A simple Spring Boot sample to publish a message to a Redis queue, with a message subscriber that consumes the message Spring Data. See README in Github for how to set up a local Redis and run the code.
In the age of Serverless & Container architectures, there is once again chatter about Java being too fat (and dying). While I can understand the “too fat” observation, I will not put my money on the “java is dying/dead” chatter. That obituary has been written multiple times and the language lives on. It is true that Java was not born in the Container/Cloud era. Yes, it was born in a different age and time, but the language and framework ecosystem has evolved. In the Microservices cloud native app world where horizontal scaling and fast startup times are expected, Java may (at times depending on the architecture) not be the fastest horse in town.
Capturing metrics from your system is critical to understanding its internal behavior and to tune its performance. Without this you are operating in the blind. In this post we will go through how you can gather metrics from a Spring Boot application using Prometheus, Grafana and Micrometer.
Most serious applications (and distributed microservices style architectures) will require to provide a log aggregation & analysis feature to its dev & operations teams. Reviewing log entires from 10s or 100s of server instances is not something to take lightly. Whether you choose to use a commercial product or an open source offering – that does not matter; just make sure you have one available.
Recently I have been deploying applications using AWS Beanstalk. You can definitely configure CloudWatch Logs to send log streams over to AWS ElasticSearch service. Log messages can be routed to a Lambda function which would break the log messages into individual attributes suitable for indexing. I wanted to try a slightly different route where I depend less on CloudWatch Logs and more on open source tools. Enter filebeat on Beanstalk.
Updated one of my previous Spring Boot sample service to run within a Docker container – https://github.com/thomasma/quote-service-docker. You can run it locally w/o Docker as a regular Spring Boot app and next run it inside a Docker container. Make sure that you have Docker setup correctly and tested prior to running this app.
To discuss Serverless Architecture we need to understand how we got here. From using physical machines we moved to virtual machines (somewhere in between a few brave folks also used linux/solaris containers). The current trend is container technologies such as Docker or CoreOS RKT which allow even more efficient use of resources. Regardless of which you use, we are often required to plan our application infrastructure needs upfront and permanently keep the “servers” running.
While SpringMVC makes it quite easy to create RESTful services, this starter project adds a few things more. It provides a consistent way to send error messages in json back to the caller and also integrates Spring Security into the mix.
Here is a slightly modified Architecture on my previous post on Getting Started With ElasticSearch. If you find yourself indexing content constantly (like 100s or even 1000s per minute) you might want to consider an asynchronous architecture towards indexing.
Extending from some of my previous posts around the 2012 Presidential political contributions, here I will use Spring Integration, ActiveMQ, JMS and Mongodb to load the CSV data into Mongodb.
Finally got around to deploying one of my old restful applications to the open source PaaS Cloudfoundry.com. I have updated the original post with new instructions at Secure RESTful Services with Maven, Spring, Apache CXF and Spring Security.