Talk about how we at Expedia are trying get to greater observability into stack using our opensourced distributed tracing and analysis system Haystack.
This document summarizes a presentation about GraphQL and serverless architectures. It introduces GraphQL as a new philosophy for building APIs that gives clients control and reduces bandwidth. It then discusses how GraphQL compares favorably to REST by allowing a single data request and being self-documenting. The document outlines how serverless GraphQL works using AWS AppSync as an example and discusses some companies that use GraphQL and AppSync.
The document discusses RIPE Atlas, an internet measurement network composed of probes and anchors around the world. RIPE Atlas allows users to perform various measurements like ping, traceroute, DNS lookups, and more to monitor and analyze internet connectivity and infrastructure. Users can access RIPE Atlas through a web interface, API, or CLI to run customized measurements and view historical data. The document provides examples of how RIPE Atlas has been used to monitor internet outages and analyze the paths traffic takes within countries. Training and support resources are available on the RIPE Atlas website.
Building an Artificial Intelligence mobile application with GeneXus - Angelo ...GeneXus
A la par con las tecnologías móvil y werable, SiwfSwim se basa en algoritmos de Inteligencia Artificial y Big Data para analizar los sensores de los dispositivos móviles y reconocer automáticamente los estilos nadados, las piletas y generar un sofisticado reporte analítico.
In this session, learn about Flagger, an open source Kubernetes operator that aims to untangle complexity. It automates the promotion of canary deployments utilizing App Mesh traffic shifting and Prometheus metrics to analyze an application’s behavior during a controlled roll-out.
The document discusses RIPE Atlas, a global active measurements platform that allows users to monitor network performance using probes hosted by volunteers. It provides statistics on the number of probes, measurements collected, and types of tests. The presentation encourages expanding coverage in Asia by deploying more probes and anchors, especially in Vietnam. It demonstrates how RIPE Atlas can be used to analyze connectivity and peerings within a country.
The document discusses changes to the RIPE IRR database to prevent "foreign objects". Specifically:
- As a result of policy discussions over several years, the RIPE NCC will no longer allow APNIC region resources to be included in the RIPE IRR database to prevent hijacking of prefixes.
- Existing foreign objects will be maintained separately but new foreign objects will not be permitted.
- The changes aim to ensure resources are always controlled by their designated holders and any routing assertions require their explicit consent.
This document summarizes a presentation about GraphQL and serverless architectures. It introduces GraphQL as a new philosophy for building APIs that gives clients control and reduces bandwidth. It then discusses how GraphQL compares favorably to REST by allowing a single data request and being self-documenting. The document outlines how serverless GraphQL works using AWS AppSync as an example and discusses some companies that use GraphQL and AppSync.
The document discusses RIPE Atlas, an internet measurement network composed of probes and anchors around the world. RIPE Atlas allows users to perform various measurements like ping, traceroute, DNS lookups, and more to monitor and analyze internet connectivity and infrastructure. Users can access RIPE Atlas through a web interface, API, or CLI to run customized measurements and view historical data. The document provides examples of how RIPE Atlas has been used to monitor internet outages and analyze the paths traffic takes within countries. Training and support resources are available on the RIPE Atlas website.
Building an Artificial Intelligence mobile application with GeneXus - Angelo ...GeneXus
A la par con las tecnologías móvil y werable, SiwfSwim se basa en algoritmos de Inteligencia Artificial y Big Data para analizar los sensores de los dispositivos móviles y reconocer automáticamente los estilos nadados, las piletas y generar un sofisticado reporte analítico.
In this session, learn about Flagger, an open source Kubernetes operator that aims to untangle complexity. It automates the promotion of canary deployments utilizing App Mesh traffic shifting and Prometheus metrics to analyze an application’s behavior during a controlled roll-out.
The document discusses RIPE Atlas, a global active measurements platform that allows users to monitor network performance using probes hosted by volunteers. It provides statistics on the number of probes, measurements collected, and types of tests. The presentation encourages expanding coverage in Asia by deploying more probes and anchors, especially in Vietnam. It demonstrates how RIPE Atlas can be used to analyze connectivity and peerings within a country.
The document discusses changes to the RIPE IRR database to prevent "foreign objects". Specifically:
- As a result of policy discussions over several years, the RIPE NCC will no longer allow APNIC region resources to be included in the RIPE IRR database to prevent hijacking of prefixes.
- Existing foreign objects will be maintained separately but new foreign objects will not be permitted.
- The changes aim to ensure resources are always controlled by their designated holders and any routing assertions require their explicit consent.
Security Events Logging at Bell with the Elastic StackElasticsearch
One of Canada’s largest telecommunications company is using Elastic to drive improved security analysis in their SOC. With a need to ingest all security logs, build threat detection models, and normalize many new types of logs, the Bell security team turned to Elastic. Learn how they’ve streamlined alerts, deepened log analysis, and addressed challenges unique to being an ISP.
Monitoring with Elastic Machine Learning at SkyElasticsearch
Learn how Sky leveraged the power of Elastic’s machine learning feature to process over seven billion documents and help discover trends, learn from real-time data, and generate alerts when anomalies occur.
See the video: https://www.elastic.co/elasticon/tour/2019/london/monitoring-with-elastic-machine-learning-at-sky
Atlassian User Group Toronto Hosted By Elasity & AWSiTMethods
The document discusses how to scale mission-critical applications on AWS. It provides an overview of Elasity, an iTMethods product that helps enterprises migrate and manage applications on AWS through continuous availability, disaster recovery, and optimization. The presentation was delivered to an Atlassian user group in Toronto by an iTMethods representative to explain how Elasity can help scale applications on AWS.
ARIN 35: Internet Number Resource Status ReportARIN
ARIN 35: Internet Number Resource Status Report by Leslie Nobile. Video archives at: https://www.arin.net/participate/meetings/reports/ARIN_35/ppm.html
The document discusses RIPE Atlas streaming, a new architecture that allows users to receive RIPE Atlas measurement results in real-time through web sockets. Over 8,200 probes currently collect over 2,500 results per second. Streaming provides live access to ping results and probe connection events. A code example demonstrates subscribing to a measurement stream and logging results to the console. Resources for participating on GitHub or providing feedback are also listed.
RIPE Atlas is the biggest internet measurement network composed of more than 8000 probes distributed worldwide.
The new RIPE Atlas streaming service allows you to tap into the real-time data flow of all the collected public results. Every time our system receives a data point or a probe connectivity event occurs, it's also delivered to the clients that are "tuned in" to that result stream. This feature is implemented using web sockets.
The document describes an industrial process historian that has many scalable and high availability features such as linear scalability, clustering, sharding, a flexible data schema with searchable metadata, stream analytics capabilities, and extensible user-defined functions. It also has tools for visualization, alarm generation, and an open source code base. This system, called Influx, provides an ecosystem for data storage, analytics and visualization for industrial processes.
SW360 is a third-party software component catalog that assigns components to products and projects. It has gone through several versions with new features added, including SPDX BOM import, improved FOSSology integration, and REST endpoints for SHA1 search and FOSSology triggering. Upcoming versions will include change logs for every record and dependency updates. The presentation provides an overview of SW360's capabilities and ecosystem projects like sw360chores and sw360vagrant that help set up the SW360 infrastructure.
This document summarizes Slack's use of Druid, an open-source analytics database, to measure performance of its API. It discusses Slack's growth from 5 employees and 350,000 active users in 2016 to 800+ employees and 6 million active users in 2017. It describes how Druid is used to store and query 500+ tables and handle 400,000 accesses to Slack's data warehouse per day. The document outlines Druid's infrastructure, including its use of Kafka for a near real-time pipeline and autoscale of historical and broker nodes. Finally, it discusses challenges faced like cascading failures, optimizing forward index fields, supporting SQL, and bridging batch and real-time tables.
The document summarizes an open project meeting for the OptiWind project. The meeting covered Work Package 7.1, which focuses on performance monitoring of wind turbines. It discussed developing systems for data collection from various sources, cleaning the data automatically, and applying data mining and modeling approaches to perform availability analysis, downtime analysis, and failure prediction based on the cleaned historical turbine performance data. The presentation tested a wide range of techniques and showed interesting tools for wind turbine performance monitoring and assessment based on the approaches.
Boost dataviz with Python, OW2online, June 2020OW2
The new R/Python widget allows to embed R and Python scripts directly within a cockpit filling a gap between datavisualization and datascience. With this widget, datascientists are able to integrate python facilities in a heterogeneous and multi-source environment and to present advanced analytics to a broader audience. Presentation by Marco Balestri, Knowage Lab, Engineering Group.
12th Meeting OpenChain Reference Tooling Work Group - 25th March - SlidesShane Coughlan
This document summarizes the 12th meeting of the OpenChain reference tooling work group in 3 sentences:
The meeting agenda included providing news from Oliver, continuing discussion on best practices for container compliance, and learning about new features in sw360 from Michael Jaeger. Haksung prepared a Korean overview of sw360 and a new branch was created in their Github repo focused on container license compliance. Communication channels like Github, Slack, and a mailing list were also listed.
Rule-Driven, Fully-Configurable Asset Tracking with GISSSP Innovations
For the last seven years MLGW has successfully implemented GIS using ArcGIS/ArcFM ™. The GIS serves as an enterprise backbone for a variety of business applications where utility assets play a crucial role: Inspection, Maintenance, New Construction, OMS, among others.To support the life cycle of MLGW’s assets, SSP has implemented a rule-driven and fully-configurable asset tracking mechanism built into the GIS. Rules specified by different business units determine: What network elements are to be tracked as assets. What attributes of those assets are to be monitored. How and when these attributes may change.
Our company has a large codebase split across many Git repositories that are reviewed using Gerrit. Gerrit allows grouping related code changes across different projects into topics but does not support checking out topics from the repo or running continuous integration feedback on all commits in a topic. To address this, a workflow was created using Gerrit topics and AWS Lambda to automatically run CI tests on all commits in a topic.
Analyze Your Smart City: Build Sensor Analytics with OGC SensorThings API SensorUp
This webinar is a hands-on tutorial to develop a sensor analytics application using the SensorThings API. SensorThings API offers a rich set of query functions that can be the basis for analytics. This tutorial will uncover these query functions.
APIdays Paris 2018 - Hack your legacy, from mutualism to Open Source! Chris W...apidays
This document discusses MAIF's transition to open source software. It summarizes MAIF's history from its creation in 1934 to its recent initiatives to build an open source software platform. Key events include MAIF releasing its first open source component, Otoroshi, in May 2017. Otoroshi is an API management tool. In January 2018, MAIF fully open sourced these initial components to reflect its values of ethics and sharing in the digital world. This open source work earned MAIF an award as the top open source project of 2018.
SensorThings API Webinar - #1 of 4 - IntroductionSensorUp
This document introduces the OGC SensorThings API. It begins with an agenda that covers introducing IoT and sensor web, an overview of the SensorThings API and its benefits, and two case studies. It then provides background on the presenter and his company SensorUp, who developed the first implementation of the SensorThings API. The rest of the document discusses what the SensorThings API is, its benefits like being open, location-intelligent, and reducing time to market, and examples of its applications in environmental monitoring and disaster response. FAQs are addressed and two case studies are described in more detail.
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
This document discusses analyzing Bitcoin transaction data as a graph using Oracle technologies. It provides an overview of modeling Bitcoin transactions as a graph with transactions and addresses as vertices and relationships between them as edges. It then describes the workflow of preparing the data, loading it into a graph database, and analyzing the graph using PGX and PGQL. Examples are given of graph queries and algorithms like PageRank and betweenness centrality that can be run on the Bitcoin transaction graph to identify important transactions and addresses.
Security Events Logging at Bell with the Elastic StackElasticsearch
One of Canada’s largest telecommunications company is using Elastic to drive improved security analysis in their SOC. With a need to ingest all security logs, build threat detection models, and normalize many new types of logs, the Bell security team turned to Elastic. Learn how they’ve streamlined alerts, deepened log analysis, and addressed challenges unique to being an ISP.
Monitoring with Elastic Machine Learning at SkyElasticsearch
Learn how Sky leveraged the power of Elastic’s machine learning feature to process over seven billion documents and help discover trends, learn from real-time data, and generate alerts when anomalies occur.
See the video: https://www.elastic.co/elasticon/tour/2019/london/monitoring-with-elastic-machine-learning-at-sky
Atlassian User Group Toronto Hosted By Elasity & AWSiTMethods
The document discusses how to scale mission-critical applications on AWS. It provides an overview of Elasity, an iTMethods product that helps enterprises migrate and manage applications on AWS through continuous availability, disaster recovery, and optimization. The presentation was delivered to an Atlassian user group in Toronto by an iTMethods representative to explain how Elasity can help scale applications on AWS.
ARIN 35: Internet Number Resource Status ReportARIN
ARIN 35: Internet Number Resource Status Report by Leslie Nobile. Video archives at: https://www.arin.net/participate/meetings/reports/ARIN_35/ppm.html
The document discusses RIPE Atlas streaming, a new architecture that allows users to receive RIPE Atlas measurement results in real-time through web sockets. Over 8,200 probes currently collect over 2,500 results per second. Streaming provides live access to ping results and probe connection events. A code example demonstrates subscribing to a measurement stream and logging results to the console. Resources for participating on GitHub or providing feedback are also listed.
RIPE Atlas is the biggest internet measurement network composed of more than 8000 probes distributed worldwide.
The new RIPE Atlas streaming service allows you to tap into the real-time data flow of all the collected public results. Every time our system receives a data point or a probe connectivity event occurs, it's also delivered to the clients that are "tuned in" to that result stream. This feature is implemented using web sockets.
The document describes an industrial process historian that has many scalable and high availability features such as linear scalability, clustering, sharding, a flexible data schema with searchable metadata, stream analytics capabilities, and extensible user-defined functions. It also has tools for visualization, alarm generation, and an open source code base. This system, called Influx, provides an ecosystem for data storage, analytics and visualization for industrial processes.
SW360 is a third-party software component catalog that assigns components to products and projects. It has gone through several versions with new features added, including SPDX BOM import, improved FOSSology integration, and REST endpoints for SHA1 search and FOSSology triggering. Upcoming versions will include change logs for every record and dependency updates. The presentation provides an overview of SW360's capabilities and ecosystem projects like sw360chores and sw360vagrant that help set up the SW360 infrastructure.
This document summarizes Slack's use of Druid, an open-source analytics database, to measure performance of its API. It discusses Slack's growth from 5 employees and 350,000 active users in 2016 to 800+ employees and 6 million active users in 2017. It describes how Druid is used to store and query 500+ tables and handle 400,000 accesses to Slack's data warehouse per day. The document outlines Druid's infrastructure, including its use of Kafka for a near real-time pipeline and autoscale of historical and broker nodes. Finally, it discusses challenges faced like cascading failures, optimizing forward index fields, supporting SQL, and bridging batch and real-time tables.
The document summarizes an open project meeting for the OptiWind project. The meeting covered Work Package 7.1, which focuses on performance monitoring of wind turbines. It discussed developing systems for data collection from various sources, cleaning the data automatically, and applying data mining and modeling approaches to perform availability analysis, downtime analysis, and failure prediction based on the cleaned historical turbine performance data. The presentation tested a wide range of techniques and showed interesting tools for wind turbine performance monitoring and assessment based on the approaches.
Boost dataviz with Python, OW2online, June 2020OW2
The new R/Python widget allows to embed R and Python scripts directly within a cockpit filling a gap between datavisualization and datascience. With this widget, datascientists are able to integrate python facilities in a heterogeneous and multi-source environment and to present advanced analytics to a broader audience. Presentation by Marco Balestri, Knowage Lab, Engineering Group.
12th Meeting OpenChain Reference Tooling Work Group - 25th March - SlidesShane Coughlan
This document summarizes the 12th meeting of the OpenChain reference tooling work group in 3 sentences:
The meeting agenda included providing news from Oliver, continuing discussion on best practices for container compliance, and learning about new features in sw360 from Michael Jaeger. Haksung prepared a Korean overview of sw360 and a new branch was created in their Github repo focused on container license compliance. Communication channels like Github, Slack, and a mailing list were also listed.
Rule-Driven, Fully-Configurable Asset Tracking with GISSSP Innovations
For the last seven years MLGW has successfully implemented GIS using ArcGIS/ArcFM ™. The GIS serves as an enterprise backbone for a variety of business applications where utility assets play a crucial role: Inspection, Maintenance, New Construction, OMS, among others.To support the life cycle of MLGW’s assets, SSP has implemented a rule-driven and fully-configurable asset tracking mechanism built into the GIS. Rules specified by different business units determine: What network elements are to be tracked as assets. What attributes of those assets are to be monitored. How and when these attributes may change.
Our company has a large codebase split across many Git repositories that are reviewed using Gerrit. Gerrit allows grouping related code changes across different projects into topics but does not support checking out topics from the repo or running continuous integration feedback on all commits in a topic. To address this, a workflow was created using Gerrit topics and AWS Lambda to automatically run CI tests on all commits in a topic.
Analyze Your Smart City: Build Sensor Analytics with OGC SensorThings API SensorUp
This webinar is a hands-on tutorial to develop a sensor analytics application using the SensorThings API. SensorThings API offers a rich set of query functions that can be the basis for analytics. This tutorial will uncover these query functions.
APIdays Paris 2018 - Hack your legacy, from mutualism to Open Source! Chris W...apidays
This document discusses MAIF's transition to open source software. It summarizes MAIF's history from its creation in 1934 to its recent initiatives to build an open source software platform. Key events include MAIF releasing its first open source component, Otoroshi, in May 2017. Otoroshi is an API management tool. In January 2018, MAIF fully open sourced these initial components to reflect its values of ethics and sharing in the digital world. This open source work earned MAIF an award as the top open source project of 2018.
SensorThings API Webinar - #1 of 4 - IntroductionSensorUp
This document introduces the OGC SensorThings API. It begins with an agenda that covers introducing IoT and sensor web, an overview of the SensorThings API and its benefits, and two case studies. It then provides background on the presenter and his company SensorUp, who developed the first implementation of the SensorThings API. The rest of the document discusses what the SensorThings API is, its benefits like being open, location-intelligent, and reducing time to market, and examples of its applications in environmental monitoring and disaster response. FAQs are addressed and two case studies are described in more detail.
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
This document discusses analyzing Bitcoin transaction data as a graph using Oracle technologies. It provides an overview of modeling Bitcoin transactions as a graph with transactions and addresses as vertices and relationships between them as edges. It then describes the workflow of preparing the data, loading it into a graph database, and analyzing the graph using PGX and PGQL. Examples are given of graph queries and algorithms like PageRank and betweenness centrality that can be run on the Bitcoin transaction graph to identify important transactions and addresses.
Les logs, traces et indicateurs au service d'une observabilité unifiéeElasticsearch
Découvrez comment Elasticsearch centralise le stockage des données et comment exploiter Kibana pour les analyser. Sans oublier l'accélération de l'identification, du diagnostic et de la résolution des problèmes.
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Analyzing Data Streams in Real Time with Amazon Kinesis: PNNL's Serverless Da...Amazon Web Services
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. In this session, we first present an end-to-end streaming data solution using Amazon Kinesis Data Streams for data ingestion, Amazon Kinesis Data Analytics for real-time processing, and Amazon Kinesis Data Firehose for persistence. We review in detail how to write SQL queries for operational monitoring using Kinesis Data Analytics.
Learn how PNNL is building their ingestion flow into their Serverless Data Lake leveraging the Kinesis Platform. At times migrating existing NiFi Processes where applicable to various parts of the Kinesis Platform, replacing complex flows on Nifi to bundle and compress the data with Kinesis Firehose, leveraging Kinesis Streams for their enrichment and transformation pipelines, and using Kinesis Analytics to Filter, Aggregate, and detect anomalies.
Instrumenting Applications for Observability Using AWS X-Ray (DEV402-R2) - AW...Amazon Web Services
AWS X-Ray enables developers to gain visibility into distributed applications as if they were running on your developer desktop. Come learn how you can instrument your applications to make use of X-Ray and reduce the time it takes to resolve issues and errors in both development and production environments from hours to minutes.
During the past years, the data deluge that prevails in the World
Wide Web has been accompanied by a number of APIs that
expose business logic. In this paper, we discuss a novel approach
to enrich existing API standards definitions with business rules.
Taking advantage of the REST principles, we aim at enabling the
creation of generic clients that can dynamically navigate through
semantically enriched web affordances with the help of Hydrabased
Hypermedia API descriptions, which encapsulate the finite
state machine of possible actions into SWRL rules.
LeverX - Live Engineering with IoT on SAP LeonardoEric Stajda
LeverX is an SAP consulting firm that helps companies leverage their SAP investments. They provide services around SAP solutions for supply chain, manufacturing, and IoT/Leonardo. LeverX presented on their work with SAP IoT, including demonstrations of their MisterX and RobotX IoT prototyping platforms which integrate sensor data with SAP using Node.js and leverage various SAP Leonardo components. Questions were invited at the end.
Have Your Front End and Monitor It, Too (ANT303) - AWS re:Invent 2018Amazon Web Services
Amazon Elasticsearch Service (Amazon ES) is both a search solution and a log monitoring solution. In this session, we address both. We build a front-end, PHP web server that provides a search experience on movie data as well as backend monitoring to send Apache web logs, syslogs, and application logs to Amazon ES. We tune the relevance for the search experience and build Kibana visualizations for the log data. In addition, we use security best practices and deploy everything into a VPC.
Here are a few ways AWS services can help automate common SAP operations:
1. Use AWS Lambda to automate tasks like adding additional application servers. A Lambda function can launch a new EC2 instance from a pre-configured AMI, run scripts to join the instance to the SAP cluster and perform other configuration tasks.
2. Leverage AWS EC2 Systems Manager to remotely execute commands on EC2 instances. This allows automating activities like patching, software installation etc across SAP application and database servers.
3. Build conversational interfaces using Amazon Lex to simplify operations for users. Lex bots can understand natural language commands to add app servers, perform backups etc by invoking the appropriate Lambda functions.
4. Use the
Beyond Infrastructure for SAP on AWS (GPSTEC322) - AWS re:Invent 2018Amazon Web Services
Enterprises are seeing big business benefits by moving their SAP workloads to AWS. However, the migration to AWS is often just the first step in their innovation journeys. In this session, we share how enterprises are realizing their business transformations in the four major areas: big data & analytics, IoT, app & APIs, and DevOps, supported by a solid foundation of machine learning and compute services on AWS. We provide demonstrations so you can learn firsthand how to help your customers innovate using AWS solutions with SAP applications. Expect to leave this session with reference architectures and best practices for implementing these innovations in their SAP landscapes.
Cardinality provides innovative analytics using big data techniques. It analyzes data from multiple sources, including at the customer, service, and network-wide levels. Cardinality's analytics platform includes components for data collection, storage, analysis, and visualization. It utilizes open-source technologies like Hadoop, Spark, and open APIs to provide customizable and scalable solutions to customers.
Extending Analytics Beyond the Data Warehouse, ft. Warner Bros. Analytics (AN...Amazon Web Services
Companies have valuable data that they might not be analyzing due to the complexity, scalability, and performance issues of loading the data into their data warehouse. With the right tools, you can extend your analytics to query data in your data lake—with no loading required. Amazon Redshift Spectrum extends the analytic power of Amazon Redshift beyond data stored in your data warehouse to run SQL queries directly against vast amounts of unstructured data in your Amazon S3 data lake. This gives you the freedom to store your data where you want, in the format you want, and have it available for analytics when you need it. Join a discussion with an Amazon Redshift lead engineer to ask questions and learn more about how you can extend your analytics beyond your data warehouse.
How to create an enterprise data lake for enterprise-wide information storage and sharing? The data lake concept, architecture principles, support for data science and some use case review.
These slides will help you to choose a PaaS solution by analyzing 6 most important dimensions: adoption, features, operational capabilities, continuous delivery and ALM, architecture and deployment principles, developer ecosystem.
Combining Logs, Metrics, and Traces for Unified ObservabilityElasticsearch
Learn how Elasticsearch efficiently combines data in a single store and how Kibana is used to analyze it. Plus, see how recent developments help identify, troubleshoot, and resolve operational issues faster.
Combinação de logs, métricas e rastreamentos para observabilidade unificadaElasticsearch
Saiba como o Elasticsearch combina com eficiência dados em um único armazenamento e como o Kibana é usado para analisá-los. Além disso, veja como os desenvolvimentos recentes ajudam a identificar e resolver problemas operacionais mais rapidamente.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
3. @ All Things Open 2018, Raleigh
Observability Events
Logs : stateless events generated by
the application
Metrics : timeseries events containing
measurements
Traces : correlated events to track
cause of ordering
6. @ All Things Open 2018, Raleigh
Traces
Span typically represents a service call
or a block of code
Trace represents a collection of spans
correlated by an identifier
7. Distribution tracing tracks production requests as they track different parts of
the architecture
@ All Things Open 2018, Raleigh
Traces – Context Propagation
8. @ All Things Open 2018, Raleigh
Traces –Why do I care
In todays microservice architecture, there’s a lot going on at the backend while serving a request. Multiple service interactions, levels of resiliency, multiple layers of caching etcs. So in case something goes wrong its not always evident as to why it happened.
Observability is the ability to understand and troubleshoot our systems in production by collecting a series of timestamped events.
These events can be either request scoped/system scoped. A garbage collection event would most likely not associated with a request, whereas a response time event is.
For the sake of this presentation we are going to talk about events, which are request scoped.
So what are the kind of events we are talking about here, I think they can be broadly classified into three types
1. Logs
2. Metrics
3. Traces
Collecting each kind of events have their own use-cases but they don’t really have very clear boundaries. For instance an audit log which logs the response time for an incoming request in the system can be used to compute the average response time metric. In this case as you see you don’t explicitly collect the metric event.
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Distribution tracing tracks production requests by correlating different service interactions in the architecture
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Context propagation
Reduce time to triage by contextualizing errors and delays
Visualizing latencies over the network
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This is the architecture of haystack system. We have kafka as central nervous system backing haystack.
1. Componentized: Haystack includes all of the necessary subsystems to make the system ready to use. But we have also ensured that the overall system is designed in such a way that you can replace any given subsystem to better meet your own needs.
2. Resilient: There is no single point of failure.
3. Scalable: We have completely decentralized our system which helps us to scale every component individually
The architecture can be broken down into 3 parts :
Subsystems : Haystack includes various subsystems to perform tracing, trending, service graph etc. We will go over these subsystems in a bit.
Data Stores : We have 3 data stores, namely Cassandra : To store the raw stitched spans ,ie, traces. ElasticSearch is used as an indexer to query the data faster and MetricTank backed by Cassandra to store trends in metrics 2.0 format.
Visualization : Haystack UI is a central place to visualize the processed data such as traces, trends, alerts from various haystack sub-systems.
Let’s see the subsystems one by one.
I will be doing deep dives about usecases and architecture about each of the current subsystems haystack has.
Traces subsystem is mandatory, others are optional. If you deploy you can configure haystack to have only a subset of them, except Traces.
Some of them are dependent on others, to be specific Anomaly detection requires Trends as you need trends to detect anomalies. Outcome of Trends goes in Kafka and Anomaly detection picks it up from there.
We would love you to feel free and add any new subsystem on top of Kafka backone. It doesn’t need to be part of haystack’s repositories, if you need something specific to your companies need, you can build that and run on top of haystack’s Kafka. Don’t need to come and talk to us about adding any new thing in.
Demo
If you know the traceId you can jump to see the timeline/waterfall showing how a single end user request got severed inside your system.
In case of this example, user request was to stark service at /stark/endpoint
You might have used Zipkin or Jaeger before
Usecase
Identifying root cause of errors
Perf bottlenecks
Understanding of flow of requests
Open tracing compliant
Use 3 IDs traceId, spanId, and parentSpanId
spanId needs to be passed on from a service to the next one, which is your logic pass it in http header or in payload. For the next service when it is logging span it will use the caller’s spanId as its parentSpanId.
We are also looking into supporting zipkin style ids, they have a slight but crucial difference in Ids.
Usecase
You might not have traceIds handy
For example, lets say your site has started showing intermittent errors for US SiteId, you might want to see traces where error = true and siteid = us and check traces for that scenario
You can setup a number of whitelisted fields and they become searchable on haystack-ui.
Click on any of these traces and you will get the timeline/waterfall view
About the architecture, two apps in traces subsystem
Indexer
Reader
The Trends subsystem is responsible for reading spans and generating vital service health trends.
Introduce a new term operation. What is [user service -> loyalty service example]
service
operation
The Trends subsystem is responsible for reading spans and generating vital service health trends.
This system is loosely coupled and can be run on demand. It has two components :
haystack-span-timeseries-transformer - This component is responsible for reading span and converting them to metrics 2.0 compatible MetricPoints. These metricpoints are then pushed back to kafka.
haystack-timeseries-aggregator - This app is responsible for reading metric points, aggregating them based on rules and pushing the aggregated metric points to Kafka. The metric points are MetricTank compliant and can be directly consumed by metrictank which is a timeseries database.
Currently we compute four trends for each combination of service and operation . These are
Total count
success_count [count]
failure_count [count]
duration [mean, median, std-dev, 99 percentile, 95 percentile]
Each trend is computed for 4 intervals [1min, 5min, 15min, 1hour].
The Trends subsystem is responsible for reading spans and generating vital service health trends.
This system is loosely coupled and can be run on demand. It has two components :
haystack-span-timeseries-transformer - This component is responsible for reading span and converting them to metrics 2.0 compatible MetricPoints. These metricpoints are then pushed back to kafka.
haystack-timeseries-aggregator - This app is responsible for reading metric points, aggregating them based on rules and pushing the aggregated metric points to Kafka. The metric points are MetricTank compliant and can be directly consumed by metrictank which is a timeseries database.
Currently we compute four trends for each combination of service and operation . These are
Total count
success_count [count]
failure_count [count]
duration [mean, median, std-dev, 99 percentile, 95 percentile]
Each trend is computed for 4 intervals [1min, 5min, 15min, 1hour].
The alerts view is used to show up alerts for any anomalous behavior in service health trends. Currently haystack alerts on total count, failure count and duration (TP99) . These alerts would be powered by adaptive alerting system which is one of the other OSS projects by Expedia.