As Elasticsearch users are pushing the limits of how much data they can store on an Elasticsearch node, they sometimes run out of heap memory before running out of disk space. This is a frustrating problem for these users, as fitting as much data per node as possible is often important to reduce costs. But why does Elasticsearch need heap memory to store data? Why doesn't it only need disk space?
This post was written by InfluxAce Nikki Attea A long time ago in a galaxy far, far away the phrase “May the Force be with you” originated to wish those good luck before parting ways, oftentimes before a journey or battle.
There’s no question that subscription-based businesses are an incredibly popular revenue model in today’s economy. While single transaction revenue models tend to fluctuate due to the seasonality of markets, subscription plans offer much more consistent and predictable revenues. Although the subscription revenue model can certainly be advantageous over one-off transactions, these businesses are also notoriously challenging to keep subscribers active on their plan.
As we connect with customers we increasingly hear the need for teams to be more predictive with their data. A big challenge is uncertainty around how to get started, especially when much of their data is unstructured. At Splunk, our goal is to make data — and machine learning — accessible for a broad range of users. The good news is, with machine learning doing even more work on your behalf, you don’t need to be a data scientist to use these advanced capabilities.
Even though most of us have spent this month working from home or furloughed due to COVID-19, glitches certainly haven’t been taking a break. We’re still relying on intricate software systems to keep the world moving, and we have little ability to maintain them in person. What’s more, changing usage patterns have stressed global networks in ways that are hard to anticipate. As a result, we find ourselves in a perfect storm for technical outages.
LogDNA is adding the ability to store and view raw lines, allowing customers to debug with logs in their unaltered form. If you’ve ever looked at your logs and noticed that the timestamp was different than what you expected, it is usually due to a long latency between the event and processing time of the logs. Depending on the size of the latency, we may update the log timestamp to order them accurately and to provide a cleaner and more intuitive search experience.
Amazon Web Services (AWS) products are countless, and at LogicMonitor, we are working tirelessly to bring monitoring support to as many of them as possible. With so many products and tools already on your plate, we want to make sure that monitoring is not a hassle, but rather a trusted companion. Here, we will focus on the analytics section of AWS and provide some tips on how to utilize the data collected from AWS Athena and Glue.
