Microservices with a Spark
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This document discusses Microservices with Spark Java, a minimalist microframework for web applications. It describes key features of Spark including its verb-route-handler structure, request and response objects, and ease of configuration. The document also discusses how Leadspace uses Spark for thin wrappers serving data, business logic, orchestrators, and queue-based services, noting it is less suitable for high concurrency web serving.
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Microservices with a Spark
- 1. MICROSERVICES WITH A Yair Galler @ygaller – Java IL November 2017
- 2. A Minimalist Microframework for Web Apps •Verb, Route & Handler •Request and Response objects
- 3. From 0 60 in 35 seconds
- 5. <?xml version="1.0" encoding="UTF-8"?> <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> <modelVersion>4.0.0</modelVersion> <groupId>com.leadspace</groupId> <artifactId>hello-spark</artifactId> <version>1.0-SNAPSHOT</version> <dependencies> <dependency> <groupId>com.sparkjava</groupId> <artifactId>spark-core</artifactId> <version>2.6.0</version> </dependency> </dependencies> </project>
- 6. The HTTP Server * Works with other HTTP servers
- 7. Common options are easy to tune • Port • Threads • Timeout Configuration
- 8. Fully configurable for advanced use cases Works with other containers Configuration
- 9. Easy access to request & response objects • Parameters • Mime Type • Response transformer – Any object String Ease of Use
- 10. Ease of Use
- 11. The Leadspace Use Case
- 13. Before Spark •Tried adapting existing Tomcat to microservices •Services were increasingly complex •Provisioning & configuration hell
- 14. Alternatives We Considered Spring Boot Dropwizard
- 15. The Road to Spark
- 16. Spark in Leadspace •Thin wrapper for serving data / configs •Business logic •Orchestrators •Queue based services
- 17. Spark in Leadspace When is it less suitable? •Web serving •High concurrency
- 18. Spark in Leadspace Testability • Spin up an instance with mock dependencies • Call endpoints with an http client • Shutdown
- 19. Additional Features & Considerations Dependency Injection Web Serving Kotlin
Editor's Notes
- Who thinks this talk is about Big Data? Goals of this talk: Explain what is Spark Present our journey from monolith to microservices using Spark Show extra features of Spark Quickly about myself: Engineering TL at Leadspace Been at Leadspace for 6 years Durring that time we moved from Monolith to Microservices – Something many have done. What’s different – We’ve ended up using Spark to build services
- Based on Sinatra RoR Actively developed – 6K stars, Java 9 coming soon What is minimalist – Route urls to functions, Request & Response objects Web app – Static + REST
- Don’t really care about “Hello world” Do care about getting to the business logic as quickly as possible Zero code to working server in 35 seconds!
- Port 4567 is the default Response is an object that undergoes a transformation Thank IntelliJ Uses Java 8 lambdas – Would not exist without it
- In the coding example, I showed the actual coding. You might wonder what else need to be done. This is all the setup overhead Omitted maven-source-plugin for Java 8
- Generic Embedded Jetty under the hood Anything you can do with Jetty, you can do with Spark
- Common options – Port, threads, timeout
- Useful for advanced use case – Connectors Other containers
- Request & response objects Access params (URL / Query) Set mime type & transform the response
- We are mid-size. Not POC / not Google or Netflix or Taboola. Don’t want upfront costs of their frameworks
- Started deconstructing our monolith POC back in 2012 Starting point - Tomcat + MySQL System was too complex, too fragile. Locks. Long builds of one huge codebase
- “Microservices” the concept was just appearing Embedded was the key Bad directions (we didn’t understand at the time) Many machines, one Tomcat / machine, multiple wars per Tomcat + RMI. Many machines, one war per Tomcat, but not embedded. NoSQL on some. Setup hell, abuse of Spring. Ansible automation
- Least opinionated Other containers that we looked at: Spring Boot – Lots of magic, very opinionated towards Spring Dropwizard – Opinionated and includes many libraries – Jetty, Jackson, JDBI, Liquibase. Mapping is Jersey based Spark does the little that it does really well There’s not much under the hood Comparisons: 6700 stars on Github (DW – 6K, Play – 9K, Spring Boot – 17K) https://zeroturnaround.com/rebellabs/sparkjava-is-an-amazing-java-web-framework-do-you-really-need-it/ https://craftsmen.nl/memory-usage-6-popular-rest-server-frameworks-compared/
- How we use Spark today Lightweight services Thin wrappers for data stores Business logic / Orchestrators / Machine Learning Processed that read queues Hundreds of requests / second
- Spark is not a fit for everything. We also use Express / Akka
- Emphasis here is on testing the endpoints, not the business logic Mocked DB