Grafana Loki is a newly developed logs aggregation system that integrated very nicely with Grafana dashboard to link metrics with logs or just use logs as a separate panel. It is open-source and has a growing community.
Slides from #PromCon2018 Munich.
https://promcon.io/2018-munich/talks/thanos-prometheus-at-scale/
Bartłomiej Płotka
Fabian Reinartz
The Prometheus Monitoring system has been thriving for several years. Along with its powerful data model, operational simplicity and reliability have been a key factor in its success. However, some questions were still largely unaddressed to this day. How can we store historical data at the order of petabytes in a reliable and cost-efficient way? Can we do so without sacrificing responsive query times? And what about a global view of all our metrics and transparent handling of HA setups?
Thanos takes Prometheus' strong foundations and extends it into a clustered, yet coordination free, globally scalable metric system. It retains Prometheus's simple operational model and even simplifies deployments further. Under the hood, Thanos uses highly cost-efficient object storage that's available in virtually all environments today. By building directly on top of the storage format introduced with Prometheus 2.0, Thanos achieves near real-time responsiveness even for cold queries against historical data. All while having virtually no cost overhead beyond that of the underlying object storage.
We will show the theoretical concepts behind Thanos and demonstrate how it seamlessly integrates into existing Prometheus setups.
LinuxCon 2015 Linux Kernel Networking WalkthroughThomas Graf
This presentation features a walk through the Linux kernel networking stack for users and developers. It will cover insights into both, existing essential networking features and recent developments and will show how to use them properly. Our starting point is the network card driver as it feeds a packet into the stack. We will follow the packet as it traverses through various subsystems such as packet filtering, routing, protocol stacks, and the socket layer. We will pause here and there to look into concepts such as networking namespaces, segmentation offloading, TCP small queues, and low latency polling and will discuss how to configure them.
VictoriaLogs: Open Source Log Management System - PreviewVictoriaMetrics
VictoriaLogs Preview - Aliaksandr Valialkin
* Existing open source log management systems
- ELK (ElasticSearch) stack: Pros & Cons
- Grafana Loki: Pros & Cons
* What is VictoriaLogs
- Open source log management system from VictoriaMetrics
- Easy to setup and operate
- Scales vertically and horizontally
- Optimized for low resource usage (CPU, RAM, disk space)
- Accepts data from Logstash and Fluentbit in Elasticsearch format
- Accepts data from Promtail in Loki format
- Supports stream concept from Loki
- Provides easy to use yet powerful query language - LogsQL
* LogsQL Examples
- Search by time
- Full-text search
- Combining search queries
- Searching arbitrary labels
* Log Streams
- What is a log stream?
- LogsQL examples: querying log streams
- Stream labels vs log labels
* LogsQL: stats over access logs
* VictoriaLogs: CLI Integration
* VictoriaLogs Recap
Kubernetes Observability with Prometheus by ExampleThomas Riley
This talk was given at Cloud Native + Kubernetes Manchester, July 2019.
Prometheus is quickly becoming the de factor open-source monitoring and alerting tool for Kubernetes. Through a series of live demos I will explain how to deploy Prometheus into Kubernetes and make use of it for monitoring Kubernetes. I will also demonstrate how to successfully run Prometheus in HA with the Thanos project and how to store years worth of metrics without requiring heaps of CPU, memory and storage for Prometheus.
The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk gives an overview of the OpenTelemetry project and then outlines some production-proven architectures for improving the observability of your applications and systems.
This document discusses Open vSwitch (OVS) and how using Data Plane Development Kit (DPDK) can improve its performance. It notes that with standard OVS, there are many components between a virtual machine and physical networking that cause scalability and performance issues due to context switches. OVS-DPDK addresses this by using polling, hugepages, pinned CPUs, and userspace I/O to bypass the kernel and reduce overhead. The document shows that using DPDK can increase OVS throughput by over 8x and reduce latency by 30-37% compared to standard OVS.
Removing performance bottlenecks with Kafka Monitoring and topic configurationKnoldus Inc.
Apache Kafka is a distributed messaging system used to build real-time data pipelines & streaming applications. Since applications rely heavily on efficient data transfer, message passing platforms like Kafka cannot afford a breakdown or poor performance.
But how do we ensure that Kafka is running well and successfully streaming messages at low latency? This is where Kafka monitoring steps in.
Here’s the agenda of the webinar -
> Why Kafka monitoring?
> Top 10 Kafka metrics to focus on
> How to change Kafka topic configuration at runtime?
Slides from #PromCon2018 Munich.
https://promcon.io/2018-munich/talks/thanos-prometheus-at-scale/
Bartłomiej Płotka
Fabian Reinartz
The Prometheus Monitoring system has been thriving for several years. Along with its powerful data model, operational simplicity and reliability have been a key factor in its success. However, some questions were still largely unaddressed to this day. How can we store historical data at the order of petabytes in a reliable and cost-efficient way? Can we do so without sacrificing responsive query times? And what about a global view of all our metrics and transparent handling of HA setups?
Thanos takes Prometheus' strong foundations and extends it into a clustered, yet coordination free, globally scalable metric system. It retains Prometheus's simple operational model and even simplifies deployments further. Under the hood, Thanos uses highly cost-efficient object storage that's available in virtually all environments today. By building directly on top of the storage format introduced with Prometheus 2.0, Thanos achieves near real-time responsiveness even for cold queries against historical data. All while having virtually no cost overhead beyond that of the underlying object storage.
We will show the theoretical concepts behind Thanos and demonstrate how it seamlessly integrates into existing Prometheus setups.
LinuxCon 2015 Linux Kernel Networking WalkthroughThomas Graf
This presentation features a walk through the Linux kernel networking stack for users and developers. It will cover insights into both, existing essential networking features and recent developments and will show how to use them properly. Our starting point is the network card driver as it feeds a packet into the stack. We will follow the packet as it traverses through various subsystems such as packet filtering, routing, protocol stacks, and the socket layer. We will pause here and there to look into concepts such as networking namespaces, segmentation offloading, TCP small queues, and low latency polling and will discuss how to configure them.
VictoriaLogs: Open Source Log Management System - PreviewVictoriaMetrics
VictoriaLogs Preview - Aliaksandr Valialkin
* Existing open source log management systems
- ELK (ElasticSearch) stack: Pros & Cons
- Grafana Loki: Pros & Cons
* What is VictoriaLogs
- Open source log management system from VictoriaMetrics
- Easy to setup and operate
- Scales vertically and horizontally
- Optimized for low resource usage (CPU, RAM, disk space)
- Accepts data from Logstash and Fluentbit in Elasticsearch format
- Accepts data from Promtail in Loki format
- Supports stream concept from Loki
- Provides easy to use yet powerful query language - LogsQL
* LogsQL Examples
- Search by time
- Full-text search
- Combining search queries
- Searching arbitrary labels
* Log Streams
- What is a log stream?
- LogsQL examples: querying log streams
- Stream labels vs log labels
* LogsQL: stats over access logs
* VictoriaLogs: CLI Integration
* VictoriaLogs Recap
Kubernetes Observability with Prometheus by ExampleThomas Riley
This talk was given at Cloud Native + Kubernetes Manchester, July 2019.
Prometheus is quickly becoming the de factor open-source monitoring and alerting tool for Kubernetes. Through a series of live demos I will explain how to deploy Prometheus into Kubernetes and make use of it for monitoring Kubernetes. I will also demonstrate how to successfully run Prometheus in HA with the Thanos project and how to store years worth of metrics without requiring heaps of CPU, memory and storage for Prometheus.
The monolith to cloud-native, microservices evolution has driven a shift from monitoring to observability. OpenTelemetry, a merger of the OpenTracing and OpenCensus projects, is enabling Observability 2.0. This talk gives an overview of the OpenTelemetry project and then outlines some production-proven architectures for improving the observability of your applications and systems.
This document discusses Open vSwitch (OVS) and how using Data Plane Development Kit (DPDK) can improve its performance. It notes that with standard OVS, there are many components between a virtual machine and physical networking that cause scalability and performance issues due to context switches. OVS-DPDK addresses this by using polling, hugepages, pinned CPUs, and userspace I/O to bypass the kernel and reduce overhead. The document shows that using DPDK can increase OVS throughput by over 8x and reduce latency by 30-37% compared to standard OVS.
Removing performance bottlenecks with Kafka Monitoring and topic configurationKnoldus Inc.
Apache Kafka is a distributed messaging system used to build real-time data pipelines & streaming applications. Since applications rely heavily on efficient data transfer, message passing platforms like Kafka cannot afford a breakdown or poor performance.
But how do we ensure that Kafka is running well and successfully streaming messages at low latency? This is where Kafka monitoring steps in.
Here’s the agenda of the webinar -
> Why Kafka monitoring?
> Top 10 Kafka metrics to focus on
> How to change Kafka topic configuration at runtime?
Video: https://www.youtube.com/watch?v=JRFNIKUROPE . Talk for linux.conf.au 2017 (LCA2017) by Brendan Gregg, about Linux enhanced BPF (eBPF). Abstract:
A world of new capabilities is emerging for the Linux 4.x series, thanks to enhancements that have been included in Linux for to Berkeley Packet Filter (BPF): an in-kernel virtual machine that can execute user space-defined programs. It is finding uses for security auditing and enforcement, enhancing networking (including eXpress Data Path), and performance observability and troubleshooting. Many new open source tools that have been written in the past 12 months for performance analysis that use BPF. Tracing superpowers have finally arrived for Linux!
For its use with tracing, BPF provides the programmable capabilities to the existing tracing frameworks: kprobes, uprobes, and tracepoints. In particular, BPF allows timestamps to be recorded and compared from custom events, allowing latency to be studied in many new places: kernel and application internals. It also allows data to be efficiently summarized in-kernel, including as histograms. This has allowed dozens of new observability tools to be developed so far, including measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more.
This talk will summarize BPF capabilities and use cases so far, and then focus on its use to enhance Linux tracing, especially with the open source bcc collection. bcc includes BPF versions of old classics, and many new tools, including execsnoop, opensnoop, funcccount, ext4slower, and more (many of which I developed). Perhaps you'd like to develop new tools, or use the existing tools to find performance wins large and small, especially when instrumenting areas that previously had zero visibility. I'll also summarize how we intend to use these new capabilities to enhance systems analysis at Netflix.
How to build a Kubernetes networking solution from scratchAll Things Open
Presented by: Antonin Bas & Jianjun Shen, VMware
Presented at All Things Open 2020
Abstract: For the non-initiated, Kubernetes (K8s) networking can be a bit like dark magic. Many clusters have requirements beyond what the default network plugin, kubenet, can provide and require the use of a third-party Container Network Interface (CNI) plugin. But what exactly is the role of these plugins, how do they differ from each other and how does the choice of one affect your cluster?
In this talk, Antonin and Jianjun will describe how a group of developers was able to build a CNI plugin - an open source project called Antrea - from scratch and bring it to production in a matter of months. This velocity was achieved by leveraging existing open-source technologies extensively: Open vSwitch, a well-established programmable virtual switch for the data plane, and the K8s libraries for the control plane. Antonin and Jianjun will explain the responsibilities of a CNI plugin in the context of K8s and will walk the audience through the steps required to create one. They will show how Antrea integrates with the rest of the cloud-native ecosystem (e.g. dashboards such as Octant and Prometheus) to provide insight into the network and ensure that K8s networking is not just dark magic anymore.
This document provides an overview of Vector Packet Processing (VPP), an open source packet processing platform developed as part of the FD.io project. VPP is based on DPDK for high performance packet processing in userspace. It includes a full networking stack and can perform L2/L3 forwarding and routing at speeds of over 14 million packets per second on a single core. VPP processing is divided into individual nodes connected by a graph. Packets are passed between nodes as vectors to support batch processing. VPP supports both single and multicore modes using different threading models. It can be used to implement routers, switches, and other network functions and topologies.
Prometheus is an open-source monitoring system that collects metrics from instrumented systems and applications and allows for querying and alerting on metrics over time. It is designed to be simple to operate, scalable, and provides a powerful query language and multidimensional data model. Key features include no external dependencies, metrics collection by scraping endpoints, time-series storage, and alerting handled by the AlertManager with support for various integrations.
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerDatabricks
Kubernetes is the most popular container orchestration system that is natively designed for Cloud. At Lyft and Cloudera, we have both emerged the next-generation, cloud-native infrastructure based on Kubernetes, which supports various distributed workloads.
Juraci Paixão Kröhling - All you need to know about OpenTelemetryJuliano Costa
OpenTelemetry is one of the newest projects in the realm of Observability at the CNCF and is already the second most active project there. In this session, Juraci Paixão Kröhling will talk about the different subprojects and how to get started using them. Even if you heard about OpenTelemetry before, you'll leave this session with a better understanding of what this is all about, the several faces of OpenTelemetry, and what you can do to make your projects more observable.
What can you do with the prometheus-specific feature of relabeling? Look how you can change, add, remove metrics, config, and label within Prometheus with this talk I have given at PromCon Munich.
FOSDEM15 SDN developer room talk
DPDK performance
How to not just do a demo with DPDK
The Intel DPDK provides a platform for building high performance Network Function Virtualization applications. But it is hard to get high performance unless certain design tradeoffs are made. This talk focuses on the lessons learned in creating the Brocade vRouter using DPDK. It covers some of the architecture, locking and low level issues that all have to be dealt with to achieve 80 Million packets per second forwarding.
Helm helps you manage Kubernetes applications — Helm Charts help you define, install, and upgrade even the most complex Kubernetes application.
https://thinkcloudly.com/
Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...HostedbyConfluent
The document discusses bringing Apache Kafka clusters into production without using ZooKeeper for coordination and metadata storage. It describes how Kafka uses ZooKeeper currently and the problems with this approach. It then introduces KRaft, which replaces ZooKeeper by using Raft consensus to replicate cluster metadata within Kafka. The key aspects of deploying, operating and troubleshooting KRaft-based Kafka clusters are covered, including formatting storage, controller setup, rolling upgrades, and examining the replicated metadata log.
Grafana Mimir and VictoriaMetrics_ Performance Tests.pptxRomanKhavronenko
VictoriaMetrics and Grafana Mimir are time series databases with support of mostly the same protocols and APIs. However, they have different architectures and components, which makes the comparison more complicated. In the talk, we'll go through the details of the benchmark where I compared both solutions. We'll see how VictoriaMetrics and Mimir are dealing with identical workloads and how efficient they’re with using the allocated resources.
The talk will cover design and architectural details, weak and strong points, trade-offs, and maintenance complexity of both solutions.
[KubeConEU] Building images efficiently and securely on Kubernetes with BuildKitAkihiro Suda
https://sched.co/MPX5
BuildKit is a modern container image builder that focuses on efficiency and security, mostly known as the backend of Docker 18.06+ and Jessie Frazelle's `img`. (But it is even useful as a standalone tool!)
In this talk, Akihiro Suda, one of founding maintainers of BuildKit, shows practical tips for running BuildKit on Kubernetes clusters.
Performance Troubleshooting Using Apache Spark MetricsDatabricks
Luca Canali, a data engineer at CERN, presented on performance troubleshooting using Apache Spark metrics at the UnifiedDataAnalytics #SparkAISummit. CERN runs large Hadoop and Spark clusters to process over 300 PB of data from the Large Hadron Collider experiments. Luca discussed how to gather, analyze, and visualize Spark metrics to identify bottlenecks and improve performance.
The document discusses Ext4 journaling and the write barrier feature. It notes that the write barrier forces a flush-to-disk call after writing the journal to ensure consistency. However, this can cause sluggishness when storage is full during OTA updates. Disabling the write barrier allows reordering of cache-to-disk writes, reducing latency and improving performance, though it introduces a small risk of filesystem corruption in the event of a power failure. Tests showed disabling the barrier reduced fsync latency and improved SQLite transactions per second on HDD and EMMC storage.
Kubernetes Networking with Cilium - Deep DiveMichal Rostecki
Cilium is open source software for providing and transparently securing network connectivity and load balancing between application workloads such as application containers or processes. Cilium operates at Layer 3/4 to provide traditional networking and security services as well as Layer 7 to protect and secure use of modern application protocols such as HTTP, gRPC and Kafka. The foundation of Cilium is the new Linux kernel technology BPF which supports the dynamic insertion of BPF bytecode into the Linux kernel at various integration points. This presentation reveals the secrets of Kubernetes networking and gives you a deep dive into Cilium and why it is awesome!
Presentation by Lorenzo Mangani of QXIP at the October 26 SF Bay Area ClickHouse meetup
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup
https://qxip.net/
Cilium - Bringing the BPF Revolution to Kubernetes Networking and SecurityThomas Graf
BPF is one of the fastest emerging technologies of the Linux kernel. The talk provides an introduction to Cilium which brings the powers of BPF to Kubernetes and other orchestration systems to provide highly scalable and efficient networking, security and load balancing for containers and microservices. The talk will provide an introduction to the capabilities of Cilium today but also deep dives into the emerging roadmap involving networking at the socket layer and service mesh datapath capabilities to provide highly efficient connectivity between cloud native apps and sidecar proxies.
Performance Wins with BPF: Getting StartedBrendan Gregg
Keynote by Brendan Gregg for the eBPF summit, 2020. How to get started finding performance wins using the BPF (eBPF) technology. This short talk covers the quickest and easiest way to find performance wins using BPF observability tools on Linux.
PromQL Deep Dive - The Prometheus Query Language Weaveworks
- What is PromQL
- PromQL operators
- PromQL functions
- Hands on: Building queries in PromQL
- Hands on: Visualizing PromQL in Grafana
- Prometheus alerts in PromQL
- Hands on: Creating an alert in Prometheus with PromQL
Getting started with Loki on Google Kubernetes Engine (GKE) involves deploying Loki, a log aggregation system, onto GKE cluster. Loki allows to collect, store, and query logs efficiently. The process includes configuring Loki to scrape logs from your GKE applications, integrating it with Grafana for visualization, and using PromQL to query logs effectively. This setup enhances your ability to monitor, troubleshoot, and gain insights into your applications' performance on GKE.
Video: https://www.youtube.com/watch?v=JRFNIKUROPE . Talk for linux.conf.au 2017 (LCA2017) by Brendan Gregg, about Linux enhanced BPF (eBPF). Abstract:
A world of new capabilities is emerging for the Linux 4.x series, thanks to enhancements that have been included in Linux for to Berkeley Packet Filter (BPF): an in-kernel virtual machine that can execute user space-defined programs. It is finding uses for security auditing and enforcement, enhancing networking (including eXpress Data Path), and performance observability and troubleshooting. Many new open source tools that have been written in the past 12 months for performance analysis that use BPF. Tracing superpowers have finally arrived for Linux!
For its use with tracing, BPF provides the programmable capabilities to the existing tracing frameworks: kprobes, uprobes, and tracepoints. In particular, BPF allows timestamps to be recorded and compared from custom events, allowing latency to be studied in many new places: kernel and application internals. It also allows data to be efficiently summarized in-kernel, including as histograms. This has allowed dozens of new observability tools to be developed so far, including measuring latency distributions for file system I/O and run queue latency, printing details of storage device I/O and TCP retransmits, investigating blocked stack traces and memory leaks, and a whole lot more.
This talk will summarize BPF capabilities and use cases so far, and then focus on its use to enhance Linux tracing, especially with the open source bcc collection. bcc includes BPF versions of old classics, and many new tools, including execsnoop, opensnoop, funcccount, ext4slower, and more (many of which I developed). Perhaps you'd like to develop new tools, or use the existing tools to find performance wins large and small, especially when instrumenting areas that previously had zero visibility. I'll also summarize how we intend to use these new capabilities to enhance systems analysis at Netflix.
How to build a Kubernetes networking solution from scratchAll Things Open
Presented by: Antonin Bas & Jianjun Shen, VMware
Presented at All Things Open 2020
Abstract: For the non-initiated, Kubernetes (K8s) networking can be a bit like dark magic. Many clusters have requirements beyond what the default network plugin, kubenet, can provide and require the use of a third-party Container Network Interface (CNI) plugin. But what exactly is the role of these plugins, how do they differ from each other and how does the choice of one affect your cluster?
In this talk, Antonin and Jianjun will describe how a group of developers was able to build a CNI plugin - an open source project called Antrea - from scratch and bring it to production in a matter of months. This velocity was achieved by leveraging existing open-source technologies extensively: Open vSwitch, a well-established programmable virtual switch for the data plane, and the K8s libraries for the control plane. Antonin and Jianjun will explain the responsibilities of a CNI plugin in the context of K8s and will walk the audience through the steps required to create one. They will show how Antrea integrates with the rest of the cloud-native ecosystem (e.g. dashboards such as Octant and Prometheus) to provide insight into the network and ensure that K8s networking is not just dark magic anymore.
This document provides an overview of Vector Packet Processing (VPP), an open source packet processing platform developed as part of the FD.io project. VPP is based on DPDK for high performance packet processing in userspace. It includes a full networking stack and can perform L2/L3 forwarding and routing at speeds of over 14 million packets per second on a single core. VPP processing is divided into individual nodes connected by a graph. Packets are passed between nodes as vectors to support batch processing. VPP supports both single and multicore modes using different threading models. It can be used to implement routers, switches, and other network functions and topologies.
Prometheus is an open-source monitoring system that collects metrics from instrumented systems and applications and allows for querying and alerting on metrics over time. It is designed to be simple to operate, scalable, and provides a powerful query language and multidimensional data model. Key features include no external dependencies, metrics collection by scraping endpoints, time-series storage, and alerting handled by the AlertManager with support for various integrations.
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerDatabricks
Kubernetes is the most popular container orchestration system that is natively designed for Cloud. At Lyft and Cloudera, we have both emerged the next-generation, cloud-native infrastructure based on Kubernetes, which supports various distributed workloads.
Juraci Paixão Kröhling - All you need to know about OpenTelemetryJuliano Costa
OpenTelemetry is one of the newest projects in the realm of Observability at the CNCF and is already the second most active project there. In this session, Juraci Paixão Kröhling will talk about the different subprojects and how to get started using them. Even if you heard about OpenTelemetry before, you'll leave this session with a better understanding of what this is all about, the several faces of OpenTelemetry, and what you can do to make your projects more observable.
What can you do with the prometheus-specific feature of relabeling? Look how you can change, add, remove metrics, config, and label within Prometheus with this talk I have given at PromCon Munich.
FOSDEM15 SDN developer room talk
DPDK performance
How to not just do a demo with DPDK
The Intel DPDK provides a platform for building high performance Network Function Virtualization applications. But it is hard to get high performance unless certain design tradeoffs are made. This talk focuses on the lessons learned in creating the Brocade vRouter using DPDK. It covers some of the architecture, locking and low level issues that all have to be dealt with to achieve 80 Million packets per second forwarding.
Helm helps you manage Kubernetes applications — Helm Charts help you define, install, and upgrade even the most complex Kubernetes application.
https://thinkcloudly.com/
Bringing Kafka Without Zookeeper Into Production with Colin McCabe | Kafka Su...HostedbyConfluent
The document discusses bringing Apache Kafka clusters into production without using ZooKeeper for coordination and metadata storage. It describes how Kafka uses ZooKeeper currently and the problems with this approach. It then introduces KRaft, which replaces ZooKeeper by using Raft consensus to replicate cluster metadata within Kafka. The key aspects of deploying, operating and troubleshooting KRaft-based Kafka clusters are covered, including formatting storage, controller setup, rolling upgrades, and examining the replicated metadata log.
Grafana Mimir and VictoriaMetrics_ Performance Tests.pptxRomanKhavronenko
VictoriaMetrics and Grafana Mimir are time series databases with support of mostly the same protocols and APIs. However, they have different architectures and components, which makes the comparison more complicated. In the talk, we'll go through the details of the benchmark where I compared both solutions. We'll see how VictoriaMetrics and Mimir are dealing with identical workloads and how efficient they’re with using the allocated resources.
The talk will cover design and architectural details, weak and strong points, trade-offs, and maintenance complexity of both solutions.
[KubeConEU] Building images efficiently and securely on Kubernetes with BuildKitAkihiro Suda
https://sched.co/MPX5
BuildKit is a modern container image builder that focuses on efficiency and security, mostly known as the backend of Docker 18.06+ and Jessie Frazelle's `img`. (But it is even useful as a standalone tool!)
In this talk, Akihiro Suda, one of founding maintainers of BuildKit, shows practical tips for running BuildKit on Kubernetes clusters.
Performance Troubleshooting Using Apache Spark MetricsDatabricks
Luca Canali, a data engineer at CERN, presented on performance troubleshooting using Apache Spark metrics at the UnifiedDataAnalytics #SparkAISummit. CERN runs large Hadoop and Spark clusters to process over 300 PB of data from the Large Hadron Collider experiments. Luca discussed how to gather, analyze, and visualize Spark metrics to identify bottlenecks and improve performance.
The document discusses Ext4 journaling and the write barrier feature. It notes that the write barrier forces a flush-to-disk call after writing the journal to ensure consistency. However, this can cause sluggishness when storage is full during OTA updates. Disabling the write barrier allows reordering of cache-to-disk writes, reducing latency and improving performance, though it introduces a small risk of filesystem corruption in the event of a power failure. Tests showed disabling the barrier reduced fsync latency and improved SQLite transactions per second on HDD and EMMC storage.
Kubernetes Networking with Cilium - Deep DiveMichal Rostecki
Cilium is open source software for providing and transparently securing network connectivity and load balancing between application workloads such as application containers or processes. Cilium operates at Layer 3/4 to provide traditional networking and security services as well as Layer 7 to protect and secure use of modern application protocols such as HTTP, gRPC and Kafka. The foundation of Cilium is the new Linux kernel technology BPF which supports the dynamic insertion of BPF bytecode into the Linux kernel at various integration points. This presentation reveals the secrets of Kubernetes networking and gives you a deep dive into Cilium and why it is awesome!
Presentation by Lorenzo Mangani of QXIP at the October 26 SF Bay Area ClickHouse meetup
https://www.meetup.com/San-Francisco-Bay-Area-ClickHouse-Meetup
https://qxip.net/
Cilium - Bringing the BPF Revolution to Kubernetes Networking and SecurityThomas Graf
BPF is one of the fastest emerging technologies of the Linux kernel. The talk provides an introduction to Cilium which brings the powers of BPF to Kubernetes and other orchestration systems to provide highly scalable and efficient networking, security and load balancing for containers and microservices. The talk will provide an introduction to the capabilities of Cilium today but also deep dives into the emerging roadmap involving networking at the socket layer and service mesh datapath capabilities to provide highly efficient connectivity between cloud native apps and sidecar proxies.
Performance Wins with BPF: Getting StartedBrendan Gregg
Keynote by Brendan Gregg for the eBPF summit, 2020. How to get started finding performance wins using the BPF (eBPF) technology. This short talk covers the quickest and easiest way to find performance wins using BPF observability tools on Linux.
PromQL Deep Dive - The Prometheus Query Language Weaveworks
- What is PromQL
- PromQL operators
- PromQL functions
- Hands on: Building queries in PromQL
- Hands on: Visualizing PromQL in Grafana
- Prometheus alerts in PromQL
- Hands on: Creating an alert in Prometheus with PromQL
Getting started with Loki on Google Kubernetes Engine (GKE) involves deploying Loki, a log aggregation system, onto GKE cluster. Loki allows to collect, store, and query logs efficiently. The process includes configuring Loki to scrape logs from your GKE applications, integrating it with Grafana for visualization, and using PromQL to query logs effectively. This setup enhances your ability to monitor, troubleshoot, and gain insights into your applications' performance on GKE.
Centralized Logging System Using ELK StackRohit Sharma
Centralized Logging System using ELK Stack
The document discusses setting up a centralized logging system (CLS) using the ELK stack. The ELK stack consists of Logstash to capture and filter logs, Elasticsearch to index and store logs, and Kibana to visualize logs. Logstash agents on each server ship logs to Logstash, which filters and sends logs to Elasticsearch for indexing. Kibana queries Elasticsearch and presents logs through interactive dashboards. A CLS provides benefits like log analysis, auditing, compliance, and a single point of control. The ELK stack is an open-source solution that is scalable, customizable, and integrates with other tools.
Strimzi - Where Apache Kafka meets OpenShift - OpenShift Spain MeetUpJosé Román Martín Gil
Apache Kafka is the most used data streaming broker by companies. It could manage millions of messages easily and it is the base of many architectures based in events, micro-services, orchestration, ... and now cloud environments. OpenShift is the most extended Platform as a Service (PaaS). It is based in Kubernetes and it helps the companies to deploy easily any kind of workload in a cloud environment. Thanks many of its features it is the base for many architectures based in stateless applications to build new Cloud Native Applications. Strimzi is an open source community that implements a set of Kubernetes Operators to help you to manage and deploy Apache Kafka brokers in OpenShift environments.
These slides will introduce you Strimzi as a new component on OpenShift to manage your Apache Kafka clusters.
Slides used at OpenShift Meetup Spain:
- https://www.meetup.com/es-ES/openshift_spain/events/261284764/
Centralization of all log (application, docker, security, ...)Thierry Gayet
The ELK Stack, which consists of Elasticsearch, Logstash, and Kibana, is an open-source tool commonly used for log analysis and visualization. Elasticsearch stores and indexes log data, Logstash collects and processes logs, and Kibana provides visualization and querying of logs stored in Elasticsearch. Fluentd is an open-source log collector that collects, processes, and forwards logs to storage systems. It supports plugins to collect data from various sources and can process and transform data in real-time using filters before forwarding to outputs like Elasticsearch.
Initial presentation of swift (for montreal user group)Marcos García
Swift is an open source object storage system that provides scalable storage and retrieval of any amount of unstructured data over HTTP. It is designed to be scalable, reliable, and inexpensive for storing large amounts of unstructured data. Some key uses of Swift include storing backups, web content like images, and large scientific data objects. Swift uses a ring architecture to distribute and replicate data across multiple servers for high availability.
Distributed Logging Architecture in the Container EraGlenn Davis
Presentation given at LinuxCon Japan 2016 by Satoshi "Moris" Tagomori (@tagomoris), Treasure Data. Describes various strategies for aggregating log data in a microservices architecture using containers, e.g. Docker.
Distributed Logging Architecture in Container EraSATOSHI TAGOMORI
Distributed Logging Architecture in Container Era
The document discusses distributed logging architecture in the container era. It covers: 1) The difficulties of logging with microservices and containers due to their ephemeral and distributed nature, 2) The need to redesign logging to push logs from containers to destinations quickly without fixed addresses or mappings; 3) Common patterns for distributed logging architectures including source aggregation, destination aggregation, and scaling; and 4) A case study using Docker and Fluentd to implement source aggregation and scaling for logging. Open source solutions are important to keep the logging layer transparent, interoperable, and able to scale independently of applications and infrastructure.
Building a Unified Logging Layer with Fluentd, Elasticsearch and KibanaMushfekur Rahman
This document discusses building a unified logging layer using Fluentd, Elasticsearch, and Kibana. It describes what a unified logging layer is and why it is needed to collect, format, filter, and forward logs from multiple sources to storage. It then provides overviews of Fluentd for log collection, Elasticsearch for storage and querying, and Kibana for real-time visualization. Details are given on the architectures, plugins, and configurations of each tool and how they can work together in a scalable and highly available logging system.
Scalable crawling with Kafka, scrapy and spark - November 2021Max Lapan
This document describes the architecture of a scalable crawling system used by Scoutbee to crawl thousands of company websites weekly. It uses Kafka to store and transfer crawled data for high throughput and low latency. Scrapy is used for distributed crawling scaled at the domain level. Spark is used for data processing pipelines to decrease data latency. Crawled data is stored long-term in S3 for efficient retrieval and reuse in machine learning pipelines. The system fulfills the contradictory requirements of fast, inexpensive crawling at scale.
The document provides an introduction to the ELK stack for log analysis and visualization. It discusses why large data tools are needed for network traffic and log analysis. It then describes the components of the ELK stack - Elasticsearch for storage and search, Logstash for data collection and parsing, and Kibana for visualization. Several use cases are presented, including how Cisco and Yale use the ELK stack for security monitoring and analyzing biomedical research data.
S. Bartoli & F. Pompermaier – A Semantic Big Data CompanionFlink Forward
This document introduces Okkam, an Italian company that uses Apache Flink for large-scale data integration and semantic technologies. It discusses Okkam's use of Flink for domain reasoning, RDF data processing, duplicate detection, entity linkage, and telemetry analysis. The document also provides lessons learned from Okkam's Flink experiences and suggestions for improving Flink.
Grafana Loki (Monitoring Tool) PresentationKnoldus Inc.
Loki — Loki is responsible for storing our logs. It indexes only the labels and metadata of each message, enabling efficient filtering and searching. Loki collects and provides log data. Grafana (or another visualization system) — This component is used to interact with Loki. Grafana queries Loki to perform filtering and select the desired results from the logs. These data can then be visualized as charts or other graphics.
This document summarizes a presentation about log forwarding at scale. It discusses how logging works internally and requires understanding the logging pipeline of parsing, filtering, buffering and routing logs. It then introduces Fluent Bit as a lightweight log forwarder that can be used to cheaply forward logs from edge nodes to log aggregators in a scalable way, especially in cloud native environments like Kubernetes. Hands-on demos show how Fluent Bit can parse and add metadata to Kubernetes logs.
This presentation was first held at the OpenSQL Camp 2009, part of the FrOSCon conference in St. Augustin, Germany. It gives a nice overview over the project, technology and how it will progress. Find more information at http://www.blackray.org
How To Download and Process SEC XBRL Data Directly from EDGARAlexander Falk
This document discusses downloading XBRL data directly from the SEC's EDGAR database, organizing the downloaded files, processing and validating the XBRL filings, and extracting useful financial information and ratios from the filings. It describes accessing SEC RSS feeds to download ZIP files containing XBRL exhibits filed since 2005, parsing the RSS feeds to get the ZIP file names, and organizing the downloaded files by date, CIK number, and ticker for easy access. It also demonstrates using the RaptorXML validation engine to validate batches of filings and extract financial ratios using a Python script passed to RaptorXML's built-in Python interpreter.
- The document discusses logging for containers using Fluentd, an open source data collector. It describes how Fluentd can provide a unified logging layer, reliably forwarding and aggregating logs from multiple containers and applications in a pluggable way.
- Key points covered include using Fluentd with the new Docker logging drivers to directly collect logs from containers, avoiding performance penalties from other approaches. A demo of Fluentd is also mentioned.
Improving Logging Ingestion Quality At Pinterest: Fighting Data Corruption An...HostedbyConfluent
This document summarizes Heng Zhang's presentation on improving logging ingestion quality at Pinterest. It discusses how Pinterest ingests large volumes of logging data at scale through a pipeline that favors scalability over consistency. This can lead to data corruption and loss issues. The presentation proposes a logging auditing framework to address these problems. It would add CRC checksums, audit headers and events at various stages to detect corrupted messages, track data loss metrics, and process audit events to remove bad data and provide alerts. The framework was tested and rolled out across Pinterest's ingestion pipelines with no downtime, improving data quality.
Similar to Linking Metrics to Logs using Loki (20)
Managing State & HTTP Requests In Ionic.Knoldus Inc.
Ionic is a complete open-source SDK for hybrid mobile app development created by Max Lynch, Ben Sperry, and Adam Bradley of Drifty Co. in 2013.The original version was released in 2013 and built on top of AngularJS and Apache Cordova. However, the latest release was re-built as a set of Web Components using StencilJS, allowing the user to choose any user interface framework, such as Angular, React or Vue.js. It also allows the use of Ionic components with no user interface framework at all.[4] Ionic provides tools and services for developing hybrid mobile, desktop, and progressive web apps based on modern web development technologies and practices, using Web technologies like CSS, HTML5, and Sass. In particular, mobile apps can be built with these Web technologies and then distributed through native app stores to be installed on devices by utilizing Cordova or Capacitor.
Facilitation Skills - When to Use and Why.pptxKnoldus Inc.
In this session, we will discuss the world of Agile methodologies and how facilitation plays a crucial role in optimizing collaboration, communication, and productivity within Scrum teams. We'll dive into the key facets of effective facilitation and how it can transform sprint planning, daily stand-ups, sprint reviews, and retrospectives. The participants will gain valuable insights into the art of choosing the right facilitation techniques for specific scenarios, aligning with Agile values and principles. We'll explore the "why" behind each technique, emphasizing the importance of adaptability and responsiveness in the ever-evolving Agile landscape. Overall, this session will help participants better understand the significance of facilitation in Agile and how it can enhance the team's productivity and communication.
Performance Testing at Scale Techniques for High-Volume ServicesKnoldus Inc.
Delve into advanced techniques for conducting performance testing at scale, aiming to simulate high-volume services and fortify applications against heavy loads. Uncover strategic approaches to optimize test scenarios, ensuring thorough evaluation and robustness in the face of increased demand. Explore methodologies that go beyond conventional testing practices, addressing the complexities associated with large-scale performance evaluations.
Snowflake and its features (Presentation)Knoldus Inc.
In this session, we will explore the groundbreaking features that make Snowflake a leader in cloud-based data warehousing, transforming the way organizations manage and analyze data. We will also explore Snowflake's multi-cluster, shared data architecture that enables simultaneous data access by multiple compute clusters, enabling efficient and parallelized data processing. We will explore Snowflake's various capabilities like its zero-copy cloning feature, Security and governance are paramount in Snowflake, with features such as encryption, multi-factor authentication, and granular access controls. Snowflake's global data replication ensures data availability and resilience by allowing replication across different regions. Lastly, we will also take a look at Snowflake's integrations with popular business intelligence tools and analytics solutions that streamline workflows, making it easy for organizations to incorporate Snowflake into their existing processes.
Terratest - Automation testing of infrastructureKnoldus Inc.
TerraTest is a testing framework specifically designed for testing infrastructure code written with HashiCorp's Terraform. It helps validate that your Terraform configurations create the desired infrastructure, and it can be used for both unit testing and integration testing.
Getting Started with Apache Spark (Scala)Knoldus Inc.
In this session, we are going to cover Apache Spark, the architecture of Apache Spark, Data Lineage, Direct Acyclic Graph(DAG), and many more concepts. Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Secure practices with dot net services.pptxKnoldus Inc.
Securing .NET services is paramount for protecting applications and data. Employing encryption, strong authentication, and adherence to best coding practices ensures resilience against potential threats, enhancing overall cybersecurity posture.
Distributed Cache with dot microservicesKnoldus Inc.
A distributed cache is a cache shared by multiple app servers, typically maintained as an external service to the app servers that access it. A distributed cache can improve the performance and scalability of an ASP.NET Core app, especially when the app is hosted by a cloud service or a server farm. Here we will look into implementation of Distributed Caching Strategy with Redis in Microservices Architecture focusing on cache synchronization, eviction policies, and cache consistency.
Introduction to gRPC Presentation (Java)Knoldus Inc.
gRPC, which stands for Remote Procedure Call, is an open-source framework developed by Google. It is designed for building efficient and scalable distributed systems. gRPC enables communication between client and server applications by defining a set of services and message types using Protocol Buffers (protobuf) as the interface definition language. gRPC provides a way for applications to call methods on a remote server as if they were local procedures, making it a powerful tool for building distributed and microservices-based architectures.
Using InfluxDB for real-time monitoring in JmeterKnoldus Inc.
Explore the integration of InfluxDB with JMeter for real-time performance monitoring. This session will cover setting up InfluxDB to capture JMeter metrics, configuring JMeter to send data to InfluxDB, and visualizing the results using Grafana. Learn how to leverage this powerful combination to gain real-time insights into your application's performance, enabling proactive issue detection and faster resolution.
Intoduction to KubeVela Presentation (DevOps)Knoldus Inc.
KubeVela is an open-source platform for modern application delivery and operation on Kubernetes. It is designed to simplify the deployment and management of applications in a Kubernetes environment. KubeVela is a modern software delivery platform that makes deploying and operating applications across today's hybrid, multi-cloud environments easier, faster and more reliable. KubeVela is infrastructure agnostic, programmable, yet most importantly, application-centric. It allows you to build powerful software, and deliver them anywhere!
Stakeholder Management (Project Management) PresentationKnoldus Inc.
A stakeholder is someone who has an interest in or who is affected by your project and its outcome. This may include both internal and external entities such as the members of the project team, project sponsors, executives, customers, suppliers, partners and the government. Stakeholder management is the process of managing the expectations and the requirements of these stakeholders.
Introduction To Kaniko (DevOps) PresentationKnoldus Inc.
Kaniko is an open-source tool developed by Google that enables building container images from a Dockerfile inside a Kubernetes cluster without requiring a Docker daemon. Kaniko executes each command in the Dockerfile in the user space using an executor image, which runs inside a container, such as a Kubernetes pod. This allows building container images in environments where the user doesn’t have root access, like a Kubernetes cluster.
Efficient Test Environments with Infrastructure as Code (IaC)Knoldus Inc.
In the rapidly evolving landscape of software development, the need for efficient and scalable test environments has become more critical than ever. This session, "Streamlining Development: Unlocking Efficiency through Infrastructure as Code (IaC) in Test Environments," is designed to provide an in-depth exploration of how leveraging IaC can revolutionize your testing processes and enhance overall development productivity.
Exploring Terramate DevOps (Presentation)Knoldus Inc.
Terramate is a code generator and orchestrator for Terraform that enhances Terraform's capabilities by adding features such as code generation, stacks, orchestration, change detection, globals, and more . It's primarily designed to help manage Terraform code at scale more efficiently . Terramate is particularly useful for managing multiple Terraform stacks, providing support for change detection and code generation 2. It allows you to create relationships between stacks to improve your understanding and control over your infrastructure . One of the key features of Terramate is its ability to detect changes at both the stack and module level. This capability allows you to identify which stacks and resources have been altered and selectively determine where you should execute commands.
Clean Code in Test Automation Differentiating Between the Good and the BadKnoldus Inc.
This session focuses on the principles of writing clean, maintainable, and efficient code in the context of test automation. The session will highlight the characteristics that distinguish good test automation code from bad, ultimately leading to more reliable and scalable testing frameworks.
Integrating AI Capabilities in Test AutomationKnoldus Inc.
Explore the integration of artificial intelligence in test automation. Understand how AI can enhance test planning, execution, and analysis, leading to more efficient and reliable testing processes. Explore the cutting-edge integration of Artificial Intelligence (AI) capabilities in Test Automation, a transformative approach shaping the future of software testing. This session will delve into practical applications, benefits, and considerations associated with infusing AI into test automation workflows.
State Management with NGXS in Angular.pptxKnoldus Inc.
NGXS is a state management pattern and library for Angular. NGXS acts as a single source of truth for your application's state - providing simple rules for predictable state mutations. In this session we will go through the main for components of NGXS -Store, Actions, State, and Select.
Authentication in Svelte using cookies.pptxKnoldus Inc.
Svelte streamlines authentication with cookies, offering a secure and seamless user experience. Effortlessly manage sessions by storing tokens in cookies, ensuring persistent logins. With Svelte's simplicity, implement robust authentication mechanisms, enhancing user security and interaction.
OAuth2 Implementation Presentation (Java)Knoldus Inc.
The OAuth 2.0 authorization framework is a protocol that allows a user to grant a third-party web site or application access to the user's protected resources, without necessarily revealing their long-term credentials or even their identity. It is commonly used in scenarios such as user authentication in web and mobile applications and enables a more secure and user-friendly authorization process.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
2. 01 What is Log Aggregation?
02 Overview of Loki
03
04
Installation Options
05
Comparisons with Existing Solutions
Our Agenda
06
Available Clients for Loki
07
What is Promtail?
Demo
3. What is Log Aggregation?
Log aggregation is the practice of gathering up disparate log files for
the purposes of organizing the data in them and making them
searchable.
4. c
Overview of
Loki?
Grafana Loki is a set of
components that can be
composed into a fully featured
logging stack.
1. Unlike other logging systems,
Loki is built around the idea of only
indexing metadata about your
logs: labels (just like Prometheus
labels).
2. Log data itself is then
compressed and stored in chunks
in object stores such as S3 or GCS,
or even locally on the filesystem.
5. Loki Overview: Motivation
○ Incident Response and Context Switching
○ Resolving Problems in Existing Solutions
○ Cost Efficiency
○ Kubernetes and Docker
6. Loki Overview: Features
● Multi-Tenancy:
○ Data between tenants is completely separated
○ Achieved through a tenant ID (which is represented as an alphanumeric string)
○ When disabled, all requests are internally given a tenant ID of "fake"
● Modes of Operation:
○ Loki is optimized for both running locally (or at small scale) and for scaling horizontally
○ Loki comes with a single process mode that runs all of the required microservices in one process
○ The microservices of Loki can be broken out into separate processes, allowing them to scale independently of each other
7. Loki Overview: Architecture
Components in Loki
● Distributor
○ Handle incoming streams by clients
● Ingester
○ Write log data to long-term storage backends (DynamoDB, S3, Cassandra, etc.)
○ Return log data for in-memory queries on the read path
● Query Frontend
○ Optional service providing the querier's API endpoints
○ Used to accelerate the read path
● Querier
○ Handles queries using the LogQL query language
○ Fetch logs both from the ingesters and long-term storage
8. Loki Overview: Architecture
To summarize, the read path works as follows:
1. The querier receives an HTTP/1 request for data.
2. The querier passes the query to all ingesters for in-memory data.
3. The ingesters receive the read request and return data matching the query, if any.
4. The querier lazily loads data from the backing store and runs the query against it if no ingesters returned data.
5.
6. The querier iterates over all received data and deduplicates, returning a final set of data over the HTTP/1
connection.
And the the flow for the write path is as follows:
1. The distributor receives an HTTP/1 request to store data for streams.
2. Each stream is hashed using the hash ring.
3. The distributor sends each stream to the appropriate ingesters and their replicas (based on the configured
replication factor).
4. Each ingester will create a chunk or append to an existing chunk for the stream's data. A chunk is unique per
tenant and per labelset.
5. The distributor responds with a success code over the HTTP/1 connection.
10. Installation Options
1. Tanka (A reimplementation of Ksonnet that Grafana Labs created after Ksonnet was deprecated)
2. Helm (Loki Helm chart in its repository:
https://github.com/grafana/loki/tree/master/production/helm/loki)
3. Docker: Loki can be installed using both Docker and Docker Compose
4. Using Binaries: Every release includes binaries for Loki which can be found on the
Releases page. We can also build Loki binaries by creating them manually from by
cloning its repositories.
11. Comparison with Elastic Stack
Loki Promtail Grafana Elastic Stack Datadog
Data is stored in a cloud storage system
such as S3, GCS, or Cassandra as well as
on-disk
Data stored on-disk as JSON objects Data stored on-disk
Indexes metadata of logs Indexes the whole logs Indexes metadata of logs
Available on premise Available on premise Not available on premise
Open Source Open Source Flexible Pricing
Visualization Tool: Grafana Visualization Tool: Kibana Visualization Tool: Datadog Dashboards
12. Available Clients for Loki
● Promtail:
○ Client of choice when you're running Kubernetes
○ Configure it to automatically scrape logs from pods running on the same node that it runs on
● Docker Driver:
○ Automatically adds labels appropriate to the running container
● Fluent Bit & Fluentd:
○ Ideal when you already have Fluentd deployed and you already have configured Parser and Filter plugins
There are three unofficial clients present as well: promtail-client(Go), push-to-loki.py(Python) and
Serilog-Sinks-Loki(C#)
13. What is Promtail?
Promtail is an agent which ships the contents of local logs to a private or cloud Loki instance. It
is usually deployed to every machine that has applications needed to be monitored.
It primarily:
1. Discovers targets
2. Attaches labels to log streams
3. Pushes them to the Loki instance.
Currently, Promtail can tail logs from two sources: local log files and the systemd journal (on
AMD64 machines only).
14. OUR CHARTInsert Your Subtitle Here
Reference
● https://github.com/grafana/loki/tree/master/docs
● https://docs.google.com/document/d/11tjK_lvp1-SVsFZjgOTr1vV3-q
6vBAsZYIQ5ZeYBkyM/view