- Basic Monitoring of Amazon EC2 instances at 5-minute intervals at no additional charge.
- Elastic Load Balancer Health Checks -Auto Scaling can now be instructed to automatically replace instances that have been deemed unhealthy by an Elastic Load Balancer.
- Alarms - You can now monitor Amazon CloudWatch metrics, with notification to the Amazon SNS topic of your choice when the metric falls outside of a defined range.
- Auto Scaling Suspend/Resume - You can now push a "big red button" in order to prevent scaling activities from being initiated.
- Auto Scaling Follow the Line -You can now use scheduled actions to perform scaling operations at particular points in time, creating a time-based scaling plan.
- Auto Scaling Policies - You now have more fine-grained control over the modifications to the size of your AutoScaling groups.
- VPC and HPC Support - You can now use AutoScaling with Amazon EC2 instances that are running within your Virtual Private Cloud or as Cluster Compute instances.
December 02, 2010
Kafka is a distributed publish-subscribe messaging system. It is designed to support the following
- Persistent messaging with O(1) disk structures that provide constant time performance even with many TB of stored messages.
- High-throughput: even with very modest hardware Kafka can support hundreds of thousands of messages per second.
- Explicit support for partitioning messages over Kafka servers and distributing consumption over a cluster of consumer machines while maintaining per-partition ordering semantics.
- Support for parallel data load into Hadoop.
Kafka is aimed at providing a publish-subscribe solution that can handle all activity stream data and processing on a consumer-scale web site. This kind of activity (page views, searches, and other user actions) are a key ingredient in many of the social feature on the modern web. This data is typically handled by "logging" and ad hoc log aggregation solutions due to the throughput requirements. This kind of ad hoc solution is a viable solution to providing logging data to an offline analysis system like Hadoop, but is very limiting for building real-time processing. Kafka aims to unify offline and online processing by providing a mechanism for parallel load into Hadoop as well as the ability to partition real-time consumption over a cluster of machines.
The use for activity stream processing makes Kafka comparable to Facebook's Scribe or Cloudera's Flume, though the architecture and primitives are very different for these systems and make Kafka more comparable to a traditional messaging system. See our design page for more details.
One of the my problems with most cloud ROI worksheets is that they are heavily weighted for use-cases where resource usage is very bursty. But what if your resource requirements arenâ€™t bursty ? And what if you have a use case where you have to maintain a small IT team to manage some on-site resources due to compliance and other issues ?