NoSQL theory: All content tagged as NoSQL theory in NoSQL databases and polyglot persistence
Download the slides, set aside 1 hour and 10 minutes of uncontended time, click the Maximize button, and watch this great presentation by Martin Thompson and Michael Barker diving into the Intel x86_64 processors and memory models for implementing lock-free algorithms. Once you’re done make sure to also read The Single Writer Principle by the same Martin Thompson.
Original title and link: Lock-Free Algorithms: How Intel X86_64 Processors and Their Memory Model Works ( ©myNoSQL)
A reminder to those thinking that networks never fail and automation can solve everything. Christina Ilvento, on behalf of the App Engine team:
The root cause of the outage was a combination of two factors during a scheduled network maintenance in one of our datacenters. As part of the scheduled maintenance, network capacity to and from this datacenter was reduced. This alone was expected, and was not a problem. However, this maintenance exposed a previously existing misconfiguration in the system that manages network bandwidth capacity.
Ordinarily, the bandwidth management system helps isolate and prioritize traffic. When capacity is reduced because of maintenance, network failure, or due to an excess of normal traffic, the bandwidth management system keeps things running smoothly by throttling back the rate of low priority traffic. However, as mentioned, the bandwidth management system had a latent misconfiguration which did not show up until capacity was reduced due to the scheduled maintenance. This misconfiguration under-reported the available network capacity to and from the datacenter, causing the network modeler to believe that there was less overall capacity than actually existed.
The configuration error in the bandwidth management system, when combined with an expected reduction in capacity due to the scheduled maintenance, led the system to conclude that there was insufficient bandwidth available for current traffic demand to and from this datacenter. (In reality, there was more than sufficient excess capacity, as otherwise the maintenance would not have been allowed to go forward.) Because of this combination of misconfiguration and scheduled maintenance, a number of services were automatically blocked from sending network traffic. […]
The outage occurred because two independent systems failed at the same time, which resulted in mistakes in our usual escalation procedures which significantly impacted the duration of the outage.
Original title and link: Networks Never Fail ( ©myNoSQL)
Scroll to minute 16:55 of this video to watch Jim Webber explain the benefits of polyglot persistence and how starting (again) the winner-takes-it-all war is just sending us back at least 10 years from the database Nirvana.
We’ve just come from the place where one-size-fits-all and we don’t want to go back there. There is a huge wonderful ecosystem of stores. Pick the right one. Don’t just assume that the one you find the easiest or the one that shouts the loudest is the one you’re going to use. Pick the one that suits your data model.
It doesn’t matter what flavor of relational or NoSQL database you prefer or have experience with or if a small or large database vendor is paying your bills. You really need to get this right as otherwise we’re just going to destroy a lot of valuable options we’ve added to our toolboxes.
Original title and link: The Database Nirvana ( ©myNoSQL)
Besides the many practical lessons emphasized in Jack Clark’s interview with Adrian Cockcroft on ZDNet—luckly I’ve had the chance to see some of Cockcroft’s presentations about Netflix architecture and also talk to him directly—one thing that sticked with me was the ending paragraph:
The thing I’ve been publicly asking for has been better IO in the cloud. Obviously I want SSDs in there. We’ve been asking cloud vendors to do that for a while. With Cassandra, we’ve had to go onto horizontal scale and use the internal disks and triple replicate across availability zones, so you end up with a triple-redundant data store that is careful not to overload the disks.
That reminded me of this old ACM article authored by Brendan Gregg:
When I/O latency is presented as a visual heat map, some intriguing and beautiful patterns can emerge. These patterns provide insight into how a system is actually performing and what kinds of latency end-user applications experience. Many characteristics seen in these patterns are still not understood, but so far their analysis is revealing systemic behaviors that were previously unknown.
I was wondering if in the NoSQL databases space (and data storage space in general) are there any of the monitoring tools that provide such advanced visualization of latency data. Do you know any?
Original title and link: Visualizing System Latency ( ©myNoSQL)