Random thoughts on mobile from a presentation slide I created a while back and some updates thoughts based on new information from talking to another expert.
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.
Spring Boot and Spring Cloud are relatively newer additions to the Spring portfolio. Boot makes it faster to spin up your project with less configuration (and an opinionated programming model). Spring Cloud brings in techniques and tools to efficiently standup distributed applications. In a previous blog I had noted my ramblings on API/Microservices style. If you take that path and have more than a handful of API’s then you will need some of the capabilities of what Spring Boot and Spring Cloud offer.
(Updated version. Originally Published on: Nov 3, 2013) Here are some things to keep in mind as you go about designing your ElasticSearch cluster. Many of these are from real life experiences and IMO are the basic common sense items you should consider. In addition to these settings noted here, there maybe other settings that are relevant to your use case.
In the tech world one cannot stay a day away from hearing the words API or Microservices. For those who work in startups or tech firms (google type) these are well understood. But once you walk into large non-tech firms, these words are not as well understood.
Experimenting with Ganglia to monitor my nodes. More detailed steps to come.
While I had the privilege of using ElasticSearch to implement media analytics in a past role, it is only recently that I have started looking at it for log analysis.
In this article we will run the example from Part-1 in pseudo-distributed single server mode. Most of the configuration details are clearly laid out on the Hadoop site at Setting Up Single Server Pseudo-Distributed mode. For the sake of additional clarity I will note them here and also run our previous job from Part-1 against the new cluster. I assume that you already have Hadoop downloaded and setup from the previous article.
Note: Updated to Hadoop 2.4.1 and re-published from original Sept 28th, 2011 blog.
Hadoop is a framework that allows you to process large sets of unstructured or semi-structured data. The unstructured/semi-structured nature of the data and the sheer size (terabytes or petabytes) make the current RDMS offerings come short. Enter Apache Hadoop.
Note: Updated to Hadoop 2.4.1 and re-published from original Sept 28th, 2011 blog. Continue reading