policies and sticky consumers.
3. It discusses the Pulsar community contributions from the Zhaopin team, including details on key-shared subscriptions, schema version handling, HDFS offloader storage, and other improvements.
LINE's messaging service architecture underlying more than 200 million monthl...kawamuray
Yuto Kawamura from LINE Corp presented on the messaging service architecture underlying LINE's 200 million monthly active users. The key points are:
1. LINE uses a distributed architecture with the LEGY gateway, talk-server application servers, and a hybrid Redis/HBase datastore to handle over 25 billion messages per day.
2. The Armeria RPC framework is used for communication between systems like the talk-servers, authentication services, and analytics.
3. Apache Kafka is used as the backbone for asynchronous task processing and data synchronization between services due to its load distribution, fail-over capabilities, and pub-sub model.
4. While LINE leverages many open source technologies, it also
[Demo session] 관리형 Kafka 서비스 - Oracle Event Hub ServiceOracle Korea
오라클 클라우드에서는 카프카를 관리형 서비스로 제공합니다. 밋업 세션에서는 관리형 카프카 서비스의 편의성을 소개하고 카프카 서비스의 데모를 진행합니다. 또한 MSA, 빅데이터 및 Blockchain의 인프라로 카프카가 핵심 위치를 갖는 것 뿐만 아니라 오라클 클라우드의 통합 핵심 컴포넌트로 카프카는 중요한 의미를 갖습니다.
오라클 클라우드의 통합 컴포넌트로 카프카의 역할과 주요 서비스의 구성을 소개합니다.
* 본 세션은 “입문자/초급자/중급자” 분들께 두루 적합한 세션입니다.
Apache Kafka and Apache Pulsar are both popular messaging frameworks. Apache Kafka has a big user base and people will want to know how Kafka and Pulsar are either the same or different in many respects. This talk will cover the key differences and how Pulsar adds new features that missing in Kafka.
We will cover:
The architectural differences and similarities in Pulsar and Kafka. Show use of BookKeeper and what that allows.
The Producer API and functionality differences. Show HelloWorld for both.
The Consumer API and functionality differences. Show HelloWorld for both.
The core use case and functionality differences. Show Pulsar as handling all of Kafka’s use cases and new ones that aren’t possible with Kafka.
This talk will allow people who are choosing between Kafka and Pulsar to have a more accurate and in-depth understanding of the differences between them. For companies considering a switch from Kafka to Pulsar, this talk will give them the cheatsheet to go back and make a more informed decision.
TGIPulsar - EP #006: Lifecycle of a Pulsar message StreamNative
1. Pulsar uses bookies to persist messages and brokers to serve clients and select bookies. ZooKeeper stores metadata.
2. When a message is produced, it is sent to a broker and written to multiple bookies. Consumers connect to brokers and receive messages from caches or by brokers reading from bookies.
3. Pulsar retains messages based on retention policies like time and size. Messages are deleted by segment once all subscriptions are caught up to avoid deleting messages still needed.
How Apache Pulsar Helps Tencent Process Tens of Billions of Transactions Effi...StreamNative
As the largest provider of Internet products and services in China, Tencent serves billions of users and over a million merchants—and these numbers are growing fast! Tencent’s enterprises generate a huge volume of financial transactions, placing a tremendous load on their billing service, which processes hundreds of millions of dollars in revenue each day.
Because Tencent had been unable to scale its current billing service to handle its rapidly growing business, the possibility of data loss had become an escalating concern. To ensure data consistency, the company decided to redesign its system’s transaction processing pipeline. After evaluating the pros and cons of several messaging systems, Tencent chose to implement its billing service using Apache Pulsar. As a result, Tencent can now run their billing service on a very large scale with virtually no data loss.
In this talk, Ningguo Chen, the Chief Architect from Tencent Billing will share their journal of adopting Pulsar in their core transaction processing engine to process tens of billions of events every day. He will also discuss the problems they have encountered in using Pulsar and the improvements they have made for meeting their scale.
This document provides an introduction to Apache Kafka. It discusses why Kafka is needed for real-time streaming data processing and real-time analytics. It also outlines some of Kafka's key features like scalability, reliability, replication, and fault tolerance. The document summarizes common use cases for Kafka and examples of large companies that use it. Finally, it describes Kafka's core architecture including topics, partitions, producers, consumers, and how it integrates with Zookeeper.
Multi-Cluster and Failover for Apache Kafka - Kafka Summit SF 17Gwen (Chen) Shapira
This document discusses disaster recovery strategies for Apache Kafka clusters running across multiple data centers. It outlines several failure scenarios like an entire data center being demolished and recommends solutions like running a single Kafka cluster across multiple near-by data centers. It then describes a "stretch cluster" approach using 3 data centers with replication between them to provide high availability. The document also discusses active-active replication between two data center clusters and challenges around consumer offsets not being identical across data centers during a failover. It recommends approaches like tracking timestamps and failing over consumers based on time.
Containers, DevOps, Apache Mesos and Cloud - Reshaping how we develop and del...Marcelo Sousa Ancelmo
Presentation made at ApacheCon: Core Europe 2015
Container technology are being evaluated by software developers and administrators with a great deal of interest. Developers want to focus on what they do best: Creating and coding new applications. That shouldn't have to change just because they need to deploy an application to a different environment. Administrators want the environment to stay reliable and stable, keeping changes at a minimum. By following a strategy that embraces good Architecture, use of Containers, DevOps philosophy, Apache Mesos and a Cloud based environment, developers and operators can create, consume and collaborate on the infrastructure configuration over the time, deploy Java EE applications and test your application infrastructure consistently regardless of the stage of the development life cycle.
This document discusses using Kafka for service messaging in a service-oriented architecture. Some key points discussed include:
- Kafka allows for one message per event, instant message passing between services, ability to replay messages, and messages having a schema.
- Kafka Streams provides a framework for building streaming applications and processing data in Kafka topics. It allows for querying state stored in Kafka.
- Real-world examples show Kafka Streams can handle high throughput workloads of 50,000 messages per second across multiple instances.
- Kafka Connect is used for simple data integration without transformations by moving data to and from Kafka.
Following up from AMQP presentation, this is a more in-depth coverage of RabbitMQ with workshop-style walkthrough, covering various aspects of the system.
Testing Kafka components with Kafka for JUnitMarkus Günther
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 discusses RabbitMQ and Apache Kafka. It provides an overview of AMQP and how it defines features like message orientation, queuing, routing, security and reliability. It also describes RabbitMQ concepts like exchanges, queues, routing and plugins. For Apache Kafka, it explains how it is distributed, replicated and uses commit logs and topics to store messages. It then discusses reliability, performance, clustering and high availability aspects of RabbitMQ and Kafka.
Kafka Connect is a framework which connects Kafka with external Systems. It helps to move the data in and out of the Kafka. Connect makes it simple to use existing connector configuration for common source and sink Connectors.
In Apache Pulsar Beijing Meetup, Sijie Guo and Yong Zhang gave a preview of transaction support in Pulsar 2.5.0. Sijie Guo started with the current state of messaging semantics in Pulsar and talked about the implementation of message deduplication introduced by PIP-6. Then he went into the details of why do we need transaction and how do we implement transaction in Pulsar. Finally Yong walked through the whole transaction execution flow.
Deep Dive into the Pulsar Binary Protocol - Pulsar Virtual Summit Europe 2021StreamNative
To achieve maximum performance, some important choices have been made when designing the Pulsar binary protocol.
This session will explain how Pulsar implements all the features of a high quality streaming protocol such as frame multiplexing, session establishment, keep-alive, flow control, authentication and authorisation, encoding, zero-copy capabilities and more.
Data processing use cases, from transformation to analytics, perform tasks that require various combinations of queuing, streaming & lightweight processing steps. Until now, supporting all of those needs has required different systems for each task--stream processing engines, messaging queuing middleware, & streaming messaging systems. That has led to increased complexity for development & operations.
In this session, well discuss the need to unify these capabilities in a single system & how Apache Pulsar was designed to address that. Apache Pulsar is a next generation distributed pub-sub system that was developed & deployed at Yahoo. Streamlios Karthik Ramasamy, will explain how the architecture & design of Pulsar provides the flexibility to support developers & applications needing any combination of queuing, messaging, streaming & lightweight compute.
Data Con LA 2018 - A Serverless Approach to Data Processing using Apache Puls...Data Con LA
This document discusses Apache Pulsar Functions, a lightweight serverless compute framework built on Apache Pulsar. Pulsar Functions allows users to run stateless and stateful functions against data streams in Pulsar. Functions are simple Java functions that process individual messages. The functions integrate seamlessly with Pulsar for scalable, low-latency processing of streaming data at the edge and in cloud environments.
RabbitMQ and Apache Kafka are two popular messaging systems. RabbitMQ uses a push model where consumers register interest in queues and brokers push messages. It offers low latency but requires back pressure. Kafka uses a pull model where consumers pull messages from topics in batches. This improves throughput but can affect processing order. Both systems provide reliability through mechanisms like persistent messages, clustering, and mirrors/replicas. However, RabbitMQ prioritizes low latency while Kafka prioritizes high throughput.
This document provides a preview of new features in Apache Pulsar 2.5.0, including transactional streaming, sticky consumers, batch receiving, and namespace change events. It also discusses messaging semantics like at least once, at most once, and effectively once delivery. Transactional streaming allows atomic multi-topic publishes and acknowledgments. Sticky consumers improve partitioning for key-based topics. Batch receiving allows consuming messages in batches. Namespace change events provide notifications of namespace changes.
How Zhaopin contributes to Pulsar communityStreamNative
This document discusses Apache Pulsar usage in Zhaopin and some key features:
1. It provides an overview of how Pulsar is used in Zhaopin and the increasing message throughput over time.
2. It describes several Pulsar features in detail, including key-shared subscriptions, schema versioning, HDFS offloading, and upcoming topics like policies and sticky consumers.
3. It discusses the Pulsar community contributions from the Zhaopin team, including details on key-shared subscriptions, schema version handling, HDFS offloader storage, and other improvements.
LINE's messaging service architecture underlying more than 200 million monthl...kawamuray
Yuto Kawamura from LINE Corp presented on the messaging service architecture underlying LINE's 200 million monthly active users. The key points are:
1. LINE uses a distributed architecture with the LEGY gateway, talk-server application servers, and a hybrid Redis/HBase datastore to handle over 25 billion messages per day.
2. The Armeria RPC framework is used for communication between systems like the talk-servers, authentication services, and analytics.
3. Apache Kafka is used as the backbone for asynchronous task processing and data synchronization between services due to its load distribution, fail-over capabilities, and pub-sub model.
4. While LINE leverages many open source technologies, it also
[Demo session] 관리형 Kafka 서비스 - Oracle Event Hub ServiceOracle Korea
오라클 클라우드에서는 카프카를 관리형 서비스로 제공합니다. 밋업 세션에서는 관리형 카프카 서비스의 편의성을 소개하고 카프카 서비스의 데모를 진행합니다. 또한 MSA, 빅데이터 및 Blockchain의 인프라로 카프카가 핵심 위치를 갖는 것 뿐만 아니라 오라클 클라우드의 통합 핵심 컴포넌트로 카프카는 중요한 의미를 갖습니다.
오라클 클라우드의 통합 컴포넌트로 카프카의 역할과 주요 서비스의 구성을 소개합니다.
* 본 세션은 “입문자/초급자/중급자” 분들께 두루 적합한 세션입니다.
Apache Kafka and Apache Pulsar are both popular messaging frameworks. Apache Kafka has a big user base and people will want to know how Kafka and Pulsar are either the same or different in many respects. This talk will cover the key differences and how Pulsar adds new features that missing in Kafka.
We will cover:
The architectural differences and similarities in Pulsar and Kafka. Show use of BookKeeper and what that allows.
The Producer API and functionality differences. Show HelloWorld for both.
The Consumer API and functionality differences. Show HelloWorld for both.
The core use case and functionality differences. Show Pulsar as handling all of Kafka’s use cases and new ones that aren’t possible with Kafka.
This talk will allow people who are choosing between Kafka and Pulsar to have a more accurate and in-depth understanding of the differences between them. For companies considering a switch from Kafka to Pulsar, this talk will give them the cheatsheet to go back and make a more informed decision.
TGIPulsar - EP #006: Lifecycle of a Pulsar message StreamNative
1. Pulsar uses bookies to persist messages and brokers to serve clients and select bookies. ZooKeeper stores metadata.
2. When a message is produced, it is sent to a broker and written to multiple bookies. Consumers connect to brokers and receive messages from caches or by brokers reading from bookies.
3. Pulsar retains messages based on retention policies like time and size. Messages are deleted by segment once all subscriptions are caught up to avoid deleting messages still needed.
How Apache Pulsar Helps Tencent Process Tens of Billions of Transactions Effi...StreamNative
As the largest provider of Internet products and services in China, Tencent serves billions of users and over a million merchants—and these numbers are growing fast! Tencent’s enterprises generate a huge volume of financial transactions, placing a tremendous load on their billing service, which processes hundreds of millions of dollars in revenue each day.
Because Tencent had been unable to scale its current billing service to handle its rapidly growing business, the possibility of data loss had become an escalating concern. To ensure data consistency, the company decided to redesign its system’s transaction processing pipeline. After evaluating the pros and cons of several messaging systems, Tencent chose to implement its billing service using Apache Pulsar. As a result, Tencent can now run their billing service on a very large scale with virtually no data loss.
In this talk, Ningguo Chen, the Chief Architect from Tencent Billing will share their journal of adopting Pulsar in their core transaction processing engine to process tens of billions of events every day. He will also discuss the problems they have encountered in using Pulsar and the improvements they have made for meeting their scale.
This document provides an introduction to Apache Kafka. It discusses why Kafka is needed for real-time streaming data processing and real-time analytics. It also outlines some of Kafka's key features like scalability, reliability, replication, and fault tolerance. The document summarizes common use cases for Kafka and examples of large companies that use it. Finally, it describes Kafka's core architecture including topics, partitions, producers, consumers, and how it integrates with Zookeeper.
Multi-Cluster and Failover for Apache Kafka - Kafka Summit SF 17Gwen (Chen) Shapira
This document discusses disaster recovery strategies for Apache Kafka clusters running across multiple data centers. It outlines several failure scenarios like an entire data center being demolished and recommends solutions like running a single Kafka cluster across multiple near-by data centers. It then describes a "stretch cluster" approach using 3 data centers with replication between them to provide high availability. The document also discusses active-active replication between two data center clusters and challenges around consumer offsets not being identical across data centers during a failover. It recommends approaches like tracking timestamps and failing over consumers based on time.
Containers, DevOps, Apache Mesos and Cloud - Reshaping how we develop and del...Marcelo Sousa Ancelmo
Presentation made at ApacheCon: Core Europe 2015
Container technology are being evaluated by software developers and administrators with a great deal of interest. Developers want to focus on what they do best: Creating and coding new applications. That shouldn't have to change just because they need to deploy an application to a different environment. Administrators want the environment to stay reliable and stable, keeping changes at a minimum. By following a strategy that embraces good Architecture, use of Containers, DevOps philosophy, Apache Mesos and a Cloud based environment, developers and operators can create, consume and collaborate on the infrastructure configuration over the time, deploy Java EE applications and test your application infrastructure consistently regardless of the stage of the development life cycle.
This document discusses using Kafka for service messaging in a service-oriented architecture. Some key points discussed include:
- Kafka allows for one message per event, instant message passing between services, ability to replay messages, and messages having a schema.
- Kafka Streams provides a framework for building streaming applications and processing data in Kafka topics. It allows for querying state stored in Kafka.
- Real-world examples show Kafka Streams can handle high throughput workloads of 50,000 messages per second across multiple instances.
- Kafka Connect is used for simple data integration without transformations by moving data to and from Kafka.
Following up from AMQP presentation, this is a more in-depth coverage of RabbitMQ with workshop-style walkthrough, covering various aspects of the system.
Docker Swarm allows managing Docker clusters remotely. The key components are swarm managers, swarm nodes, and a scheduler. Swarm managers oversee nodes in the cluster using Docker APIs. The scheduler uses strategies and filters to determine where to place containers on nodes. Discovery services help register and discover nodes in the cluster.
Kubernetes Architecture and Introduction – Paris Kubernetes MeetupStefan Schimanski
The document provides an overview of Kubernetes architecture and introduces how to deploy Kubernetes clusters on different platforms like Mesosphere's DCOS, Google Container Engine, and Mesos/Docker. It discusses the core components of Kubernetes including the API server, scheduler, controller manager and kubelet. It also demonstrates how to interact with Kubernetes using kubectl and view cluster state.
Kubernetes is an open-source system for managing containerized applications across multiple hosts. It includes key components like Pods, Services, ReplicationControllers, and a master node for managing the cluster. The master maintains state using etcd and schedules containers on worker nodes, while nodes run the kubelet daemon to manage Pods and their containers. Kubernetes handles tasks like replication, rollouts, and health checking through its API objects.
Swarm in a nutshell
• Exposes several Docker Engines as a single virtual Engine
• Serves the standard Docker API
• Extremely easy to get started
• Batteries included but swappable
Traditional virtualization technologies have been used by cloud infrastructure providers for many years in providing isolated environments for hosting applications. These technologies make use of full-blown operating system images for creating virtual machines (VMs). According to this architecture, each VM needs its own guest operating system to run application processes. More recently, with the introduction of the Docker project, the Linux Container (LXC) virtualization technology became popular and attracted the attention. Unlike VMs, containers do not need a dedicated guest operating system for providing OS-level isolation, rather they can provide the same level of isolation on top of a single operating system instance.
An enterprise application may need to run a server cluster to handle high request volumes. Running an entire server cluster on Docker containers, on a single Docker host could introduce the risk of single point of failure. Google started a project called Kubernetes to solve this problem. Kubernetes provides a cluster of Docker hosts for managing Docker containers in a clustered environment. It provides an API on top of Docker API for managing docker containers on multiple Docker hosts with many more features.
Removing performance bottlenecks with Kafka Monitoring and topic configurationKnoldus Inc.
Apache Kafka is a distributed messaging system used to build real-time data pipelines & streaming applications. Since applications rely heavily on efficient data transfer, message passing platforms like Kafka cannot afford a breakdown or poor performance.
But how do we ensure that Kafka is running well and successfully streaming messages at low latency? This is where Kafka monitoring steps in.
Here’s the agenda of the webinar -
> Why Kafka monitoring?
> Top 10 Kafka metrics to focus on
> How to change Kafka topic configuration at runtime?
Apache Kafka is a fast, scalable, and distributed messaging system. It is designed for high throughput systems and can replace traditional message brokers due to its better throughput, built-in partitioning for scalability, replication for fault tolerance, and ability to handle large message processing applications. Kafka uses topics to organize streams of messages, partitions to distribute data, and replicas to provide redundancy and prevent data loss. It supports reliable messaging patterns including point-to-point and publish-subscribe.
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 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 is a distributed publish-subscribe messaging system that provides high throughput and low latency for processing streaming data. It is used to handle large volumes of data in real-time by partitioning topics across multiple servers or brokers. Kafka maintains ordered and immutable logs of messages that can be consumed by subscribers. It provides features like replication, fault tolerance and scalability. Some key Kafka concepts include producers that publish messages, consumers that subscribe to topics, brokers that handle data streams, topics to categorize related messages, and partitions to distribute data loads across clusters.
In this session you will learn:
1. Kafka Overview
2. Need for Kafka
3. Kafka Architecture
4. Kafka Components
5. ZooKeeper Overview
6. Leader Node
For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
How to use kakfa for storing intermediate data and use it as a pub/sub model with each of the Producer/Consumer/Topic configs deeply and the Internals working of it.
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.
This document provides an overview of Apache Kafka including:
- Apache Kafka is a distributed streaming platform that allows for publishing and subscribing to streams of records.
- It introduces key Apache Kafka concepts like topics, producers, consumers, brokers, and components.
- Use cases for Apache Kafka are also discussed such as messaging, metrics collection, and event sourcing.
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.
The document provides an overview of key concepts for working with Apache Kafka including:
1. Kafka only provides at-most-once or at-least-once delivery out of the box and discusses how messages can be lost or duplicated. Exactly-once delivery was introduced in later versions.
2. Using more partitions can increase unavailability if a broker fails uncleanly, since leadership elections must occur for each partition, and can increase latency by serializing replication across partitions.
3. The schema registry helps enforce schemas when using Avro to serialize data to Kafka, avoiding issues from schema changes.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
ndependent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. How can me make sure that all these event are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amount of messages between a source and a target.
This session will start with an introduction into Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table. Additionally the Kafka ecosystem will be covered as well as the integration of Kafka in the Oracle Stack, with products such as Golden Gate, Service Bus and Oracle Stream Analytics all being able to act as a Kafka consumer or producer.
In this Kafka Tutorial, we will discuss Kafka Architecture. In this Kafka Architecture article, we will see API’s in Kafka. Moreover, we will learn about Kafka Broker, Kafka Consumer, Zookeeper, and Kafka Producer. Also, we will see some fundamental concepts of Kafka.
This document provides an overview of Apache Kafka, a distributed streaming platform and messaging queue. It discusses the two main types of messaging queues - traditional queues that delete messages after consumption and pub/sub models that persist messages. It explains how Kafka combines these approaches by persisting messages like a pub/sub system but allowing parallel consumption through consumer groups and partitioning like a traditional queue. The document also covers key Kafka concepts like producers, brokers, consumers, topics, partitions, offsets, and how Zookeeper is used to manage the Kafka cluster. It provides examples of using Kafka for real-time data ingestion, request queuing, data replication, and describes basic Kafka configurations.
Learn All Aspects Of Apache Kafka step by step, Enhance your skills & Launch Your Career, On-Demand Course
for apache kafka online training visit: https://mindmajix.com/apache-kafka-training
Apache Kafka is a distributed messaging system that handles large volumes of real-time data efficiently. It allows for publishing and subscribing to streams of records and storing them reliably and durably. Kafka clusters are highly scalable and fault tolerant, providing throughput higher than other message brokers with latency of less than 10ms.
Kafka uses a publish-subscribe messaging model with topics that can be partitioned across multiple servers. Messages are organized into topics which are distributed and stored across partitions. Producers write data to topics in the form of messages which are consumed by subscribers. The messages are distributed to partitions in a partitioned topic for scalability and fault tolerance.
Apache Kafka is a distributed publish-subscribe messaging system that allows for high volumes of data to be passed from endpoints to endpoints. It uses a broker-based architecture with topics that messages are published to and persisted on disk for reliability. Producers publish messages to topics that are partitioned across brokers in a Kafka cluster, while consumers subscribe to topics and pull messages from brokers. The ZooKeeper service coordinates the Kafka brokers and notifies producers and consumers of changes.
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.
Similar to Cluster_Performance_Apache_Kafak_vs_RabbitMQ (20)
1. Y790 – Independent Study
Streaming Performances with Apache Kafka and RabbitMQ
Shameera Rathnayaka Yodage(syodage@indiana.edu)
Introduction
The demand for stream processing is increasing rapidly these days. It is not enough to process data
in big volumes. Data has to be processed fast so that users can identify the nature of data in real
time. This is required for fraud detection, trading, social network event processing and many more.
Source can be anything which publishes the changes of data in high rate. There can be more than
one data source, in that case application needs to handle all these event streams in real time.
Backend event processing component needs to process these data at same speed it comes. But the
reality is backend can not operate in the same speed. Event processing may require more time to
process the data and in the mean time data published will be added. In order to handle this high
rates of data event streams, data stream processing component needs to process these events in
parallel. Apache Kafka and RabbitMQ are two popular message broker implementations which
can be used to control real-time event streaming and processing with reliable way. These brokers
provide guaranteed delivery, means, every event will be delivered to backend when backend is
ready to process it. In this study, we have measured round trip latency of Apache Kafka and
RabbitMQ with different message sizes and compared the results.
Data Streaming
Data streaming is action of generating data continuously from large number of data sources, which
are typically sent simultaneously and data records are in small size. We can easily see this kind of
data sources widely used in industry. Twitter is one major data streaming application, which
generated large number of twitter record under millions of users. Another few applications are log
file generated by customers using web applications, ecommerce purchases, information from
social networks, financial trading floors, connected IoT devices. These data need to be processed
sequentially. Data stream processed by record by record or set of record using small time window.
To get near real time action according to the behavior of this data stream, stream processing
engines need to process these data as soon as it received. Stream processing technique is used by
wide verity of analytics including correlations, aggregations, filtering and sampling. Information
derived from such analysis gives companies more visibility of their business and help to take
important decisions without any delay. Every message comes with this data streams is valuable
and needs to process without losing. To achieve this, we need to use reliable message brokers in
between streaming data sources and stream processing engine.
Apache Kafka
Apache Kafka[1] is a distributed, partitioned, replicated commit log service, in another words
Kafka is a high-throughput distributed messaging system. Apache Kafka is an open source project
2. developed under Apache Software Foundation. Kafka is designed to allow a single cluster to serve
as central data backbone for a large organization. It can be elastically and transparently expanded
without downtime. Messages are persisted on disk and replicated within the cluster to prevent data
loss. Kafka has a modern cluster-centric design that offers strong durability and fault-tolerance
guarantee.
Kafka topic is feed name to which messages are published. Kafka maintain multiple partition for
a topic, and each partition can have multiple replications. This is how Kafka provide high fault-
tolerance to its data. Kafka recommend to setup partition count equal to the number of instances
in cluster and at least replication factor as 2 to provide fault-tolerance service.
Figure 1: Kafka Topic partitions
Apache Kafka use Apache Zookeeper [2] for store configurations and as a distributed coordinator
for its cluster. Kafka store all topics, partitions, replications, consumers, producers related
configurations on zookeeper. Figure 2 shows 3 node Kafka cluster with 3 partitions and replication
factor 2.
Kafka select one leader per partition and leader is the one who serve for consumers. Kafka
publisher can route messages to specific broker instance and there is not any introversion routing
tier. Client controls which partition it publishes messages to. This can be done at random,
implementing a kind of random load balancing in software level.
According to the Kafka design, Kafka consumer can start consuming messages from any location.
Either from latest or from previous offset. User can configure these through client topic
configuration. The Kafka consumer works by issuing fetch requests to the brokers leading the
portions it wants to consume. The consumer specifies its offset in the log with each request and
receives back a chunk of log beginning form that position. The consumer thus has significant
control over this consuming position and can rewind it to re-consume data if needed. This special
design enables to build a high fault-tolerance consumer framework. Kafka keep all messages
comes to the broker till retention time exceed for that topic. Once the retention time comes it delete
messages to free the disk space to new messages.
3. RabbitMQ
RabbitMQ [3] is a messaging broker which provides a common platform to send and receive
messages for applications. Also it provides a reliable storage for the messages till they are getting
delivered. RabbitMQ is designed to offer several important features as reliability, persistence,
guaranteed delivery and high availability in messaging.
RabbitMQ supports messaging over a variety of messaging protocols and is using AMQP 0.9.1
protocol. AMQP [4], The Advanced Message Queuing Protocol is an open standard for passing
business messages between applications or organizations.
Kafka Cluster
Broker 1
P1
R1
P3
R2
Broker 2
P2
R1
P1
R2
Broker 3
P3
R1
P2
R2
Producer
Consumer 1
Consumer 2
Consumer 3
Px
Ry
P3
R2
Leader of partition y and
replica number is x
Partition y and
replica number is x
Figure 2: Kafka Cluster
4. Figure 3: RabbitMQ Topic Routing
RabbitMQ's AMQP based messaging model is based on producer/subscriber, exchange, bindings
and queues/topics. The core idea in this messaging model is that the producer never sends any
messages directly to a specified queue.
Once published, the producer doesn't even know if a message will be delivered to any
queue. Instead, the producer only sends messages to an exchange. Messages are routed through
exchanges before arriving at queues. The exchange is like the middle man between producer and
queue/topic. It receives messages from producers and on the other side it forwards them to queues.
The exchange knows exactly what to do with a message it receives; whether it needs to be added
to a particular queue, to many queues or whether to discard the message. The rules for this selection
are defined by the exchange type. A binding is a relationship between an exchange and a queue.
In other words, binding implies the queue is interested in messages from this exchange. Once the
producer sends the message it is first added to exchange. Then based on which queue the message
is addressed to, a binding between exchange and specific queue is created and message is
transferred to queue through the binding. This is the RabbitMQ messaging model in brief.
RabbitMQ support high availability, this is similar to Apach Kafka replication factor. By default,
RabbitMQ queues located on a single node in a cluster. To archive fault tolerance, we need to alter
this default behavior and increase high availability factor to 2. RabbitMQ queues can optionally
be made mirrored across multiple nodes. By increasing high availability factor of cluster we say
RabbitMQ to make two mirror of one queue. Each mirrored queue consists of one master and one
5. or more slaves, which depend on HA factor. Oldest slave being promoted to the new master if the
old master disappears for any reason.
Deployment
Here we used hardware that is available at Indiana Universities Digital Science Center. We used
Juliet SuperMicro HPC Cluster to deploy all clusters and run all clients. Following is Juliet cluster
node configurations [5].
Juliet Compute Resource
System Type : SuperMicro HPC Cluster
# Nodes : 128
# CPUs : 256
# Cores : 3456
RaM(GB) : 16384
Storage(TB) : 1024 (HDD), 50 (SSD)
Node configuration
# CPUs : 48
Core(s) per socket: 12
Socket(s): 2
NUMA node(s): 2
Memory: 125G
SSD : 367G
Apache Kafka Cluster
Test has been run with 3 node Apache Kafka cluster with 3 node Apache Zookeeper cluster.
Apache Zookeeper recommends to run 3 node cluster to get robust behavior. Each Zookeeper node
runs on separate Juliet node and communicate via TCP. Each Kafka cluster node runs on separate
node on Juliet cluster. In test we used one Kafka producer and three partition consumer, one
consumer for one partition ( This is Apache Kafka recommendation). All these clients run on one
separate node in juliet cluster. Figure 4 show the Apache Kafka deployment on Juliet Cluster.
Apache Kafka : kafka_2.10-0.8.2.2
Apache Kafka Client : 0.8.2.0
Apache Zookeeper: 3.4.6
6. Figure 4: Apache Kafka Cluster Setup
RabbitMQ Cluster
Test has been run with 3 node RabbitMQ cluster with different configurations. Erlang is installed
on each machine and used RabbitMQ rpm download package available in RabbitMQ download
page. Each RabbitMQ is installed on different nodes on Juliet cluster. All producer and consumer
clients are runs on one different node on Juliet cluster.
RabbitMQ : 3.6.1
Erlang : 18.3
7. Figure 5: RabbitMQ Cluster Setup
Performance Comparison
All test cases run with two difference configurations, one with fault tolerance and another without
fault tolerance. In the first round we measure round trip latency with 3000 messages, here we ran
with different message sizes, from 8kb to 8MB. Figure 6 shows results with small message sizes
and Figure 7 shows results with small to large message sizes. For every test run, fresh topic was
created in Apache Kafka cluster and fresh vhost created on RabbitMQ cluster.
9. Table 1: Variance of Round Trip Latency
Kafka – rep 1 Kafka – rep 2 RabbitMQ – HA 1 RabbitMQ – HA 2
8k 3.687 0.431 0.571 0.415
16k 0.493 0.814 0.580 0.634
32k 0.615 1.063 0.610 0.634
64k 1.012 1.078 0.899 0.707
128k 0.923 1.004 1.200 0.889
256k 1.076 1.129 1.764 1.542
512k 1.284 1.328 2.834 2.738
1Mb 1.957 2.094 5.03 4.375
2Mb 4.622 14.29 9.799 8.979
4Mb 6.547 29.88 19.35 4.699
8Mb 23.853 107.786 8.205 37.02
As second round of test, we used the same configuration changes, but we give warm up time to
clusters with first 100 messages. Readings are taken after first 100 messages. Figure 8 show the
results for small messages with all four test configuration with Apache Kafka and RabbitMQ.
Figure 9 is same as Figure 8 but it has small to large messages.
Figure 8: Kafka vs RabbitMQ small message sizes
11. Conclusion
Without enabling fault-tolerance both Apache Kafka and RabbitMQ give the same level
performance of round trip latency (Figure 6 & 7). It is obvious that round trip latency is getting
increased with message size, as it requires to read and write more data from and to I/O devices
buffers. With enabling fault-tolerance factor by 2, Apache Kafka has slightly large round trip
latency than RabbitMQ. Fault-tolerance round trip latency of each broker is always grater when
compared to the non fault-tolerant round trip latency of the same broker. For small size messages
RabbitMQ shows almost same round trip latency for both with and without high availability. The
difference of fault tolerance round trip latency is getting increase with message size.
After warm up the cluster with hundred messages, there is slight performance improvement with
Apache Kafka results (Figure 8 & 9). Apache Kafka has high latency at startup and then it comes
down and cluster calibrate to the environment after first hundreds of messages. Still RabbitMQ
has the lowest round trip latency with both configurations. The fault-tolerance round trip latency
gap has been decrease as a result of warm up step.
According to the results, it seems there is no considerably big different with two broker but
RabbitMQ has given best readings for round trip latency compare to Apache Kafka for both fault-
tolerance and non fault tolerance configurations.
Future Works
Each broker can be configured differently to get more performance according to the nature of
application. In this performance test mostly default properties comes with each broker and few
properties are changed to match with testing environment. Here we used one producer, but it is
more interesting to know the break point of both brokers with high load. We can run same test
with increasing the number of publishers/producers and check the stability of both clusters with
load. Further we can run this load test with increasing fault tolerance factor of each broker and
checking the latency changes.
Reference:
[1] http://kafka.apache.org
[2] https://zookeeper.apache.org
[3] https://www.rabbitmq.com
[4] https://www.amqp.org
[5] http://cloudmesh.github.io/introduction_to_cloud_computing/hardware/indiana.html