�ݺ�ߣshows by User: bobrik

�ݺ�ߣshows by User: bobrik / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: bobrik / Wed, 31 Oct 2018 22:31:17 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: bobrik LISA18: Hidden Linux Metrics with Prometheus eBPF Exporter /slideshow/lisa18-hidden-linux-metrics-with-prometheus-ebpf-exporter/121348825 lisa18hiddenlinuxmetricswithprometheusebpfexporter-181031223117
Presented at LISA18: https://www.usenix.org/conference/lisa18/presentation/huynh While there are plenty of readily available metrics for monitoring Linux kernel, many gems remain hidden. With the help of recent developments in eBPF, it is now possible to run safe programs in the kernel to collect arbitrary information with little to no overhead. A few examples include: * Disk latency and io size histograms * Run queue (scheduler) latency * Page cache efficiency * Directory cache efficiency * LLC (aka L3 cache) efficiency * Kernel timer counters * System-wide TCP retransmits Practically any event from "perf list" output and any kernel function can be traced, analyzed and turned into a Prometheus metric with almost arbitrary labels attached to it. If you are already familiar with BCC tools, you may think if ebpf_exporter as bcc tools turned into prometheus metrics. In this tutorial we’ll go over eBPF basics, how to write programs and get insights into a running system.]]>
Presented at LISA18: https://www.usenix.org/conference/lisa18/presentation/huynh While there are plenty of readily available metrics for monitoring Linux kernel, many gems remain hidden. With the help of recent developments in eBPF, it is now possible to run safe programs in the kernel to collect arbitrary information with little to no overhead. A few examples include: * Disk latency and io size histograms * Run queue (scheduler) latency * Page cache efficiency * Directory cache efficiency * LLC (aka L3 cache) efficiency * Kernel timer counters * System-wide TCP retransmits Practically any event from "perf list" output and any kernel function can be traced, analyzed and turned into a Prometheus metric with almost arbitrary labels attached to it. If you are already familiar with BCC tools, you may think if ebpf_exporter as bcc tools turned into prometheus metrics. In this tutorial we’ll go over eBPF basics, how to write programs and get insights into a running system.]]> Wed, 31 Oct 2018 22:31:17 GMT /slideshow/lisa18-hidden-linux-metrics-with-prometheus-ebpf-exporter/121348825 bobrik@slideshare.net(bobrik) LISA18: Hidden Linux Metrics with Prometheus eBPF Exporter bobrik Presented at LISA18: https://www.usenix.org/conference/lisa18/presentation/huynh While there are plenty of readily available metrics for monitoring Linux kernel, many gems remain hidden. With the help of recent developments in eBPF, it is now possible to run safe programs in the kernel to collect arbitrary information with little to no overhead. A few examples include: * Disk latency and io size histograms * Run queue (scheduler) latency * Page cache efficiency * Directory cache efficiency * LLC (aka L3 cache) efficiency * Kernel timer counters * System-wide TCP retransmits Practically any event from "perf list" output and any kernel function can be traced, analyzed and turned into a Prometheus metric with almost arbitrary labels attached to it. If you are already familiar with BCC tools, you may think if ebpf_exporter as bcc tools turned into prometheus metrics. In this tutorial we’ll go over eBPF basics, how to write programs and get insights into a running system. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lisa18hiddenlinuxmetricswithprometheusebpfexporter-181031223117-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Presented at LISA18: https://www.usenix.org/conference/lisa18/presentation/huynh While there are plenty of readily available metrics for monitoring Linux kernel, many gems remain hidden. With the help of recent developments in eBPF, it is now possible to run safe programs in the kernel to collect arbitrary information with little to no overhead. A few examples include: * Disk latency and io size histograms * Run queue (scheduler) latency * Page cache efficiency * Directory cache efficiency * LLC (aka L3 cache) efficiency * Kernel timer counters * System-wide TCP retransmits Practically any event from "perf list" output and any kernel function can be traced, analyzed and turned into a Prometheus metric with almost arbitrary labels attached to it. If you are already familiar with BCC tools, you may think if ebpf_exporter as bcc tools turned into prometheus metrics. In this tutorial we’ll go over eBPF basics, how to write programs and get insights into a running system.

LISA18: Hidden Linux Metrics with Prometheus eBPF Exporter from Ivan Babrou

]]> 919 2 https://cdn.slidesharecdn.com/ss_thumbnails/lisa18hiddenlinuxmetricswithprometheusebpfexporter-181031223117-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Debugging linux issues with eBPF /slideshow/debugging-linux-issues-with-ebpf/121231755 lisa18debugginglinuxissueswithebpf-181030194841
Presented at LISA18: https://www.usenix.org/conference/lisa18/presentation/babrou This is a technical dive into how we used eBPF to solve real-world issues uncovered during an innocent OS upgrade. We'll see how we debugged 10x CPU increase in Kafka after Debian upgrade and what lessons we learned. We'll get from high-level effects like increased CPU to flamegraphs showing us where the problem lies to tracing timers and functions calls in the Linux kernel. The focus is on tools what operational engineers can use to debug performance issues in production. This particular issue happened at Cloudflare on a Kafka cluster doing 100Gbps of ingress and many multiple of that egress.]]>
Presented at LISA18: https://www.usenix.org/conference/lisa18/presentation/babrou This is a technical dive into how we used eBPF to solve real-world issues uncovered during an innocent OS upgrade. We'll see how we debugged 10x CPU increase in Kafka after Debian upgrade and what lessons we learned. We'll get from high-level effects like increased CPU to flamegraphs showing us where the problem lies to tracing timers and functions calls in the Linux kernel. The focus is on tools what operational engineers can use to debug performance issues in production. This particular issue happened at Cloudflare on a Kafka cluster doing 100Gbps of ingress and many multiple of that egress.]]> Tue, 30 Oct 2018 19:48:41 GMT /slideshow/debugging-linux-issues-with-ebpf/121231755 bobrik@slideshare.net(bobrik) Debugging linux issues with eBPF bobrik Presented at LISA18: https://www.usenix.org/conference/lisa18/presentation/babrou This is a technical dive into how we used eBPF to solve real-world issues uncovered during an innocent OS upgrade. We'll see how we debugged 10x CPU increase in Kafka after Debian upgrade and what lessons we learned. We'll get from high-level effects like increased CPU to flamegraphs showing us where the problem lies to tracing timers and functions calls in the Linux kernel. The focus is on tools what operational engineers can use to debug performance issues in production. This particular issue happened at Cloudflare on a Kafka cluster doing 100Gbps of ingress and many multiple of that egress. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lisa18debugginglinuxissueswithebpf-181030194841-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> Presented at LISA18: https://www.usenix.org/conference/lisa18/presentation/babrou This is a technical dive into how we used eBPF to solve real-world issues uncovered during an innocent OS upgrade. We'll see how we debugged 10x CPU increase in Kafka after Debian upgrade and what lessons we learned. We'll get from high-level effects like increased CPU to flamegraphs showing us where the problem lies to tracing timers and functions calls in the Linux kernel. The focus is on tools what operational engineers can use to debug performance issues in production. This particular issue happened at Cloudflare on a Kafka cluster doing 100Gbps of ingress and many multiple of that egress.

Debugging linux issues with eBPF from Ivan Babrou

]]> 1774 3 https://cdn.slidesharecdn.com/ss_thumbnails/lisa18debugginglinuxissueswithebpf-181030194841-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 https://cdn.slidesharecdn.com/profile-photo-bobrik-48x48.jpg?cb=1533816677 bobrik.name https://cdn.slidesharecdn.com/ss_thumbnails/lisa18hiddenlinuxmetricswithprometheusebpfexporter-181031223117-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/lisa18-hidden-linux-metrics-with-prometheus-ebpf-exporter/121348825 LISA18: Hidden Linux M... https://cdn.slidesharecdn.com/ss_thumbnails/lisa18debugginglinuxissueswithebpf-181030194841-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/debugging-linux-issues-with-ebpf/121231755 Debugging linux issues...