Paul will outline his vision around the platform and give the latest updates on Flux (a new query language), the decoupling of query and storage, the impact of hybrid cloud environments on architecture, cardinality, and discuss the technical directions of the platform. This talk will walk through the vision and architecture with demonstrations of working prototypes of the projects.
In this InfluxDays NYC 2019 talk, InfluxData Founder & CTO Paul Dix will outline his vision around the platform and its new data scripting and query language Flux, and he will give the latest updates on InfluxDB time series database. This talk will walk through the vision and architecture with demonstrations of working prototypes of the projects.
Timeseries - data visualization in GrafanaOCoderFest
Presentation deals with proper handling of the application and resources monitoring. It mentions tools that help with presentation layer - Grafana, storage - InfluxDB, and communication between the measurements and their destination - Telegraf.
Presented by Marek Szymeczko
This session defines what time series data is (and isn’t), how the problem domain time series differs from more traditional data workloads like full-text search and examines how InfluxData is differentiated from other proposed solutions. This session also includes a review of the most common use cases and a brief demo of InfluxDB.
Metaflow: The ML Infrastructure at NetflixBill Liu
Metaflow was started at Netflix to answer a pressing business need: How to enable an organization of data scientists, who are not software engineers by training, build and deploy end-to-end machine learning workflows and applications independently. We wanted to provide the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for the foundational infrastructure: data, compute, orchestration, and versioning.
Today, the open-source Metaflow powers hundreds of business-critical ML projects at Netflix and other companies from bioinformatics to real estate.
In this talk, you will learn about:
- What to expect from a modern ML infrastructure stack.
- Using Metaflow to boost the productivity of your data science organization, based on lessons learned from Netflix.
- Deployment strategies for a full stack of ML infrastructure that plays nicely with your existing systems and policies.
https://www.aicamp.ai/event/eventdetails/W2021080510
With the rise of the Internet of Things (IoT) and low-latency analytics, streaming data becomes ever more important. Surprisingly, one of the most promising approaches for processing streaming data is SQL. In this presentation, Julian Hyde shows how to build streaming SQL analytics that deliver results with low latency, adapt to network changes, and play nicely with BI tools and stored data. He also describes how Apache Calcite optimizes streaming queries, and the ongoing collaborations between Calcite and the Storm, Flink and Samza projects.
This talk was given Julian Hyde at Apache Big Data conference, Vancouver, on 2016/05/09.
In this InfluxDays NYC 2019 talk, InfluxData Founder & CTO Paul Dix will outline his vision around the platform and its new data scripting and query language Flux, and he will give the latest updates on InfluxDB time series database. This talk will walk through the vision and architecture with demonstrations of working prototypes of the projects.
Timeseries - data visualization in GrafanaOCoderFest
Presentation deals with proper handling of the application and resources monitoring. It mentions tools that help with presentation layer - Grafana, storage - InfluxDB, and communication between the measurements and their destination - Telegraf.
Presented by Marek Szymeczko
This session defines what time series data is (and isn’t), how the problem domain time series differs from more traditional data workloads like full-text search and examines how InfluxData is differentiated from other proposed solutions. This session also includes a review of the most common use cases and a brief demo of InfluxDB.
Metaflow: The ML Infrastructure at NetflixBill Liu
Metaflow was started at Netflix to answer a pressing business need: How to enable an organization of data scientists, who are not software engineers by training, build and deploy end-to-end machine learning workflows and applications independently. We wanted to provide the best possible user experience for data scientists, allowing them to focus on parts they like (modeling using their favorite off-the-shelf libraries) while providing robust built-in solutions for the foundational infrastructure: data, compute, orchestration, and versioning.
Today, the open-source Metaflow powers hundreds of business-critical ML projects at Netflix and other companies from bioinformatics to real estate.
In this talk, you will learn about:
- What to expect from a modern ML infrastructure stack.
- Using Metaflow to boost the productivity of your data science organization, based on lessons learned from Netflix.
- Deployment strategies for a full stack of ML infrastructure that plays nicely with your existing systems and policies.
https://www.aicamp.ai/event/eventdetails/W2021080510
With the rise of the Internet of Things (IoT) and low-latency analytics, streaming data becomes ever more important. Surprisingly, one of the most promising approaches for processing streaming data is SQL. In this presentation, Julian Hyde shows how to build streaming SQL analytics that deliver results with low latency, adapt to network changes, and play nicely with BI tools and stored data. He also describes how Apache Calcite optimizes streaming queries, and the ongoing collaborations between Calcite and the Storm, Flink and Samza projects.
This talk was given Julian Hyde at Apache Big Data conference, Vancouver, on 2016/05/09.
Telegraf is an open-source server agent designed to collect metrics from stacks, sensors, and systems — with nearly 300 inputs and outputs. Telegraf Operator makes it easy to use Telegraf for monitoring your Kubernetes workloads. It enables developers to define a common output destination for all metrics, and configure Sidecar monitoring on your application pods using annotations. With the Telegraf sidecar container added, it will collect data and start pushing the metrics to a time series database, like InfluxDB. Discover how to use the Telegraf Operator as a control center for managing individual Telegraf instances which are deployed throughout Kubernetes clusters. Find out how to use the InfluxDB and Telegraf Operator to monitor and get metrics from your Kubernetes workloads.
Join this webinar as InfluxData's Pat Gaughen and Wojciech Kocjan provide:
InfluxDB & Telegraf overview
Telegraf Operator deep-dive
Live demos of sample deployments!
Using Grafana with InfluxDB 2.0 and Flux Lang by Jacob LisiInfluxData
Flux, the new InfluxData data scripting and query language (formerly IFQL), super-charges queries both for analytics and data science. Jacob Lisi from Grafana Labs will give a quick overview of the language features as well as the moving parts for a working deployment. Grafana is an open source dashboard solution that shares Flux’s passion for analytics and data science. For that reason, they are very excited to showcase the new Flux support within Grafana, and a couple of common analytics use cases to get the most out of your data.
In this InfluxDays NYC 2019 talk, Jacob Lisi will share the latest updates they have made with their Flux builder in Grafana.
Introducing BinarySortedMultiMap - A new Flink state primitive to boost your ...Flink Forward
Flink Forward San Francisco 2022.
Probably everyone who has written stateful Apache Flink applications has used one of the fault-tolerant keyed state primitives ValueState, ListState, and MapState. With RocksDB, however, retrieving and updating items comes at an increased cost that you should be aware of. Sometimes, these may not be avoidable with the current API, e.g., for efficient event-time stream-sorting or streaming joins where you need to iterate one or two buffered streams in the right order. With FLIP-220, we are introducing a new state primitive: BinarySortedMultiMapState. This new form of state offers you to (a) efficiently store lists of values for a user-provided key, and (b) iterate keyed state in a well-defined sort order. Both features can be backed efficiently by RocksDB with a 2x performance improvement over the current workarounds. This talk will go into the details of the new API and its implementation, present how to use it in your application, and talk about the process of getting it into Flink.
by
Nico Kruber
Introduction to Apache ZooKeeper | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kvXlPd
This CloudxLab Introduction to Apache ZooKeeper tutorial helps you to understand ZooKeeper in detail. Below are the topics covered in this tutorial:
1) Data Model
2) Znode Types
3) Persistent Znode
4) Sequential Znode
5) Architecture
6) Election & Majority Demo
7) Why Do We Need Majority?
8) Guarantees - Sequential consistency, Atomicity, Single system image, Durability, Timeliness
9) ZooKeeper APIs
10) Watches & Triggers
11) ACLs - Access Control Lists
12) Usecases
13) When Not to Use ZooKeeper
Beautiful Monitoring With Grafana and InfluxDBleesjensen
Query your data streams with the time series database InfluxDB and then visualize the results with stunning Grafana dashboards. Quick and easy to set up. Fully scalable to millions of metrics per second.
Monitoring containerised apps creates a whole new set of challenges that traditional monitoring systems struggle with. In this talk, Brice Fernandes from Weaveworks will introduce and demo the open source Prometheus monitoring toolkit and its integration with Kubernetes. After this talk, you'll be able to use Prometheus to monitor your microservices on a Kubernetes cluster. We'll cover:
- An introduction to Kubernetes to manage containers;
- The monitoring maturity model;
- An overview of whitebox and blackbox monitoring;
- Monitoring with Prometheus;
- Using PromQL (the Prometheus Query Language) to monitor your app in a dynamic system
PromQL Deep Dive - The Prometheus Query Language Weaveworks
- What is PromQL
- PromQL operators
- PromQL functions
- Hands on: Building queries in PromQL
- Hands on: Visualizing PromQL in Grafana
- Prometheus alerts in PromQL
- Hands on: Creating an alert in Prometheus with PromQL
In the session, we discussed the End-to-end working of Apache Airflow that mainly focused on "Why What and How" factors. It includes the DAG creation/implementation, Architecture, pros & cons. It also includes how the DAG is created for scheduling the Job and what all steps are required to create the DAG using python script & finally with the working demo.
Near real-time statistical modeling and anomaly detection using Flink!Flink Forward
Flink Forward San Francisco 2022.
At ThousandEyes we receive billions of events every day that allow us to monitor the internet; the most important aspect of our platform is to detect outages and anomalies that have a potential to cause serious impact to customer applications and user experience. Automatic detection of such events at lowest latency and highest accuracy is extremely important for our customers and their business. After launching several resilient and low latency data pipelines in production using Flink we decided to take it up a notch; we leveraged Flink to build statistical models in near real-time and apply them on incoming stream of events to detect anomalies! In this session we will deep dive into the design as well as discuss pitfalls and learnings while developing our real-time platform that leverages Debezium, Kafka, Flink, ElasticCache and DynamoDB to process events at scale!
by
Kunal Umrigar & Balint Kurnasz
Optimizing the Grafana Platform for FluxInfluxData
Flux, the new InfluxData Data Scripting Language (formerly IFQL), super-charges queries both for analytics and data science. Matt will give a quick overview of the language features as well as the moving parts for a working deployment. Grafana is an open source dashboard solution that shares Flux’s passion for analytics and data science. For that reason, they are very excited to showcase the new Flux support within Grafana, and a couple of common analytics use cases to get the most out of your data.
In this talk, Matt Toback from Grafana Labs will share the latest updates they have made with their Flux builder in Grafana.
9:40 am InfluxDB 2.0 and Flux – The Road Ahead Paul Dix, Founder and CTO | ...InfluxData
Paul will continue to chart the road ahead by outlining the next phase of development for InfluxDB 2.0 and for Flux, InfluxData’s new data scripting and query language. He will discuss Flux’s role in multi-data source environments and explain how InfluxDB can be deployed in on-premise, multi-cloud, and hybrid environments.
Telegraf is an open-source server agent designed to collect metrics from stacks, sensors, and systems — with nearly 300 inputs and outputs. Telegraf Operator makes it easy to use Telegraf for monitoring your Kubernetes workloads. It enables developers to define a common output destination for all metrics, and configure Sidecar monitoring on your application pods using annotations. With the Telegraf sidecar container added, it will collect data and start pushing the metrics to a time series database, like InfluxDB. Discover how to use the Telegraf Operator as a control center for managing individual Telegraf instances which are deployed throughout Kubernetes clusters. Find out how to use the InfluxDB and Telegraf Operator to monitor and get metrics from your Kubernetes workloads.
Join this webinar as InfluxData's Pat Gaughen and Wojciech Kocjan provide:
InfluxDB & Telegraf overview
Telegraf Operator deep-dive
Live demos of sample deployments!
Using Grafana with InfluxDB 2.0 and Flux Lang by Jacob LisiInfluxData
Flux, the new InfluxData data scripting and query language (formerly IFQL), super-charges queries both for analytics and data science. Jacob Lisi from Grafana Labs will give a quick overview of the language features as well as the moving parts for a working deployment. Grafana is an open source dashboard solution that shares Flux’s passion for analytics and data science. For that reason, they are very excited to showcase the new Flux support within Grafana, and a couple of common analytics use cases to get the most out of your data.
In this InfluxDays NYC 2019 talk, Jacob Lisi will share the latest updates they have made with their Flux builder in Grafana.
Introducing BinarySortedMultiMap - A new Flink state primitive to boost your ...Flink Forward
Flink Forward San Francisco 2022.
Probably everyone who has written stateful Apache Flink applications has used one of the fault-tolerant keyed state primitives ValueState, ListState, and MapState. With RocksDB, however, retrieving and updating items comes at an increased cost that you should be aware of. Sometimes, these may not be avoidable with the current API, e.g., for efficient event-time stream-sorting or streaming joins where you need to iterate one or two buffered streams in the right order. With FLIP-220, we are introducing a new state primitive: BinarySortedMultiMapState. This new form of state offers you to (a) efficiently store lists of values for a user-provided key, and (b) iterate keyed state in a well-defined sort order. Both features can be backed efficiently by RocksDB with a 2x performance improvement over the current workarounds. This talk will go into the details of the new API and its implementation, present how to use it in your application, and talk about the process of getting it into Flink.
by
Nico Kruber
Introduction to Apache ZooKeeper | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kvXlPd
This CloudxLab Introduction to Apache ZooKeeper tutorial helps you to understand ZooKeeper in detail. Below are the topics covered in this tutorial:
1) Data Model
2) Znode Types
3) Persistent Znode
4) Sequential Znode
5) Architecture
6) Election & Majority Demo
7) Why Do We Need Majority?
8) Guarantees - Sequential consistency, Atomicity, Single system image, Durability, Timeliness
9) ZooKeeper APIs
10) Watches & Triggers
11) ACLs - Access Control Lists
12) Usecases
13) When Not to Use ZooKeeper
Beautiful Monitoring With Grafana and InfluxDBleesjensen
Query your data streams with the time series database InfluxDB and then visualize the results with stunning Grafana dashboards. Quick and easy to set up. Fully scalable to millions of metrics per second.
Monitoring containerised apps creates a whole new set of challenges that traditional monitoring systems struggle with. In this talk, Brice Fernandes from Weaveworks will introduce and demo the open source Prometheus monitoring toolkit and its integration with Kubernetes. After this talk, you'll be able to use Prometheus to monitor your microservices on a Kubernetes cluster. We'll cover:
- An introduction to Kubernetes to manage containers;
- The monitoring maturity model;
- An overview of whitebox and blackbox monitoring;
- Monitoring with Prometheus;
- Using PromQL (the Prometheus Query Language) to monitor your app in a dynamic system
PromQL Deep Dive - The Prometheus Query Language Weaveworks
- What is PromQL
- PromQL operators
- PromQL functions
- Hands on: Building queries in PromQL
- Hands on: Visualizing PromQL in Grafana
- Prometheus alerts in PromQL
- Hands on: Creating an alert in Prometheus with PromQL
In the session, we discussed the End-to-end working of Apache Airflow that mainly focused on "Why What and How" factors. It includes the DAG creation/implementation, Architecture, pros & cons. It also includes how the DAG is created for scheduling the Job and what all steps are required to create the DAG using python script & finally with the working demo.
Near real-time statistical modeling and anomaly detection using Flink!Flink Forward
Flink Forward San Francisco 2022.
At ThousandEyes we receive billions of events every day that allow us to monitor the internet; the most important aspect of our platform is to detect outages and anomalies that have a potential to cause serious impact to customer applications and user experience. Automatic detection of such events at lowest latency and highest accuracy is extremely important for our customers and their business. After launching several resilient and low latency data pipelines in production using Flink we decided to take it up a notch; we leveraged Flink to build statistical models in near real-time and apply them on incoming stream of events to detect anomalies! In this session we will deep dive into the design as well as discuss pitfalls and learnings while developing our real-time platform that leverages Debezium, Kafka, Flink, ElasticCache and DynamoDB to process events at scale!
by
Kunal Umrigar & Balint Kurnasz
Optimizing the Grafana Platform for FluxInfluxData
Flux, the new InfluxData Data Scripting Language (formerly IFQL), super-charges queries both for analytics and data science. Matt will give a quick overview of the language features as well as the moving parts for a working deployment. Grafana is an open source dashboard solution that shares Flux’s passion for analytics and data science. For that reason, they are very excited to showcase the new Flux support within Grafana, and a couple of common analytics use cases to get the most out of your data.
In this talk, Matt Toback from Grafana Labs will share the latest updates they have made with their Flux builder in Grafana.
9:40 am InfluxDB 2.0 and Flux – The Road Ahead Paul Dix, Founder and CTO | ...InfluxData
Paul will continue to chart the road ahead by outlining the next phase of development for InfluxDB 2.0 and for Flux, InfluxData’s new data scripting and query language. He will discuss Flux’s role in multi-data source environments and explain how InfluxDB can be deployed in on-premise, multi-cloud, and hybrid environments.
Paul will outline his vision around the platform and give the latest updates on IFQL ( a new query language), the decoupling of query and storage, the impact of hybrid cloud environments on architecture, cardinality, and discuss the technical directions of the platform. This talk will walk through the vision and architecture with demonstrations of working prototypes of the projects.
Optimizing InfluxDB Performance in the Real World by Dean Sheehan, Senior Dir...InfluxData
Dean will provide practical tips and techniques learned from helping hundreds of customers deploy InfluxDB and InfluxDB Enterprise. This includes hardware and architecture choices, schema design, configuration setup, and running queries.
You use InfluxData to monitor the performance of your infrastructure and apps—so it is equally important to keep your InfluxEnterprise instance up and running. Tim Hall, InfluxData VP of Products, will outline why and how you can monitor InfluxEnterprise with InfluxDB.
Towards an Integration of the Actor Model in an FRP Language for Small-Scale ...Takuo Watanabe
This paper presents an integration of the Actor model in Emfrp, a functional reactive programming language designed for resource constrained embedded systems. In this integration, actors not only express nodes that represent time-varying values, but also present communication mechanism. The integration provides a higher-level view of the internal representation of nodes, representations of time-varying values, as well as an actor-based inter-device communication mechanism.
Kapacitor - Real Time Data Processing EnginePrashant Vats
Kapacitor is a native data processing engine.Kapacitor is a native data processing engine.It can process both stream and batch data from InfluxDB.It lets you plug in your own custom logic or user-defined functions to process alerts with dynamic thresholds. Key Kapacitor Capabilities
-Alerting
-ETL (Extraction, Transformation and Loading)
-Action Oriented
-Streaming Analytics
-Anomaly Detection
Kapacitor uses a DSL (Domain Specific Language) called TICKscript to define tasks.
A detailed overview of Kapacitor, InfluxDB’s native data processing engine. How to install, configure and build custom TICKscripts enable alerting and anomaly detection
Writing a TSDB from scratch_ performance optimizations.pdfRomanKhavronenko
I'm one of maintainers of the open source TimeSeries database VictoriaMetrics, written in Go. It is used for APM or Kubernetes monitoring. The average VictoriaMetrics installation is processing 2-4 million samples/s on the ingestion path, 20-40 million samples/s on the read path. The biggest installations have more than 100 million samples/s on the ingestion path for a single cluster. This requires being clever with data processing to keep it efficient and scalable. In the talk, I'll cover the following optimizations for keeping the database fast:
1. String interning for lowering GC pressure. We use string interning for storing time series metadata (aka labels). However, this approach has downside of increased memory usage. When it is worth it to use string interning?
2. Metadata processing may require many regular expression matching and strings modification operations. Caching results of such operations helps to save CPU. But the downside could be increased memory usage. Which operations should be cached and which are not?
3. Limiting the number of concurrently running goroutines with CPU-bound load by the number of available CPU cores. This helps to control the memory usage on load spikes (which is a frequent event in monitoring). The limit also improves the processing speed of each goroutine, since it reduces the number of context switches. The downside of the approach is its complexity - it is easy to make a mistake and end up with a deadlock or inefficient resource utilization.
4. The better understanding of `sync.Pool`. For us, `sync.Pool` shows itself the best when used in CPU-bound code, while in IO-bound code it leads to excessive memory usage. The CPU-bound code has short ownership over the objects retrieved from the pool. In combination with p.3 (limited number of goroutines processing CPU-bound code) it gives the most efficient processing speed and memory usage since the chance to get a "hot" object from the pool is much higher.
Codemotion Rome 2015 - Building a drone from scratch with spare parts is a challenging business. To accomplish this journey, a Linux embedded stability control system is developed entirely from 0.This is a journey starting from the hardware choosing (a home WIFI router), to a stable and real flight. Unconventional implementations are one of the main topic, like using WiFi as communication between drone and pilot, HTML5 and COMET to show telemetry from the router web server, and implementing a entirely new protocol based on 802.11 Beacon Frames to prevent deauthentication attacks.
"Kafka Streams has a rich set of metrics for monitoring application health. Through these metrics, you can uncover performance issues, resource allocation concerns, and improve the performance of your application through deployment and configuration changes.
Providing dashboards around all of these metrics can be rather challenging. In addition, the vast amount of metrics is extensive. Which metrics are important depends on the type of application you’re building. Let's uncover what you should be monitoring, why you should be monitoring it, and leave you with properly monitored Kafka Streams applications.
Not only will you gain an understanding of task-id, sub-topology, and partition-id, but you will also see how to visualize that topology in a dashboard. Explore the new metrics added to Kafka Streams, since 3.0 was released, and go in-depth with the awesome end-to-end latency metrics. Finally, learn how to use these metrics to determine the number of instances an application needs when being deployed.
Unleash your Kafka Stream Application metrics making it easier to run your applications effectively."
InfluxData is excited to announce InfluxDB Clustered, the self-managed version of InfluxDB 3.0 with unparalleled flexibility, speed, performance, and scale. The evolution of InfluxDB Enterprise, InfluxDB Clustered is delivered as a collection of Kubernetes-based containers and services, which enables you to run and operate InfluxDB 3.0 where you need it, whether that's on-premises or in a private cloud environment. With this new enterprise offering, we’re excited to provide our customers with real-time queries, low-cost object storage, unlimited cardinality, and SQL language support – all with improved data access, support, and security! The newest version of InfluxDB was built on Apache Arrow, and through the open source ecosystem and integrations, extends the value of your time-stamped data.
Join this webinar to learn more about InfluxDB Clustered, and how to manage your large mission-critical workloads in the highly available database service offering!
In this webinar, Balaji Palani and Gunnar Aasen will dive into:
Key features of the new InfluxDB Clustered solution
Use cases for using the newest version of the purpose-built time series database
Live demo
During this 1-hour technical webinar, you’ll also get a chance to ask your questions live.
Best Practices for Leveraging the Apache Arrow EcosystemInfluxData
Apache Arrow is an open source project intended to provide a standardized columnar memory format for flat and hierarchical data. It enables more efficient analytics workloads for modern CPU and GPU hardware, which makes working with large data sets easier and cheaper.
InfluxData and Dremio are both members of the Apache Software Foundation (ASF). Dremio is a data lakehouse management service known for its scalability and capacity for direct querying across diverse data sources. InfluxDB is the purpose-built time series database, and InfluxDB 3.0 has a new columnar storage engine and uses the Arrow format for representing data and moving data to and from Parquet. Discover how InfluxDB and Dremio have advanced their solutions by relying on the Apache Arrow framework.
Join this live panel as Alex Merced and Anais Dotis-Georgiou dive into:
Advantages to utilizing the Apache Arrow ecosystem
Tips and tricks for implementing the columnar data structure
How developers can best utilize the ASF to innovate and contribute to new industry standards
How Bevi Uses InfluxDB and Grafana to Improve Predictive Maintenance and Redu...InfluxData
Bevi are the creators of smart water dispensers which empower people to choose their desired beverage — flat or sparkling, their desired flavor and temperature. Since 2014, Bevi users have saved more than 350 million bottles and cans. Their "smart" water coolers have prevented the extraction of 1.4 trillion oz of oil from Earth and have saved 21.7 billion grams of CO2 from the atmosphere.
Discover how Bevi uses a time series database to enable better predictive maintenance and alerting of their entire ecosystem — including the hardware and software. They are using InfluxDB to collect sensor data in real-time remotely from their internet-connected machines about their status and activity — i.e., flavor and CO2 levels, water temp, filter status, etc. They a7re using these metrics to improve their customer experience and continuously improve their sustainability practices. Gain tips and tricks on how to best utilize InfluxDB's schema-less design.
Join this webinar as Spencer Gagnon dives into:
Bevi's approach to reducing organizations' carbon footprint — they are saving 50K+ bottles and cans annually
Their entire system architecture — including InfluxDB Cloud, Grafana, Kafka, and DigitalOcean
The importance of using time-stamped data to extend the life of their machines
Power Your Predictive Analytics with InfluxDBInfluxData
If you're using InfluxDB to store and manage your time series data, you're already off to a great start. But why stop there? In our upcoming webinar, we'll show you how to take your data analysis to the next level by building predictive analytics using a variety of tools and techniques.
We will demonstrate how to use Quix to create custom dashboards and visualizations that allow you to monitor your data in real-time. We'll also introduce you to Hugging Face, a powerful tool for building models that can predict future trends and identify anomalies. With these tools at your disposal, you'll be able to extract valuable insights from your data and make more informed decisions about the future. Don't miss out on this opportunity to improve your data analysis skills and take your business to the next level!
What you will learn:
Use InfluxDB to store and manage time series data
Utilize Quix and Hugging Face to build models, visualize trends, and identify anomalies
Extract valuable insights from your data
Improve your data analysis skills to make informed decision
How Teréga Replaces Legacy Data Historians with InfluxDB, AWS and IO-Base InfluxData
Are you considering replacing your legacy data historian and moving your OT data to the cloud? Join this technical webinar to learn how to adopt InfluxDB and IO Base - a digital platform used to improve operational efficiencies!
Teréga Solutions are the creators of digital solutions used to improve energy efficiencies and to address decarbonization challenges. Their network includes 5,000+ km of gas pipelines within France; they aim to help France attain carbon neutrality by 2050. With these impressive goals in mind, Teréga has created IO-Base — the digital platform to improve industrial performance, and increase profitability. Creating digital twins for their clients allows them to collect data from all production sites and view it in real time, from anywhere and at any time.
Discover how Teréga uses InfluxDB, Docker, and AWS to monitor its gas and hydrogen pipeline infrastructure. They chose to replace their legacy data historian with InfluxDB — the purpose built time series database. They are collecting more than 100K different metrics at various frequencies — some are collected every 5 seconds to only every 1-2 minutes. THey have reduced overall IT spend by 50% and collect 2x the amount of data at 20x frequency! By using various industrial protocols (Modbus, OPC-UA, etc.), Teréga improved output, reduced the TCO, and is now able to create added-value services: forecast, monitoring, predictive maintenance.
Join this webinar as Thomas Delquié dives into:
Teréga's approach to modernizing fossil fuel pipelines IT systems while improving yields and safety
Their centralized methodology to collecting sensor, hardware, and network metrics
The importance of time series data and why they chose InfluxDB
Build an Edge-to-Cloud Solution with the MING StackInfluxData
FlowForge enables organizations to reliably deliver Node-RED applications in a continuous, collaborative, and secure manner. Node-RED is the popular, low-code programming solution that makes it easy to connect different services using a visual programming environment. InfluxData is the creator of InfluxDB, the purpose-built time series database run by developers at scale and in any environment in the cloud, on-premises, or at the edge.
Jump-start monitoring your industrial IoT devices and discover how to build an edge-to-cloud solution with the MING stack. The MING stack includes Mosquitto/MQTT, InfluxDB, Node-RED, and Grafana. This solution can be used to improve fleet management, enable predictive maintenance of industrial machines and power generation equipment (i.e. turbines and generators) and increase safety practices (i.e. buildings, construction sites). Join this webinar to learn best practices from industrial IoT SME's.
In this webinar, Robert Marcer and Jay Clifford dive into:
Best practices for monitoring sensor data collected by everyone — from the edge to the factory
Tips and tricks for using Node-RED and InfluxDB together
Demo — see Node-RED and InfluxDB live
Meet the Founders: An Open Discussion About Rewriting Using RustInfluxData
Rust is a systems programming language designed for high performance, type safety, and concurrency. According to Stack Overflow’s annual survey in 2022, Rust is the most loved language with 87% of developers saying they want to continue using it. The same survey also reported that nearly 20% of developers aren’t currently using Rust, but want to start developing using it.
Ockam’s suite of programming libraries, command line tools, and managed cloud services enable developers to orchestrate end-to-end encryption. InfluxDB is the purpose-built time series database developed to handle time series data for IoT, monitoring, and real-time analytics. Ockam was originally developed using C, and InfluxDB was originally written using Go; both solutions have been completely rewritten in Rust. Discover why two founders decided to rewrite their developer tools using Rust, and gain insight into the strategy beforehand and the entire process.
Join this live panel as Mrinal Wadhwa and Paul Dix dive into:
Their approach to rewriting a project in Rust
How to build and train engineering teams
Tips and tricks learned along the way - pitfalls to look out for!
Join this webinar as there will be a live discussion with Q&A
InfluxData is excited to announce the general availability of InfluxDB Cloud Dedicated! It is a fully managed time series database service running on cloud infrastructure resources that are dedicated to a single tenant. With this new offering, we’re excited to provide our customers with additional security options, and more custom configuration options to best suit customers’ workload requirements. Join this webinar to learn more about InfluxDB Cloud, and the new dedicated database service offering!
In this webinar, Balaji Palani and Gary Fowler will dive into:
Key features of the new InfluxDB Cloud Dedicated solution
Use cases for using the newest version of the purpose-built time series database
Live demo
During this 1-hour technical webinar, you’ll also get a chance to ask your questions live.
Gain Better Observability with OpenTelemetry and InfluxDB InfluxData
Many developers and DevOps engineers have become aware of using their observability data to gain greater insights into their infrastructure systems. InfluxDB is the purpose-built time series database used to collect metrics and gain observability into apps, servers, containers, and networks. Developers use InfluxDB to improve the quality and efficiency of their CI/CD pipelines. Start using InfluxDB to aggregate infrastructure and application performance monitoring metrics to enable better anomaly detection, root-cause analysis, and alerting.
This session will demonstrate how to record metrics, logs, and traces with one library — OpenTelemetry — and store them in one open source time series database — InfluxDB. Zoe will demonstrate how easy it is to set up the OpenTelemetry Operator for Kubernetes and to store and analyze your data in InfluxDB.
How a Heat Treating Plant Ensures Tight Process Control and Exceptional Quali...InfluxData
American Metal Processing Company ("AMP") is the US' largest commercial rotary heat treat facility with customers in the automotive, construction, military, and agriculture industries. They use their atmosphere-protected rotary retort furnaces to provide their clients with three primary hardening services: neutral hardening (quench and temper), carburizing, and carbonitriding.
This furnace style ensures consistent, uniform heat treatment process vs. traditional batch-or-belt-style furnaces; excels at processing high volumes of smaller parts with tight tolerances; and improves the strength and toughness of plain carbon steels. Discover why AMP’s use of Telegraf, InfluxDB, Node-RED, and Grafana allows them to gain 24/7 insights into their plant operations and metallurgical results. Learn how they use time-stamped data to gain accurate metrics about their consumables usage, furnace profiles, and machine status.
Join this webinar as Grant Pinkos dives into:
American Metal Processing's approach to heat treating in a digitized environment through connected systems
Their approach to collecting and measuring sensor data to enable predictive maintenance and improve product quality
Why they need a time series database for managing and analyzing vast amounts of time-stamped data
How Delft University's Engineering Students Make Their EV Formula-Style Race ...InfluxData
Delft University is the oldest and largest technical university in the Netherlands with 25,000+ students. Since 1999, they have had a team of students (undergraduate and graduate) designing, building, and racing cars, as part of the Formula Student worldwide competition. The competition has grown to include teams from 1K+ universities in 20+ countries. Students are responsible for all aspects of car manufacturing (research, construction, testing, developing, marketing, management, and fundraising). Delft University's team includes 90 students across disciplines.
Discover how Delft University's team uses Marple and InfluxDB to collect telemetry and sensor metrics while they develop, test, and race their electrics cars. They collect sensor data about their EV's control systems using a time series platform. During races, they are collecting IoT data about their batteries, accelerometer, gyroscope, tires, etc. The engineers are able to share important car stats during races which help the drivers tweak their driving decisions — all with the goal of winning. After races, the entire team are able to analyze data in Marple to understand what to do better next time. By using Marple + InfluxDB, their team are able to collect, share and analyze high frequency car data used to make their car faster at competitions.
Join this webinar as Robbin Baauw and Nero Vanbiervliet dive into:
Marple's approach to empowering engineers to organize, analyze, and visualize their data
Delft University's collaborative methodology to building and racing their Formula-style race car
How InfluxDB is crucial to their collaborative engineering and racing process
Introducing InfluxDB’s New Time Series Database Storage EngineInfluxData
InfluxData is excited to announce the general availability of InfluxDB Cloud's new storage engine! It is a cloud-native, real-time, columnar database optimized for time series data. InfluxDB's rebuilt core was coded in Rust and sits on top of Apache Arrow and DataFusion. InfluxData's team picked Apache Parquet as the persistent format. In this webinar, Paul Dix and Balaji Palani will demonstrate key product features including the removal of cardinality limits!
They will dive into:
The next phase of the InfluxDB platform
How using Apache Arrow's ecosystem has improved InfluxDB's performance and scalability
Key features of InfluxDB Cloud's new core — including SQL native support
Start Automating InfluxDB Deployments at the Edge with balena InfluxData
balena.io helps companies develop, deploy, update, and manage IoT devices. By using Linux containers and other cloud technologies, balena enables teams to quickly and easily build fleets of connected devices. Developers are able to use containers with the language of choice and pull IoT sensor data from 70+ different single board computers into balenaCloud. Discover how to use balena.io to automate your InfluxDB deployments at the edge!
During this one-hour session, experts from balena and InfluxData will demonstrate how to build and deploy your own air quality IoT solution. You will learn:
The fundamentals of IoT sensor deployment and management using balena.
How to use a time series platform to collect and visualize metrics from edge devices.
Tips and tricks to using balenaCloud to automate InfluxDB deployments and Telegraf configurations.
How to use InfluxDB's Edge Data Replication feature to collect sensor data and push it to InfluxDB Cloud for analysis.
No coding experience required, just a curiosity to start your own IoT adventure.
Understanding InfluxDB’s New Storage EngineInfluxData
Learn more about InfluxDB’s new storage engine! The team developed a cloud-native, real-time, columnar database optimized for time series data. We built it all in Rust and it sits on top of Apache Arrow and DataFusion. We chose Apache Parquet as the persistent format, which is an open source columnar data file format. This new storage engine provides InfluxDB Cloud users with new functionality, including the removal of cardinality limits, so developers can bring in massive amounts of time series data at scale.
In this webinar, Anais Dotis-Georgiou will dive into:
Requirements for rebuilding InfluxDB’s core
Key product features and timeline
How Apache Arrow’s ecosystem is used to meet those requirements
Stick around for a demo and live Q&A
Streamline and Scale Out Data Pipelines with Kubernetes, Telegraf, and InfluxDBInfluxData
RudderStack — the creators of the leading open source Customer Data Platform (CDP) — needed a scalable way to collect and store metrics related to customer events and processing times (down to the nanosecond). They provide their clients with data pipelines that simplify data collection from applications, websites, and SaaS platforms. RudderStack's solution enables clients to stream customer data in real time — they quickly deploy flexible data pipelines that send the data to the customer's entire stack without engineering headaches. Customers are able to stream data from any tool using their 16+ SDK's, and they are able to transform the data in-transit using JavaScript or Python. How does RudderStack use a time series platform to provide their customers with real-time analytics?
Join this webinar as Ryan McCrary dives into:
RudderStack's approach to streamlining data pipelines with their 180+ out-of-the-box integrations
Their data architecture including Kapacitor for alerting and Grafana for customized dashboards
Why using InfluxDB was crucial for them for fast data collection and providing single-sources of truths for their customers
Ward Bowman [PTC] | ThingWorx Long-Term Data Storage with InfluxDB | InfluxDa...InfluxData
Customers using ThingWorx and the Manufacturing Solutions often need to store property data longer than the Solutions default to. These customers are recommended to use InfluxDB, and this presentation will cover the key considerations for moving to InfluxDB vs the standard ThingWorx value streams. Join this session as Ward highlights ThingWorx’s solution and its easy implementation process.
Scott Anderson [InfluxData] | New & Upcoming Flux Features | InfluxDays 2022InfluxData
Two new features are coming to Flux that add flexibility
and functionality to your data workflow—polymorphic
labels and dynamic types. This session walks through
these new features and shows how they work.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
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.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
TOP 10 B TECH COLLEGES IN JAIPUR 2024.pptxnikitacareer3
Looking for the best engineering colleges in Jaipur for 2024?
Check out our list of the top 10 B.Tech colleges to help you make the right choice for your future career!
1) MNIT
2) MANIPAL UNIV
3) LNMIIT
4) NIMS UNIV
5) JECRC
6) VIVEKANANDA GLOBAL UNIV
7) BIT JAIPUR
8) APEX UNIV
9) AMITY UNIV.
10) JNU
TO KNOW MORE ABOUT COLLEGES, FEES AND PLACEMENT, WATCH THE FULL VIDEO GIVEN BELOW ON "TOP 10 B TECH COLLEGES IN JAIPUR"
https://www.youtube.com/watch?v=vSNje0MBh7g
VISIT CAREER MANTRA PORTAL TO KNOW MORE ABOUT COLLEGES/UNIVERSITITES in Jaipur:
https://careermantra.net/colleges/3378/Jaipur/b-tech
Get all the information you need to plan your next steps in your medical career with Career Mantra!
https://careermantra.net/
24. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
25. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
Comments
26. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
Named Arguments
27. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
String Literals
28. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
Buckets, not DBs
29. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
Duration Literal
30. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:2018-11-07T00:00:00Z)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
Time Literal
31. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
Pipe forward operator
32. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => r._measurement == "cpu" and r._field == "usage_system")
Anonymous Function
33. // get all data from the telegraf db
from(bucket:”telegraf/autogen”)
// filter that by the last hour
|> range(start:-1h)
// filter further by series with a specific measurement and field
|> filter(fn: (r) => (r._measurement == "cpu" or r._measurement == “cpu")
and r.host == “serverA")
Predicate Function
45. Table
_measurement host region _field _time _value
mem A west free 2018-06-14T09:15:00 10
mem A west free 2018-06-14T09:14:50 10
46. _measurement host region _field _time _value
mem A west free 2018-06-14T09:15:00 10
mem A west free 2018-06-14T09:14:50 10
Column
47. _measurement host region _field _time _value
mem A west free 2018-06-14T09:15:00 10
mem A west free 2018-06-14T09:14:50 10
Record
48. _measurement host region _field _time _value
mem A west free 2018-06-14T09:15:00 10
mem A west free 2018-06-14T09:14:50 10
Group Key
_measurement=mem,host=A,region=west,_field=free
49. _measurement host region _field _time _value
mem A west free 2018-06-14T09:15:00 10
mem A west free 2018-06-14T09:14:50 10
Every record has
the same value!
_measurement=mem,host=A,region=west,_field=free
50. Table Per Series
_measurement host region _field _time _value
mem A west free 2018-06-14T09:15:00 10
mem A west free 2018-06-14T09:14:50 11
_measurement host region _field _time _value
mem B west free 2018-06-14T09:15:00 20
mem B west free 2018-06-14T09:14:50 22
_measurement host region _field _time _value
cpu A west usage_user 2018-06-14T09:15:00 45
cpu A west usage_user 2018-06-14T09:14:50 49
_measurement host region _field _time _value
cpu A west usage_system 2018-06-14T09:15:00 35
cpu A west usage_system 2018-06-14T09:14:50 38
52. input tables -> function -> output tables
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> sum()
53. input tables -> function -> output tables
What to sum on?
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> sum()
54. input tables -> function -> output tables
Default columns argument
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> sum(columns: [“_value”])
55. input tables -> function -> output tables
_meas
ureme
host region _field _time _valu
e
mem A west free 2018-06-
14T09:1
10
mem A west free 2018-06-
14T09:1
11
_meas
ureme
host region _field _time _valu
emem B west free 2018-06-
14T09:15
20
mem B west free 2018-06-
14T09:14
22
Input in table form
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> sum()
56. input tables -> function -> output tables
_meas
ureme
host region _field _time _valu
e
mem A west free 2018-06-
14T09:1
10
mem A west free 2018-06-
14T09:1
11
_meas
ureme
host region _field _time _valu
emem B west free 2018-06-
14T09:15
20
mem B west free 2018-06-
14T09:14
22
sum()
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> sum()
57. input tables -> function -> output tables
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:50, start:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> sum()
_meas
ureme
host region _field _time _valu
e
mem A west free 2018-06-
14T09:1
10
mem A west free 2018-06-
14T09:1
11
_meas
ureme
host region _field _time _valu
emem B west free 2018-06-
14T09:15
20
mem B west free 2018-06-
14T09:14
22
sum()
_meas
ureme
host region _field _time _valu
e
mem A west free 2018-06-
14T09:1
21
_meas
ureme
host region _field _time _valu
e
mem B west free 2018-06-
14T09:15
42
60. window
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> window(every:20s)
30s of data (4 samples)
61. window
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> window(every:20s)
split into 20s windows
62. window
_meas host region _field _time _valu
mem A west free …14:30 10
mem A west free …14:40 11
mem A west free …14:50 12
mem A west free …15:00 13
_meas host region _field _time _valu
mem B west free …14:30 20
mem B west free …14:40 22
mem B west free …14:50 23
mem B west free …15:00 24
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> window(every:20s)
Input
63. window
_meas host region _field _time _valu
mem A west free …14:30 10
mem A west free …14:40 11
mem A west free …14:50 12
mem A west free …15:00 13
_meas host region _field _time _valu
mem B west free …14:30 20
mem B west free …14:40 22
mem B west free …14:50 23
mem B west free …15:00 24
window(
every:20s)
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> window(every:20s)
64. window
_meas host region _field _time _valu
mem A west free …14:30 10
mem A west free …14:40 11
mem A west free …14:50 12
mem A west free …15:00 13
_meas host region _field _time _valu
mem B west free …14:30 20
mem B west free …14:40 22
mem B west free …14:50 23
mem B west free …15:00 24
window(
every:20s)
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> window(every:20s) _meas
ureme
host region _field _time _valu
emem A west free …14:30 10
mem A west free …14:40 11
_meas
ureme
host region _field _time _valu
emem B west free …14:50 23
mem B west free …15:00 24
_meas
ureme
host region _field _time _valu
emem B west free …14:30 20
mem B west free …14:40 22
_meas
ureme
host region _field _time _valu
emem A west free …14:50 12
mem A west free …15:00 13
65. window
_meas host region _field _time _valu
mem A west free …14:30 10
mem A west free …14:40 11
mem A west free …14:50 12
mem A west free …15:00 13
_meas host region _field _time _valu
mem B west free …14:30 20
mem B west free …14:40 22
mem B west free …14:50 23
mem B west free …15:00 24
window(
every:20s)
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> window(every:20s) _meas
ureme
host region _field _time _valu
emem A west free …14:30 10
mem A west free …14:40 11
_meas
ureme
host region _field _time _valu
emem B west free …14:50 23
mem B west free …15:00 24
_meas
ureme
host region _field _time _valu
emem B west free …14:30 20
mem B west free …14:40 22
_meas
ureme
host region _field _time _valu
emem A west free …14:50 12
mem A west free …15:00 13
N to M tables
67. group
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> group(keys:[“region"])
68. group
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> group(keys:[“region"])
new group key
69. group
_meas host region _field _time _valu
mem A west free …14:30 10
mem A west free …14:40 11
mem A west free …14:50 12
mem A west free …15:00 13
_meas host region _field _time _valu
mem B west free …14:30 20
mem B west free …14:40 22
mem B west free …14:50 23
mem B west free …15:00 24
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> group(keys:[“region"])
70. group
_meas host region _field _time _valu
mem A west free …14:30 10
mem A west free …14:40 11
mem A west free …14:50 12
mem A west free …15:00 13
_meas host region _field _time _valu
mem B west free …14:30 20
mem B west free …14:40 22
mem B west free …14:50 23
mem B west free …15:00 24
group(
keys:
[“region”])
// example query
from(db:"telegraf")
|> range(start:2018-06-14T09:14:30, end:2018-06-14T09:15:01)
|> filter(fn: r => r._measurement == “mem" and
r._field == “free”)
|> group(keys:[“region"])
_meas
ureme
host region _field _time _valu
emem A west free …14:30 10
mem B west free …14:30 20
mem A west free …14:40 11
mem B west free …14:40 21
mem A west free …14:50 12
mem B west free …14:50 22
mem B west free …15:00 13
mem B west free …15:00 23
N to M tables
M == cardinality(group keys)
107. option task = {name: "slack critical alerts", every: 1m}
import "slack"
lastNotificationTime = from(bucket: "notificatons")
|> filter(fn: (r) => r.level == "critical" and r._field == "alert_time")
|> group(none:true)
|> last()
|> recordValue(column:"_value")
from(bucket: "alerts")
|> range(start: lastNotificationTime)
|> filter(fn: (r) => r.level == "critical")
// shape the alert data to what we care about in notifications
|> renameColumn(from: "_time", to: "alert_time")
|> renameColumn(from: "_value", to: "used_percent")
// set the time the notification is being sent
|> addColumn(key: "_time", value: now())
// get rid of unneeded columns
|> drop(columns: ["_start", "_stop"])
// write the message
|> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%")
|> slack.to(config: loadSecret(name: “slack_alert_config”), message: “_value”)
|> to(bucket: “notifications")
108. option task = {name: "slack critical alerts", every: 1m}
import "slack"
lastNotificationTime = from(bucket: "notificatons")
|> filter(fn: (r) => r.level == "critical" and r._field == "alert_time")
|> group(none:true)
|> last()
|> recordValue(column:"_value")
from(bucket: "alerts")
|> range(start: lastNotificationTime)
|> filter(fn: (r) => r.level == “critical”)
// shape the alert data to what we care about in notifications
|> renameColumn(from: "_time", to: "alert_time")
|> renameColumn(from: "_value", to: "used_percent")
// set the time the notification is being sent
|> addColumn(key: "_time", value: now())
// get rid of unneeded columns
|> drop(columns: ["_start", "_stop"])
// write the message
|> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%")
|> slack.to(config: loadSecret(name: "slack_alert"))
|> to(bucket: “notifications")
We have state so we don’t resend
109. option task = {name: "slack critical alerts", every: 1m}
import "slack"
lastNotificationTime = from(bucket: "notificatons")
|> filter(fn: (r) => r.level == "critical" and r._field == "alert_time")
|> group(none:true)
|> last()
|> recordValue(column:"_value")
from(bucket: "alerts")
|> range(start: lastNotificationTime)
|> filter(fn: (r) => r.level == "critical")
// shape the alert data to what we care about in notifications
|> renameColumn(from: "_time", to: "alert_time")
|> renameColumn(from: "_value", to: "used_percent")
// set the time the notification is being sent
|> addColumn(key: "_time", value: now())
// get rid of unneeded columns
|> drop(columns: ["_start", "_stop"])
// write the message
|> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%")
|> slack.to(config: loadSecret(name: "slack_alert"))
|> to(bucket: “notifications")
Use last time as argument to range
110. option task = {name: "slack critical alerts", every: 1m}
import "slack"
lastNotificationTime = from(bucket: "notificatons")
|> filter(fn: (r) => r.level == "critical" and r._field == "alert_time")
|> group(none:true)
|> last()
|> recordValue(column:"_value")
from(bucket: "alerts")
|> range(start: lastNotificationTime)
|> filter(fn: (r) => r.level == "critical")
// shape the alert data to what we care about in notifications
|> renameColumn(from: "_time", to: "alert_time")
|> renameColumn(from: "_value", to: "used_percent")
// set the time the notification is being sent
|> addColumn(key: "_time", value: now())
// get rid of unneeded columns
|> drop(columns: ["_start", "_stop"])
// write the message
|> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%")
|> slack.to(config: loadSecret(name: "slack_alert"))
|> to(bucket: “notifications")
Now function for current time
111. option task = {name: "slack critical alerts", every: 1m}
import "slack"
lastNotificationTime = from(bucket: "notificatons")
|> filter(fn: (r) => r.level == "critical" and r._field == "alert_time")
|> group(none:true)
|> last()
|> recordValue(column:"_value")
from(bucket: "alerts")
|> range(start: lastNotificationTime)
|> filter(fn: (r) => r.level == "critical")
// shape the alert data to what we care about in notifications
|> renameColumn(from: "_time", to: "alert_time")
|> renameColumn(from: "_value", to: "used_percent")
// set the time the notification is being sent
|> addColumn(key: "_time", value: now())
// get rid of unneeded columns
|> drop(columns: ["_start", "_stop"])
// write the message
|> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%")
|> slack.to(config: loadSecret(name: "slack_alert"))
|> to(bucket: “notifications")
Map function to iterate
over values
112. option task = {name: "slack critical alerts", every: 1m}
import "slack"
lastNotificationTime = from(bucket: "notificatons")
|> filter(fn: (r) => r.level == "critical" and r._field == "alert_time")
|> group(none:true)
|> last()
|> recordValue(column:"_value")
from(bucket: "alerts")
|> range(start: lastNotificationTime)
|> filter(fn: (r) => r.level == "critical")
// shape the alert data to what we care about in notifications
|> renameColumn(from: "_time", to: "alert_time")
|> renameColumn(from: "_value", to: "used_percent")
// set the time the notification is being sent
|> addColumn(key: "_time", value: now())
// get rid of unneeded columns
|> drop(columns: ["_start", "_stop"])
// write the message
|> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%")
|> slack.to(config: loadSecret(name: "slack_alert"))
|> to(bucket: “notifications")
String interpolation
113. option task = {name: "slack critical alerts", every: 1m}
import "slack"
lastNotificationTime = from(bucket: "notificatons")
|> filter(fn: (r) => r.level == "critical" and r._field == "alert_time")
|> group(none:true)
|> last()
|> recordValue(column:"_value")
from(bucket: "alerts")
|> range(start: lastNotificationTime)
|> filter(fn: (r) => r.level == "critical")
// shape the alert data to what we care about in notifications
|> renameColumn(from: "_time", to: "alert_time")
|> renameColumn(from: "_value", to: "used_percent")
// set the time the notification is being sent
|> addColumn(key: "_time", value: now())
// get rid of unneeded columns
|> drop(columns: ["_start", "_stop"])
// write the message
|> map(fn: (r) => r._value = "{r.host} disk usage is at {r.used_percent}%")
|> slack.to(config: loadSecret(name: "slack_alert"))
|> to(bucket: “notifications")
Send to Slack and
record in InfluxDB