 Okay, so next up is Sherry with Metrics and Logs in a unified solution. Thank you. Hi everyone, my name is Sherry, I work at Red Hat on the open project. I would like to talk to you today about Metrics and Logs in an unified solution and I do it fast because I'm selecting the day on five minutes. So when we talk about Metrics, we usually talk about time series data and key value pairs, like CPU usage or manual usage. And when we talk about logs, we usually talk about semi-structured data that includes timestamp and the message itself, maybe a little bit of metadata like log level. So both Metrics and Logs are time based. When you search for a submission, we wanted a submission that you can aggregate all this data to a central location. And to be able to add additional metadata like tags and labels to the data before it is sent to the central location, so you can slice and dice the data. You've all heard, if you've heard the discussion before, they talked about it a lot. It's important, like where data comes from, which cluster, which environment. And for both Metrics and Logs, we want to be able to do actions like learning or visualization and analysis. So in open source, we have several components that relates to monitoring. First are the Collectors, the Collected Metrics and Logs from the environment. For Metrics, we have PCP, CollectD, and Prometheus. We also have Office Log, FluidD, Telegraph, and Logspatch. In visualization, like you've seen before, we have Rufana, Kibana, and Chronograph, very fast to be. And for Logs analysis, basically the most common is Kibana. For learning, we have Prometheus, Capacitor, Sentinel, and Elastalert for Elastic Search. So what we wanted to achieve in having both Metrics and Logs in one solution, less maintenance, less upgrades, less components that we need to support. This was very important for us. An additional value that we have when we combine Metrics and Logs in one solution is that you can correlate between them. So if you have an error in Logs, you want to understand what's going on, you can go and see the dashboard. In one dashboard, you can see that in that time, your disk is going that specific host. The disk can pinpoint your issues, read that. And when we examined the foreign stacks, Elastic Search, Prometheus, Graphite, and Influx, the main issue for us, the first point was whether it was fully open source. So we took out Influx before the equation. And we wanted to have Logs and Metrics, of course, support, a flexible schema, long-term storage, and be able to scale the monitoring solution for large environments. So for the open project, what we chose for the collectors, for the Metrics, we chose CollectD, FluidD for Logs, Kibana for, and Graphana for visualization, and Kibana for Logs analysis. For Logs, it is still to be decided. So what I wanted to take out of this presentation is that having both Metrics and Logs in one solution is less maintenance. A lot more, when you have one solution, you need to have less maintenance, less upgrades, as I said before, and you get a value of correlation. Thank you. If you want to see the Metrics, I can show you tomorrow. Thank you very much. Next up is Brian.