Docker 1.5 came out a few weeks ago and with it the new stats api arrived. Before 1.5 there was no standard way to collect the metrics of running docker containers without writing custom scripts to parse files stored in the proc memory file system.
Release 2.5.1 includes enhancements to JMX functionality, improvements to Logscape roll detection aswell as significant search performance optimisation.
2.5.1 Introduces new linking functionality. This allows you to flawlessly link your workspaces and transfer context between them providing a troubleshooting workflow for non-expert users of the Logscape system.
The release notes are here.
Deploying Logscape in the cloud has its challenges. Logscape is a cpu and i/o intensive application and finding an optimal configuration between disk, i/o and cost requires extensive benchmarking and tests.
Recently we started looking at Amazon. We have now have three stages to our benchmarking.
In preparation for release Logscape as a SAAS solution, the Collectd App has been updated. The CollectdApp is one of the first apps to support Logcape Cloud.
- write_graphite – This release now uses the graphite plugin to import collectd metrics.
- Workspaces Update – the workspaces have all been updated
- Zip – The app is now available as a zip. Whereas before it was distributed as a config file.
Docker has been around for roughly a year, it was open-sourced by the guys at dotCloud ( the PAAS provider) and has since caused a revolution in the devops community. It has the backing of industry heavy weights such as Google, Rackspace and Redhat . In this blog post, I will attempt to give an initial look at monitoring docker, with a specific focus on system health. The technology stack under Docker is a large topic so I will not be going into much detail on setting up a docker environment. Before we dig right in let’s find out a bit about docker.
In Part 1 I built a Groovy WebSocket
Server and a Java and HTML Client. In Part 2 I’ll deploy it into AWS, fire up the Clients and add the Github link. With WebSocket Clients, I can run Logscape in the ‘wild’ and make use of the Alert-Feed WebSocket functionality to stream data to my local servers.
AWS Deployment: Before running on the AWS server I need to find the right AMI – one with Java installed. The OpenJDK is installed on most Linux flavours, and I prefer to work with Ubuntu. In the following grab you can see where I’ve fired up the AMI instance.
This is a 2 part post where in Part 1 I build the ‘spike’ using Groovy to run a WebSocketServer to stream data to HTML5-WebSocket & JavaWebSocket Clients. The HTML Client uses the elegant smoothie charts (great for streaming). In Part 2 Ill show you how to run it on Amazons AWS.
At the end we have a real-time feed plotting the data from the cloud; it looks something like the grab on the right.