Whenever you build a service and expose a set of endpoints to provide API access to that service, you’ll likely need to track their availability and response times, aside from ensuring their functionality. But to actually know that “something is down” or just “not performing” you need to consistently monitor your services day in day out and that’s how Heartbeat from the Elastic Beat family helps you with Uptime Monitoring.
Millions of people already use Kibana for a wide range of purposes, but it was still a challenge for the average business user to quickly learn. Visualizations often require quite a bit of experimentation and several iterations to get the results “just right” and this Kibana Lens tutorial will get you started quickly.
With the new Streama capability announced today, you no longer have to choose what to monitor and what to drop to manage your logging costs. For years, our customers have enjoyed the benefits of a log analytics platform that enables them to autonomously manage and analyze data in their cloud applications. Our machine learning engine empowers users to improve their system stability and accelerate their release cycles.
Running an ELK stack provides unrivaled benefits for your organization, however, ELK issues will inevitably crop up. ELK is scalable, and largely agnostic of internal infrastructure, making it a great asset for SMEs and enterprises. However, successfully deploying and running an ELK stack is not without its difficulties. In order to keep your ELK stack running at optimum performance, you need to familiarize yourself with some of the most common ELK issues.
Ingesting various events and documents into Elasticsearch is great for detailed analysis but when it comes to the common need to analyze data from a higher level, we need to aggregate the individual event data for more interesting insights. This is where Elasticsearch Data Frames come in. Aggregation queries do a lot of this heavy lifting, but sometimes we need to prebake the aggregations for better performance and more options for analysis and machine learning.
Log maintenance has a hidden cost. Engineers optimize their instance types, storage, networking, dependencies, and much more. However, we rarely consider the engineers themselves. A DevOps culture encourages engineers to own the solutions they build. While this increases team autonomy, it risks splitting the precious bandwidth that the team has. Automation is what makes the DevOps cycle work, and it has to cover log analysis to do a thorough job of catching issues.
Fundamentally, there are logs that will be of intrinsic value to you, and others that are less business-critical. Are you aware of the logging cost to handle, analyze and store these different types of logs? Should you really have the same approach for mission-critical logs as you do for info or telemetry logs? Differentiating your approach for different logs is challenging. If no two logs are truly the same then why should you treat them the same?
The ELK stack has become a staple of log analytics in recent years, but so too have the stories of complex maintenance and poor scalability. We’re going to discuss some of the problems with a self-hosted ELK stack and the advantages of a SaaS offering like Coralogix.
Observability is an indispensable concept in continuous delivery, but it can be a little bewildering. Luckily for us, there are a number of tools and techniques to make our job easier! One way to aid in improving observability in a continuous delivery environment is by monitoring and analyzing key metrics from builds and deploys. With tools such as Prometheus and their integrations into CI/CD pipelines, gathering and analysis of metrics is simple. Tracking these things early on is essential.
Java is used by at least 7.6 Million developers worldwide. Java logging has been a staple of the Java platform since day one, boasting extensive, resourceful documentation and rich API’s. The cornerstone of monitoring your application is efficient and widespread logging. At Coralogix, we know that logs have become one of the most important components of a modern monitoring function.
