DOCSLIB.ORG

Message Broker Integration

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Message Broker Integration 1 Event Driven Architectures with Camel Gnanaguru Sattanathan Prajod Vettiyattil Twitter:@gnanagurus Twitter: @prajods Website: bushorn.com 2 What this session is about v Application Integration v Apache Camel v Event Driven Architectures with Camel 3 Application Integration 4 Integration JMS, 1. Validate, Enrich, Route, RMI, Transform JDBC, 2. Diverse protocol support FTP, SNMP, TCP Middleware https, SFTP, SNMP, 1. Enterprise Service Bus TCP, 2. Integration Framework CORBA 5 The Chameleon 6 Camel is a reptile ! 7 The Chameleon It can change color with the container J Camel Camel Camel Camel Camel Camel Oracle JVM Tomcat JBoss Websphere Jetty Fusion 8 Apache Camel • Integration framework • Multiple deployment options • Feature rich • Simple to program • Unique scalability features • Apache License 9 Apache Camel: Components • More than 100 readymade components • Covering – PaaS & Public Clouds – Social Networks – Devices – Almost all standard protocols 10 Apache Camel: Components AWS HDFS MongoDB ActiveMQ Twitter FILE JDBC JMS SFTP Streams JGroups AMQP MQTT NETTY SQL SIP UDP Cache Esper Hazelcast Zoo Keeper WebSockets SMTP SSH SMPP SOLR RMI Velocity Smooks XQuery XSLT NMR TCP 11 Coding with Camel 12 Camel – Code your way JAVA Domain Specific Language SPRING (DSL) GROOVY SCALA 13 A simple file integration File File System Input Output Directory Directory 14 Simple file integration: Sample Code <route> <from uri="file://d:\Input"/> <to uri="file://d:\Output"/> </route> using Spring XML 15 File System – Message Broker Integration File File System Message Broker ( ActiveMQ) Input Output Directory Queue 16 File System to Message Broker Integration: Sample Code <route> <from uri="file://d:\Input"/> <to uri="activemq:queue:Output"/> </route> using Spring XML 17 Adding a simple transformation File File System Message Broker ( ActiveMQ) Input Output Directory Queue Transformer Transformer 18 A simple transformation: Sample Code <route> <from uri="file://d:\Input"/> <to uri="xslt:file://d:\transform.xsl"/> <to uri="activemq:queue:Output"/> </route> using Spring XML 19 A simple Twitter application Twitter.com Message Broker ( ActiveMQ) Tweets TweetsTweets Queue 20 A simple Twitter application: Sample Code <route> <from uri="twitter://search ?type=direct &keywords=JUDCON”/ > <to uri="activemq:queue:Output"/> </route>21 Event Driven Architectures with Camel 22 Event Driven Architecture • EDA • A software architecture pattern • Event: Significant change in state • Parts of the architecture – Event source – Event channel – Event processor 23 EDA Source Source Source Source Source Source Sources 1 2 3 4 5 6 Channels Channels Event Event Event Processor 1 Processor 2 Processor 3 Processors 24 Staged Event Driven Architecture(SEDA) • EDA – Direct and tight coupling of sources and processors – Event processor may get swamped with events • Reduces performance • Possible loss of messages • SEDA – EDA decomposed into stages and connected with queues 25 SEDA: one view Source Source Source Source Source Source 1 2 3 4 5 6 Stage 1 Channels Channels using Queues Channels using Queues Event Event Event Processor 1 Processor 2 Processor 3 Stage 2 26 SEDA: another view Processor Queue Source 2 1 Queue Processor 1 Source 2 Processor Queue 3 Stage 1 Channels using Stage 2 Channels using Stage 3 Queues Queues 27 SEDA with Camel • SEDA – Scalability feature in Camel – Uses thread pools and internal queues – Enables parallel processing – Avoids waiting threads 28 Camel with SEDA: comparison Waiting thread without SEDA Parallel processing with SEDA Receive Receive SEDA Queue 1 Validate Thread 1 Thread 2 Validate Normalize Thread 1 Normalize Enrich SEDA Queue 2 Split Split Enrich Route Route Thread 3 • Impact • For 100 concurrent requests – significant reduction in thread – Without SEDA: 100 threads; With SEDA: 30 threads 29 consumption Camel as a part of EDA • Camel as – Event channel – Event processor Camel 30 Camel as Event Channel • SEDA queues • VM queues • Multi-threaded flow • Load balanced flow • Asynchronous API 31 Camel as Event Processor • Not ideal as the final Event Processor • Can be used as an intermediate Event processor • Routing based on events • Transformation of events • Combining simple events • Event auditing, logging 32 Camel and the Actor Model with Akka • Actor Model Actor – Actors Behavior • Similar to objects, many other features • Mailbox, behavior/s, internal state Mailbox State – No shared state – Messages between actors – Locking and Threads are abstracted – Each actor instance runs in its own thread • Akka-Camel – Integrate Akka to other applications – Two way: Akka to Camel and Camel to Akka 33 Summary • Camel – Integration Framework – Simplicity and flexibility – Components • EDA with Camel – SEDA – SEDA within Camel – Camel as part of SEDA • Akka and Camel for EDA 34 Questions Gnanaguru Sattanathan Prajod Vettiyattil Twitter:@gnanagurus Twitter: @prajods Website: bushorn.com Our Open Source Middleware Group on LinkedIn http://tinyurl.com/be6e93q 35 .
Load more

Recommended publications
  • Akka Java Documentation Release 2.2.5
    Akka Java Documentation Release 2.2.5 Typesafe Inc February 19, 2015 CONTENTS 1 Introduction 1 1.1 What is Akka?............................................1 1.2 Why Akka?..............................................3 1.3 Getting Started............................................3 1.4 The Obligatory Hello World.....................................7 1.5 Use-case and Deployment Scenarios.................................8 1.6 Examples of use-cases for Akka...................................9 2 General 10 2.1 Terminology, Concepts........................................ 10 2.2 Actor Systems............................................ 12 2.3 What is an Actor?.......................................... 14 2.4 Supervision and Monitoring..................................... 16 2.5 Actor References, Paths and Addresses............................... 19 2.6 Location Transparency........................................ 25 2.7 Akka and the Java Memory Model.................................. 26 2.8 Message Delivery Guarantees.................................... 28 2.9 Configuration............................................. 33 3 Actors 65 3.1 Actors................................................ 65 3.2 Typed Actors............................................. 84 3.3 Fault Tolerance............................................ 88 3.4 Dispatchers.............................................. 103 3.5 Mailboxes.............................................. 106 3.6 Routing................................................ 111 3.7 Building Finite State Machine
    [Show full text]
  • Up up and Out: Scaling Software with Akka
    UP UP AND OUT: SCALING SOFTWARE WITH AKKA Jonas Bonér CTO Typesafe @jboner Scaling software with Jonas Bonér CTO Typesafe @jboner ScalingScaling softwaresoftware with with ScalingScaling softwaresoftware with with Akka (Áhkká) The name comes from the goddess in the Sami (native swedes) mythology that represented all the wisdom and beauty in the world. It is also the name of a beautiful mountain in Laponia in the north part of Sweden ScalingScaling softwaresoftware with with Manage System Overload Scale UP & Scale OUT How can we achieve this? How can we achieve this? Let’s use Actors How can we achieve this? What is an Actor? What is an Actor? What is an Actor? • Akka's unit of code organization is called an Actor What is an Actor? • Akka's unit of code organization is called an Actor • Like Java EE servlets and session beans, Actors is a model for organizing your code that keeps many “policy decisions” separate from the business logic What is an Actor? • Akka's unit of code organization is called an Actor • Like Java EE servlets and session beans, Actors is a model for organizing your code that keeps many “policy decisions” separate from the business logic • Actors may be new to many in the Java community, but they are a tried-and-true concept (Hewitt 1973) used for many years in telecom systems with 9 nines uptime Program at a Higher Level Program at a Higher Level Program at a Higher Level • Never think in terms of shared state, state visibility, threads, locks, concurrent collections, thread notifications etc.
    [Show full text]
  • A Software Framework for the Actor Model Focusing on the Optimization of Message Passing
    AICT 2018 : The Fourteenth Advanced International Conference on Telecommunications Actor4j: A Software Framework for the Actor Model Focusing on the Optimization of Message Passing David Alessandro Bauer, Juho Mäkiö Department of Informatics and Electronics University of Applied Sciences Emden/Leer Emden, Germany Email: [email protected], [email protected] Abstract—Common actor implementations often use also the Scale Cube by Abbott [6], which describes the three standardized thread pools without special optimization for the dimensions of scalability. message passing. For that, a high-performance solution was To ensure high parallelization, its one benefit to use worked out. The actor-oriented software framework Akka uses multi-core systems. The computer world of the last few internally a ForkJoinPool that is intended for a MapReduce years has been characterized by a change ("The Free Lunch approach. However, the MapReduce approach is not relevant for message passing, as it may lead to significant performance Is Over" [7]) from constantly increasing computing power losses. One solution is to develop a thread pool that focuses on to multi-core systems due to technical limitations. In the message passing. In the implementation of the Actor4j particular, technical progress always lags behind practical framework, the message queue of the actors is placed in requirements (Wirth's law [8]). Up to now, Moore's law was threads to enable an efficient message exchange. The actors are “that the number of transistors available to semiconductor operated directly from this queue (injecting the message), manufacturers would double approximately every 18 to 24 without further ado.
    [Show full text]
  • Reactive Programming with Scala, Lagom, Spark, Akka and Play
    Issue October 2016 | presented by www.jaxenter.com #53 The digital magazine for enterprise developers Reactive Programming with Scala, Lagom, Spark, Akka and Play Interview with Scala creator Martin Odersky The state of Scala The Lagom Framework Lagom gives the developer a clear path DevOpsCon 2016: Our mission statement This is how we interpret modern DevOps ©istockphoto.com/moorsky Editorial Reactive programming is gaining momentum “We believe that a coherent approach to systems architec- If the definition “stream of events” does not satisfy your ture is needed, and we believe that all necessary aspects are thirst for knowledge, get ready to find out what reactive pro- already recognized individually: we want systems that are Re- gramming means to our experts in Scala, Lagom, Spark, Akka sponsive, Resilient, Elastic and Message Driven. We call these and Play. Plus, we talked to Scala creator Martin Odersky Reactive Systems.” – The Reactive Manifesto about the impending Scala 2.12, the current state of this pro- Why should anyone adopt reactive programming? Because gramming language and the technical innovations that await it allows you to make code more concise and focus on im- us. portant aspects such as the interdependence of events which Thirsty for more? Open the magazine and see what we have describe the business logic. Reactive programming means dif- prepared for you. ferent things to different people and we are not trying to rein- vent the wheel or define this concept. Instead we are allowing Gabriela Motroc, Editor our authors to prove how Scala, Lagom, Spark, Akka and Play co-exist and work together to create a reactive universe.
    [Show full text]
  • Lightweight Affinity and Object Capabilities in Scala
    http://www.diva-portal.org Postprint This is the accepted version of a paper presented at ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA). Citation for the original published paper: Haller, P., Loiko, A. (2016) LaCasa: Lightweight Affinity and Object Capabilities in Scala. In: Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (pp. 272-291). Association for Computing Machinery (ACM) https://doi.org/10.1145/3022671.2984042 N.B. When citing this work, cite the original published paper. © Author | ACM 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications, http://dx.doi.org/10.1145/3022671.2984042. Permanent link to this version: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-197902 LACASA: Lightweight Affinity and Object Capabilities in Scala Philipp Haller Alex Loiko KTH Royal Institute of Technology, Sweden Google, Sweden ∗ [email protected] [email protected] Abstract difficulty of reasoning about program behavior and software Aliasing is a known source of challenges in the context of architecture [3], and it can introduce data races in concur- imperative object-oriented languages, which have led to im- rent programs. These observations have informed the devel- portant advances in type systems for aliasing control. How- opment of a number of type disciplines aimed at providing ever, their large-scale adoption has turned out to be a surpris- static aliasing properties, such as linear types [33, 51, 64], ingly difficult challenge.
    [Show full text]
  • Comparing Languages for Engineering Server Software: Erlang, Go, and Scala with Akka
    Comparing Languages for Engineering Server Software: Erlang, Go, and Scala with Akka Ivan Valkov, Natalia Chechina, and Phil Trinder School of Computing Science, University of Glasgow G12 8RZ, United Kingdom [email protected], {Natalia.Chechina, Phil.Trinder}@glasgow.ac.uk Abstract functional or object-oriented, with high-level coordination models, Servers are a key element of current IT infrastructures, and must e.g. actors as in Erlang [2] or a process algebra as in Go [6]. Indeed, often deal with large numbers of concurrent requests. The program- the success of some server-based companies is even attributed to ming language used to construct the server has an important role their use of specific languages. As examples WhatsApp’s success in engineering efficient server software, and must support massive is attributed to their use of Erlang [27]; the video streaming leader concurrency on multicore machines with low communication and Twitch uses Go to serve millions of users a day [13]. Research synchronisation overheads. contributions of this paper include the following: This paper investigates 12 highly concurrent programming lan- • A survey of programming language characteristics relevant for guages suitable for engineering servers, and analyses three repre- servers (Section 2.1). sentative languages in detail: Erlang, Go, and Scala with Akka. • The design and implementation of three benchmarks to analyse We have designed three server benchmarks that analyse key per- the multicore server capabilities of Erlang, Go, and Scala with formance characteristics of the languages. The benchmark results Akka (Section 3). suggest that where minimising message latency is crucial, Go and Erlang are best; that Scala with Akka is capable of supporting the • An evaluation of Erlang, Go, and Scala with Akka for key largest number of dormant processes; that for servers that frequently server capabilities, i.e.
    [Show full text]
  • Akka Java Documentation Release 2.2.4
    Akka Java Documentation Release 2.2.4 Typesafe Inc March 04, 2014 CONTENTS 1 Introduction 1 1.1 What is Akka?............................................1 1.2 Why Akka?..............................................3 1.3 Getting Started............................................3 1.4 The Obligatory Hello World.....................................7 1.5 Use-case and Deployment Scenarios.................................8 1.6 Examples of use-cases for Akka...................................9 2 General 10 2.1 Terminology, Concepts........................................ 10 2.2 Actor Systems............................................ 12 2.3 What is an Actor?.......................................... 14 2.4 Supervision and Monitoring..................................... 16 2.5 Actor References, Paths and Addresses............................... 19 2.6 Location Transparency........................................ 25 2.7 Akka and the Java Memory Model.................................. 26 2.8 Message Delivery Guarantees.................................... 28 2.9 Configuration............................................. 33 3 Actors 65 3.1 Actors................................................ 65 3.2 Typed Actors............................................. 84 3.3 Fault Tolerance............................................ 88 3.4 Dispatchers.............................................. 103 3.5 Mailboxes.............................................. 106 3.6 Routing................................................ 111 3.7 Building Finite State Machine
    [Show full text]
  • Operations Hub Open Source Software List
    GE Operations Hub Open Source Software List (for Version 1.7) In accordance with certain software license terms, the General Electric Company (“GE”) makes available the following software package installations. This code is provided to you on an “as is” basis, and GE makes no representations or warranties for the use of this code by you independent of any GE provided software or services. Refer to the licenses and copyright notices files for each package for any specific license terms that apply to each software bundle, associated with this product release. NOTE: These software package versions may change or be removed as needed for updates to this product. Copyright © 2018 General Electric Company. All rights reserved. Page 1 of 67 Software Name & Company Link License Name & Copyright Version Version (by Component) HikariCP https://github.com/brettwooldr Apache 2.0 3.4.2 idge/HikariCP Not found MsSql Jdbc 7.4.1.jre8 Copyright(c) 2019 Microsoft MIT https://www.microsoft.com/ Corporation snakeyaml http://www.snakeyaml.org Copyright (c) 2008, Apache License 2.0 1.2.6 http://www.snakeyaml.org COMMON DEVELOPMENT HK2 Class-Model 2.5.0-b06 https://javaee.github.io/glassfish/ AND DISTRIBUTION LICENSE Copyright (c) 2010-2015 Oracle and/or (CDDL) Version 1.1 its affiliates. All rights reserved. https://code.google.com/archive/p/ Copyright (C) 2003-2013 Virginia Tech. VT Crypt Library 2.1.4 Apache License 2.0 vt-middleware/ All rights reserved. https://code.google.com/archive/p/ Copyright (C) 2003-2013 Virginia Tech. VT Dictionary Libraries 3.0 Apache License 2.0 vt-middleware/ All rights reserved.
    [Show full text]
  • Akka Documentation Release 2.1.4
    Akka Documentation Release 2.1.4 Typesafe Inc May 14, 2013 CONTENTS 1 Introduction 1 1.1 What is Akka?............................................1 1.2 Why Akka?..............................................3 1.3 Getting Started............................................4 1.4 Use-case and Deployment Scenarios.................................7 1.5 Examples of use-cases for Akka...................................7 2 General 9 2.1 Terminology, Concepts........................................9 2.2 Actor Systems............................................ 11 2.3 What is an Actor?.......................................... 13 2.4 Supervision and Monitoring..................................... 15 2.5 Actor References, Paths and Addresses............................... 18 2.6 Location Transparency........................................ 24 2.7 Akka and the Java Memory Model.................................. 25 2.8 Message Delivery Guarantees.................................... 27 2.9 Configuration............................................. 31 3 Common utilities 51 3.1 Duration............................................... 51 3.2 Circuit Breaker............................................ 52 4 Java API 56 4.1 Actors (Java)............................................. 56 4.2 Typed Actors (Java)......................................... 70 4.3 Logging (Java)............................................ 75 4.4 Event Bus (Java)........................................... 79 4.5 Scheduler (Java)........................................... 82 4.6 Futures
    [Show full text]
  • Big Data SMACK
    Big Data SMACK A Guide to Apache Spark, Mesos, Akka» Cassandra, and Kafka Raul Estrada Isaac Ruiz Apress® Contents About the Authors xix About the Technical Reviewer xxi Acknowledgments xxiii Introduction xxv •Part I: Introduction 1 •Chapter 1: Big Data, Big Challenges 3 Big Data Problems 3 Infrastructure Needs 3 ETL 4 Lambda Architecture 5 Hadoop 5 Data Center Operation 5 The Open Source Reign 6 The Data Store Diversification 6 Is SMACK the Solution? 7 •Chapter 2: Big Data, Big Solutions 9 Traditional vs. Modern (Big) Data 9 SMACK in a Nutshell 11 Apache Spark vs. MapReduce 12 The Engine 14 The Model 15 The Broker 15 vii CONTENTS The Storage 16 The Container 16 Summary 16 Part II: Playing SMACK 17 •Chapter 3: The Language: Scala 19 Functional Programming 19 Predicate 19 Literal Functions 20 Implicit Loops 20 Collections Hierarchy 21 Sequences 21 Maps 22 Sets 23 Choosing Collections 23 Sequences 23 Maps 24 Sets 25 Traversing 25 foreach 25 for 26 Iterators 27 Mapping 27 Flattening 28 Filtering 29 Extracting 30 Splitting 31 Unicity 32 Merging 32 Lazy Views 33 Sorting 34 viii Streams 35 Arrays 35 ArrayBuffers 36 Queues 37 Stacks 38 Ranges 39 Summary 40 •Chapter 4: The Model: Akka 41 The Actor Model 41 Threads and Labyrinths 42 Actors 101 42 Installing Akka 44 Akka Actors 51 Actors 51 Actor System 53 Actor Reference 53 Actor Communication 54 Actor Lifecycle 56 Starting Actors 58 Stopping Actors 60 Killing Actors 61 Shutting down the Actor System 62 Actor Monitoring 62 Looking up Actors 63 Actor Code of Conduct 64 Summary 66 •Chapter 5: Storage:
    [Show full text]
  • Modern Functional Programming and Actors with Scala and Akka
    Modern Functional Programming and Actors With Scala and Akka Aaron Kosmatin Computer Science Department San Jose State University San Jose, CA 95192 707-508-9143 [email protected] Abstract sons for this. Functional programming languages place a strong emphasis on minimization of side ef- For many years, functional programming has had fects, and immutable states. Functional program- an academic presence in computer science, but ming is an expansion of lambda calculus. The has been poorly represented in popular languages. idea of functional programming is that the pro- Functional programming has been making a resur- gram executes as a series of mathematical func- gence lately. With Java 8, Oracle has introduced tion evaluations[2]. From its mathematical nature, lambda functions, an essential part of functional every function call that has the same arguments programming, to Java. Much of the resurgent in- should produce the same return statement. terest in functional programming originates with Martin Odersky, designer of Scala and Co-founder of Typesafe. The Actor Model of concurrent programming The emphasis on reduction of side effects has pre-dates Scala, with implementations in languages hurt the adoption of functional languages in the such as Erlang, but has been an underutilized industry, many programs require some form of side model. The Actor Model is well adapted to multi- effect. Side effects include any modification of a core and multi-machine programming. The Actor state, such as modifying a variable, printing to the Model has regained interest recently due to the console and writing to files. As well, for many pro- work of Jonas Bon´er,developer of the Akka Ac- grammers, it is easier to conceptualize problems by tor library for Java and Scala and Co-founder of modifying states.
    [Show full text]
  • A New Initiative for Tiling, Stitching and Processing Geospatial Big Data in Distributed Computing Environments
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume III-4, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic A NEW INITIATIVE FOR TILING, STITCHING AND PROCESSING GEOSPATIAL BIG DATA IN DISTRIBUTED COMPUTING ENVIRONMENTS A. Olasz a*, B. Nguyen Thai b, D. Kristóf a a Department of Geoinformation, Institute of Geodesy, Cartography and Remote Sensing (FÖMI),5. Bosnyák sqr. Budapest, 1149 (olasz.angela, kristof.daniel)@fomi.hu b Department of Cartography and Geoinformatics, Eötvös Loránd University (ELTE), 1/A Pázmány Péter sétány, Budapest, 1117 Hungary, [email protected] Commission ICWG IV/II KEYWORDS: Distributed computing, GIS processing, raster data tiling, data assimilation, remote sensing data analysis, geospatial big data, spatial big data ABSTRACT Within recent years, several new approaches and solutions for Big Data processing have been developed. The Geospatial world is still facing the lack of well-established distributed processing solutions tailored to the amount and heterogeneity of geodata, especially when fast data processing is a must. The goal of such systems is to improve processing time by distributing data transparently across processing (and/or storage) nodes. These types of methodology are based on the concept of divide and conquer. Nevertheless, in the context of geospatial processing, most of the distributed computing frameworks have important limitations regarding both data distribution and data partitioning methods. Moreover, flexibility and expendability for handling various data types (often in binary formats) are also strongly required. This paper presents a concept for tiling, stitching and processing of big geospatial data. The system is based on the IQLib concept (https://github.com/posseidon/IQLib/) developed in the frame of the IQmulus EU FP7 research and development project (http://www.iqmulus.eu).
    [Show full text]