Monitoring large-scale application clusters

Most software engineering organizations build applications with some hooks in place to allow functional tests. Some organizations continuously build and test all software automatically at check-in. And then there are those who have learnt from mistakes, and have built a suite of tests which get triggered at startup to look for problems which could indicate a failed initialization.

The next step in building a scalable web application, is creating some form of self-monitoring logic (sometimes called a watchdog) which could periodically test itself (or monitor performance statistics) for problems worth escalating to operations team.

Arnon has a couple of (1)interesting  (2)posts on the topic which I came across today. He summarized the whole suggestion using 3 acronymns. And I’m going to add one more to make it complete

  1. BBIT : Build time Built in Tests.
  2. PBIT: Power-on Built in Tests
  3. CBIT: Continuous Built in Tests
  4. IBIT: Initiated Built in Tests

The organization I work for is relatively small in the number of servers but one thing we learnt early on was that waiting for customers to report problems was the worst way to find out about issues. Setting up monitoring using something like Nagios/Openview/curl_scripts/etc may work, but its not very scalable. Besides blackbox testing without insight into the application may not be sufficient/ideal for more complex applications.

To increase automation, reduce dependence on central monitoring infrastructure and to speed up failure detection (prediction), it might be important to have some tests in all of the 4 zones listed above.

Comments

Application monitoring said…
SysOrb is an higher server and network monitoring system with many features and yet it has a very easy and accessible web interface which means short completion time and considerably less training wanted than with other competing systems.
6:00 PM
Post a Comment

Popular posts from this blog

Chrome Frame - How to add command line parameters

Image
Chrome frame intentionally does stuff without getting in the way of the user. This sometimes makes things harder to debug. For example how can one debug an issue if chrome frame doesn't even launch ? Apparently there is a flag for that. But you have to know how to enable it. Here are the steps. Make sure chrome frame is installed. We can enable startup flags for dumping debug logs using a policy called AdditionalLaunchParameters If this is just for one desktop, I recommend doing a registry edit (it can be pushed via GPO as well) Add a REG_SZ property " AdditionalLaunchParameters " to " SoftwarePoliciesGoogleChromeAdditionalLaunchParameters " with the value "--enable-logging --v=1" (also documented here  and mentioned here )  [ Attachment 1 ] Next kill the IE browser and make sure chrome is also dead by checking taskmgr Restart IE and go to " gcf:about:version " and confirm that the parameters you added show up next to "Command Line:"
Read more

Creating your first chrome app on a Chromebook

Image
Doing development on Chromebook usually means developing online. There are a lot of sites around for that. But with the APIs getting more mature, its just a matter of time before someone builds a kick-ass IDE which runs natively on Chromebooks without network connectivity. One such tool which I've been exploring for a last few weeks is from Google and called " Chrome Dev Editor " If you have never built and run a chrome app or extension, I'll show you how to do this in 3 easy steps.     Step 1 Launch the app and click on the "+" button to add a new project. Pick a project name and select "JavaScript Chrome App" in the drop down menu.       Step 2 Notice how it automatically adds the minimum code required for it to run. This will be the template you are going to work with. You are almost done, but lets review the files in there to understand why they are there.   manifest.json - This is the most important file. Modify the app name, version number
Read more

Brewers CAP Theorem on distributed systems

Image
Large distributed systems run into a problem which smaller systems don’t usually have to worry about. “ Brewers CAP Theorem ” [ Ref 1 ] [ Ref 2 ] [ Ref 3 ]  defines this problem in a very simple way. It states, that though its desirable to have Consistency, High-Availability and Partition-tolerance in every system, unfortunately no system can achieve all three at the same time . C onsistent : A fully Consistent system is one where the system can guarantee that once you store a state (lets say “x=y”) in the system, it will report the same state in every subsequent operation until the state is explicitly changed by something outside the system. [ Example 1 ] A single MySQL database instance is automatically fully consistent since there is only one node keeping the state. [ Example 2 ] If two MySQL servers are involved, and if the system is designed in such a way that all keys starting “a” to “m” is kept on server 1, and keys “n” to “z” are kept on server
Read more