Maintaining an endpoint, especially a customer-facing one, requires constant monitoring, whether using REST or GraphQL. As the industry has looked for solutions to build a more adaptive endpoint technology, it is also a must to monitor these endpoints. GraphQL and REST are two different technologies that allow user-facing clients to link to databases and platform logic. Both GraphQL and REST include monitoring techniques.
When your Elasticsearch cluster ingests data, it needs to understand how the data is constructed. To do this, your Elasticsearch cluster undergoes a process called mapping. Mapping involves defining the type for each field in a given document. For example, a number or a string of text. But how do you know the health of the mapping process? And why do you need to monitor it? This is where mapping statistics come in. Mapping statistics give you an overall view of the mapping process.
Continuous integration (CI) and continuous delivery or deployment (CD) cover the process of automatically merging, building, and testing code changes ready for release, and – in the case of continuous deployment – releasing those changes to users. If you’re developing software for others to use, you’ll need to go through some form of build and test process before you make your latest changes available.
Compute functions that run on Amazon’s Elastic Container Service (ECS) require regular monitoring to ensure proper running and managing of containerized functions on AWS – in short, ECS monitoring is a must. ECS can manage containers with either EC2 or Fargate compute functions. While EC2 and Fargate are compute services, EC2 allows users to configure virtually every functional aspect. Fargate is more limited in its available settings but is simpler to set up.
The Log4J library is one of the most widely-used logging libraries for Java code. On the 24th of November 2021, Alibaba’s Cloud Security Team found a vulnerability in the Log4J, also known as log4shell, framework that provides attackers with a simple way to run arbitrary code on any machine that uses a vulnerable version of the Log4J. This vulnerability was publicly disclosed on the 9th of December 2021.
Life is all about perspective, and the way we look at things often defines us as individuals, professionals, business entities, and products. How you understand the world is influenced by many details, or in the case of your application – many data sources. At Coralogix, we not only preach comprehensive data analysis but strive to enable it by continuously adding new ways to collect data.
When your development team is under pressure to keep releasing new functionality in order to stay ahead of the competition, the time spent on quality assurance (QA) activities can feel like one overhead that you could do without. After all, with automated CI/CD pipelines enabling multiple deployments per day, you can get a fix out pretty quickly if something does go wrong – so why invest the time in testing before release? The reality is that scrimping on software testing is a false economy.
Logfile analysis plays a central role in enhancing the observability of your IT estate, helping operations teams and SRE engineers to identify issues as they emerge and track down the cause of failures quickly. As the number of log entries generated on any given day in a medium-sized business easily numbers in the thousands, viewing and analyzing logs manually to realize these benefits is not a realistic option. This is where automated real-time log analysis comes in.
Coralogix is excited to announce the launch of our Stateful Streaming Data Platform that is now available on the Red Hat Marketplace. Built for modern architectures and workflows, the Coralogix platform produces real-time insights and trend analysis for logs, metrics, and security with no reliance on storage or indexing. Making it a perfect match for the Red Hat Marketplace. Request a Demo.
Observability is one of the most popular topics in technology at the moment, and that isn’t showing any sign of changing soon. Agentless log collection, automated analysis, and machine learning insights are all features and tools that organizations are investigating to optimize their systems’ observability. However, there is a new kid on the block that has been gaining traction at conferences and online: the Extended Berkeley Packet Filter, or eBPF. So, what is eBPF?
