The Big List of NoSQL Use Cases

«  »In the NoSQL space this kind of real-world data is still a bit vague, » writes Todd Hoff at High Scalability. « When asked, vendors tend to give very general answers like NoSQL is good for BigData or key-value access. What does that mean for for the developer in the trenches faced with the task of solving a specific problem and there are a dozen confusing choices and no obvious winner? Not a lot. » »



The Linux-HA project maintains a set of building blocks for high availability cluster systems, including a cluster messaging layer, a huge number of resource agents for a variety of applications, and a plumbing library and error reporting toolkit.

You usually want to deploy the Pacemaker Cluster Resource Manager on top of the Heartbeat infrastructure layer. If you happen to be still under the impression that this CRM would be part of Heartbeat, please see Why was the Pacemaker Project Created?


DRBD® refers to block devices designed as a building block to form high availability (HA) clusters. This is done by mirroring a whole block device via an assigned network. DRBD can be understood as network based raid-1.

In the illustration above, the two orange boxes represent two servers that form an HA cluster. The boxes contain the usual components of a Linux™ kernel: file system, buffer cache, disk scheduler, disk drivers, TCP/IP stack and network interface card (NIC) driver. The black arrows illustrate the flow of data between these components.

The orange arrows show the flow of data, as DRBD mirrors the data of a high availably service from the active node of the HA cluster to the standby node of the HA cluster.

Scaling Lucene and Solr | Enterprise Search support for Apache Lucene and Solr by Lucid Imagination

While many Lucene/Solr applications will never outgrow a single, well-configured machine, the fact is, more and more applications are pushing beyond the single machine limit due to either index size or query volume. In discussing Lucene and Solr best practices for performance and scaling, Mark Miller explains how to get the most out of a single machine, as well as how to scale out to harness multiple machines to handle large indexes, large query volume, or both.