Back your app with MySQL and Redis on Cloud Foundry

May 12, 20161 like2,001 views

In this session, we will build a minimum viable Spring Data web service with REST API, add a MySQL backing service as the primary data store, and a Redis Labs backing service for caching. We will demonstrate performance metrics without Redis caching enabled and then with Redis caching enabled. I will also provide an intro-level explanation of the platform capabilities within Pivotal Web Services

Unless otherwise indicated, these slides are © 2016 Pivotal Software, Inc. and licensed under a

Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
Back your App with MySQL & Redis, the Cloud
Foundry Way
Kenny Bastani, Spring Developer Advocate, Pivotal
@kennybastani

What are we talking about today?
! Spring Data Redis
! Spring Boot
! We’re going to build a Spring Boot web service with MySQL storage
! Add in Redis caching with Spring Data Redis
! Deploy to the cloud! (Cloud Foundry)
! Stream metrics and compare caching vs. no caching
3

Why did the database have problems
ingesting data?
5

Because it had ACID indigestion
6
Why did the database have problems
ingesting data?

Ok let’s get serious
Let’s talk about architecture
7

Microservices & Polyglot Persistence
If you’re into that kind of thing
8

What is Spring Data Redis?
! Easy access to Redis from your Spring applications
! Provides a variety of abstractions for you to interact with Redis
• Spring Data Repositories (NEW!)
• RedisTemplate
• CacheManager
! Available as a Spring Boot starter project from http://start.spring.io
! Provides a CacheManager implementation so you can use annotation-
driven caching on methods
11
+

How does Spring Data work?
Abstractions…
12

Cloud Native Application with Redis Caching
13

Spring Boot
A JVM micro-framework for building microservices
14

Spring Initializr
16
supports rapid
development of
production-ready
applications and services
Bootstrap your application

Add Redis Caching
Configure Redis connection and Spring Data cache manager
18

Deploy to the cloud!
Using Cloud Foundry
29

Cloud Foundry
! Open-source platform as a service (PaaS)
! Supports multiple cloud providers
! Supports build packs for multiple application
frameworks
! Supports Linux container deployments
using Diego
! Supports Docker container registries
30

Application Server vs Linux Container deployments
31

Application Server Deployment
! Load balancing requires
provisioning of new VMs and app
server installations
! Poor resource isolation; memory
leaks can cause other
applications to become
unavailable
! Runtime environment is driven by
the operator
32
Virtual Machine 
App
Linux Kernel
App App
Hardware Infrastructure

Linux Container Deployment
! Development team drives the
application runtime of a container
! Containers are resource isolated,
allowing efficient scheduling onto
a grid of VMs
! Containers take seconds to start,
VMs take minutes
! It’s not a rule that there is one
microservice per container
33
Virtual Machine 
Container
Linux Kernel
App App
App App
Container
App App
App App
Container
App App
App App
Hardware Infrastructure

Microservices - Container Deployment
! Each microservice can be containerized with their application dependencies
! Containers get scheduled on virtual machines with an allotted resource
policy
34

Auto-scaling
! Minutes to start a VM, but seconds to start a container
! An elastic runtime handles auto-scaling of VMs with cloud providers
35

38
Learn More. Stay Connected.
! Twitter: @kennybastani
! Spring: spring.io/team/kbastani
! Blog: kennybastani.com
! GitHub: github.com/kbastani
Twitter: twitter.com/springcentral
YouTube: spring.io/video
LinkedIn: spring.io/linkedin
Google Plus: spring.io/gplus

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In this talk I will introduce you to a Docker container that provides you an easy way to do distributed graph processing using Apache Spark GraphX and a Neo4j graph database. You'll learn how to analyze big data graphs that are exported from Neo4j and consequently updated from the results of a Spark GraphX analysis. The types of analysis I will be talking about are PageRank, connected components, triangle counting, and community detection. Database technologies have evolved to be able to store big data, but are largely inflexible. For complex graph data models stored in a relational database there may be tedious transformations and shuffling around of data to perform large scale analysis. Fast and scalable analysis of big data has become a critical competitive advantage for companies. There are open source tools like Apache Hadoop and Apache Spark that are providing opportunities for companies to solve these big data problems in a scalable way. Platforms like these have become the foundation of the big data analysis movement. Speakers

Document Classification with Neo4jKenny Bastani

This document discusses document classification using graphs and Neo4j. It introduces hierarchical pattern recognition (HPR) for graph-based document classification. HPR learns deep feature representations in a hierarchy using finite state machines. The features are mapped to a vector space model for classification. The document demonstrates HPR by classifying US presidential speeches by political affiliation, achieving over 70% similarity for predicted vs actual labels. It encourages attendees to get involved in the Neo4j community.

Neo4j Graph Data ModelingKenny Bastani

Building a Graph-based Analytics PlatformKenny Bastani

Meetup is a valuable source of data for understanding trends around products or brands. Meetup does not support an analytics package to track group statistics overtime unless you are an administrator of a group. There are no third-party tools or websites that analyze Meetup trends to understand how communities grow. In this talk I will present a graph-based analytics platform that uses the Meetup.com API to collect and analyze membership statistics over time. This talk will cover: How to poll and import periodic data from the Meetup.com API into Neo4j using Node.js. How to track meetup group growth over time using a Neo4j graph database using Node.js. How to apply tags to meetup groups and report combined growth of all groups over time. How to build an interactive documented analytics API to support applications using Node.js and Neo4j. How to build a business dashboard to visualize time-based statistics and reports using a Node.js based REST API that queries Neo4j.

Building Killer Apps with Neo4j 2.0Kenny Bastani

As companies like Facebook and Google have introduced us to Graph Search and the Knowledge Graph, developers are learning the benefits of graph database architectures. Graph databases, like Neo4j, have increased in popularity by nearly 250% from last year - the highest among all other DBMS categories, according to db-engines.com. Join Kenny Bastani as we look at the benefits of using a graph database, explore various use cases and walkthrough creating a movie recommendation app on Neo4j 2.0.

Natural Language Processing with Neo4jKenny Bastani

Recent natural language processing advancements have propelled search engine and information retrieval innovations into the public spotlight. People want to be able to interact with their devices in a natural way. In this talk I will be introducing you to natural language search using a Neo4j graph database. I will show you how to interact with an abstract graph data structure using natural language and how this approach is key to future innovations in the way we interact with our devices.

Natural language search using Neo4jKenny Bastani

Using Docker, Neo4j, and Spring Cloud for Developing MicroservicesKenny Bastani

Open Source Big Graph Analytics on Neo4j with Apache SparkKenny Bastani

Big Graph Analytics on Neo4j with Apache SparkKenny Bastani

Document Classification with Neo4jKenny Bastani

Neo4j Graph Data ModelingKenny Bastani

Building a Graph-based Analytics PlatformKenny Bastani

Building Killer Apps with Neo4j 2.0Kenny Bastani

Natural Language Processing with Neo4jKenny Bastani

Natural language search using Neo4jKenny Bastani

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