The speaker Jaskey(RocketMQ Committer) shared core features of RocketMQ, including domain model, order message, transactional message, batch message, message filter by SQL92 and so on.
Effectively-once semantics in Apache PulsarMatteo Merli
“Exactly-once” is a controversial term in the messaging landscape. In this presentation we offer a detailed look at effectively-once delivery semantics in Apache Pulsar and how this is achieved without sacrificing performance.
October 2016 HUG: Pulsar, a highly scalable, low latency pub-sub messaging s...Yahoo Developer Network
Yahoo recently open-sourced Pulsar, a highly scalable, low latency pub-sub messaging system running on commodity hardware. It provides simple pub-sub messaging semantics over topics, guaranteed at-least-once delivery of messages, automatic cursor management for subscribers, and cross-datacenter replication. Pulsar is used across various Yahoo applications for large scale data pipelines. Learn more about Pulsar architecture and use-cases in this talk.
Speakers:
Matteo Merli from Pulsar team at Yahoo
High performance messaging with Apache PulsarMatteo Merli
Apache Pulsar is being used for an increasingly broad array of data ingestion tasks. When operating at scale, it's very important to ensure that the system can make use of all the available resources. Karthik Ramasamy and Matteo Merli share insights into the design decisions and the implementation techniques that allow Pulsar to achieve high performance with strong durability guarantees.
Pulsar - flexible pub-sub for internet scaleMatteo Merli
Pub-Sub messaging is a very convenient abstraction that allows system and application developers to decouple components and let them communicate, by acting as durable buffer for transient data, or as a persistent log from where to recover after crashes. This talk will present an overview of Apache Pulsar, the reasons that led to its development and how it enabled many teams at Yahoo and to build scalable and reliable applications. Apache Pulsar has become the defacto pub-sub messaging at Yahoo serving 100+ applications and processing 100’s of billions of messages for over 3+ years.
In this talk, we will explore in detail different categories of use cases that highlight how Pulsar can be applied to solve a broad range of problems thanks to its flexible messaging model that supports both queuing and streaming semantics with a focus on durability and transaction guarantees.
This document provides an overview of Apache Kafka. It begins with defining Kafka as a distributed streaming platform and messaging system. It then lists the agenda which includes what Kafka is, why it is used, common use cases, major companies that use it, how it achieves high performance, and core concepts. Core concepts explained include topics, partitions, brokers, replication, leaders, and producers and consumers. The document also provides examples to illustrate these concepts.
Lessons from managing a Pulsar cluster (Nutanix)StreamNative
In this presentation, we will cover:
- How to performance test and optimize a Pulsar cluster. We will present how we load tested Pulsar with locust and, following this, how we tuned our configurations for our use cases.
- Event sourcing pattern with Apache Pulsar. Avro schema usage, compatibility choices and schema evolution on pulsar topics that worked for us.
- Bonus: How we source Apache Flink from apache pulsar and run our workflows.
By attending this webinar, you can expect to come away with:
- How to performance test a Pulsar cluster for your use case.
- How to leverage the highly configurable broker and Bookkeeper to suit your needs.
- Event sourcing patterns on top of Apache Pulsar.
- Avro schema usage, compatibility choices, and evolution.
- Familiarise with pulsar connector for Flink and possible use cases.
At Clever Cloud, we are working on extremely light virtual machines to run WebAssembly binaries. As it’s WASM, we can write code using a lot of languages. We use a custom unikernel to run this WASM as Function-as-a-Service, using one VM per function execution. These VM can run on events from messages coming through Pulsar, or from HTTP invocation, the run is on-demand as only the consumers stay up. This can be a new model: Pulsar functions for real isolation in multi-tenancy use cases. This talk will show the use case, explain the virtualization underneath and demonstrate the multi-tenancy use case.
Effectively-once semantics in Apache PulsarMatteo Merli
“Exactly-once” is a controversial term in the messaging landscape. In this presentation we offer a detailed look at effectively-once delivery semantics in Apache Pulsar and how this is achieved without sacrificing performance.
October 2016 HUG: Pulsar, a highly scalable, low latency pub-sub messaging s...Yahoo Developer Network
Yahoo recently open-sourced Pulsar, a highly scalable, low latency pub-sub messaging system running on commodity hardware. It provides simple pub-sub messaging semantics over topics, guaranteed at-least-once delivery of messages, automatic cursor management for subscribers, and cross-datacenter replication. Pulsar is used across various Yahoo applications for large scale data pipelines. Learn more about Pulsar architecture and use-cases in this talk.
Speakers:
Matteo Merli from Pulsar team at Yahoo
High performance messaging with Apache PulsarMatteo Merli
Apache Pulsar is being used for an increasingly broad array of data ingestion tasks. When operating at scale, it's very important to ensure that the system can make use of all the available resources. Karthik Ramasamy and Matteo Merli share insights into the design decisions and the implementation techniques that allow Pulsar to achieve high performance with strong durability guarantees.
Pulsar - flexible pub-sub for internet scaleMatteo Merli
Pub-Sub messaging is a very convenient abstraction that allows system and application developers to decouple components and let them communicate, by acting as durable buffer for transient data, or as a persistent log from where to recover after crashes. This talk will present an overview of Apache Pulsar, the reasons that led to its development and how it enabled many teams at Yahoo and to build scalable and reliable applications. Apache Pulsar has become the defacto pub-sub messaging at Yahoo serving 100+ applications and processing 100’s of billions of messages for over 3+ years.
In this talk, we will explore in detail different categories of use cases that highlight how Pulsar can be applied to solve a broad range of problems thanks to its flexible messaging model that supports both queuing and streaming semantics with a focus on durability and transaction guarantees.
This document provides an overview of Apache Kafka. It begins with defining Kafka as a distributed streaming platform and messaging system. It then lists the agenda which includes what Kafka is, why it is used, common use cases, major companies that use it, how it achieves high performance, and core concepts. Core concepts explained include topics, partitions, brokers, replication, leaders, and producers and consumers. The document also provides examples to illustrate these concepts.
Lessons from managing a Pulsar cluster (Nutanix)StreamNative
In this presentation, we will cover:
- How to performance test and optimize a Pulsar cluster. We will present how we load tested Pulsar with locust and, following this, how we tuned our configurations for our use cases.
- Event sourcing pattern with Apache Pulsar. Avro schema usage, compatibility choices and schema evolution on pulsar topics that worked for us.
- Bonus: How we source Apache Flink from apache pulsar and run our workflows.
By attending this webinar, you can expect to come away with:
- How to performance test a Pulsar cluster for your use case.
- How to leverage the highly configurable broker and Bookkeeper to suit your needs.
- Event sourcing patterns on top of Apache Pulsar.
- Avro schema usage, compatibility choices, and evolution.
- Familiarise with pulsar connector for Flink and possible use cases.
At Clever Cloud, we are working on extremely light virtual machines to run WebAssembly binaries. As it’s WASM, we can write code using a lot of languages. We use a custom unikernel to run this WASM as Function-as-a-Service, using one VM per function execution. These VM can run on events from messages coming through Pulsar, or from HTTP invocation, the run is on-demand as only the consumers stay up. This can be a new model: Pulsar functions for real isolation in multi-tenancy use cases. This talk will show the use case, explain the virtualization underneath and demonstrate the multi-tenancy use case.
Kafka meetup JP #3 - Engineering Apache Kafka at LINEkawamuray
This document summarizes a presentation about engineering Apache Kafka at LINE. Some key points:
- LINE uses Apache Kafka as a central data hub to pass data between services, handling over 140 billion messages per day.
- Data stored in Kafka includes application logs, data mutations, and task requests. This data is used for tasks like data replication, analytics, and asynchronous processing.
- Performance optimizations have led to target latencies below 1ms for 50% of produces and below 10ms for 99% of produces.
- SystemTap, a Linux tracing tool, helped identify slow disk reads causing delayed Kafka responses, improving performance.
- Having a single Kafka cluster as a data hub makes inter-service
Building a Messaging Solutions for OVHcloud with Apache Pulsar_Pierre ZembStreamNative
OVHcloud is the biggest European cloud provider. From dedicated servers to Managed Kubernetes, from VMware® based Hosted Private Cloud to OpenStack-based Public Cloud, we have over 1.4 million customers worldwide.
Internally, we have been running Apache Kafka for years, and despite all the skills obtained operating multiples clusters with millions of messages per second, we decided to shift and build the foundation of our 'topic-as-a-service' product called ioStream on Apache Pulsar.
In this talk, you will have the insights of why we decided to use Apache Pulsar instead of Apache Kafka as the core of ioStream. We will tell you our journey to use Apache Pulsar, from our deployments to the management, what did work and what did not.
This document discusses using Event Sourcing (ES) with Kafka. It explains that ES persists raw events immutably as they occur rather than storing mutations. Kafka is well-suited for ES because it can handle high volumes of messages, provides durability and ordering, and scales horizontally. It also supports use cases like messaging, analytics, and stream processing. The document outlines Kafka's architecture including topics, partitions, producers, and consumers, and how it provides reliability, parallelism, and ordering guarantees.
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 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.
Apache Kafka is a distributed publish-subscribe messaging system that was originally created by LinkedIn and contributed to the Apache Software Foundation. It is written in Scala and provides a multi-language API to publish and consume streams of records. Kafka is useful for both log aggregation and real-time messaging due to its high performance, scalability, and ability to serve as both a distributed messaging system and log storage system with a single unified architecture. To use Kafka, one runs Zookeeper for coordination, Kafka brokers to form a cluster, and then publishes and consumes messages with a producer API and consumer API.
Pulsar Summit Asia - Running a secure pulsar clusterShivji Kumar Jha
This document provides an overview of securing Apache Pulsar. It discusses securing the different cluster components like Zookeeper, Bookkeeper and brokers. It describes how to enable TLS for securing communication between these components. It also covers setting up TLS, keystores and truststores for brokers and clients. The document references Pulsar and Zookeeper documentation for more details on configuring security.
This document provides an overview of lightweight messaging and remote procedure call (RPC) systems in distributed systems. It discusses messaging systems, typical peer-to-peer and broker-based messaging topologies, characteristics and features of messaging systems, main classes of messaging systems including enterprise service buses (ESBs), JMS implementations, AMQP implementations, and lightweight modern systems. It also covers RPC, serialization libraries, differences between messaging and RPC, examples of ZeroMQ for peer-to-peer messaging, Apache Kafka for broker-based messaging, and Twitter Finagle for scalable RPC.
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 Kafka including basic concepts like topics, brokers, and partitions. It discusses how to install and run Kafka, configure topics and clusters, and monitor and troubleshoot Kafka. It also demonstrates producing, consuming, and loading test scenarios. Key lessons learned are around balancing replication, partitions, and leaders across brokers while ensuring adequate disk space, IOPS, and retention periods. Automating cluster changes and backing up messages is also recommended.
Introduction to Apache BookKeeper Distributed StorageStreamlio
A brief technical introduction to Apache BookKeeper, the scalable, fault-tolerant, and low-latency storage service optimized for real-time and streaming workloads.
Streaming millions of Contact Center interactions in (near) real-time with Pu...Frank Kelly
Cogito uses Apache Pulsar to stream millions of contact center interactions in near real-time. They break each customer call into intervals, with each interval generating two Pulsar topics for real-time audio and analytics. With 15,000 concurrent users, they estimate creating 1.5-2 million topics per day. Key challenges include Zookeeper memory usage, broker configuration tuning, and bookie throughput optimization to handle their high throughput workload.
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013mumrah
Apache Kafka is a distributed publish-subscribe messaging system that allows both publishing and subscribing to streams of records. It uses a distributed commit log that provides low latency and high throughput for handling real-time data feeds. Key features include persistence, replication, partitioning, and clustering.
Strata London 2018: Multi-everything with Apache PulsarStreamlio
Ivan Kelly offers an overview of Apache Pulsar, a durable, distributed messaging system, underpinned by Apache BookKeeper, that provides the enterprise features necessary to guarantee that your data is where is should be and only accessible by those who should have access. Ivan explores the features built into Pulsar that will help your organization stay in compliance with key requirements and regulations, for multi-data center replication, multi-tenancy, role-based access control, and end-to-end encryption. Ivan concludes by explaining why Pulsar’s multi-data center story will alleviate headaches for the operations teams ensuring compliance with GDPR.
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.
Apache Kafka is a fast, scalable, and distributed messaging system. It is designed for high throughput systems and can serve as a replacement for traditional message brokers. Kafka uses a publish-subscribe messaging model where messages are published to topics that multiple consumers can subscribe to. It provides benefits such as reliability, scalability, durability, and high performance.
This document discusses using pulsarctl and pulsar-manager to manage a Pulsar cluster. It introduces pulsarctl as a CLI tool developed in Go for managing Pulsar clusters that addresses some issues with the existing Pulsar admin tool. It then covers how to use the Admin API and CLI features of pulsarctl. Finally, it outlines some future plans, including adding more features to pulsarctl and pulsar-manager.
Scaling customer engagement with apache pulsarStreamNative
Iterable's platform is used by marketers to reach hundreds of millions of users every day, and those numbers are quickly growing. Iterable's infrastructure is built with pub-sub messaging at it's core, so the reliability, scalability and flexibility provided by that system are business critical.
In this talk we'll discuss why Iterable chose Pulsar as a pub-sub messaging system, as well as how Iterable is taking advantage of some of more recently added features in Pulsar. We'll also talk about some of the challenges we encountered, where we think Pulsar can improve, and some contributions we've made to the open source community around Pulsar.
JavaOne 2016
JMS is pretty simple, right? Once you’ve mastered topics and queues, the rest can appear trivial, but that isn’t the case. The queuing system, whether ActiveMQ, OpenMQ, or WebLogic JMS, provides many more features and settings than appear in the Java EE documentation. This session looks at some of the important extended features and configuration settings. What would you need to optimize if your messages are large or you need to minimize prefetching? What is the best way to implement time-delayed messages? The presentation also looks at dangerous bugs that can be introduced via simple misconfigurations with pooled beans. The JMS APIs are deceptively simple, but getting an implementation into production and tuned correctly can be a bit trickier.
Kafka meetup JP #3 - Engineering Apache Kafka at LINEkawamuray
This document summarizes a presentation about engineering Apache Kafka at LINE. Some key points:
- LINE uses Apache Kafka as a central data hub to pass data between services, handling over 140 billion messages per day.
- Data stored in Kafka includes application logs, data mutations, and task requests. This data is used for tasks like data replication, analytics, and asynchronous processing.
- Performance optimizations have led to target latencies below 1ms for 50% of produces and below 10ms for 99% of produces.
- SystemTap, a Linux tracing tool, helped identify slow disk reads causing delayed Kafka responses, improving performance.
- Having a single Kafka cluster as a data hub makes inter-service
Building a Messaging Solutions for OVHcloud with Apache Pulsar_Pierre ZembStreamNative
OVHcloud is the biggest European cloud provider. From dedicated servers to Managed Kubernetes, from VMware® based Hosted Private Cloud to OpenStack-based Public Cloud, we have over 1.4 million customers worldwide.
Internally, we have been running Apache Kafka for years, and despite all the skills obtained operating multiples clusters with millions of messages per second, we decided to shift and build the foundation of our 'topic-as-a-service' product called ioStream on Apache Pulsar.
In this talk, you will have the insights of why we decided to use Apache Pulsar instead of Apache Kafka as the core of ioStream. We will tell you our journey to use Apache Pulsar, from our deployments to the management, what did work and what did not.
This document discusses using Event Sourcing (ES) with Kafka. It explains that ES persists raw events immutably as they occur rather than storing mutations. Kafka is well-suited for ES because it can handle high volumes of messages, provides durability and ordering, and scales horizontally. It also supports use cases like messaging, analytics, and stream processing. The document outlines Kafka's architecture including topics, partitions, producers, and consumers, and how it provides reliability, parallelism, and ordering guarantees.
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 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.
Apache Kafka is a distributed publish-subscribe messaging system that was originally created by LinkedIn and contributed to the Apache Software Foundation. It is written in Scala and provides a multi-language API to publish and consume streams of records. Kafka is useful for both log aggregation and real-time messaging due to its high performance, scalability, and ability to serve as both a distributed messaging system and log storage system with a single unified architecture. To use Kafka, one runs Zookeeper for coordination, Kafka brokers to form a cluster, and then publishes and consumes messages with a producer API and consumer API.
Pulsar Summit Asia - Running a secure pulsar clusterShivji Kumar Jha
This document provides an overview of securing Apache Pulsar. It discusses securing the different cluster components like Zookeeper, Bookkeeper and brokers. It describes how to enable TLS for securing communication between these components. It also covers setting up TLS, keystores and truststores for brokers and clients. The document references Pulsar and Zookeeper documentation for more details on configuring security.
This document provides an overview of lightweight messaging and remote procedure call (RPC) systems in distributed systems. It discusses messaging systems, typical peer-to-peer and broker-based messaging topologies, characteristics and features of messaging systems, main classes of messaging systems including enterprise service buses (ESBs), JMS implementations, AMQP implementations, and lightweight modern systems. It also covers RPC, serialization libraries, differences between messaging and RPC, examples of ZeroMQ for peer-to-peer messaging, Apache Kafka for broker-based messaging, and Twitter Finagle for scalable RPC.
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 Kafka including basic concepts like topics, brokers, and partitions. It discusses how to install and run Kafka, configure topics and clusters, and monitor and troubleshoot Kafka. It also demonstrates producing, consuming, and loading test scenarios. Key lessons learned are around balancing replication, partitions, and leaders across brokers while ensuring adequate disk space, IOPS, and retention periods. Automating cluster changes and backing up messages is also recommended.
Introduction to Apache BookKeeper Distributed StorageStreamlio
A brief technical introduction to Apache BookKeeper, the scalable, fault-tolerant, and low-latency storage service optimized for real-time and streaming workloads.
Streaming millions of Contact Center interactions in (near) real-time with Pu...Frank Kelly
Cogito uses Apache Pulsar to stream millions of contact center interactions in near real-time. They break each customer call into intervals, with each interval generating two Pulsar topics for real-time audio and analytics. With 15,000 concurrent users, they estimate creating 1.5-2 million topics per day. Key challenges include Zookeeper memory usage, broker configuration tuning, and bookie throughput optimization to handle their high throughput workload.
Introduction and Overview of Apache Kafka, TriHUG July 23, 2013mumrah
Apache Kafka is a distributed publish-subscribe messaging system that allows both publishing and subscribing to streams of records. It uses a distributed commit log that provides low latency and high throughput for handling real-time data feeds. Key features include persistence, replication, partitioning, and clustering.
Strata London 2018: Multi-everything with Apache PulsarStreamlio
Ivan Kelly offers an overview of Apache Pulsar, a durable, distributed messaging system, underpinned by Apache BookKeeper, that provides the enterprise features necessary to guarantee that your data is where is should be and only accessible by those who should have access. Ivan explores the features built into Pulsar that will help your organization stay in compliance with key requirements and regulations, for multi-data center replication, multi-tenancy, role-based access control, and end-to-end encryption. Ivan concludes by explaining why Pulsar’s multi-data center story will alleviate headaches for the operations teams ensuring compliance with GDPR.
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.
Apache Kafka is a fast, scalable, and distributed messaging system. It is designed for high throughput systems and can serve as a replacement for traditional message brokers. Kafka uses a publish-subscribe messaging model where messages are published to topics that multiple consumers can subscribe to. It provides benefits such as reliability, scalability, durability, and high performance.
This document discusses using pulsarctl and pulsar-manager to manage a Pulsar cluster. It introduces pulsarctl as a CLI tool developed in Go for managing Pulsar clusters that addresses some issues with the existing Pulsar admin tool. It then covers how to use the Admin API and CLI features of pulsarctl. Finally, it outlines some future plans, including adding more features to pulsarctl and pulsar-manager.
Scaling customer engagement with apache pulsarStreamNative
Iterable's platform is used by marketers to reach hundreds of millions of users every day, and those numbers are quickly growing. Iterable's infrastructure is built with pub-sub messaging at it's core, so the reliability, scalability and flexibility provided by that system are business critical.
In this talk we'll discuss why Iterable chose Pulsar as a pub-sub messaging system, as well as how Iterable is taking advantage of some of more recently added features in Pulsar. We'll also talk about some of the challenges we encountered, where we think Pulsar can improve, and some contributions we've made to the open source community around Pulsar.
JavaOne 2016
JMS is pretty simple, right? Once you’ve mastered topics and queues, the rest can appear trivial, but that isn’t the case. The queuing system, whether ActiveMQ, OpenMQ, or WebLogic JMS, provides many more features and settings than appear in the Java EE documentation. This session looks at some of the important extended features and configuration settings. What would you need to optimize if your messages are large or you need to minimize prefetching? What is the best way to implement time-delayed messages? The presentation also looks at dangerous bugs that can be introduced via simple misconfigurations with pooled beans. The JMS APIs are deceptively simple, but getting an implementation into production and tuned correctly can be a bit trickier.
This document provides an overview of Apache Kafka. It begins with background on messaging systems and defines the key types as point-to-point and publish-subscribe. It then defines Kafka as a distributed commit log that evolved from a messaging queue to a full streaming platform. The benefits of Kafka are outlined as reliability, scalability, durability, and performance. Core concepts are explained including topics, partitions, producers, consumers, brokers, clusters, and Zookeeper. An example Kafka deployment is described with brokers, topics, partitions, producers, and consumer groups. Finally, steps to produce the example are listed.
The document summarizes messaging services on AWS. It provides overviews and details of Amazon MQ, Amazon SQS, Amazon Kinesis, Amazon SNS, Amazon PinPoint, and AWS IoT Message Broker. These services enable event-driven architectures and the exchange of information between distributed systems and microservices through queuing, streaming, and publishing of messages. Key features highlighted include scalability, reliability, encryption, and integration with other AWS services.
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.
The 100% open source WSO2 Message Broker is a lightweight, easy-to-use, distributed message-brokering server. It features high availability (HA) support with a complete hot-to-hot continuous availability mode, the ability to scale up to several servers in a cluster, and no single point of failure. It is designed to manage persistent messaging and large numbers of queues, subscribers and messages.
Internet companies with huge traffic and millions of users have tasks involved that cannot be served in a request. RabbitMQ can process tasks or communication between different app components asynchronously but close to real time.
Apache Kafka is a fast, scalable, and distributed messaging system. It is designed for high throughput systems and can serve as a replacement for traditional message brokers. Kafka uses a publish-subscribe messaging model where messages are published to topics that multiple consumers can subscribe to. It provides benefits such as reliability, scalability, durability, and high performance.
The document provides an overview of Java Message Service (JMS) and Apache ActiveMQ. It discusses JMS concepts like messaging domains, message consumption, and message types. It also covers ActiveMQ configuration such as persistence options, transports, clustering, and performance tuning. The document outlines integrating JMS with Spring and monitoring ActiveMQ using the web console, JMX, or command agent. It proposes evaluating performance using JMeter and references additional JMS and ActiveMQ documentation.
At Hootsuite, we've been transitioning from a single monolithic PHP application to a set of scalable Scala-based microservices. To avoid excessive coupling between services, we've implemented an event system using Apache Kafka that allows events to be reliably produced + consumed asynchronously from services as well as data stores.
In this presentation, I talk about:
- Why we chose Kafka
- How we set up our Kafka clusters to be scalable, highly available, and multi-data-center aware.
- How we produce + consume events
- How we ensure that events can be understood by all parts of our system (Some that are implemented in other programming languages like PHP and Python) and how we handle evolving event payload data.
This document discusses various Azure Platform Services including storage, caching, relaying, queuing, and topics. Storage in Azure provides blobs, drives, tables and queues for structured storage needs. Caching services improve application performance. Service Bus provides relaying for connectivity between applications and queuing/topics for messaging with publish/subscribe capabilities. Platform as a Service (PaaS) allows building and hosting applications on Azure's scalable infrastructure.
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 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.
An overview of the Brokered Messaging feature on the Windows Azure Platform. Brokered Messaging supports Queues, Topics and Subscriptions providing message-based sollutions for load balancing, load leveling and pub/sub scenarios.
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.
Kafka is a distributed, replicated, and partitioned platform for handling real-time data feeds. It allows both publishing and subscribing to streams of records, and is commonly used for applications such as log aggregation, metrics, and streaming analytics. Kafka runs as a cluster of one or more servers that can reliably handle trillions of events daily.
In the following slides, our dear colleagues Dimosthenis Botsaris and Alexandros Koufatzis are trying to explore Kafka and Event-Driven Architecture. They define what is the Kafka platform, how does it work and analyze Kafka API's like ConsumerAPI, ProducerAPI, StreamsAPI. They also take a look on some core Kafka's configuration before they deploy it on production and discuss a few best approaches to have a reliable data delivery system using Kafka.
Check out the repository: https://github.com/arconsis/Eshop-EDA
In the following slides, we are trying to explore Kafka and Event-Driven Architecture. We try to define what is Kafka platform, how does it work, analyze Kafka API's like ConsumerAPI, ProducerAPI, StreamsAPI. Also we take a look on some core Kafka's configuration before we deploy it on production and we discuss a few best approaches to have a reliable data delivery system using Kafka.
Check out our repository: https://github.com/arconsis/Eshop-EDA
Similar to 1. Core Features of Apache RocketMQ (20)
Alluxio Webinar | 10x Faster Trino Queries on Your Data PlatformAlluxio, Inc.
Alluxio Webinar
June. 18, 2024
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
- Jianjian Xie (Staff Software Engineer, Alluxio)
As Trino users increasingly rely on cloud object storage for retrieving data, speed and cloud cost have become major challenges. The separation of compute and storage creates latency challenges when querying datasets; scanning data between storage and compute tiers becomes I/O bound. On the other hand, cloud API costs related to GET/LIST operations and cross-region data transfer add up quickly.
The newly introduced Trino file system cache by Alluxio aims to overcome the above challenges. In this session, Jianjian will dive into Trino data caching strategies, the latest test results, and discuss the multi-level caching architecture. This architecture makes Trino 10x faster for data lakes of any scale, from GB to EB.
What you will learn:
- Challenges relating to the speed and costs of running Trino in the cloud
- The new Trino file system cache feature overview, including the latest development status and test results
- A multi-level cache framework for maximized speed, including Trino file system cache and Alluxio distributed cache
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1. Core Features of Apache RocketMQ
1. 1
• Introduction
• Core Concepts
• Message Store
• Load Balance
• Core Features
Core Features of Apache RocketMQ
Jaskey Lam 2017 12
2. 2
Introduction
Low Latency
More than 99.6% response latency within 1 millisecond under high pressure.
Finance Oriented
High availability with tracking and auditing features.
Industry Sustainable
Trillion-level message capacity guaranteed.
Massive Accumulation
Given sufficient disk space, accumulate messages without performance loss.
3. Features
• High availability
• Low Latency Messaging
• At least once
• Message Index
• Massive Accumulation
• Order Message
• Transactional Message
• Scheduled Message
• SQL Filter
• Batch Produce
• LogAppender Support
5. NameServer
• Name Server serves as the routing information provider. Producer/
Consumer clients look up topics to find the corresponding broker list.
• Name Server is stateless and designed as share-nothing.
• Name Server does not store datas, all states are registered from
brokers and stay in memory only
• A client will only connect to one instance in the Name Server clusters
randomly.
6. Broker
• Each group of brokers contains only one master and zero or some slaves.
• Slave synchronize data from master in Sync or Async way depending on the
configuration.
• Consumers can consume message from master or slave. By default, they
consume from master unless master is offline.
• Broker will connect to Name Server and register its topic route informations
Broker is a major component of the RocketMQ system. It receives
messages sent from producers, store them and prepare to handle pull
requests from consumers.
7. Consumer Group
• Identify a kind of consumers, which usually consume the same kind of
messages and have the same consume logic.
• Achieving goals of load-balance and fault-tolerance, in terms of message
consuming, is super easy.
8. Topic
Topic is a category in
which producers deliver
messages and
consumers pull
messages
11. • There will be a consume queue file for every message queues
• Messages are actually stored in CommitLog
• Messages of different topics/queues are actually store in the
same commit logs
18. Order Message
MQ
Create Pay Finish
An order produces three messages, they are order creation, order payment, order finishing. The
consumer need to consume them in order or it does not make sense.
However, we hope the messages of different orders can be consumed concurrently.
20. Order Message
When producing order messages, we can use the order id as the sharding key, by
which we can send the messages into the same message queue in order.
Then it is possible for the consumer to consume these messages belong to the
same order in the order it produces.
21. Scheduled Message
Scheduled messages differ from normal messages in that they
won’t be delivered until a provided time later. This can be used to
solve some scenarios that there is a time window between
production and consumption or used for schedule a delay task.
RocketMQ now only supports the scheduled message with a fixed accuracy ,
but does not support the one with arbitrary precision.
messageDelayLevel = 1s 5s 10s 30s 1m 2m 3m 4m 5m 6m 7m 8m 9m 10m 20m
30m 1h 2h
30. SQL Filter Gramma
1 Numeric comparison, like >, >=, <, <=, BETWEEN, =;
2 Character comparison, like =, <>, IN;
3 IS NULL or IS NOT NULL;
4 Logical AND, OR, NOT;
1 Numeric, like 123, 3.1415;
2 Character, like ‘abc’, must be made with single quotes;
3 NULL, special constant;
4 Boolean, TRUE or FALSE;
Type
Grammars
31. SQL Filter Example
// only subsribe messages have property a, also a >=0 and a <= 3
consumer.subscribe("TopicTest", MessageSelector.bySql("a between 0 and 3");
Message msg = new Message("TopicTest",
tag,
("Hello RocketMQ " + i).getBytes(RemotingHelper.DEFAULT_CHARSET)
);
// Set some properties.
msg.putUserProperty("a", 2);
SendResult sendResult = producer.send(msg);
Producer
Consumer