I’d like to share some of the best practices we’ve learned on our journey to battle performance issues with the Jaeger tracing tool. Some may say we are experts in logging. We log for a living, and have our log analytics service (which we based on open source ELK Stack) to prove it. We’ve mastered logging to the level where debugging and troubleshooting our system is a no-brainer.
Elastic is prepping for Elasticsearch 8.0, but in the meantime is rolling out upgrades and features with Elasticsearch 7.7. The new version introduces asynchronous search as well as changes with Elasticsearch clusters, mapping, SQL enhancements, snapshots and machine learning. This post will cover just a few of the highlights of the new release. Besides asynchronous search, ES 7.7 also introduces multi-class classification, reduced heap usage, inference time features, and better password security.
Last week, the first OpenObservability conference took place. This event had amazing content contributions from open source project leaders, users, and influencers. We’ve seen massive growth and adoption in the open source observability space from the inspiring work being done across tracing, logging, and especially metrics. The new data stores and capabilities are growing at breakneck speed. There are more choices— yet more complexity—than ever before.
Current cloud investments have been a boost for flexibility and productivity. The challenge is that there is a single point of failure with these platforms, requiring diversity of providers to reduce dependencies. The future of cloud computing will see effects from the ambitions and expansion of cloud providers themselves, as well as potential competition. This is especially true with Amazon, Google, and to a lesser extent Microsoft.
These days, “SIEM” (Security Information and Event Management) is all over the place. SIEM tools work by collecting data from multiple systems and noticing patterns in the data. This adds immediate value to the business by providing insights, security recommendations, and actionable intelligence. Despite being helpful tools for many companies, SIEM tools do have their drawbacks. This article will describe the four main ones and offer suggestions for how they might be overcome.
The large volumes of logs, metrics, and traces generated by scaling cloud environments can be overwhelming, but they must be collected to identify and respond to production issues or other signals showing business or application issues. To collect, monitor, and analyze this data, many teams choose between open source or proprietary observability solutions.
Computing environments are constantly changing. Back when an on-premises server hosted your work, your infrastructure and applications were easy to track. Now that you’re developing in the cloud, things are more challenging. You’re learning that each team within your organization uses a different monitoring tool. At this point, you may be wondering if it’s time to build your own monitoring solution with open source tools at its core that everyone can use.
Elasticsearch and MongoDb are the two most popular distributed datastores used to manage NoSQL data. Both of these technologies are highly scalable and have document-oriented design at the core. There are differences between the two technologies, however, and it’s important to understand these differences in order to choose the right one for your use case. This blog post will examine the differences between these two technologies in a number of critical areas.
Considering the scale of log data that modern cloud environments generate, it’s oftentimes prohibitively expensive to index all of it. For monitoring and logging, cost management is just as important as in other parts of the business. Whether sudden spikes of log data overwhelm databases or good business generates more activity in your environment, teams should anticipate and mitigate the steep costs that result from high log volumes.
Out of the box monitoring solutions are getting more and more difficult to implement. In addition, they may not have the features you need, and they’re always becoming more expensive. It’s no wonder you’re trying to create a cost-effective solution via infrastructure monitoring cost management on your own by using a combination of your on-site development talent and a few open-source libraries.
