Amazon EC2 is the cornerstone for any Amazon-based cloud deployment. Enabling you to provision and scale compute resources with different memory, CPU, networking and storage capacity in multiple regions all around the world, EC2 is by far Amazon’s most popular and widely used service.
Logging your Java applications is essential to getting visibility into application performance and troubleshooting problems. But, with a variety of available libraries and configuration options, getting started logging in Java can be challenging.
In a previous post we looked at 6 key considerations to keep in mind when selecting a log management solution: data collection, search experience, scalability, security, advanced analytics and cost effectiveness. Hopefully, you’ve managed to use this list to finally select your solution. What now?
Serverless computing is a cloud-based application architecture where the application’s infrastructure and support services layer is completely abstracted from the software layer. Any computer program needs hardware to run on, so serverless applications are not really “serverless” - they do run on servers - it’s just that the servers are not exposed as physical or virtual machines to the developer running the code.
As shown in Elasticsearch Key Metrics, the setup, tuning, and operations of Elasticsearch require deep insights into the performance metrics such as index rate, query rate, query latency, merge times, and many more. Sematext provides an excellent alternative to other Elasticsearch monitoring tools.
An important element of operating Kubernetes is monitoring. Hosted Kubernetes services simplify the deployment and management of clusters, but the task of setting up logging and monitoring is mostly up to us. Yes, Kubernetes offer built-in monitoring plumbing, making it easier to ship logs to either Stackdriver or the ELK Stack, but these two endpoints, as well as the data pipeline itself, still need to be set up and configured.
Logstash plays an extremely important role in any ELK-based data pipeline but is still considered as one of the main pain points in the stack. Like any piece of software, Logstash has a lot of nooks and crannies that need to be mastered to be able to log with confidence. One super-important nook and cranny is the Logstash configuration file (not the software’s configuration file (/etc/logstash/logstash.yml), but the .conf file responsible for your data pipeline).
Famed management thinker Peter Drucker is often quoted as saying, “You can’t manage what you can’t measure.” Tracking and analyzing data of a system provides metrics to measure, predict, and improve the underlining health of the system. Logging data is the simplest act of collecting data for measurement and plays an important role in modern enterprises, as it provides a way to measure the health of hardware devices and software applications alike.
Scenario You are running a large production environment with many Windows servers. There are multiple forests in the network and some forests have multiple domain controllers. Your Windows server security is paramount – you want to track and audit suspicious activities and view detailed Windows reports extracted from the Windows servers event logs.
Organizations that depend on distributed systems often write their applications in Go to take advantage of concurrency features like channels and goroutines (e.g., Heroku, Basecamp, Cockroach Labs, and Datadog). If you are responsible for building or supporting Go applications, a well-considered logging strategy can help you understand user behavior, localize errors, and monitor the performance of your applications.
