Breaking Prometheus (Promcon Berlin '16)
•
6 likes•8,344 views
This document discusses strategies for scaling Prometheus monitoring to millions of machines. It describes the stages of scaling from manual Prometheus configuration to per-region Prometheus instances. Issues with input/output and retention led to further scaling techniques like sharding, using Kafka as a message bus, downsampling metrics, and developing an agent and query API. The talk introduces "Vulcan" which implements Prometheus APIs with Cassandra storage and Kafka input to enable monitoring at large scale.
Report
Share
Report
Share
Download to read offline
Recommended
Monitoring_with_Prometheus_Grafana_Tutorial
This document provides an overview of setting up monitoring for MySQL and MongoDB servers using Prometheus and Grafana. It discusses installing and configuring Prometheus, Grafana, exporters for collecting metrics from MySQL, MongoDB and systems, and dashboards for visualizing the metrics in Grafana. The tutorial hands-on sets up Prometheus and Grafana in two virtual machines to monitor a MySQL master-slave replication setup and MongoDB cluster.
Distributed scheduler hell (MicroXChg 2017 Berlin)
Containers are the future for all microservice based apps. Where do you deploy them? How do you manage them? At Digital Ocean we went through growing pains of trying out 5 of the top major Docker container schedulers, Mesos, Kubernetes, Docker Swarm, Nomad and we even tried manual scheduling of containers. Let us walk you through how we chose different schedulers for different applications, and tips and tricks for choosing a scheduler to use.
Monitoring Kafka w/ Prometheus
This document discusses monitoring Apache Kafka clusters and applications with Prometheus. It provides an overview of the architecture used, including deploying Prometheus servers, Kafka and HBase exporters, and a JSON exporter for YARN applications. Specific exporters are discussed for Kafka brokers using JMX, Kafka clients using the Prometheus Java library, and exposing application metrics via HTTP. Important Prometheus configurations and query functions are also covered. The summary highlights the key components of the monitoring architecture and some of the exporters and techniques discussed.
Prometheus Is Good for Your Small Startup - ShuttleCloud Corp. - 2016
In this talk, we'll explain our journey from having near-zero monitoring to having all of our infrastructure monitored with the necessary metrics and alerts.
We will share with the audience some of the mistakes we did and what lessons we have learned. We currently have around 200 instances monitored with a comfortable cost-effective in-house monitoring stack based on Prometheus.
We want to demonstrate that you don't need to have a big fleet to embrace Prometheus and that it is a non-expensive solution for monitoring.
----------
ShuttleCloud is a small startup specialized in email and contacts migrations. We developed a reliable migration platform in high availability used by clients like Gmail, GContacts and Comcast.
For example, Gmail alone has imported data for 3 million users with our API and we process hundreds of terabytes every month.
-------------
Follow us on Twitter:
@ShuttleCloud: https://twitter.com/ShuttleCloud
@ShuttleCloudEng: https://twitter.com/ShuttleCloudEng
ShuttleCloud.com
Prometheus – a next-gen Monitoring System
Prometheus a modern monitoring system and time series database. It is based on a multi-dimensional data model and pull based ingestion.
Monitoring with Prometheus
This document provides an overview of using Prometheus for monitoring and alerting. It discusses using Node Exporters and other exporters to collect metrics, storing metrics in Prometheus, querying metrics using PromQL, and configuring alert rules and the Alertmanager for notifications. Key aspects covered include scraping configs, common exporters, data types and selectors in PromQL, operations and functions, and setting up alerts and the Alertmanager for routing alerts.
Monitoring a Kubernetes-backed microservice architecture with Prometheus
As many startups of the last decade, SoundCloud’s architecture started as a Ruby-on-Rails monolith, which later had to be broken into microservices to cope with the growing size and complexity of the site. The microservices initially ran on an in-house container management and deployment platform. Recently, the company has started to migrate to Kubernetes.
With the introduction of microservices, the existing conventional monitoring setup failed both conceptually and in terms of scalability. Thus, starting in 2012, SoundCloud invested heavily into the development of the open-source monitoring system Prometheus, which was designed for large-scale highly dynamic service-oriented architectures.
Migrating to Kubernetes, it became apparent that Prometheus and Kubernetes are a match made in open-source heaven. The talk will demonstrate the current Prometheus setup at SoundCloud, monitoring a large-scale Kubernetes cluster.
Monitoring infrastructure with prometheus
This document discusses using Prometheus to monitor infrastructure. It describes using Prometheus to monitor over 600 servers handling high traffic loads. Prometheus scrapes metrics from exporters, services, and custom sources. It stores metrics in Prometheus and visualizes them using Grafana dashboards. The document provides examples of configuring Prometheus to scrape metrics from Google Compute Engine, Kubernetes, Hystrix, and custom sources using the Prometheus client library. It also discusses using Alertmanager for alerting.
Recommended
Monitoring_with_Prometheus_Grafana_Tutorial
This document provides an overview of setting up monitoring for MySQL and MongoDB servers using Prometheus and Grafana. It discusses installing and configuring Prometheus, Grafana, exporters for collecting metrics from MySQL, MongoDB and systems, and dashboards for visualizing the metrics in Grafana. The tutorial hands-on sets up Prometheus and Grafana in two virtual machines to monitor a MySQL master-slave replication setup and MongoDB cluster.
Distributed scheduler hell (MicroXChg 2017 Berlin)
Containers are the future for all microservice based apps. Where do you deploy them? How do you manage them? At Digital Ocean we went through growing pains of trying out 5 of the top major Docker container schedulers, Mesos, Kubernetes, Docker Swarm, Nomad and we even tried manual scheduling of containers. Let us walk you through how we chose different schedulers for different applications, and tips and tricks for choosing a scheduler to use.
Monitoring Kafka w/ Prometheus
This document discusses monitoring Apache Kafka clusters and applications with Prometheus. It provides an overview of the architecture used, including deploying Prometheus servers, Kafka and HBase exporters, and a JSON exporter for YARN applications. Specific exporters are discussed for Kafka brokers using JMX, Kafka clients using the Prometheus Java library, and exposing application metrics via HTTP. Important Prometheus configurations and query functions are also covered. The summary highlights the key components of the monitoring architecture and some of the exporters and techniques discussed.
Prometheus Is Good for Your Small Startup - ShuttleCloud Corp. - 2016
In this talk, we'll explain our journey from having near-zero monitoring to having all of our infrastructure monitored with the necessary metrics and alerts.
We will share with the audience some of the mistakes we did and what lessons we have learned. We currently have around 200 instances monitored with a comfortable cost-effective in-house monitoring stack based on Prometheus.
We want to demonstrate that you don't need to have a big fleet to embrace Prometheus and that it is a non-expensive solution for monitoring.
----------
ShuttleCloud is a small startup specialized in email and contacts migrations. We developed a reliable migration platform in high availability used by clients like Gmail, GContacts and Comcast.
For example, Gmail alone has imported data for 3 million users with our API and we process hundreds of terabytes every month.
-------------
Follow us on Twitter:
@ShuttleCloud: https://twitter.com/ShuttleCloud
@ShuttleCloudEng: https://twitter.com/ShuttleCloudEng
ShuttleCloud.com
Prometheus – a next-gen Monitoring System
Prometheus a modern monitoring system and time series database. It is based on a multi-dimensional data model and pull based ingestion.
Monitoring with Prometheus
This document provides an overview of using Prometheus for monitoring and alerting. It discusses using Node Exporters and other exporters to collect metrics, storing metrics in Prometheus, querying metrics using PromQL, and configuring alert rules and the Alertmanager for notifications. Key aspects covered include scraping configs, common exporters, data types and selectors in PromQL, operations and functions, and setting up alerts and the Alertmanager for routing alerts.
Monitoring a Kubernetes-backed microservice architecture with Prometheus
As many startups of the last decade, SoundCloud’s architecture started as a Ruby-on-Rails monolith, which later had to be broken into microservices to cope with the growing size and complexity of the site. The microservices initially ran on an in-house container management and deployment platform. Recently, the company has started to migrate to Kubernetes.
With the introduction of microservices, the existing conventional monitoring setup failed both conceptually and in terms of scalability. Thus, starting in 2012, SoundCloud invested heavily into the development of the open-source monitoring system Prometheus, which was designed for large-scale highly dynamic service-oriented architectures.
Migrating to Kubernetes, it became apparent that Prometheus and Kubernetes are a match made in open-source heaven. The talk will demonstrate the current Prometheus setup at SoundCloud, monitoring a large-scale Kubernetes cluster.
Monitoring infrastructure with prometheus
This document discusses using Prometheus to monitor infrastructure. It describes using Prometheus to monitor over 600 servers handling high traffic loads. Prometheus scrapes metrics from exporters, services, and custom sources. It stores metrics in Prometheus and visualizes them using Grafana dashboards. The document provides examples of configuring Prometheus to scrape metrics from Google Compute Engine, Kubernetes, Hystrix, and custom sources using the Prometheus client library. It also discusses using Alertmanager for alerting.
Kafka monitoring and metrics
Kafka monitoring and metrics
With Docker, Grafana, Prometheus, JMX and JConsole
By Touraj Ebrahimi
Senior Java Developer and Java Architect
github: toraj58
bitbucket: toraj58
twitter: @toraj58
https://www.youtube.com/channel/UCcLcw6sTk_8G6EgfBr0E5uA
Your data is in Prometheus, now what? (CurrencyFair Engineering Meetup, 2016)
Prometheus is a next-generation monitoring system with a time series database at it's core. Once you have a time series database, what do you do with it though? This talk will look at getting data in, and more importantly how to use the data you collect productively.
Contact us at prometheus@robustperception.io
Infrastructure & System Monitoring using Prometheus
The document introduces infrastructure and system monitoring using Prometheus. It discusses the importance of monitoring, common things to monitor like services, applications, and OS metrics. It provides an overview of Prometheus including its main components and data format. The document demonstrates setting up Prometheus, adding host metrics using Node Exporter, configuring Grafana, monitoring Docker containers using cAdvisor, configuring alerting in Prometheus and Alertmanager, instrumenting application code, and integrating Consul for service discovery. Live code demos are provided for key concepts.
Monitoring using Prometheus and Grafana
This is a talk on how you can monitor your microservices architecture using Prometheus and Grafana. This has easy to execute steps to get a local monitoring stack running on your local machine using docker.
Monitoring Kubernetes with Prometheus
Kubernetes provides a powerful framework and great tooling to control hundreds of heterogenous workloads on thousands of machines. In a production environment, however, the collection of metrics to automatically detect and act on issues in such a cluster is essential. Prometheus was created to meet such needs: highly dynamic scheduling, automatic service discovery, and reliable operations.
Prometheus (Microsoft, 2016)
A talk given to engineers in Microsoft on using Prometheus for monitoring, and how blackbox and whitebox monitoring are best used.
Service Discovery in Prometheus
This document discusses service discovery options in Prometheus. It describes setting up infrastructure with Prometheus, Grafana, Consul and Spring Boot applications. It then covers different service discovery mechanisms like static configuration, DNS, and Consul. DNS and Consul configurations are demonstrated, with Consul used to register microservices that Prometheus then discovers and scrapes. The document aims to showcase service discovery capabilities and options in Prometheus.
An Introduction to Prometheus (GrafanaCon 2016)
Often what you monitor and get alerted on is defined by your tools, rather than what makes the most sense to you and your organisation. Alerts on metrics such as CPU usage which are noisy and rarely spot real problems, while outages go undetected. Monitoring systems can also be challenging to maintain, and overall provide a poor return on investment.
In the past few years several new monitoring systems have appeared with more powerful semantics and which are easier to run, which offer a way to vastly improve how your organisation operates and prepare you for a Cloud Native environment. Prometheus is one such system. This talk will look at the monitoring ideal and how whitebox monitoring with a time series database, multi-dimensional labels and a powerful querying/alerting language can free you from midnight pages.
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Prometheus is a next-generation monitoring system. Since being publicly announced last year it has seen wide-spread interest and adoption. This talk will look at the concepts behind monitoring with Prometheus, and how to use it with Kubernetes which has direct support for Prometheus.
What is your application doing right now? An introduction to Prometheus
Slides from my talk at the "DevOps and the search for the Holy Grail" meetup in Stockholm on May 28, 2105
A short introduction on application monitoring & metrics with Prometheus, the monitoring system and time-series database.
The use of Prometheus is illustrated with examples in Docker and Java. We'll use Grafana as a metrics dashboard on top of Prometheus and create a simple integration with Slack for alarm triggering.
Meetup: http://www.meetup.com/DevOps-Stockholm/events/222471316/
Recording: https://youtu.be/Z0LlilNpX1U
Server monitoring using grafana and prometheus
This blog defines how grafana and Prometheus be used in monitoring the basic system performance at its core level.
PostgreSQL Terminology
In the “Sharing is caring” spirit, we came up with a series of internal talks called, By Showmaxers, for Showmaxers, and we recently started making them public. There are already talks about Networks, and Android app building, available.
Our latest talk focuses on PostgreSQL Terminology, and is led by Angus Dippenaar. He worked on Showmax projects from South Africa, and moved to work with us in Prague, Czech Republic.
The talk was meant to fill some holes in our knowledge of PostgreSQL. So, it guides you through the basic PostgreSQL terminology you need to understand when reading the official documentation and blogs.
You may learn what all these POstgreSQL terms mean:
Command, query, local or global object, non-schema local objects, relation, tablespace, database, database cluster, instance and its processes like postmaster or backend; session, connection, heap, file segment, table, TOAST, tuple, view, materialized (view), transaction, commit, rollback, index, write-ahead log, WAL record, WAL file, checkpoint, Multi-version concurrency control (MVCC), dead tuples (dead rows), or transaction exhaustion.
The terminology is followed by a demonstration of transaction exhaustion.
Get the complete explanation and see the demonstration of the transaction exhaustion and of tuple freezing in the talk on YouTube: https://youtu.be/E-RkI3Ws7gM.
Getting Started Monitoring with Prometheus and Grafana
Introduction to network monitoring with open source solution prometheus as monitoring engine and grafana as dashboard to visualization.
Prometheus with Grafana - AddWeb Solution
Prometheus is an open source monitoring tool that collects metrics from monitored applications and stores them for querying and alerting. Grafana is used to visualize the metrics collected by Prometheus through customizable dashboards. It connects Prometheus as a data source and fires queries to retrieve and display time-series data as graphs and visualizations. This allows monitoring of server status, resources, containers, probes, and other metrics to more easily track system performance and issues over time.
Kafka on ZFS: Better Living Through Filesystems
(Hugh O'Brien, Jet.com) Kafka Summit SF 2018
You’re doing disk IO wrong, let ZFS show you the way. ZFS on Linux is now stable. Say goodbye to JBOD, to directories in your reassignment plans, to unevenly used disks. Instead, have 8K Cloud IOPS for $25, SSD speed reads on spinning disks, in-kernel LZ4 compression and the smartest page cache on the planet. (Fear compactions no more!)
Learn how Jet’s Kafka clusters squeeze every drop of disk performance out of Azure, all completely transparent to Kafka.
-Striping cheap disks to maximize instance IOPS
-Block compression to reduce disk usage by ~80% (JSON data)
-Instance SSD as the secondary read cache (storing compressed data), eliminating >99% of disk reads and safe across host redeployments
-Upcoming features: Compressed blocks in memory, potentially quadrupling your page cache (RAM) for free
We’ll cover:
-Basic Principles
-Adapting ZFS for cloud instances (gotchas)
-Performance tuning for Kafka
-Benchmarks
Streaming huge databases using logical decoding
This talk was delivered on FOSDEM PGDay 2016, on 29th of January. It discusses the options to stream consistent snapshot of the data existing in a database prior to creating a logical replication slot.
Chronix as Long-Term Storage for Prometheus
Cloud Native Con 2016, Seattle: Talk by Moritz Kammerer (@phxql, Senior Software Engineer at QAware).
Abstract: Prometheus is great when it comes to monitoring and alerting. But the long term storage opportunities are comparatively weak compared to related time series databases (missing data distribution, sharding etc.). At this point Chronix enters the stage. Chronix is an open source time series database. It focuses on an efficient long term storage both in terms of storage volume and access times. Chronix achieves a compression rate of 98% compared to data in CSV files while an average query took 21 milliseconds, determined in a benchmark asking 96 queries for different time ranges and time series. Chronix offers a multi-dimensional generic data model for storing all kinds of time series, functions for anomaly detection used in the frameworks EGADS and SAX, and an integration with Apache Spark allows for distributed time series processing. In this code-intense session we show the integration of Prometheus and Chronix. We also dig into the details of Chronix and explain why Chronix <3 Prometheus and vice versa. Furthermore we demonstrate a toolchain: collect data with Prometheus, pipe them to Chronix, visualize both data sources in Grafana [5], and easily analyze tons of data with Spark and Apache Zeppelin.
Prometheus - Intro, CNCF, TSDB,PromQL,Grafana
https://www.youtube.com/playlist?list=PLAiEy9H6ItrKC5PbH7KiELiSEIKv3tuov
-What is Prometheus?
-Difference Between Nagios vs Prometheus
-Architecture
-Alertmanager
-Time series DB
-PromQL (Prometheus Query Language)
-Live Demo
-Grafana
Using eBPF to Measure the k8s Cluster Health
As a k8s cluster-admin your app teams have a certain expectation of your cluster to be available to deploy services at any time without problems. While there is no shortage on metrics in k8s its important to have the right metrics to alert on issues and giving you enough data to react to potential availability issues. Prometheus has become a standard and sheds light on the inner behaviour of Kubernetes clusters and workloads. Lots of KPIs (CPU, IO, network. Etc) in our On-Premise environment are less precise when we start to work in a Cloud environment. Ebpf is the perfect technology that fulfills that requirement as it gives us information down to the kernel level. In 2018 Cloudflare shared an opensource project to expose custom ebpf metrics in Prometheus. Join this session and learn about: • What is ebpf? • What type of metrics we can collect? • How to expose those metrics in a K8s environment. This session will try to deliver a step-by-step guide on how to take advantage of the ebpf exporter.
Kafka Summit SF 2017 - One Day, One Data Hub, 100 Billion Messages: Kafka at ...
LINE is a messaging service with 200+ million active users. I will introduce why we feed 100+ billion daily messages into Kafka and how various systems such as data sync, abuse detection and analysis are depending on and leveraging it. It will be also introduced how we leverage dynamic tracing tools like SystemTap to inspect broker’s performance on production system, which led me to fix KAFKA-4614.
Presented by Yuto Kawamura, LINE Corporation
Systems Track
John adams talk cloudy
My presentation at Microsoft's Mountain View Conference Center during the "Talk Cloudy to me" day.
Thanks to Sebastian and Scalr for a great day.
[OpenInfra Days Korea 2018] Day 2 - E6 - OpenInfra monitoring with Prometheus
[OpenInfra Days Korea 2018] Day 2 - E6 - OpenInfra monitoring with PrometheusOpenStack Korea Community
This document discusses using Prometheus for open infrastructure and cloud monitoring. It introduces Prometheus as a time series database and monitoring tool. Key features covered include metrics collection, service discovery, graphing, and alerting. The architecture of Prometheus is explained, including scrapping metrics directly or via exporters. A demo of Prometheus and Grafana is proposed to monitor Kubernetes clusters and visualize CPU usage. Alerting configuration and routes in Prometheus and Alertmanager are also summarized.More Related Content
What's hot
Kafka monitoring and metrics
Kafka monitoring and metrics
With Docker, Grafana, Prometheus, JMX and JConsole
By Touraj Ebrahimi
Senior Java Developer and Java Architect
github: toraj58
bitbucket: toraj58
twitter: @toraj58
https://www.youtube.com/channel/UCcLcw6sTk_8G6EgfBr0E5uA
Your data is in Prometheus, now what? (CurrencyFair Engineering Meetup, 2016)
Prometheus is a next-generation monitoring system with a time series database at it's core. Once you have a time series database, what do you do with it though? This talk will look at getting data in, and more importantly how to use the data you collect productively.
Contact us at prometheus@robustperception.io
Infrastructure & System Monitoring using Prometheus
The document introduces infrastructure and system monitoring using Prometheus. It discusses the importance of monitoring, common things to monitor like services, applications, and OS metrics. It provides an overview of Prometheus including its main components and data format. The document demonstrates setting up Prometheus, adding host metrics using Node Exporter, configuring Grafana, monitoring Docker containers using cAdvisor, configuring alerting in Prometheus and Alertmanager, instrumenting application code, and integrating Consul for service discovery. Live code demos are provided for key concepts.
Monitoring using Prometheus and Grafana
This is a talk on how you can monitor your microservices architecture using Prometheus and Grafana. This has easy to execute steps to get a local monitoring stack running on your local machine using docker.
Monitoring Kubernetes with Prometheus
Kubernetes provides a powerful framework and great tooling to control hundreds of heterogenous workloads on thousands of machines. In a production environment, however, the collection of metrics to automatically detect and act on issues in such a cluster is essential. Prometheus was created to meet such needs: highly dynamic scheduling, automatic service discovery, and reliable operations.
Prometheus (Microsoft, 2016)
A talk given to engineers in Microsoft on using Prometheus for monitoring, and how blackbox and whitebox monitoring are best used.
Service Discovery in Prometheus
This document discusses service discovery options in Prometheus. It describes setting up infrastructure with Prometheus, Grafana, Consul and Spring Boot applications. It then covers different service discovery mechanisms like static configuration, DNS, and Consul. DNS and Consul configurations are demonstrated, with Consul used to register microservices that Prometheus then discovers and scrapes. The document aims to showcase service discovery capabilities and options in Prometheus.
An Introduction to Prometheus (GrafanaCon 2016)
Often what you monitor and get alerted on is defined by your tools, rather than what makes the most sense to you and your organisation. Alerts on metrics such as CPU usage which are noisy and rarely spot real problems, while outages go undetected. Monitoring systems can also be challenging to maintain, and overall provide a poor return on investment.
In the past few years several new monitoring systems have appeared with more powerful semantics and which are easier to run, which offer a way to vastly improve how your organisation operates and prepare you for a Cloud Native environment. Prometheus is one such system. This talk will look at the monitoring ideal and how whitebox monitoring with a time series database, multi-dimensional labels and a powerful querying/alerting language can free you from midnight pages.
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Prometheus is a next-generation monitoring system. Since being publicly announced last year it has seen wide-spread interest and adoption. This talk will look at the concepts behind monitoring with Prometheus, and how to use it with Kubernetes which has direct support for Prometheus.
What is your application doing right now? An introduction to Prometheus
Slides from my talk at the "DevOps and the search for the Holy Grail" meetup in Stockholm on May 28, 2105
A short introduction on application monitoring & metrics with Prometheus, the monitoring system and time-series database.
The use of Prometheus is illustrated with examples in Docker and Java. We'll use Grafana as a metrics dashboard on top of Prometheus and create a simple integration with Slack for alarm triggering.
Meetup: http://www.meetup.com/DevOps-Stockholm/events/222471316/
Recording: https://youtu.be/Z0LlilNpX1U
Server monitoring using grafana and prometheus
This blog defines how grafana and Prometheus be used in monitoring the basic system performance at its core level.
PostgreSQL Terminology
In the “Sharing is caring” spirit, we came up with a series of internal talks called, By Showmaxers, for Showmaxers, and we recently started making them public. There are already talks about Networks, and Android app building, available.
Our latest talk focuses on PostgreSQL Terminology, and is led by Angus Dippenaar. He worked on Showmax projects from South Africa, and moved to work with us in Prague, Czech Republic.
The talk was meant to fill some holes in our knowledge of PostgreSQL. So, it guides you through the basic PostgreSQL terminology you need to understand when reading the official documentation and blogs.
You may learn what all these POstgreSQL terms mean:
Command, query, local or global object, non-schema local objects, relation, tablespace, database, database cluster, instance and its processes like postmaster or backend; session, connection, heap, file segment, table, TOAST, tuple, view, materialized (view), transaction, commit, rollback, index, write-ahead log, WAL record, WAL file, checkpoint, Multi-version concurrency control (MVCC), dead tuples (dead rows), or transaction exhaustion.
The terminology is followed by a demonstration of transaction exhaustion.
Get the complete explanation and see the demonstration of the transaction exhaustion and of tuple freezing in the talk on YouTube: https://youtu.be/E-RkI3Ws7gM.
Getting Started Monitoring with Prometheus and Grafana
Introduction to network monitoring with open source solution prometheus as monitoring engine and grafana as dashboard to visualization.
Prometheus with Grafana - AddWeb Solution
Prometheus is an open source monitoring tool that collects metrics from monitored applications and stores them for querying and alerting. Grafana is used to visualize the metrics collected by Prometheus through customizable dashboards. It connects Prometheus as a data source and fires queries to retrieve and display time-series data as graphs and visualizations. This allows monitoring of server status, resources, containers, probes, and other metrics to more easily track system performance and issues over time.
Kafka on ZFS: Better Living Through Filesystems
(Hugh O'Brien, Jet.com) Kafka Summit SF 2018
You’re doing disk IO wrong, let ZFS show you the way. ZFS on Linux is now stable. Say goodbye to JBOD, to directories in your reassignment plans, to unevenly used disks. Instead, have 8K Cloud IOPS for $25, SSD speed reads on spinning disks, in-kernel LZ4 compression and the smartest page cache on the planet. (Fear compactions no more!)
Learn how Jet’s Kafka clusters squeeze every drop of disk performance out of Azure, all completely transparent to Kafka.
-Striping cheap disks to maximize instance IOPS
-Block compression to reduce disk usage by ~80% (JSON data)
-Instance SSD as the secondary read cache (storing compressed data), eliminating >99% of disk reads and safe across host redeployments
-Upcoming features: Compressed blocks in memory, potentially quadrupling your page cache (RAM) for free
We’ll cover:
-Basic Principles
-Adapting ZFS for cloud instances (gotchas)
-Performance tuning for Kafka
-Benchmarks
Streaming huge databases using logical decoding
This talk was delivered on FOSDEM PGDay 2016, on 29th of January. It discusses the options to stream consistent snapshot of the data existing in a database prior to creating a logical replication slot.
Chronix as Long-Term Storage for Prometheus
Cloud Native Con 2016, Seattle: Talk by Moritz Kammerer (@phxql, Senior Software Engineer at QAware).
Abstract: Prometheus is great when it comes to monitoring and alerting. But the long term storage opportunities are comparatively weak compared to related time series databases (missing data distribution, sharding etc.). At this point Chronix enters the stage. Chronix is an open source time series database. It focuses on an efficient long term storage both in terms of storage volume and access times. Chronix achieves a compression rate of 98% compared to data in CSV files while an average query took 21 milliseconds, determined in a benchmark asking 96 queries for different time ranges and time series. Chronix offers a multi-dimensional generic data model for storing all kinds of time series, functions for anomaly detection used in the frameworks EGADS and SAX, and an integration with Apache Spark allows for distributed time series processing. In this code-intense session we show the integration of Prometheus and Chronix. We also dig into the details of Chronix and explain why Chronix <3 Prometheus and vice versa. Furthermore we demonstrate a toolchain: collect data with Prometheus, pipe them to Chronix, visualize both data sources in Grafana [5], and easily analyze tons of data with Spark and Apache Zeppelin.
Prometheus - Intro, CNCF, TSDB,PromQL,Grafana
https://www.youtube.com/playlist?list=PLAiEy9H6ItrKC5PbH7KiELiSEIKv3tuov
-What is Prometheus?
-Difference Between Nagios vs Prometheus
-Architecture
-Alertmanager
-Time series DB
-PromQL (Prometheus Query Language)
-Live Demo
-Grafana
Using eBPF to Measure the k8s Cluster Health
As a k8s cluster-admin your app teams have a certain expectation of your cluster to be available to deploy services at any time without problems. While there is no shortage on metrics in k8s its important to have the right metrics to alert on issues and giving you enough data to react to potential availability issues. Prometheus has become a standard and sheds light on the inner behaviour of Kubernetes clusters and workloads. Lots of KPIs (CPU, IO, network. Etc) in our On-Premise environment are less precise when we start to work in a Cloud environment. Ebpf is the perfect technology that fulfills that requirement as it gives us information down to the kernel level. In 2018 Cloudflare shared an opensource project to expose custom ebpf metrics in Prometheus. Join this session and learn about: • What is ebpf? • What type of metrics we can collect? • How to expose those metrics in a K8s environment. This session will try to deliver a step-by-step guide on how to take advantage of the ebpf exporter.
Kafka Summit SF 2017 - One Day, One Data Hub, 100 Billion Messages: Kafka at ...
LINE is a messaging service with 200+ million active users. I will introduce why we feed 100+ billion daily messages into Kafka and how various systems such as data sync, abuse detection and analysis are depending on and leveraging it. It will be also introduced how we leverage dynamic tracing tools like SystemTap to inspect broker’s performance on production system, which led me to fix KAFKA-4614.
Presented by Yuto Kawamura, LINE Corporation
Systems Track
What's hot (20)
Your data is in Prometheus, now what? (CurrencyFair Engineering Meetup, 2016)
Your data is in Prometheus, now what? (CurrencyFair Engineering Meetup, 2016)
Infrastructure & System Monitoring using Prometheus
Infrastructure & System Monitoring using Prometheus
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
Monitoring Kubernetes with Prometheus (Kubernetes Ireland, 2016)
What is your application doing right now? An introduction to Prometheus
What is your application doing right now? An introduction to Prometheus
Getting Started Monitoring with Prometheus and Grafana
Getting Started Monitoring with Prometheus and Grafana
Kafka Summit SF 2017 - One Day, One Data Hub, 100 Billion Messages: Kafka at ...
Kafka Summit SF 2017 - One Day, One Data Hub, 100 Billion Messages: Kafka at ...
Similar to Breaking Prometheus (Promcon Berlin '16)
John adams talk cloudy
My presentation at Microsoft's Mountain View Conference Center during the "Talk Cloudy to me" day.
Thanks to Sebastian and Scalr for a great day.
[OpenInfra Days Korea 2018] Day 2 - E6 - OpenInfra monitoring with Prometheus
[OpenInfra Days Korea 2018] Day 2 - E6 - OpenInfra monitoring with PrometheusOpenStack Korea Community
This document discusses using Prometheus for open infrastructure and cloud monitoring. It introduces Prometheus as a time series database and monitoring tool. Key features covered include metrics collection, service discovery, graphing, and alerting. The architecture of Prometheus is explained, including scrapping metrics directly or via exporters. A demo of Prometheus and Grafana is proposed to monitor Kubernetes clusters and visualize CPU usage. Alerting configuration and routes in Prometheus and Alertmanager are also summarized.Hacking apache cloud stack
The document provides an agenda and overview of a session on hacking Apache CloudStack. The agenda includes introductions, a session on introducing CloudStack, and a hands-on session with DevCloud. The overview discusses what CloudStack is, how it works as an orchestration platform for IAAS clouds, its architecture and core components, and how users can consume and manage resources through it.
Square Peg Round Hole: Serverless Solutions For Non-Serverless Problems
Serverless is the new hotness. It works great for parallelized workflows and event-driven systems. But what happens when your boss comes along and needs you to build a serverless system that works with a SQL database? Or you need to scrape a website, but you need to rate limit your scraping? For that matter, how can you build a serverless state machine?
Running Legacy Applications with Containers
Enterprise data centers have to support a diverse of set of workloads: cloud native, big data, high performance computing, and legacy applications. While cloud native applications are ideal to run in Docker clusters, bare metal and virtualization infrastructures must still be supported in the data center. The result is a proliferation of clusters and technologies running in individual silos, resulting in high management costs and low utilization. This talk describes the challenges and experiences in implementing a shared cluster infrastructure based on Kubernetes to support big data, high performance computing, and VM-based workloads. The talk will show the deployment and scaling of a high performance computing workload manager, Spark, and OpenStack, and how the VM and Docker management can be integrated together.
End to End Processing of 3.7 Million Telemetry Events per Second using Lambda...
End to End Processing of 3.7 Million Telemetry Events per Second using Lambda...DataWorks Summit/Hadoop Summit
This document discusses end-to-end processing of 3.7 million telemetry events per second using a lambda architecture at Symantec. It provides an overview of Symantec's security data lake infrastructure, the telemetry data processing architecture using Kafka, Storm and HBase, tuning targets for the infrastructure components, and performance benchmarks for Kafka, Storm and Hive.Server 2016 sneak peek
Windows Server 2016 introduces several new features including software defined storage, Hyper-V improvements, automation enhancements, and Nano Server. Storage Spaces Direct enables hyper-converged infrastructure, Storage Replica allows for volume replication, and Resilient File System provides improved integrity and performance. Hyper-V gains features like cluster rolling upgrades and shielded VMs. Automation is focused on a graphical authoring experience and Linux/Azure support. Nano Server offers a headless deployment optimized for cloud workloads.
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Twitter's operations team manages software performance, availability, capacity planning, and configuration management for Twitter. They use metrics, logs, and analysis to find weak points and take corrective action. Some techniques include caching everything possible, moving operations to asynchronous daemons, and optimizing databases to reduce replication delay and locks. The team also created several open source projects like CacheMoney for caching and Kestrel for asynchronous messaging.
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter and Finding your own Fail Whale document discusses Twitter operations. The operations team manages software performance, availability, capacity planning, and configuration management using metrics, logs, and data-driven analysis to find weak points and take corrective action. They use managed services for infrastructure to focus on computer science problems. The document outlines Twitter's rapid growth and challenges in maintaining performance as traffic increases. It provides recommendations around caching, databases, asynchronous processing, and other techniques Twitter uses to optimize performance under heavy load.
Fixing twitter
Fixing Twitter and Finding your own Fail Whale document discusses Twitter operations. The Twitter operations team focuses on software performance, availability, capacity planning, and configuration management using metrics, logs, and science. They use a dedicated managed services team and run their own servers instead of cloud services. The document outlines Twitter's rapid growth and challenges in maintaining performance. It discusses strategies for monitoring, analyzing metrics to find weak points, deploying changes, and improving processes through configuration management and peer reviews.
Fixing_Twitter
Twitter's operations team manages software performance, availability, capacity planning, and configuration management. They use metrics, logs, and analysis to find weak points and take corrective action. Some techniques include caching everything possible, moving operations to asynchronous daemons, optimizing databases, and instrumenting all systems. Their goal is to process requests asynchronously when possible and avoid overloading relational databases.
Speeding up R with Parallel Programming in the Cloud
Presented to eRum (Budapest), May 2018
There are many common workloads in R that are "embarrassingly parallel": group-by analyses, simulations, and cross-validation of models are just a few examples. In this talk I'll describe the doAzureParallel package, a backend to the "foreach" package that automates the process of spawning a cluster of virtual machines in the Azure cloud to process iterations in parallel. This will include an example of optimizing hyperparameters for a predictive model using the "caret" package.
Scalability strategies for cloud based system architecture
- Scalability & Availability for the Global Markets
- Global scaled Scalability, Availability and Security
- Architecture for 100, 1K, 100K, 500K, 1M and 10M global users
- Auto-Scaling
- Understand Cloud Services
- Cloud Demo(AWS, GCP, Azure and Cloudflare)
- Wrap-Up
Cortex: Prometheus as a Service, One Year On
Presented by Tom Wilkie at PromCon 2017
With Speaker Notes: https://goo.gl/V9qGva
At PromCon 2016, I presented "Project Frankenstein: A multitenant, horizontally scalable Prometheus as a service". It's now one year later, and lots has changed - not least the name! This talk will discuss what we've learnt running a Prometheus service for the past year, the architectural changes we made from the original design, and the improvements we've made to the Cortex user experience.
MariaDB on Docker
Getting started with MariaDB? Whether it is on your laptop or server, containers are great ephemeral vessels for your applications. But what about the data that drives your business? It must survive containers coming and going, maintain its availability and reliability, and grow when you need it.
Robotics technical Presentation
HIGH PERFORMANCE EDGE DATA PROCESSING SOFTWARE FOR ROBOTIC APPLICATIONS
Robotic edge autonomous systems is a fast growing market with as fast growing challenges:
Sophisticated applications,
Increasing demand in complexity:
Communications with other systems,
Heavy algorithms: AI, vision navigation, etc. Hardware has improved substantially:
- Better embedded computers
- Improved sensors (more and faster data)
https://klepsydra.com/demos-media/#earth-observation-throughput
FreeSWITCH as a Microservice
This document discusses microservices architecture compared to a monolithic architecture. A microservices architecture breaks an application into smaller, independent services that each perform discrete functions. This allows for more rapid development and improved scalability. However, a microservices architecture is also more complex to deploy and manage. The document provides an example of how a VoIP application could use a microservices approach by breaking components like billing, fraud detection, and call analytics into separate services. It also discusses using Docker containers and services to deploy and scale the microservices architecture.
6. DISZ - Webalkalmazások skálázhatósága a Google Cloud Platformon
Az előadás témája hogyan építhető fel egy rugalmas, jól skálázható szolgáltatás a felhőszolgáltatók platformjain. Hogyan lehet megoldani, hogy a szolgáltatás, amelynek induláskor legfeljebb néhány tíz vagy száz felhasználót kell kiszolgálnia, akár több ezer vagy nagyságrendekkel több felhasználót is képes legyen kiszolgálni rugalmasan? Hátradőlni és csodálni az autoscaling funkciót a Black Friday napján. Beszélni fogunk virtualizációról, platformszintű virtualizációről, szuperkönnyű alkalmazáskonténerekről, a munkaterhek közel valósidejű “pakolgatásával”. Bemutatásra kerül a Google Cloud Platform számos komponense. Bankok, biztosítók, webshopok és így tovább mind a cloudban látják a kitörési pontot.
.NET Conf 2022 - Networking in .NET 7
.NET Conf 2022 - 2022/11/8-10
Talk: Networking in .NET 7.0
https://sessionize.com/s/karel-zikmund/networking-in-.net-7.0/56535
Tupperware: Containerized Deployment at FB
Tupperware is Facebook's system for containerized deployment of services at scale. It handles provisioning machines, distributing binaries, monitoring processes, and failover to ensure services run smoothly in production. Engineers focus on application logic rather than deployment details. Tupperware uses Linux containers to isolate over 300,000 processes across 15,000+ services running on thousands of machines. It incorporates features like service discovery, logging, resource limits, and automated rollouts to efficiently manage infrastructure. Lessons learned include releasing often, using canaries for rollouts, and providing sane defaults to reduce user burden.
Similar to Breaking Prometheus (Promcon Berlin '16) (20)
[OpenInfra Days Korea 2018] Day 2 - E6 - OpenInfra monitoring with Prometheus
[OpenInfra Days Korea 2018] Day 2 - E6 - OpenInfra monitoring with Prometheus
Square Peg Round Hole: Serverless Solutions For Non-Serverless Problems
Square Peg Round Hole: Serverless Solutions For Non-Serverless Problems
End to End Processing of 3.7 Million Telemetry Events per Second using Lambda...
End to End Processing of 3.7 Million Telemetry Events per Second using Lambda...
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Speeding up R with Parallel Programming in the Cloud
Speeding up R with Parallel Programming in the Cloud
Scalability strategies for cloud based system architecture
Scalability strategies for cloud based system architecture
6. DISZ - Webalkalmazások skálázhatósága a Google Cloud Platformon
6. DISZ - Webalkalmazások skálázhatósága a Google Cloud Platformon
More from Matthew Campbell
Practical Plasma: Gaming. Upbit Developers conference 2018
A practical guide on how Plasma helps scale games like Zombie Battlegrounds. We also dive into the internals of Plasma
Microservices Python bangkok
This document provides an introduction to microservices, including:
- Microservices are an organizational tool for scaling services independently. Monoliths work well too but do not scale as easily.
- Common communication methods between microservices include RPC and message-driven interactions using protocols like GRPC and Protobuf.
- Logging and metrics are important for microservices. The REK stack of Rsyslog, Elasticsearch and Kibana is recommended for logging. Prometheus is given as an example for metrics.
- Containerization using Docker is ideal for deploying and managing microservices. Kubernetes is a popular orchestration platform for containers both in the cloud and on-premise.
Intro to microservices GopherDay Taipei '17
Microservices are an architectural approach that structures an application as a collection of loosely coupled services. The document discusses various techniques for building microservices using Go including:
- Using gRPC and protocol buffers for service definition and communication between microservices.
- Implementing structured logging, metrics collection, and configuration via environment variables to provide observability.
- Deploying microservices using containers with Docker for isolation and portability across environments.
- Leveraging tools like Kubernetes for orchestration and continuous integration/deployment via Ansible and Docker.
Distributed Timeseries Database In Go (gophercon India 17)
How we built a distributed time series database called Vulcan at Digitalocean. Spoken at Gophercon India 2017
DigitalOcean Microservices Talk Rocket Internet Conf '16
This document discusses building cloud applications using a microservices architecture. It describes how microservices can help with organizational scaling and that most large companies started as monolithic applications. It then provides examples of different types of applications and services that may be needed for a cloud platform. The document outlines best practices for developing, deploying, monitoring, and maintaining microservices including using Docker, feature flags, service discovery, distributed tracing, and logging. It concludes by inviting questions and noting the company is hiring.
Cloud in your Cloud
Cloud in your Cloud. How we use Microservices to build Digital Ocean's cloud platform. (GOphercon India conference 2016)
presentation-chaos-monkey
Chaos testing involves intentionally introducing failures into a system to test its resiliency. The document discusses using tools like Vagrant, Docker, and integration tests to simulate failures like slowness, database failovers, and network issues. It introduces Chaos Gopher, a set of simple Unix/Go tools for chaos testing, and recommends improving integration test suites to better handle intermittent failures. The author is available to answer any other questions about chaos testing approaches.
Making Wallstreet talk with GO (GO India Conference 2015)
A talk about how we built one of the worlds largest Instant Messenger servers for stock traders. This talk was given at GO India Conference in Bangalore in 2015
Intro to GO (Bangkok Launchpad 2014)
The document introduces GO and discusses why a consulting firm chose to build an instant messaging server using GO. It provides an overview of GO, including that it is a lightweight programming language developed by Google that uses garbage collection and makes concurrency easy using goroutines and channels. The document also provides code samples and discusses getting started with GO development.
More from Matthew Campbell (9)
Practical Plasma: Gaming. Upbit Developers conference 2018
Practical Plasma: Gaming. Upbit Developers conference 2018
Distributed Timeseries Database In Go (gophercon India 17)
Distributed Timeseries Database In Go (gophercon India 17)
DigitalOcean Microservices Talk Rocket Internet Conf '16
DigitalOcean Microservices Talk Rocket Internet Conf '16
Making Wallstreet talk with GO (GO India Conference 2015)
Making Wallstreet talk with GO (GO India Conference 2015)
Recently uploaded
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Recently uploaded (20)
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slack
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Breaking Prometheus (Promcon Berlin '16)
- 1. Breaking Prometheus Scaling prometheus to a million machines
- 2. About Me • Technical Lead Digital Ocean • Microservices in GO Book • Lives in Bangkok
- 3. Dark Days • Graphite • InfluxDB • OpenTSDB (*sigh*)
- 5. Manual Issues • Lots of Prometheus servers • Mismatched versions • New machines = update config • Missing matches
- 6. Consul + Prometheus = Peanut butter + Jelly
- 8. Datacenter wide 10,000s of nodes Stage 2:
- 9. Prometheus Per Region Prometheus Grafana Prometheus Prometheus Prometheus Prometheus Prometheus
- 10. I/O Problems • Modify retention windows • Drop metrics from node_exporter • Larger and larger machines
- 12. Sharding
- 15. Alert Manager
- 16. Shard Problems • Shard redistribution • Over provisioning • Data loss • Limited data windows
- 17. Kafka Bus
- 19. Million VMs
- 20. Digital Ocean Agent • Installable Metrics Agent • Authenticated Push Gateway • “Reverse node exporter”
- 21. Query Api • Customer facing API • GRPC / Json • Authenticated per customer • Prometheus queries
- 23. Vulcan • Prometheus Api • Cassandra storage • Kafka incoming • Standard Scrapers
- 24. Cassandra Store
- 25. Downsampling • In memory shared promethues • Driven from data in kafka • Reusable for Alerting
- 27. Future • New Scrape Sources (Kafka) • Per series expiry TTLs • Plugin Storage Model (In Memory) • Alerting High Availability