Monitoring with Graylog - a modern approach to monitoring?

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Speaker: Christoph Petrausch Event: inovex Brownbag 06. November 2015 weiter Vorträge von inovex: https://www.inovex.de/de/content-pool/vortraege

Monitoring with Graylog
A modern approach to monitoring?
Christoph Petrausch
Karlsruhe, 06.11.2015

Christoph Petrausch
Systems Engineer @ inovex GmbH
〉 Platform Engineering
〉 System Automation & Development (DevOps)
〉 Blog, Slides & Videos at inovex.de
2

1. Why Graylog?
2. What is Graylog?
3. Demo
4. Conclusions
5. Q&A
Agenda
3

Existing monitoring solutions (Nagios, Icinga, Zabbix) have problems:
〉 Some of them lack of APIs
〉 Integration with configuration management is very time-
consuming
〉 Do not scale well
〉 High Availability is not considered by the System Architecture
Why Graylog?
4

〉 Open Source Project, GPLv3
〉 Project started in 2009
〉 Company Graylog Inc. is the main developer
〉 Around a dozen contributors on GitHub
Project Overview
5

〉 Receives messages from multiple input protocols
〉 GELF via HTTP/UDP/TCP, Syslog, Apache Kafka, ….
〉 Assigns messages to streams
〉 Triggers user-defined alerts per stream
〉 Stores messages in ElasticSearch for graphing
〉 Routes messages to different outputs based on streams
〉 Provides search and graphing capabilities for stored messages
〉 Uses MongoDB to store metadata and alerts
What does Graylog?
6

〉 Compressed or uncompressed JSON string
〉 JSON Hash with mandatory fields:
〉 host, version, short_message, full_message, timestamp, level
〉 Additional custom fields start with an underscore
GELF (Graylog Extended Log Format)
7

Architecture
http://docs.graylog.org/en/1.2/pages/architecture.html#bigger-production-setup 8

Message Processing
http://docs.graylog.org/en/1.2/pages/streams.html#how-are-streams-processed-internally 9

〉 Are written in Java
〉 Nearly all parts of Graylog are extensible
〉 Inputs
〉 Outputs
〉 Services
〉 Alarm callbacks
〉 Filters
〉 REST API resources
〉 Periodical tasks
Plugins
10

Hands on: Demo

Overview
Collectd Logstash Graylog
OpsGenie
ElasticSearch
Logstorage
Simple Python
Script
Alarming

〉 Only one index for all messages
〉 Same named fields have to have the same semantics
〉 Rudimentary graphs
〉 No interactive zooming
〉 Rudimentary alarming configuration
〉 High configuration effort to get “per-host” monitoring
〉 But you can do it via a REST-API!
Conclusion
13

Q&A

Thank You!
Christoph Petrausch
IT Engineering & Operations
inovex GmbH
Ludwig-Erhard-Allee 6
76131 Karlsruhe (GER)
christoph.petrausch@inovex.de
CC BY-NC-ND inovex.de

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Das Ökosystem rund um Kubernetes wächst täglich. Insbesondere cloud-native Continuous-Deployment-Strategien stehen Hoch im Kurs und werden in diversen Open-Source-Projekten vorangetrieben. In einer Reihe von Evalutionen nimmt inovex diese Tools genauer unter die Lupe - den Anfang macht Jenkins X. Jenkins X wurde im März 2018 veröffentlicht. Das Konzept hinter dem Tool ist primär, bestehende Teillösungen (Helm, Skaffold, Prow, Tekton) einzusetzen, um sie abstrahiert in ein Kommandozeilen-Interface zu packen. Der Vortrag beschreibt sowohl die klassische Architektur als auch den "Severless"-Ansatz. Des weiteren werden das Kommandozeilen-Tool "jx", der allgemeine Entwicklungs-Workflow sowie diverse Features vorgestellt. Bei unseren Tests im Rahmen der Evaluation sind uns einige Stolpersteine aufgefallen. Es sind vor allem die vielen eingesetzten Dritt-Tools, die den Betrieb und den Upkeep eines mit Jenkins X erstellten Clusters verkomplizieren. Als Fazit stellen wir Jenkins X im Mai 2019 ein "befriedigend" aus und beobachten gespannt, wie sich das Tool in den kommmenden Monaten und Jahren weiterentwickeln wird. Event: Talk4Nerds, 29.04.2019 Speaker: Simon Kienzler, Johannes M. Scheuermann (beide inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Jenkins X – CI/CD in wolkigen Umgebungen

Jenkins X – CI/CD in wolkigen Umgebungen

Jenkins X – CI/CD in wolkigen Umgebungen

Neben dem großen Machine-Learning-Trend in der Cloud zeichnet sich zunehmend die Tendenz ab, bestimmte Aufgaben direkt auf Edge-Geräten auszuführen. Wir erkunden die Vorteile von Auswertungen direkt an der Quelle der Daten und die damit verbundenen Herausforderungen. Denn die Rechenleistung der Cloud steht uns hier leider nicht zur Verfügung. Zur Lösung stehen uns verschiedene Hardwareoptionen wie CPUs, GPUs, FPGAs oder spezielle ASICs und Frameworks zur Verfügung, die wir am Beispiel von einem Convolutional Neural Network evaluieren. Dabei gibt es praktische Tipps und Erfahrungen aus realen Projekten sowie anschauliche Demos auf verschiedenen Hardwareplattformen. Vorkenntnisse: Vorkenntnisse über tiefe neuronale Netze sind von Vorteil. Lernziele: - Verständnis über die Vorteile von AI auf Edge-Geräten und den damit verbundenen Herausforderungen. - Wissen über die verschiedenen Hard- und Softwarelösungen erlangen, um diese in eigenen Projekten einzusetzen. Event: building IoT, 03.04.2019 Speaker: Dominik Helleberg, inovex Mehr Tech-Vorträge: inovex.de/vortraege Mehr Blog-Artikel: inovex.de/blog

AI auf Edge-Geraeten

AI auf Edge-Geraeten

AI auf Edge-Geraeten

Der Talk auf der Konferenz „Talk4Nerds“ der R+V Versicherung bot eine Einführung in Prometheus als Monitoring-Lösung. Dabei ging inovexler Christoph auf die Anforderungen an ein modernes Monitoring Tool ein, wie Prometheus diesen Anforderungen entspricht und warum es zum defacto Standard im Kubernetes-Umfeld geworden ist. Abschließen beleuchtete Christoph die Non-Goals und wie man diese mit zusätzlichen Tools dennoch erreichen kann. Speaker: Christoph Petrausch (inovex) Event: Talk4Nerds Datum: 29.04.2019 Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Prometheus on Kubernetes

Prometheus on Kubernetes

Prometheus on Kubernetes

Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance. Join Marcel Kurovski to explore a use case for vehicle recommendations at mobile.de, Germany’s biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings—providing a holistic solution for candidate generation and ranking. The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds. Event: O'Reilly Artificial Intelligence Conference, New York, 18.04.2019 Speaker: Marcel Kurovski, inovex GmbH Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Deep Learning for Recommender Systems

Deep Learning for Recommender Systems

Deep Learning for Recommender Systems

In seinem Meetup Talk berichtete Maximilian von den aktuellen Problemen von Cloud Computing – insbesondere im Internet of Things – und wie diese durch Edge Computing mitigiert werden können. Er erklärte, wie eine generische Edge-Computing-Architektur aussehen kann und zeigte Anwendungsfälle, von denen manche auch schon in existierenden Produkten umgesetzt sind. Im Anschluss stellte er Azure IoT Edge vor und erläuterte, wie es das bestehende IoT Framework von Microsoft erweitert sowie die Grundkonzepte, die IoT Edge bereitstellt. Auch die Probleme in dem noch jungen Produkt wurden angesprochen, aber auch die Vorteile und Features, die es liefert. In der gemeinsamen Demo mit Eli haben dann beide Speaker die technischen Details von Azure IoT Edge gezeigt und demonstriert, beispielsweise wie Code automatisiert von einer CI/CD-Pipeline in Azure DevOps auf ein IoT-Gerät deployed werden kann. Event: inovex Meetup, 12.03.& 19.03.2019 Speaker: Maximilian Bischoff, inovex Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Azure IoT Edge

Es liegt in der Natur des Menschen das Unvorhersehbare vorherzusagen: Wetter, Aktienkurse, Krankheitsverläufe, die Reaktion eines Menschen. Neueste Deep Learning Ansätze sind in der Lage solche sequentielle Sachverhalte immer genauer zu prognostizieren, setzen aber auch immer größere Datenmengen und Rechenleistungen voraus, die sowohl in Forschung als auch in der Praxis häufig nicht vorliegen. Wie kann man gute Ergebnisse erreichen, wenn nur wenig Daten vorliegen? Marisa Mohr stellte in ihrem Vortrag einen neuen und vielversprechenden informationstheoretischen Ansatz zum Feature Learning von sequentiellen Daten vor, der potenziell auch mit wenigen Daten auskommt. Dabei ging es speziell um ordinale Muster in Zeitreihen, wie sie beispielsweise als Veränderung von Emotionen im Gesprächsverlauf zu finden sind. Eine solche Entwicklung ist für Menschen in der Regel leicht zu erkennen. Chatbots hingegen können nicht intuitiv auf solche Emotionsverläufe reagieren, sondern müssen entsprechend programmiert werden. Details: Deep-Learning-Ansätze wie LSTMs, RNNs oder TCNs haben sich im Umgang mit sequentiellen Daten bewährt. Neuronale Netzwerke sind tief im technischen Sinn, weil sie mehrere (verborgene) Schichten besitzen, aber nicht weil sie ein tiefes Verständnis von Problemen entwickeln. In diesem Vortrag stellte Marisa einen symbolischen informationstheoretischen Ansatz des Representation Learnings von Zeitreihen vor und damit eine Möglichkeit, konzeptionelle Schichten zu konstruieren. Die Idee hinter der sogenannten Permutationsentropie besteht darin, anstelle der Werte einer Zeitreihe die Ordnungsrelation zwischen den Werten zu betrachten, und so auf das natürliche Auf und Ab des zugrundeliegenden dynamischen Systems zurückzugreifen. Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019 Speakerin: Marisa Mohr (inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Representation Learning von Zeitreihen

Representation Learning von Zeitreihen

Representation Learning von Zeitreihen

Menschliche Kommunikation folgt zwar einer ganzen Reihe von Regeln, diese lassen sich aber schwer formalisieren. Nicht zuletzt deshalb, weil in unseren Interaktionen immer auch eine Fülle von Welt- und implizitem Kontextwissen eine Rolle spielt. Rein regelbasierte Chatbots sind daher nicht nur äußert komplex in der Programmierung, sondern stoßen in vielen Anwendungsbereichen schnell an ihre Grenzen. In diesem Vortrag gab Anna Weißhaar einen Überblick über die aktuellen Lösungen und Herausforderungen im Bereich digitale Assistenten. Der Fokus lag dabei auf Ansätzen, die Chatbots „chatty“ machen, sie also möglichst adäquat auf im Voraus unbekannte Nutzereingaben reagieren zu lassen. Event: inovex Meetup: Das Unvorhersehbare vorhersagen: Zeitreihen und Chatbots, 26.03.2019 Speaker: Anna Weißhaar (inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Talk to me – Chatbots und digitale Assistenten

Talk to me – Chatbots und digitale Assistenten

Talk to me – Chatbots und digitale Assistenten

Nicht zuletzt durch die medienwirksame Erfolge des maschinellen Lernens durch DeepMind, OpenAI und Kollegen ist Künstliche Intelligenz im Moment wieder in aller Munde. Einerseits locken zahlreiche neue, vorher undenkbare Anwendungen wie die automatische Diagnose von Krankheiten, autonome Fahrzeuge und Drohnen, oder die automatische Übersetzung gesprochener Wörter. Andererseits warnen mahnenden Stimmen wird vor dem zunehmendem Einflussnahme der „Algorithmen“ auf fast alle Bereiche unseres Lebens sowie vor unerwünschten Folgen von sich verselbstständigenden Computern gewarnt. Einige träumen von – oder fürchten sich vor – der vermeintlich unausweichlichen Singularität, an der sich nichts weniger als das Schicksal der gesamten Menschheit entscheiden wird. Doch was verbirgt sich hinter dem Begriff Künstliche Intelligenz? Je nachdem, wen man fragt, erhält man unterschiedliche, bisweilen gegensätzliche Antworten. Dieser Vortrag stellt einige dieser Antworten vor und versucht sie (nicht nur) anhand von Beispielen aus Forschung und Anwendung einzuordnen. Event: Business Analytics Day, 07.03.2019 Speaker: Dr. Matthias Richter, Dr. Stefan Igel (inovex) Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Künstlich intelligent?

Künstlich intelligent?

Künstlich intelligent?

In den letzten drei Jahren haben wir die Infrastruktur der Fernseh-Plattform waipu.tv gebaut. Dabei haben wir angefangen Tools für den Betrieb in Golang zu schreiben. Aus einigen der Tools wurden Core-Services, die auch die Last einer Fußball-WM-Übertragung locker wegstecken. Wir wollen euch zeigen, wie wir mit der selben Tool-Chain (Golang & Co) Betriebs-Probleme lösen und kritische Business-Applikationen entwickeln. Klassisch DevOps oder Golden Hammer? Speaker: Christoph Petrausch, Igor Lankin (beide inovex) Event: DevOpsConference, 04.12.2018 Mehr Tech-Vorträge: inovex.de/vortraege Mehr Tech-Artikel: inovex.de/blog

Dev + Ops = Go

Das Android Open Source Project, kurz AOSP, ist das Betriebssystem, das auf den meisten heutigen und wahrscheinlich auch auf deinem Smartphone läuft. Es ist die Basis für das Android-App-Universum und wird von Millionen Nutzern und Entwicklern auf der Welt verwendet. Wegen der offenen Verfügbarkeit des Source Codes ist es auch die Basis für bekannte Custom ROMs wie LineageOS. Der erste Teil des Talks gab eine Übersicht über die Architektur des Betriebssystems, das App-Ökosystem, den Hardware Abstraction Layer (HAL), die Sicherheitskonzepte und einige neue Betriebssystementwicklungen wie Project Treble in Android 8.0. Der zweite Teil des Talks gab einen Einblick in den Quellcode und die Struktur des AOSP: Wie lädt man sich den Source Code herunter, wie baut man das AOSP für unterstützte Geräte und wie kann man die eigenen ROMs auf ein Smartphone flashen? Zum Spaß wurde auch noch in einige Implementierungsdetails von Android-App-API-Funktionen geblickt, die man als App Developer schon aufgerufen hat. Speaker: Stefan Lengfeld, inovex Event: inovex Meetup Köln, 23.10.2018 Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

Das Android Open Source Project

Das Android Open Source Project

Das Android Open Source Project

Interpretable Machine Learning describes the process of revealing causes of predictions and explaining a derived decision in a way that is understandable to humans. The ability to understand the causes that lead to a certain prediction enables data scientists to ensure that the model is consistent to the domain knowledge of an expert. Furthermore, interpretability is critical to obtain trust in a model and to be able to tackle problems like unfair biases or discrimination against particular subgroups. This talk covers an introduction to the concept of interpretability and an overview of popular interpretability techniques. Speaker: Marcel Spitzer, inovex Event: Kaggle Munich Meetup, 20.11.2018 Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

Machine Learning Interpretability

Machine Learning Interpretability

Machine Learning Interpretability

Online vehicle marketplaces are embracing artificial intelligence to ease the process of selling a vehicle on their platform. The tedious work of copying information from the vehicle registration document into some web form can be automated with the help of smart text-spotting systems, in which the seller takes a picture of the document, and the necessary information is extracted automatically. Florian Wilhelm details the components of a text-spotting system, including the subtasks of object detection and optical character recognition (OCR). Florian elaborates on the challenges of OCR in documents with various distortions and artifacts, which rule out off-the-shelf products for this task. After offering an overview of semisupervised learning based on generative adversarial networks (GANs), Florian evaluates the performance gains of this method compared to supervised learning. More specifically, for a varying amount of labeled data, he compares the accuracy of a convolution neural network (CNN) to a GANthat uses additional unlabeled data during the training phase, showing that GANs significantly outperform classical CNNs in use cases with a lack of labeled data. What you'll learn: Understand how semisupervised learning with GANs works Explore beneficial semisupervised methods based on GANs for use cases with a limited amount of labeled data Gain insight into an interesting OCR use case of an online vehicle marketplace Event: O'Reilly Artificial Intelligence Conference, London, 11.10.2018 Speaker: Dr. Florian Wilhelm Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

Performance evaluation of GANs in a semisupervised OCR use case

Performance evaluation of GANs in a semisupervised OCR use case

Performance evaluation of GANs in a semisupervised OCR use case

IT im 21. Jahrhundert – What a time to be alive! Es gibt einen (unüberschaubaren) Zoo an Methoden und Produkten die uns so viel Freude an der Arbeit bereiten! Sie sind modern, weil sie neu sind. Sie fordern unser Können heraus, weil sie komplex sind. Sie lösen einige Probleme, die wir vorher nicht hatten. Jeder will sie verwenden, weil Google, Netflix & Co. sie propagieren und Hand auf’s Herz: Will nicht jeder gerne so arbeiten wie Google, Netflix & Co.? Aber macht das wirklich Sinn? In diesem Vortrag blicken wir auf diverse Erkenntnisse aus dem Einsatz agiler Produktentwicklung, DevOps, Continuous Integration/Delivery, Infrastructure as Code, Immutable Infrastructure (bspw. Docker/Kubernetes), Application Logging und Service Monitoring. Learning Goals: - Wir müssen den Einsatz von Methoden und Tools an die Menschen ausrichten, die sie (weiter-)entwickeln und benutzen sollen. - Manchmal lösen wir mit neuen Tools Probleme, die wir vorher nicht hatten. - Die Suche nach einfachen Lösungen für komplexe Probleme ist essentiell, aber nicht immer einfach. Event: Continuous Lifecycle, 15.11.2018 Speaker: Arnold Bechtoldt Mehr Tech-Vorträge: www.inovex.de/vortraege Mehr Tech-Artikel: www.inovex.de/blog

People & Products – Lessons learned from the daily IT madness

People & Products – Lessons learned from the daily IT madness

People & Products – Lessons learned from the daily IT madness

Pulumi (pulumi.io) offers an open source platform to create/manage and deploy your infrastructure in realy programming languages like JavaScript/TypeScript, Go and Python. As Cloud platforms the major 3 cloud providers are supported and additionally you can also use Pulumi with OpenStack and Kubernetes to deploy your applications in the cloud. In this talk we will take a look how Pulumi is different to traditional solutions like Terraform or the Cloud Provider specific solutions (e.g. CloudFormation). The main focus will be on deploying your services on top of Kubernetes. The talk will contain a little theory part about Pulumi, the rest of the talk is more focused on demos and practical parts. One focus of the talk is the difference of Pulumi to kubectl and helm (or to be precise how they complement each other. As a takeaway of this talk you should understand the basics of Pulumi and know what are the differences to the traditional deployment tools. Event: CNCF Meetup Hamburg & Stuttgart, 29.10.2018 & 07.11.2018 Speaker: Johannes M. Scheuermann, inovex Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/ Mehr Tech-Artikel: https://www.inovex.de/blog/

Infrastructure as (real) Code – Manage your K8s resources with Pulumi

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Talk to me – Chatbots und digitale Assistenten

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Das Android Open Source Project

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Monitoring with Graylog - a modern approach to monitoring?

1. Monitoring with Graylog A modern approach to monitoring? Christoph Petrausch Karlsruhe, 06.11.2015

2. Christoph Petrausch Systems Engineer @ inovex GmbH 〉 Platform Engineering 〉 System Automation & Development (DevOps) 〉 Blog, Slides & Videos at inovex.de 2

3. 1. Why Graylog? 2. What is Graylog? 3. Demo 4. Conclusions 5. Q&A Agenda 3

4. Existing monitoring solutions (Nagios, Icinga, Zabbix) have problems: 〉 Some of them lack of APIs 〉 Integration with configuration management is very time- consuming 〉 Do not scale well 〉 High Availability is not considered by the System Architecture Why Graylog? 4

5. 〉 Open Source Project, GPLv3 〉 Project started in 2009 〉 Company Graylog Inc. is the main developer 〉 Around a dozen contributors on GitHub Project Overview 5

6. 〉 Receives messages from multiple input protocols 〉 GELF via HTTP/UDP/TCP, Syslog, Apache Kafka, …. 〉 Assigns messages to streams 〉 Triggers user-defined alerts per stream 〉 Stores messages in ElasticSearch for graphing 〉 Routes messages to different outputs based on streams 〉 Provides search and graphing capabilities for stored messages 〉 Uses MongoDB to store metadata and alerts What does Graylog? 6

7. 〉 Compressed or uncompressed JSON string 〉 JSON Hash with mandatory fields: 〉 host, version, short_message, full_message, timestamp, level 〉 Additional custom fields start with an underscore GELF (Graylog Extended Log Format) 7

8. Architecture http://docs.graylog.org/en/1.2/pages/architecture.html#bigger-production-setup 8

9. Message Processing http://docs.graylog.org/en/1.2/pages/streams.html#how-are-streams-processed-internally 9

10. 〉 Are written in Java 〉 Nearly all parts of Graylog are extensible 〉 Inputs 〉 Outputs 〉 Services 〉 Alarm callbacks 〉 Filters 〉 REST API resources 〉 Periodical tasks Plugins 10

11. Hands on: Demo

12. Overview Collectd Logstash Graylog OpsGenie ElasticSearch Logstorage Simple Python Script Alarming

13. 〉 Only one index for all messages 〉 Same named fields have to have the same semantics 〉 Rudimentary graphs 〉 No interactive zooming 〉 Rudimentary alarming configuration 〉 High configuration effort to get “per-host” monitoring 〉 But you can do it via a REST-API! Conclusion 13

15. Thank You! Christoph Petrausch IT Engineering & Operations inovex GmbH Ludwig-Erhard-Allee 6 76131 Karlsruhe (GER) christoph.petrausch@inovex.de CC BY-NC-ND inovex.de