etsy/statsd OpenTSDB - A Distributed, Scalable Monitoring System Analyzing the Analyzers Distributed monitoring - op5 Distributed network monitoring involves multiple pollers distributed around your network measuring performance from multiple locations. op5 Monitor Enterprise version includes a set of options for distributed monitoring and highly scalable setups with potentially thousands of pollers that can monitor many tens of thousands of devices in large and complex networks. The performance and capacity of op5 Monitor, together with an easy-to-use interface, provides you with a powerful solution for IT monitoring and management of applications, regardless of where they reside – in the data center or in a private, hybrid or public cloud environment. Distributed setup op5 Monitor support distributed setups using peers and pollers to meet the performance and capacity needs of most organizations. Pollers can be geographically distributed and cloud based. Download full 30 day trial version, including all features, API and op5 App support Download op5 Monitor Remote and central administration
Mining Time-series with Trillions of Points: Dynamic Time Warping at scale Take a similarity measure that's already well-known to researchers who work with time-series, and devise an algorithm to compute it efficiently at scale. Suddenly intractable problems become tractable, and Big Data mining applications that use the metric are within reach. The classification, clustering, and searching through time series have important applications in many domains. In medicine EEG and ECG readings translate to time-series data collections with billions (even trillions) of points. In fact many research hospitals have trillions of points of EEG data. The problem is that existing algorithms don't scale1 to sequences with hundreds of billions or trillions of points. Recently a team of researchers led by Eamonn Keogh of UC Riverside introduced a set of tools for mining time-series with trillions of points. What is Dynamic Time Warping? SQRT[ Σ (xi - yi)2 ] While ED is easy to define, it performs poorly as a similarity score. As a distance measure, DTW is hard to beat. 1. 1.
i-enable rmf (remote monitoring framework) is a scalable distributed monitoring system for geographically diversified i-enable rmf (remote monitoring framework) is a scalable distributed monitoring system for geographically diversified, multi-site IT infrastructures. Distributed monitoring brings in scalability and control by designing the monitoring process in a distributed environment. The deployment architecture is made simple as the same tool / software runs as Manager as well as Aggregator with only the characteristics changing, based on the configuration. i-enable rmf MCM (Metrics Collection Manager) is installed at each site (could be a customer location / data center) where it collects and processes the data for the monitored metrics. Once the data for monitored metrics is received at MCA2, it will be processed and stored. Advantages of Distributed Monitoring include:
etsy/oculus GitHub - mikeaddison93/briefly: Briefly - A Python Meta-programming Library for Job Flow Control etsy/skyline Monitor Performance with Ganglia - Amazon Elastic MapReduce The Ganglia open source project is a scalable, distributed system designed to monitor clusters and grids while minimizing the impact on their performance. When you enable Ganglia on your cluster, you can generate reports and view the performance of the cluster as a whole, as well as inspect the performance of individual node instances. For more information about the Ganglia open-source project, go to To add Ganglia to a cluster using the console Open the Amazon EMR console at Create cluster.Under the Additional Applications list, choose Ganglia and Configure and add.Proceed with creating the cluster with configurations as appropriate. To add Ganglia to a cluster using the AWS CLI In the AWS CLI, you can add Ganglia to a cluster by using create-cluster subcommand with the --applications parameter. Ganglia provides a web-based user interface that you can use to view the metrics Ganglia collects. DAGScheduler.
Introducing Kale Posted by Abe Stanway | Filed under data, monitoring, operations In the world of Ops, monitoring is a tough problem. It gets harder when you have lots and lots of critical moving parts, each requiring constant monitoring. At Etsy, we’ve got a bunch of tools that we use to help us monitor our systems. You might be familiar with some of them: Nagios, StatsD, Graphite, and Ganglia. This tool is designed to solve the problem of metrics overload. Of course, if a graph isn’t being watched, it might misbehave and no one would know about it. We’d like to introduce you to the Kale stack, which is our attempt to fix both of these problems. Skyline Skyline is an anomaly detection system. You can hover over all the metric names and view the graphs directly. Once you’ve found a metric that looks suspect, you can click through to Oculus and analyze it for correlations with other metrics! Oculus Oculus is the anomaly correlation component of the Kale system. monitoring <3, Abe and Jon