spring xd - today and tomorrow

SPRINGONE2GX WASHINGTON, DC

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Spring XD – Today and Tomorrow By Mark Pollack

@markpollack

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The following is intended to outline the general direction of Pivotal's offerings. It is intended for information purposes only and may not be incorporated into any contract. Any information regarding pre-release of Pivotal offerings, future updates or other planned modifications is subject to ongoing evaluation by Pivotal and is subject to change. This information is provided without warranty or any kind, express or implied, and is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions regarding Pivotal's offerings. These purchasing decisions should only be based on features currently available. The development, release, and timing of any features or functionality described for Pivotal's offerings in this presentation remain at the sole discretion of Pivotal. Pivotal has no obligation to update forward looking information in this presentation.

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Contents

•  Big Data ecosystem driving new features

•  Cloud Native principles driving next generation architecture

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Since last year…

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Release Date Features

1.0 7/30/15 Stream & Batch processing, Analytics, Machine Learning DSL, Shell Runtime: Standlone FT, YARN

1.1 2/15/15 Kafka Message Bus Reactor, RxJava, Spark, Python processing. Sqoop Integration Role based authorization

1.2 6/17/15 Flo UI Performance Improvements Ambari HAWQ/GPDB Integration


Flo Overview

•  HTML 5 canvas application

•  Drag and Drop interface

•  Health Monitoring

•  Visualization of Metrics

•  Also available for SI

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Python

•  XD 1.0 provides evaluation of models in PMML format

•  Java Evaluator Predictive Model Markup Language (PMML)

•  JPMML-Evaluator

•  https://github.com/jpmml/jpmml-evaluator

•  Python has rich libraries for numerical analysis, machine learning

•  Numpy, scikit

•  XD 1.1 – Python in stream definitions

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Apache Kafka

•  “Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.”

•  Why it is popular?

•  Fast – can easily saturate network

•  Store tons of data – TBs

•  Replay

•  Clustered/HA

•  Partitioning –

o  Topics have multiple partitions

o  You divide the data based on the value of a field in the data

o  Can subscribe to specific partitions

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RxJava

•  Reactive X – http://reactivex.io/

•  “An API for asynchronous programming with observable streams”

•  “ReactiveX is a combination of the best ideas from the Observer pattern, the Iterator pattern, and functional programming”

•  Java Version - NetflixOSS project: https://github.com/ReactiveX/RxJava

•  Functional style programming with asynchronous events

•  Map, filter, reduce

•  Functional transformations are a natural way of describing stream processing

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Spark

•  Large scale data processing system

•  RDD

•  Resilient distributed data set

•  Immutable

•  Partitioned across machines

•  Computation

•  DAG of connected RDDs

•  Split DAGs into stages to compute

•  Batch and Stream style processing

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Spark Streaming

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Word Count

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Move to Data Flow

•  Analogy with tomcat/servlet containers

•  Some downsides

•  New advantages (right now)

•  Don’t need to run hsqldb if you are only doing stream processing

•  Can scale up/down individual modules in a stream at runtime.

•  Resource allocation per module (memory)

•  Better ‘composition’ to co-locate modules

•  Entire stream can be deployed to avoid message hops

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What’s next…

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Spring Cloud Data Flow

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Motivations

•  Parent/Child class load issues, inadvertent spral of xd/lib

•  Use cases where streams should avoid any network hops

•  Additional requirments

•  Scale up/down, upgrade/downgrade versions

•  Resource allocaiton

•  Would require ‘us’ to add these features to the runtime….

•  Many other people are doing this…

•  Develop a SPI to delegate to other systems to launch, scale, monitor, etc.

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Modules -> ‘Microservice’ Applications Runtime -> SPI (Lattice, CF, YARN, local)

Analogy with Web Apps and servlet contaier


Container Container

Spring XD 1.x

Host

Spring Cloud Stream

•  Modules run inside containers •  Container is a Boot app •  Modules are ApplicationContexts

•  Modules are executable Boot apps •  Easier to use Spring Cloud features •  Easier to test, consistent dev experience •  Portable and “cloud-native”

M

Host

M

M M

M M M

M M

M

ZooKeeper Spring Cloud Data Flow (optional) SPI Implementation


•  OOTB modules •  Executable Boot Apps

•  Boot for Spring Integration and Batch •  Auto-Configuration, Bindings

XD

Container

Modules

Admin

Spring Cloud Data Flow

Spring Cloud Stream

Modules

Spring Cloud Task

Modules

Spring Cloud Stream

Spring Cloud Task

•  REST API, Shell, UI •  Module Deployer SPI:

•  Singlenode •  Lattice •  YARN •  Cloud Foundry

XD 1.x --> Spring Cloud Data Flow


spring-cloud-dataflow

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shell ui

admin (REST API)

dsl-parser

stream-repository

module-registrymodule-deployer

local-deployer cloudfoundry-deployer

yarn-deployer lattice-deployer

task-repository


http://cloud.spring.io/spring-cloud-dataflow/

http://cloud.spring.io/spring-cloud-stream/

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Code Slide

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// This is Arial: 18 pt or higher please public class TransferServiceImpl implements TransferService { public TransferServiceImpl(AccountRepository ar) { this.accountRepository = ar; } … }


Safe Harbor Statement

The following is intended to outline the general direction of Pivotal's offerings. It is intended for information purposes only and may not be incorporated into any contract. Any information regarding pre-release of Pivotal offerings, future updates or other planned modifications is subject to ongoing evaluation by Pivotal and is subject to change. This information is provided without warranty or any kind, express or implied, and is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions regarding Pivotal's offerings. These purchasing decisions should only be based on features currently available. The development, release, and timing of any features or functionality described for Pivotal's offerings in this presentation remain at the sole discretion of Pivotal. Pivotal has no obligation to update forward looking information in this presentation.

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Sample Pie Chart

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Sample Bar Chart

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Sample Line Chart

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Sample Table

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2007 2008 2009 2010

Category 1 2.4 6.4 4.1 6.6

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Category 3 4.6 3.2 1.9 9.6

Category 4 6.7 3.3 3.4 2.2

Category 5 4.3 5.6 7.1 3.4

spring xd - today and tomorrow

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