This document provides an overview of Apache ActiveMQ and messaging with JMS. It discusses what JMS is and how it abstracts message brokers. It then describes what ActiveMQ is and its goals as open source message-oriented middleware. The document outlines examples, configurations, transports, topologies and high availability options for ActiveMQ. It also discusses security, monitoring, visualization and integration with Apache Camel.
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.
The document discusses common problems clients face when using ActiveMQ and provides solutions. It addresses questions around creating JMS clients from scratch, efficiently managing connections, consuming only certain messages, and why ActiveMQ may lock up or freeze. Solutions recommended include using Spring JMS instead of rolling your own client, connection pooling via PooledConnectionFactory or CachingConnectionFactory, message selectors, and ensuring proper memory settings and prefetch limits.
Distributed & Highly Available server applications in Java and ScalaMax Alexejev
This document summarizes a presentation about distributed and highly available server applications in Java and Scala. It discusses the Talkbits architecture, which uses lightweight SOA principles with stateless edge services and specialized systems to manage state. The presentation describes using the Finagle library as a distributed RPC framework with Apache Zookeeper for service discovery. It also covers configuration, deployment, monitoring and logging of services using tools like SLF4J, Logback, CodaHale metrics, Jolokia, Fabric, and Datadog.
Kafka is a real-time, fault-tolerant, scalable messaging system.
It is a publish-subscribe system that connects various applications with the help of messages - producers and consumers of information.
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.
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.
The document discusses message brokers and Apache Kafka. It defines a message broker as middleware that exchanges messages in computer networks. It then discusses how message brokers work using queuing and publish-subscribe models. The document focuses on describing Apache Kafka, a distributed streaming platform. It explains key Kafka concepts like topics, partitions, logs, producers, consumers, and guarantees around ordering and replication. It also discusses how Zookeeper is used to manage and monitor the Kafka cluster.
NServiceBus - introduction to a message based distributed architectureMauro Servienti
This document provides an introduction and overview of NServiceBus, an open source toolkit for building distributed applications using a message-based architecture. It discusses key concepts like messages, components, services, and endpoints. It also demonstrates request/reply and event-based messaging patterns. The document highlights features for handling failures, scaling out to multiple endpoints, and implementing long-running processes through sagas.
This document introduces NServiceBus, an open source service bus for .NET. It discusses why a service bus is useful, describing it as providing fundamental services like messaging for complex architectures. It covers common messaging patterns in NServiceBus like point-to-point, publish/subscribe, and request/response. It also discusses capabilities like scalability, long-running workflows, and handling failures. The key benefits of a service bus are less coupling between services, flexibility, and handling issues like reliability. NServiceBus aims to provide messaging infrastructure while leaving applications to focus on domain logic.
This document discusses implementing high availability in Exchange Server. It covers configuring highly available mailbox databases using database availability groups (DAGs) and deploying highly available non-mailbox servers. DAGs allow up to 16 copies of each database across multiple servers and enable automatic failover. The document demonstrates how to create and configure a DAG, monitor replication health, and deploy highly available hub transport and client access servers.
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.
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.
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.
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.
Making communication across boundaries simple with Azure Service BusParticular Software
There are times when you should consider setting up secure communications between your software components across network boundaries.
Here are just a few:
* Your application is enormous (e.g., the global deployment of a marketing site targeting billions of people)
* Remoteness (e.g., your company has branch office locations around the globe)
* Your network constraints prevent communication (e.g., your machines in Azure Cloud Services are unable to talk to each other directly)
* You don't know the network conditions (e.g., IoT or mobile devices)
Yves Goeleven and Sean Feldman show how to overcome such challenges using Azure Service Bus.
This document provides an overview of Apache Kafka. It discusses Kafka's key capabilities including publishing and subscribing to streams of records, storing streams of records durably, and processing streams of records as they occur. It describes Kafka's core components like producers, consumers, brokers, and clustering. It also outlines why Kafka is useful for messaging, storing data, processing streams in real-time, and its high performance capabilities like supporting multiple producers/consumers and disk-based retention.
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
This document provides an overview of Apache ActiveMQ, an open source messaging system. It discusses what ActiveMQ is, its basics like topics and queues, techniques for scaling such as vertical, horizontal and hybrid approaches, ensuring high availability, and its future direction with ActiveMQ Apollo. The presentation aims to explain how ActiveMQ works and how to configure it for different deployment needs.
Messaging for Web and Mobile with Apache ActiveMQdejanb
This document summarizes a presentation on messaging for web and mobile applications using Apache ActiveMQ. The presentation covered challenges with HTTP messaging, advantages of STOMP and MQTT protocols, and examples of using STOMP over WebSocket for browser messaging and MQTT for mobile apps. It also provided an overview of Apache ActiveMQ's support for STOMP, including client examples in Java.
This document describes a server load balancing system for structured data. The objectives are to develop a load balancer that can manage large amounts of data and provide functionality for uploading, downloading, and deleting data, while providing reliability, scalability, and high performance. The system uses a master server to distribute loads to slave servers and track their locations. Clients communicate directly with slave servers to access data using unique keys. This allows for horizontal scaling and fault tolerance. The system is designed to handle large volumes of data across multiple servers and provide reliable access even if servers fail.
Enterprise Integration Patterns with ActiveMQRob Davies
This document discusses enterprise integration patterns and deployments using Apache ActiveMQ. It provides an overview of key integration concepts like message channels, routing, types of messages, push and pull integration models, request/reply patterns, and job processing. It also covers deployment patterns such as hub and spoke and failover between data centers. Finally, it introduces Apache Camel as a powerful integration framework that supports these patterns and can be used with ActiveMQ.
This document provides an overview and agenda for a presentation on Apache ActiveMQ 5.9.x and Apache Apollo. The presentation will cover new features in ActiveMQ 5.9.x including AMQP 1.0 support, REST management, a new default file-based store using LevelDB, and high availability replication of the store. It will also introduce Apache Apollo and allow for a question and discussion period.
ActiveMQ is an open source message broker that implements the Java Message Service (JMS) API. It allows applications written in different languages to communicate asynchronously. Apache Camel is an open source integration framework that can be used to build messaging routes between different transports and APIs using a simple domain-specific language. It provides mediation capabilities and supports common integration patterns. Both ActiveMQ and Camel aim to simplify integration between disparate systems through message-based communication.
Apache ActiveMQ is an open-source messaging and integration pattern server that allows for message throttling, redelivery, and delay. This document discusses how to install and configure ActiveMQ, including setting up dead letter queues and clustering multiple ActiveMQ instances. The key steps are: 1) Installing ActiveMQ on each node, 2) Configuring dead letter queues by setting redelivery policies in activemq.xml, and 3) Configuring clustering by giving each broker a unique name, connecting them to a shared SQL database, and starting one as the master node.
RabbitMQ/ActiveMQ 와 같은 비동기 메시징 미들웨어를 이용하여 다량의 서버를 orchestration(command & control) 할 수 있는 mcollective에 대한 한글 ppt 자료입니다. 상세한 내용은 http://wiki.tunelinux.pe.kr/x/LQAy 를 참고하시면 됩니다.
The document introduces Apache Apollo, a new message broker project that was branched from ActiveMQ. It was created to better utilize high core counts on modern processors. The key components discussed are HawtDispatch, the reactor-based threading model; connectivity support for STOMP, MQTT, JMS, and OpenWire; and the use of LevelDB for storage. Future areas of development are also mentioned.
This document discusses client-side load balancing in a cloud computing environment. It describes how a client-side load balancer can distribute requests across backend web servers in a scalable way without requiring control of the infrastructure. The proposed architecture uses static anchor pages hosted on Amazon S3 that contain JavaScript code to select a web server based on its reported load. The JavaScript then proxies the request to that server and updates the page content. This approach achieves high scalability and adaptiveness without hardware load balancers or layer 2 optimizations.
Messaging is the backbone of many top enterprises. It affords reliable, asynchronous data passing to achieve loosely coupled, highly scalable distributed systems. As enterprises large and small become more interconnected, demand for remote and limited devices to be integrated with enterprise systems is surging. Come see how the most widely used, open-source messaging broker, Apache ActiveMQ, fits nicely and how it supports polyglot messaging.
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.
Apache Kafka is a distributed streaming platform used at WalmartLabs for various search use cases. It decouples data pipelines and allows real-time data processing. The key concepts include topics to categorize messages, producers that publish messages, brokers that handle distribution, and consumers that process message streams. WalmartLabs leverages features like partitioning for parallelism, replication for fault tolerance, and low-latency streaming.
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 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.
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.
This document provides an overview of connecting applications with Red Hat JBoss A-MQ. It discusses key features of message-oriented middleware including robustness, time and location independence, latency hiding, scalability, and event-driven communication. It describes messaging concepts like message channels, routing with selectors and wildcards, delivery modes, and features of message brokers. The document focuses on Apache ActiveMQ, covering its use, protocols, persistence storage options, high availability, broker networks, and integration with Apache Camel. It discusses use cases for messaging like the Internet of Things and provides an IoT demo overview using Arduino.
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.
This document provides an overview of Apache Kafka and discusses common misconceptions, semantics, partitioning, replication, consumer groups, performance tuning, and observability. It addresses topics such as at-least-once, at-most-once, and exactly-once delivery semantics, how partitions are organized on disk, tuning configurations for producers, brokers, and consumers, and key metrics to monitor for the brokers, producers, and consumers. The document aims to help readers better understand and optimize their use of Apache Kafka.
This document provides an overview of WSO2 Message Broker 2.2.0. It introduces the presenters, describes WSO2 as a company and what they deliver. It then explains messaging models like queues and topics. The key highlights of WSO2 MB 2.2.0 include improvements to clustering, the addition of a dead letter channel and flow control capabilities. Example use cases are also presented, such as for asynchronous and reliable messaging.
This document provides an overview of Apache Kafka fundamentals. It discusses key Kafka concepts like producers, brokers, consumers, topics, partitions, and how they interact in the Kafka architecture. The session also covers how Kafka uses ZooKeeper for coordination and configuration, and how producers, consumers, and brokers work to provide a scalable streaming platform.
Watch this talk here: https://www.confluent.io/online-talks/apache-kafka-architecture-and-fundamentals-explained-on-demand
This session explains Apache Kafka’s internal design and architecture. Companies like LinkedIn are now sending more than 1 trillion messages per day to Apache Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
This talk provides a comprehensive overview of Kafka architecture and internal functions, including:
-Topics, partitions and segments
-The commit log and streams
-Brokers and broker replication
-Producer basics
-Consumers, consumer groups and offsets
This session is part 2 of 4 in our Fundamentals for Apache Kafka series.
Apache Kafka is a distributed streaming platform that allows for publishing and subscribing to streams of records. It uses a broker system and partitions topics to allow for scaling and parallelism. LinkedIn's Camus is a MapReduce job that moves data from Kafka to HDFS in distributed fashion. It consists of three stages: setup, the MapReduce job, and cleanup.
Matteo Merli, the tech lead for Cloud Messaging Service at Yahoo, went through their design decisions, how they reached that and how they leverage Apache BookKeeper to implement a multi-tenant messaging service.
Apache Kafka is a distributed publish-subscribe messaging system that allows for high-throughput, persistent storage of messages. It provides decoupling of data pipelines by allowing producers to write messages to topics that can then be read from by multiple consumer applications in a scalable, fault-tolerant way. Key aspects of Kafka include topics for categorizing messages, partitions for scaling and parallelism, replication for redundancy, and producers and consumers for writing and reading messages.
Kafka is 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.
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/
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.
Beyond Horizontal Scalability: Concurrency and Messaging Using SpringBruce Snyder
The document discusses how software systems are growing larger and more complex due to increasing hardware capabilities. It describes typical application architectures and assumptions that rely on sequential execution in a single JVM. It advocates for concurrency and messaging using tools from Spring to address these challenges by removing assumptions, simplifying interactions, and allowing asynchronous and distributed execution. Specifically, it covers how Spring supports concurrency using TaskExecutor and messaging using JMS templates and message listeners to enable looser coupling and horizontal scalability beyond simply adding more machines.
The document discusses Spring support for synchronous and asynchronous JMS messaging. For synchronous messaging, Spring provides the JmsTemplate class which allows sending and receiving messages. For asynchronous messaging, Spring supports message-driven POJOs using the DefaultMessageListenerContainer and SimpleMessageListenerContainer. The DefaultMessageListenerContainer supports dynamic scaling and transactions while the SimpleMessageListenerContainer provides basic functionality.
Styles of Applicaton Integration Using SpringBruce Snyder
The document discusses styles of application integration using Spring. It covers the differences between tight and loose coupling, and how loose coupling is more difficult but provides long term benefits. Integrations are commonly tightly coupled but should be loosely coupled. Commands are used often for integrations but events are a more natural approach. Spring Integration provides tools for messaging, concurrency and integration within and between applications, as well as with external systems.
Using Enterprise Integration Patterns as Your Camel JockeyBruce Snyder
This document provides an overview of using Apache Camel as an integration framework. It discusses options for integration like doing it yourself, buying a solution, or adopting an open source framework. It then covers key Camel concepts like Enterprise Integration Patterns, components, routing and mediation. The document includes examples of common EIP patterns implemented in Camel's Java and Spring DSLs and discusses features like error handling, type conversions and business activity monitoring.
Service-Oriented Integration With Apache ServiceMixBruce Snyder
This document provides an overview of Service Oriented Integration with Apache ServiceMix. It discusses what an Enterprise Service Bus (ESB) is, introduces Java Business Integration (JBI) and its normalized message format. It then describes Apache ServiceMix, an open source ESB and JBI container, covering its architecture, features, and how it supports common integration patterns like content-based routing through the use of Apache Camel. Configuration and tooling options for ServiceMix are also reviewed.
This document provides an overview of Apache Camel and how it can be used for system integration and implementing enterprise integration patterns. It discusses how Camel supports routing messages between different components and endpoints, transforming data between formats, and implementing common integration patterns like content-based routing, filtering, splitting, aggregating, and more through a fluent Java-based or XML configuration. It also covers how Camel supports binding beans and methods to endpoints, type conversions, remoting, and business activity monitoring.
This document provides an overview of enterprise integration patterns (EIPs) and how they are implemented using Apache Camel and Project Fuji frameworks. It discusses core EIP principles like asynchronous messaging for integration. It also describes various EIP implementations like content-based routing, dead letter channels, and message transformation patterns. Code examples are shown using the Java and Spring DSLs for Apache Camel and the DSL and web UI for Project Fuji.
Service Oriented Integration With ServiceMixBruce Snyder
This document summarizes a presentation about Service Oriented Integration with Apache ServiceMix. The presentation introduces Enterprise Service Buses and their purpose in facilitating integration. It then discusses key aspects of Apache ServiceMix, an open source ESB, including its support for various protocols and engines. The presentation provides examples of how ServiceMix can be used to configure routing and mediation using tools like Apache Camel and content-based routing. It concludes by discussing newer developments in ServiceMix 4 that utilize OSGi and build upon integration patterns.
The document discusses Apache Camel, an open source framework for integration and routing messages between various systems. It provides an overview of Camel's capabilities including support for Enterprise Integration Patterns, components for connecting to different systems, and ways to configure routing and processing of messages using Java DSL or XML. The document also includes examples of how to implement common routing patterns like content-based routing, splitting, aggregating, and error handling with Camel.
Apache Camel is an open source integration framework that allows for routing and mediation using enterprise integration patterns. It supports message routing between various transports and protocols and includes components for common systems as well as language support for writing routing rules in various scripting languages. The history and use of Camel contexts are also discussed.
Understanding Insider Security Threats: Types, Examples, Effects, and Mitigat...Bert Blevins
Today’s digitally connected world presents a wide range of security challenges for enterprises. Insider security threats are particularly noteworthy because they have the potential to cause significant harm. Unlike external threats, insider risks originate from within the company, making them more subtle and challenging to identify. This blog aims to provide a comprehensive understanding of insider security threats, including their types, examples, effects, and mitigation techniques.
TrustArc Webinar - 2024 Data Privacy Trends: A Mid-Year Check-InTrustArc
Six months into 2024, and it is clear the privacy ecosystem takes no days off!! Regulators continue to implement and enforce new regulations, businesses strive to meet requirements, and technology advances like AI have privacy professionals scratching their heads about managing risk.
What can we learn about the first six months of data privacy trends and events in 2024? How should this inform your privacy program management for the rest of the year?
Join TrustArc, Goodwin, and Snyk privacy experts as they discuss the changes we’ve seen in the first half of 2024 and gain insight into the concrete, actionable steps you can take to up-level your privacy program in the second half of the year.
This webinar will review:
- Key changes to privacy regulations in 2024
- Key themes in privacy and data governance in 2024
- How to maximize your privacy program in the second half of 2024
The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
November 18, 2021
Implementations of Fused Deposition Modeling in real worldEmerging Tech
The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries:
1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes.
2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions.
3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines.
4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors.
5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering.
6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands.
7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems.
8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering.
9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively.
Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.
Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptxSynapseIndia
Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation
7 Most Powerful Solar Storms in the History of Earth.pdfEnterprise Wired
Solar Storms (Geo Magnetic Storms) are the motion of accelerated charged particles in the solar environment with high velocities due to the coronal mass ejection (CME).
Mitigating the Impact of State Management in Cloud Stream Processing SystemsScyllaDB
Stream processing is a crucial component of modern data infrastructure, but constructing an efficient and scalable stream processing system can be challenging. Decoupling compute and storage architecture has emerged as an effective solution to these challenges, but it can introduce high latency issues, especially when dealing with complex continuous queries that necessitate managing extra-large internal states.
In this talk, we focus on addressing the high latency issues associated with S3 storage in stream processing systems that employ a decoupled compute and storage architecture. We delve into the root causes of latency in this context and explore various techniques to minimize the impact of S3 latency on stream processing performance. Our proposed approach is to implement a tiered storage mechanism that leverages a blend of high-performance and low-cost storage tiers to reduce data movement between the compute and storage layers while maintaining efficient processing.
Throughout the talk, we will present experimental results that demonstrate the effectiveness of our approach in mitigating the impact of S3 latency on stream processing. By the end of the talk, attendees will have gained insights into how to optimize their stream processing systems for reduced latency and improved cost-efficiency.
How Social Media Hackers Help You to See Your Wife's Message.pdfHackersList
In the modern digital era, social media platforms have become integral to our daily lives. These platforms, including Facebook, Instagram, WhatsApp, and Snapchat, offer countless ways to connect, share, and communicate.
How RPA Help in the Transportation and Logistics Industry.pptxSynapseIndia
Revolutionize your transportation processes with our cutting-edge RPA software. Automate repetitive tasks, reduce costs, and enhance efficiency in the logistics sector with our advanced solutions.
Comparison Table of DiskWarrior Alternatives.pdfAndrey Yasko
To help you choose the best DiskWarrior alternative, we've compiled a comparison table summarizing the features, pros, cons, and pricing of six alternatives.
Details of description part II: Describing images in practice - Tech Forum 2024BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
3. A Crash Course in Messaging
:: JMS is:
:: An API for enterprise messaging
:: Included in Java EE
:: Also available stand alone
:: Loosely coupled
:: JMS is not:
:: A message broker implementation
3
13. Wire Formats
:: OpenWire
:: The default in ActiveMQ; a binary protocol
:: Clients for C++, Java and .NET
:: STOMP
:: Simple Text Oriented Messaging Protocol; a text based protocol
:: Clients for C, Javascript, Perl, PHP, Python, Ruby and more
:: XMPP
:: The Jabber XML protocol
:: REST
:: HTTP POST and GET
:: AMQP
:: Not yet fully supported
13
15. Transport Connectors
:: Client-to-broker connections
:: Similar to JDBC connections to a database
:: Protocols are supported:
:: TCP
:: UDP
:: NIO
:: SSL
:: HTTP/S
:: VM
:: XMPP
15
17. Networks of Brokers
:: Provides large scalability
:: ActiveMQ store-and-forward allows
messages to traverse brokers
:: Demand-based forwarding
:: Some people call this distributed queues
:: Many possible configurations or topologies
are supported
17
24. AMQ Message Store
:: Transactional message storage solution
:: Fast and reliable
:: Composed of two parts:
:: Data Store - holds messages in a transactional journal
:: Reference store - stores message locations for fast
retrieval
:: The default message store in ActiveMQ 5
24
26. Journaled JDBC
:: Transactional message storage solution
:: Reliable and faster than non-journaled
:: Two-piece store
:: Journal - A high-performance, transactional journal
:: Database - A relational database of your choice
:: Default database in ActiveMQ 4.x is Apache
Derby
26
27. Message Cursors
:: Messages are no longer stored in memory
:: Previous to 5.1, message references were stored in
memory
:: Messages are paged in from storage when
space is available in memory
27
29. Three Types of Master/Slave
:: Pure master/slave
:: Shared filesystem master/slave
:: JDBC master/slave
29
30. Pure Master/Slave
:: Shared nothing, fully replicated topology
:: Does not depend on shared filesystem or database
:: A Slave broker consumes all message states
from the Master broker (messages, acks, tx
states)
:: Slave does not start any networking or
transport connectors
30
31. Pure Master/Slave
:: Master broker will only respond to client when a
message exchange has been successfully
passed to the slave broker
31
32. Pure Master/Slave
:: If the master fails, the slave optionally has two
modes of operation:
:: Start up all it’s network and transport connectors
:: All clients connected to failed Master resume on Slave
:: Close down completely
:: Slave is simply used to duplicate state from Master
32
33. Shared Filesystem Master/Slave
:: Utilizes a directory on a shared filesystem
:: No restriction on number of brokers
:: Simple configuration (point to the data dir)
:: One master selected at random
33
34. JDBC Master/Slave
:: Recommended when using a shared
database
:: No restriction on the number of brokers
:: Simple configuration
:: Clustered database negates single point of
failure
:: One master selected at random
34
35. Client Connectivity With Master/Slave
:: Again, clients should use failover transport:
failover:(tcp://broker1:61616,tcp://broker2:61616,
tcp://broker3:61616)?initialReconnectDelay=100
35
36. Tips for HA and Fault Tolerance
:: RAIDed disks
:: A Storage Area Network
:: Clustered relational databases
:: Clustered JDBC via C-JDBC
:: http://c-jdbc.objectweb.org/
36
38. Broker Security
:: Authentication
:: I.e., are you allowed to connect to ActiveMQ?
:: File based implementation
:: JAAS based implementation
:: Authorization
:: I.e., do you have permission to use that ActiveMQ
resource?
:: Destination level
:: Message level via custom plugin
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40. Message Prefetch
:: Used for slow consumer situations
:: Consumer is flooded by messages from the broker
:: FIFO buffer on the consumer side
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41. Async Dispatch
:: Asynchronous message delivery to consumers
:: Default is true
:: Useful for slow consumers
:: Incurs a bit of overhead
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42. Exclusive Consumers
:: Anytime more than one consumer is consuming from
a queue, message order is lost
:: Allows a single consumer to consume all messages
on a queue to maintain message ordering
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43. Consumer Priority
:: Just like it sounds
:: Gives a consumer priority for message delivery
:: Allows for the weighting of consumers to optimize
network traversal for message delivery
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44. Message Groups
:: Uses the JMSXGroupID property to define which
message group a message belongs
:: Guarantees ordered processing of related messages across a
single destination
:: Load balancing of message processing across multiple consumers
:: HA/failover if consumer goes down
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45. Retroactive Consumer
:: Message replay at start of a subscription
:: At the start of every subscription, send any old
messages that the consumer may have missed
:: Configurable via policies
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50. Message Selectors
:: Used to attach a filter to a subscription
:: Defined using a subset SQL 92 syntax
:: JMS selectors
:: Filters only message properties
:: JMSType = ‘stock’ and trader = ‘bob’ and price < ‘105’
:: XPath selectors
:: Filters message bodies that contain XML
:: ‘/message/cheese/text() = 'swiss'’
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51. Retroactive Consumer
:: Used to go back in time
:: In terms of messages
:: At the start of a subscription, send old
messages the consumer may have missed
:: Configurable via timed or fixed size recovery
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52. Slow Consumer Strategies
:: Various configurable strategies for handling slow
consumers
:: Slow consumer situations are very common
:: Caused by:
:: Slow network connections
:: Unreliable network connections
:: Busy network situations
:: Busy JVM situations
:: Half disconnects with sockets
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53. Use Message Limit Strategies
:: PendingMessageLimitStrategy
:: Calculates the max number of pending messages to
be held in memory for a consumer above its prefetch
size
:: ConstantPendingMessageLimitStrategy
:: A constant limit for all consumers
:: PrefetchRatePendingMessageLimitStrategy
:: Calculates the max number of pending messages
using a multiplier of the consumers prefetch size
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54. Use Prefetch and an Eviction Policy
:: Use the prefetch policy
:: The prefetch policy has a property named
maximumPendingMessageLimit that can be used on a
per connection or per consumer basis
:: Use a message eviction policy
:: OldestMessageEvictionStrategy
:: Evict the oldest messages first
:: OldestMessageWithLowestPriorityEvictionStrategy
:: Evict the oldest messages with the lowest priority first
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55. Use Destination Policies
:: Configured on the destination policies in the
ActiveMQ XML configuration file
:: Combined with wildcards, this is very powerful
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56. Additional Tips
:: Consider configuring different message cursors
:: The status of slow consumers can be monitored via JMX
properties
:: discarded - The count of how many messages have been
discarded during the lifetime of the subscription due to it being a
slow consumer
:: matched - The current number of messages matched and to be
dispatched to the subscription as soon as some capacity is
available in the prefetch buffer. So a non-zero value implies that
the prefetch buffer is full for this subscription
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57. Monitoring
:: JMX
:: ActiveMQ web console
:: Additional consumers
:: Camel routes
:: SpringSource AMS
:: Based on Hyperic
:: IONA FuseHQ
:: Based on Hyperic
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