This document provides a tutorial overview of Apache Flink, including what Flink is, why it is useful, how it processes both bounded and unbounded data, the anatomy of a Flink application, windowing in Flink, and how it handles event time and process time. Flink is an open-source stream and batch processing platform that can process infinite datasets continuously while maintaining accuracy and recovering from failures. It provides exactly-once semantics through checkpointing and handles both bounded finite datasets and unbounded streaming data through its DataStream and DataSet APIs. The tutorial then discusses windowing concepts in Flink and provides code examples of word count applications with and without windows. It also explains the concepts of event time and processing time in F
Aljoscha Krettek - Apache Flink® and IoT: How Stateful Event-Time Processing ...Ververica
This document discusses Apache Flink and how it enables accurate analytics for Internet of Things (IoT) applications through stateful event-time stream processing. It begins by defining IoT and event-time stream processing, explaining that IoT data is continuously generated and has timestamps. It then discusses challenges like time mismatches between event time and processing time. The document also covers Flink's capabilities for stateful stream processing including failure handling through checkpoints, updating applications using savepoints, and high availability of the JobManager. It positions Flink as a stateful stream processor well-suited for IoT use cases.
2018-04 Kafka Summit London: Stephan Ewen - "Apache Flink and Apache Kafka fo...Ververica
Learn how the combination of Apache Kafka and Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable.
The new world of applications and fast data architectures has broken up the database: Raw data persistence comes in the form of event logs, and the state of the world is computed by a stream processor. Apache Kafka provides a strong solution for the event log, while Apache Flink forms a powerful foundation for the computation over the event streams.
In this talk we discuss how Flink’s abstraction and management of application state have evolved over time and how Flink’s snapshot persistence model and Kafka’s log work together to form a base to build ‘versioned applications’. We will also show how end-to-end exactly-once processing works through a smart integration of Kafka’s transactions and Flink’s checkpointing mechanism.
Debunking Common Myths in Stream ProcessingKostas Tzoumas
This document discusses stream processing with Apache Flink. It begins by defining streaming as the continuous processing of never-ending data streams. It then debunks four common myths about stream processing: 1) that there is always a throughput/latency tradeoff, showing that Flink can achieve high throughput and low latency; 2) that exactly-once processing is not possible, but Flink provides exactly-once state guarantees with checkpoints; 3) that streaming is only for real-time applications, whereas it can also be used for historical data; and 4) that streaming is too hard, whereas most data problems are actually streaming problems. The document concludes by discussing Flink's community and examples of companies using Flink in production.
Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...Ververica
Stream Processing is emerging as a popular paradigm for data processing architectures, because it handles the continuous nature of most data and computation and gets rid of artificial boundaries and delays.
The fact that stream processing is gaining rapid adoption is also due to more powerful and maturing technology (much of it open source at the ASF) that has solved many of the hard technical challenges.
We discuss Apache Flink's approach to high performance stream processing with state, strong consistency, low latency, and sophisticated handling of time. With such building blocks, Apache Flink can handle classes of problems previously considered out of reach for stream processing. We also take a sneak preview at the next steps for Flink.
Flink Forward Berlin 2017: Kostas Kloudas - Complex Event Processing with Fli...Flink Forward
Pattern matching over event streams is increasingly being employed in many areas including financial services and click stream analysis. Flink, as a true stream processing engine, emerges as a natural candidate for these usecases. In this talk, we will present FlinkCEP, a library for Complex Event Processing (CEP) based on Flink. At the conceptual level, we will see the different patterns the library can support, we will present the main building blocks we implemented to support them, and we will discuss possible future additions that will further enhance the coverage of the library. At the practical level, we will show how the integration of FlinkCEP with Flink allows the former to take advantage of Flink's rich ecosystem (e.g. connectors) and its stream processing capabilities, such as support for event-time processing, exactly-once state semantics, fault-tolerance, savepoints and high throughput.
Aljoscha Krettek - Apache Flink for IoT: How Event-Time Processing Enables Ea...Ververica
Back to the program
Apache Flink for IoT: How Event-Time Processing Enables Easy and Accurate Analytics
Thursday 17th
from 18:00 to 18:40
Theatre 19
-
Keynote
In this talk I’ll give a very short introduction to stream processing in general and then dive into event-time based stream processing. I will outline how this is important for IoT applications and also why it is such a challenging topic. Afterwards we’ll look at some real-world IoT use cases that are enabled by the support for robust event-time based stream processing provided by Apache Flink™. We will especially focus on easy of use and on correctness of results in the face of errors.
In the first half of the talk we’ll cover the basics of stream processing. We will look at the differences between event-time based and processing-time and at stateful stream processing. While on this, we’ll also highlight how the combination of these features is essential for doing robust stream processing in an IoT setting.
In the second part, we will look at how Flink solves some of the challenges that arise in event-time based processing and how that enables novel applications in the IoT space. We will do the latter by looking at a collection of real-world IoT use cases.
Some of the topics covered will be:
- Apache Flink
- Stateful Stream Processing
- Event Time vs. Processing Time Windowing
- Processing of out-of-order events
- IoT use cases
Tzu-Li (Gordon) Tai - Stateful Stream Processing with Apache FlinkVerverica
As Apache Flink continues to push the boundaries of stateful stream processing as an integral part of its past releases, increasing numbers of users are starting to realize the potential of stateful stream processing as a promising paradigm for robust and reactive data analytics as well as event-driven applications.
This talk aims at covering the general idea and motivations of stateful stream processing, and how Flink enables it with its powerful set of state management features and programming APIs. In addition to that, we will also take a look at the recent advancements related to Flink's state management and large state handling that were driven by our team at data Artisans team in the latest version 1.3 (expected release by end of May / early June).
Aljoscha Krettek - Apache Flink® and IoT: How Stateful Event-Time Processing ...Ververica
This document discusses Apache Flink and how it enables accurate analytics for Internet of Things (IoT) applications through stateful event-time stream processing. It begins by defining IoT and event-time stream processing, explaining that IoT data is continuously generated and has timestamps. It then discusses challenges like time mismatches between event time and processing time. The document also covers Flink's capabilities for stateful stream processing including failure handling through checkpoints, updating applications using savepoints, and high availability of the JobManager. It positions Flink as a stateful stream processor well-suited for IoT use cases.
2018-04 Kafka Summit London: Stephan Ewen - "Apache Flink and Apache Kafka fo...Ververica
Learn how the combination of Apache Kafka and Apache Flink is making stateful stream processing even more expressive and flexible to support applications in streaming that were previously not considered streamable.
The new world of applications and fast data architectures has broken up the database: Raw data persistence comes in the form of event logs, and the state of the world is computed by a stream processor. Apache Kafka provides a strong solution for the event log, while Apache Flink forms a powerful foundation for the computation over the event streams.
In this talk we discuss how Flink’s abstraction and management of application state have evolved over time and how Flink’s snapshot persistence model and Kafka’s log work together to form a base to build ‘versioned applications’. We will also show how end-to-end exactly-once processing works through a smart integration of Kafka’s transactions and Flink’s checkpointing mechanism.
Debunking Common Myths in Stream ProcessingKostas Tzoumas
This document discusses stream processing with Apache Flink. It begins by defining streaming as the continuous processing of never-ending data streams. It then debunks four common myths about stream processing: 1) that there is always a throughput/latency tradeoff, showing that Flink can achieve high throughput and low latency; 2) that exactly-once processing is not possible, but Flink provides exactly-once state guarantees with checkpoints; 3) that streaming is only for real-time applications, whereas it can also be used for historical data; and 4) that streaming is too hard, whereas most data problems are actually streaming problems. The document concludes by discussing Flink's community and examples of companies using Flink in production.
Keynote: Stephan Ewen - Stream Processing as a Foundational Paradigm and Apac...Ververica
Stream Processing is emerging as a popular paradigm for data processing architectures, because it handles the continuous nature of most data and computation and gets rid of artificial boundaries and delays.
The fact that stream processing is gaining rapid adoption is also due to more powerful and maturing technology (much of it open source at the ASF) that has solved many of the hard technical challenges.
We discuss Apache Flink's approach to high performance stream processing with state, strong consistency, low latency, and sophisticated handling of time. With such building blocks, Apache Flink can handle classes of problems previously considered out of reach for stream processing. We also take a sneak preview at the next steps for Flink.
Flink Forward Berlin 2017: Kostas Kloudas - Complex Event Processing with Fli...Flink Forward
Pattern matching over event streams is increasingly being employed in many areas including financial services and click stream analysis. Flink, as a true stream processing engine, emerges as a natural candidate for these usecases. In this talk, we will present FlinkCEP, a library for Complex Event Processing (CEP) based on Flink. At the conceptual level, we will see the different patterns the library can support, we will present the main building blocks we implemented to support them, and we will discuss possible future additions that will further enhance the coverage of the library. At the practical level, we will show how the integration of FlinkCEP with Flink allows the former to take advantage of Flink's rich ecosystem (e.g. connectors) and its stream processing capabilities, such as support for event-time processing, exactly-once state semantics, fault-tolerance, savepoints and high throughput.
Aljoscha Krettek - Apache Flink for IoT: How Event-Time Processing Enables Ea...Ververica
Back to the program
Apache Flink for IoT: How Event-Time Processing Enables Easy and Accurate Analytics
Thursday 17th
from 18:00 to 18:40
Theatre 19
-
Keynote
In this talk I’ll give a very short introduction to stream processing in general and then dive into event-time based stream processing. I will outline how this is important for IoT applications and also why it is such a challenging topic. Afterwards we’ll look at some real-world IoT use cases that are enabled by the support for robust event-time based stream processing provided by Apache Flink™. We will especially focus on easy of use and on correctness of results in the face of errors.
In the first half of the talk we’ll cover the basics of stream processing. We will look at the differences between event-time based and processing-time and at stateful stream processing. While on this, we’ll also highlight how the combination of these features is essential for doing robust stream processing in an IoT setting.
In the second part, we will look at how Flink solves some of the challenges that arise in event-time based processing and how that enables novel applications in the IoT space. We will do the latter by looking at a collection of real-world IoT use cases.
Some of the topics covered will be:
- Apache Flink
- Stateful Stream Processing
- Event Time vs. Processing Time Windowing
- Processing of out-of-order events
- IoT use cases
Tzu-Li (Gordon) Tai - Stateful Stream Processing with Apache FlinkVerverica
As Apache Flink continues to push the boundaries of stateful stream processing as an integral part of its past releases, increasing numbers of users are starting to realize the potential of stateful stream processing as a promising paradigm for robust and reactive data analytics as well as event-driven applications.
This talk aims at covering the general idea and motivations of stateful stream processing, and how Flink enables it with its powerful set of state management features and programming APIs. In addition to that, we will also take a look at the recent advancements related to Flink's state management and large state handling that were driven by our team at data Artisans team in the latest version 1.3 (expected release by end of May / early June).
Un tratado de libre comercio es un acuerdo entre países para establecer reglas comunes sobre el comercio entre ellos y crear una zona de libre comercio, siendo negociado por los gobiernos y administrado por el Ministerio de Economía a través de la Dirección de Administración de Tratados.
Becoming an Influencer: Strategies for ChangeDr. Ed Cabellon
In Higher Education, change moves slowly. As the Academy continues to shift due to external forces and changing student demographics, this keynote session, presented at the 2017 NIRSA Conference, explores how to become an Influencer and move positive change efforts forward.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Este documento presenta resúmenes de 5 aplicaciones móviles útiles: DU Battery Saver ayuda a ahorrar batería hasta en un 50%; GO Speed limpia archivos basura para aumentar la velocidad hasta en un 60%; Brightest Flashlight utiliza todas las luces del dispositivo como linterna; InkPad permite crear notas y listas de tareas; y App 2 SD mueve aplicaciones a tarjetas SD y libera espacio.
To set up test-driven development (TDD) with Python, pytest, and Vim:
1. Install pytest and pytest-xdist using pip. Create a "tests" folder and name test files with "test_" prefix.
2. In Vim, open files by project directory and use :vsplit to view code and tests side-by-side in split windows. Ctrl-wx swaps windows.
3. Run pytest from the project root with -f -v to watch for changes and rerun tests automatically on save.
Tired of reading textbooks on product management theory? In this session at a PDMA Chicago event, Ram Sarabu and I discussed the practical real world issues, concerns, and best practices when developing and communicating roadmaps. We try to debunk the preconceived notions of what a roadmap is as well as its recommended uses.
Value-Based Payments and Managed Care Contracting - Crash Course Webinar SeriesEpstein Becker Green
Epstein Becker Green Webinar with Attorney Basil Kim - Value-Based Payments Crash Course Webinar Series - May 31, 2016.
As value-based payment relationships continue to grow in prevalence and complexity, a question remains: How do I effectively capture this arrangement on paper?
Topics include:
* Some of the key strategic questions to deliberate with regard to contracting in a value-based payment relationship
* Considerations for contracting under a value-based payment framework.
http://www.ebglaw.com/events/value-based-payments-and-managed-care-contracting-value-based-payments-crash-course-webinar-series/
These materials have been provided for informational purposes only and are not intended and should not be construed to constitute legal advice. The content of these materials is copyrighted to Epstein Becker & Green, P.C. ATTORNEY ADVERTISING.
Reseña con motivo del Octogésimo Cuarto Aniversario de la Ciudad de El Tigre, Estado Anzoátegui, Venezuela; ocasión que nos invita a los hijos e hijas de este pueblo (hoy ciudad) a seguir investigando y estudiando los verdaderos orígenes de nuestra tierra que no es tan joven como la pintan. "Que no se pierda la historia y no se perderá la Patria".
Data privacy awareness is on the rise. Users become more and more concerned with how online service providers collect and protect their personal information. And so should you. Discover how to balance the risks and benefits of collecting data in the age of customer centricity.
This document discusses emerging technologies and their impact on society and business models. In 3 sentences:
It describes how networks and platforms are disrupting traditional industries through mobile technology, AI, robotics, and data. Jobs are being eliminated as certain roles become obsolete due to automation and self-driving vehicles. Companies must reinvent themselves and think more like startups and networks to survive the ongoing technological disruption of existing business models.
The digipaks for Halsey's albums Room 93 and Badlands appeal to her target audience through their aesthetics and additional content. The Room 93 digipak contains a booklet discussing each song's producer and writer. Badlands' color scheme and themes are inspired by films. Overall, the albums present Halsey as nostalgic and motivational, appealing to fans seeking self-esteem through her lyrics about self-love. Her early fans were highly engaged online, discussing her music on social media with dedicated accounts.
This document provides an overview of performance analysis tools for Linux systems. It describes Brendan Gregg's background and work analyzing performance at Netflix. It then discusses different types of tools, including observability tools to monitor systems, benchmarking tools to test performance, and tuning tools to optimize systems. A number of command line monitoring tools are outlined, such as vmstat, iostat, mpstat, and netstat, as well as more advanced tools like strace and tcpdump.
Continuous Processing with Apache Flink - Strata London 2016Stephan Ewen
Task from the Strata & Hadoop World conference in London, 2016: Apache Flink and Continuous Processing.
The talk discusses some of the shortcomings of building continuous applications via batch processing, and how a stream processing architecture naturally solves many of these issues.
Apache Flink Crash Course by Slim Baltagi and Srini PalthepuSlim Baltagi
In this hands-on Apache Flink presentation, you will learn in a step-by-step tutorial style about:
• How to setup and configure your Apache Flink environment: Local/VM image (on a single machine), cluster (standalone), YARN, cloud (Google Compute Engine, Amazon EMR, ... )?
• How to get familiar with Flink tools (Command-Line Interface, Web Client, JobManager Web Interface, Interactive Scala Shell, Zeppelin notebook)?
• How to run some Apache Flink example programs?
• How to get familiar with Flink's APIs and libraries?
• How to write your Apache Flink code in the IDE (IntelliJ IDEA or Eclipse)?
• How to test and debug your Apache Flink code?
• How to deploy your Apache Flink code in local, in a cluster or in the cloud?
• How to tune your Apache Flink application (CPU, Memory, I/O)?
Streaming, Database & Distributed Systems Bridging the DivideBen Stopford
There is something very interesting about stream processing. It builds upon messaging, rather than using a file system, as a more typical database does. But stream processing engines themselves are really a type of database. A database designed specifically to blend streams and tables so you can query continuous results. As such they span an architectural no-mans-land that sits between Database and Distributed Systems fields.
This talk will look at Stateful Stream Processing. Can a streaming engine provide the guarantees of a database? When is a streaming engine best? How do they work, under the covers?
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
This document discusses fast data and streaming systems. It provides a history of big data processing from MapReduce to streaming. Fast data refers to data in motion that is processed in real-time from streaming sources. Streaming systems allow for processing unbounded datasets using techniques like windows, watermarks and triggers. The document discusses streaming architectures and the SMACK stack (Spark, Mesos, Akka, Cassandra and Kafka) that provides technologies for building high performing streaming systems. It provides an example IoT application and how machine learning could be added. Streaming systems like Flink and Spark Streaming are compared.
Un tratado de libre comercio es un acuerdo entre países para establecer reglas comunes sobre el comercio entre ellos y crear una zona de libre comercio, siendo negociado por los gobiernos y administrado por el Ministerio de Economía a través de la Dirección de Administración de Tratados.
Becoming an Influencer: Strategies for ChangeDr. Ed Cabellon
In Higher Education, change moves slowly. As the Academy continues to shift due to external forces and changing student demographics, this keynote session, presented at the 2017 NIRSA Conference, explores how to become an Influencer and move positive change efforts forward.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Este documento presenta resúmenes de 5 aplicaciones móviles útiles: DU Battery Saver ayuda a ahorrar batería hasta en un 50%; GO Speed limpia archivos basura para aumentar la velocidad hasta en un 60%; Brightest Flashlight utiliza todas las luces del dispositivo como linterna; InkPad permite crear notas y listas de tareas; y App 2 SD mueve aplicaciones a tarjetas SD y libera espacio.
To set up test-driven development (TDD) with Python, pytest, and Vim:
1. Install pytest and pytest-xdist using pip. Create a "tests" folder and name test files with "test_" prefix.
2. In Vim, open files by project directory and use :vsplit to view code and tests side-by-side in split windows. Ctrl-wx swaps windows.
3. Run pytest from the project root with -f -v to watch for changes and rerun tests automatically on save.
Tired of reading textbooks on product management theory? In this session at a PDMA Chicago event, Ram Sarabu and I discussed the practical real world issues, concerns, and best practices when developing and communicating roadmaps. We try to debunk the preconceived notions of what a roadmap is as well as its recommended uses.
Value-Based Payments and Managed Care Contracting - Crash Course Webinar SeriesEpstein Becker Green
Epstein Becker Green Webinar with Attorney Basil Kim - Value-Based Payments Crash Course Webinar Series - May 31, 2016.
As value-based payment relationships continue to grow in prevalence and complexity, a question remains: How do I effectively capture this arrangement on paper?
Topics include:
* Some of the key strategic questions to deliberate with regard to contracting in a value-based payment relationship
* Considerations for contracting under a value-based payment framework.
http://www.ebglaw.com/events/value-based-payments-and-managed-care-contracting-value-based-payments-crash-course-webinar-series/
These materials have been provided for informational purposes only and are not intended and should not be construed to constitute legal advice. The content of these materials is copyrighted to Epstein Becker & Green, P.C. ATTORNEY ADVERTISING.
Reseña con motivo del Octogésimo Cuarto Aniversario de la Ciudad de El Tigre, Estado Anzoátegui, Venezuela; ocasión que nos invita a los hijos e hijas de este pueblo (hoy ciudad) a seguir investigando y estudiando los verdaderos orígenes de nuestra tierra que no es tan joven como la pintan. "Que no se pierda la historia y no se perderá la Patria".
Data privacy awareness is on the rise. Users become more and more concerned with how online service providers collect and protect their personal information. And so should you. Discover how to balance the risks and benefits of collecting data in the age of customer centricity.
This document discusses emerging technologies and their impact on society and business models. In 3 sentences:
It describes how networks and platforms are disrupting traditional industries through mobile technology, AI, robotics, and data. Jobs are being eliminated as certain roles become obsolete due to automation and self-driving vehicles. Companies must reinvent themselves and think more like startups and networks to survive the ongoing technological disruption of existing business models.
The digipaks for Halsey's albums Room 93 and Badlands appeal to her target audience through their aesthetics and additional content. The Room 93 digipak contains a booklet discussing each song's producer and writer. Badlands' color scheme and themes are inspired by films. Overall, the albums present Halsey as nostalgic and motivational, appealing to fans seeking self-esteem through her lyrics about self-love. Her early fans were highly engaged online, discussing her music on social media with dedicated accounts.
This document provides an overview of performance analysis tools for Linux systems. It describes Brendan Gregg's background and work analyzing performance at Netflix. It then discusses different types of tools, including observability tools to monitor systems, benchmarking tools to test performance, and tuning tools to optimize systems. A number of command line monitoring tools are outlined, such as vmstat, iostat, mpstat, and netstat, as well as more advanced tools like strace and tcpdump.
Continuous Processing with Apache Flink - Strata London 2016Stephan Ewen
Task from the Strata & Hadoop World conference in London, 2016: Apache Flink and Continuous Processing.
The talk discusses some of the shortcomings of building continuous applications via batch processing, and how a stream processing architecture naturally solves many of these issues.
Apache Flink Crash Course by Slim Baltagi and Srini PalthepuSlim Baltagi
In this hands-on Apache Flink presentation, you will learn in a step-by-step tutorial style about:
• How to setup and configure your Apache Flink environment: Local/VM image (on a single machine), cluster (standalone), YARN, cloud (Google Compute Engine, Amazon EMR, ... )?
• How to get familiar with Flink tools (Command-Line Interface, Web Client, JobManager Web Interface, Interactive Scala Shell, Zeppelin notebook)?
• How to run some Apache Flink example programs?
• How to get familiar with Flink's APIs and libraries?
• How to write your Apache Flink code in the IDE (IntelliJ IDEA or Eclipse)?
• How to test and debug your Apache Flink code?
• How to deploy your Apache Flink code in local, in a cluster or in the cloud?
• How to tune your Apache Flink application (CPU, Memory, I/O)?
Streaming, Database & Distributed Systems Bridging the DivideBen Stopford
There is something very interesting about stream processing. It builds upon messaging, rather than using a file system, as a more typical database does. But stream processing engines themselves are really a type of database. A database designed specifically to blend streams and tables so you can query continuous results. As such they span an architectural no-mans-land that sits between Database and Distributed Systems fields.
This talk will look at Stateful Stream Processing. Can a streaming engine provide the guarantees of a database? When is a streaming engine best? How do they work, under the covers?
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
This document discusses fast data and streaming systems. It provides a history of big data processing from MapReduce to streaming. Fast data refers to data in motion that is processed in real-time from streaming sources. Streaming systems allow for processing unbounded datasets using techniques like windows, watermarks and triggers. The document discusses streaming architectures and the SMACK stack (Spark, Mesos, Akka, Cassandra and Kafka) that provides technologies for building high performing streaming systems. It provides an example IoT application and how machine learning could be added. Streaming systems like Flink and Spark Streaming are compared.
How to build an ETL pipeline with Apache Beam on Google Cloud DataflowLucas Arruda
This document provides an overview of building an ETL pipeline with Apache Beam on Google Cloud Dataflow. It introduces key Beam concepts like PCollections, PTransforms, and windowing. It explains how Beam can be used for both batch and streaming ETL workflows on bounded and unbounded data. The document also discusses how Cloud Dataflow is a fully managed Apache Beam runner that integrates with other Google Cloud services and provides reliable, auto-scaled processing. Sample architecture diagrams demonstrate how Cloud Dataflow fits into data analytics platforms.
Using bluemix predictive analytics service in Node-REDLionel Mommeja
This document describes how to use the IBM Bluemix Predictive Analytics service with Node-RED to enable collaboration between data scientists and developers for Internet of Things applications. It provides a step-by-step example of building a predictive model using sensor data from a TI SensorTag to detect failures, deploying the model on the Predictive Analytics service, and calling it from a Node-RED application. This allows data scientists to build models and developers to easily integrate predictive capabilities into their IoT solutions.
Prometheus is an open-source monitoring system that collects metrics from configured targets, stores time-series data, and allows users to query and visualize the data. It works by scraping metrics over HTTP from applications and servers, storing the data in its time-series database, and providing a UI and query language to analyze the data. Prometheus is useful for monitoring system metrics like CPU usage and memory as well as application metrics like HTTP requests and errors.
This document provides a high-level summary of streaming data processing and the Lambda architecture. It begins with a brief history of batch and streaming systems for big data. It then introduces the Lambda architecture as a way to handle both batch and streaming data using separate batch and speed layers. The document discusses advantages and disadvantages of the Lambda architecture, as well as use cases, implementation tips, and approaches that have emerged beyond the Lambda architecture like Kappa and FastData architectures.
The document discusses using APIs to create graphical user interfaces in Windows programs. It provides an example of using the WNetConnectionDialog API function to start a network connection dialog box. The function requires a window handle and resource type as parameters. It returns error codes like NO_ERROR if successful. The document also discusses getting window handles, declaring external functions, and writing logs to the Windows event log using the API.
Getting More Out of the Node.js, PHP, and Python Agents - AppSphere16AppDynamics
Hear from our product management and engineering teams about three topics that will help you unlock more value from our dynamic languages agents:
• Diagnosing a slow business transaction in Node.js
• Using the agent APIs to create custom transactions and exit calls
• Getting the Java proxy out of your Docker containers and connecting multiple agents to one proxy
For more information, go to: www.appdynamics.com
Introduction to Stream Processing with Apache Flink (2019-11-02 Bengaluru Mee...Timo Walther
Apache Flink is a distributed, stateful stream processor. It features exactly-once state consistency, sophisticated event-time support, high throughput and low latency processing, and APIs at different levels of abstraction (Java, Scala, SQL). In my talk, I'll give an introduction to Apache Flink, its features and discuss the use cases it solves. I'll explain why batch is just a special case of stream processing, how its community evolves Flink into a truly unified stream and batch processor and what this means for its users.
https://www.meetup.com/de-DE/Bangalore-Apache-Kafka-Group/events/265285812/
https://www.youtube.com/watch?v=Ych5bbmDIoA&list=PLvkUPePDi9sa27SG9eGNXH25cfUeo_WY9&index=2
Voxxed days thessaloniki 21/10/2016 - Streaming Engines for Big DataStavros Kontopoulos
This document discusses streaming engines for big data and provides a case study on Spark Streaming. It begins with an overview of streaming concepts like streams, stream processing, and time in modern data stream analysis. Next, it covers key design considerations for streaming engines and examples of state-of-the-art stream analysis tools like Apache Flink, Spark Streaming, and Apache Beam. It then focuses on Spark Streaming, describing its DStream and Structured Streaming APIs. Code examples are provided for the DStream API and Structured Streaming. The document concludes with a recommendation to first consider Flink, Spark, or Kafka Streams when choosing a streaming engine.
Event Driven Architecture with a RESTful Microservices Architecture (Kyle Ben...confluent
Tinder’s Quickfire Pipeline powers all things data at Tinder. It was originally built using AWS Kinesis Firehoses and has since been extended to use both Kafka and other event buses. It is the core of Tinder’s data infrastructure. This rich data flow of both client and backend data has been extended to service a variety of needs at Tinder, including Experimentation, ML, CRM, and Observability, allowing backend developers easier access to shared client side data. We perform this using many systems, including Kafka, Spark, Flink, Kubernetes, and Prometheus. Many of Tinder’s systems were natively designed in an RPC first architecture.
Things we’ll discuss decoupling your system at scale via event-driven architectures include:
– Powering ML, backend, observability, and analytical applications at scale, including an end to end walk through of our processes that allow non-programmers to write and deploy event-driven data flows.
– Show end to end the usage of dynamic event processing that creates other stream processes, via a dynamic control plane topology pattern and broadcasted state pattern
– How to manage the unavailability of cached data that would normally come from repeated API calls for data that’s being backfilled into Kafka, all online! (and why this is not necessarily a “good” idea)
– Integrating common OSS frameworks and libraries like Kafka Streams, Flink, Spark and friends to encourage the best design patterns for developers coming from traditional service oriented architectures, including pitfalls and lessons learned along the way.
– Why and how to avoid overloading microservices with excessive RPC calls from event-driven streaming systems
– Best practices in common data flow patterns, such as shared state via RocksDB + Kafka Streams as well as the complementary tools in the Apache Ecosystem.
– The simplicity and power of streaming SQL with microservices
OpenWhisk - A platform for cloud native, serverless, event driven appsDaniel Krook
Cloud computing has recently evolved to enable developers to write cloud native applications better, faster, and cheaper using serverless technology.
OpenWhisk provides an open source platform to enable cloud native, serverless, event driven applications.
This presentation lays out the technical and business drivers behind the rise of serverless architectures, and provides an intro to the OpenWhisk open source project.
Presented at Cloud Native Day in Toronto, Canada on August 25, 2016.
This document introduces Redux, a state management library for JavaScript apps. It discusses why state management is needed, other approaches like Flux and MobX, and how Redux addresses these issues using principles of single source of truth, immutable state updates via pure functions, and unidirectional data flow. Key aspects of Redux like actions, action creators, reducers, and connecting React components to the store are explained. Considerations for when Redux is a good fit and potential gotchas are also covered.
Cloud Operations with Streaming Analytics using Apache NiFi and Apache FlinkDataWorks Summit
The amount of information coming from a Cloud deployment, that could be used to have a better situational awareness, and operate it efficiently is huge. Tools as the ones provided by Apache foundation can be used to build a solution to that challenge.
Nowadays Cloud deployments are pervasive in businesses, with scalability and multi tenancy as their core capabilities. This means that these deployments can grow easily beyond 1000 nodes and efficient operation of these huge clusters requires real time log analysis, metrics, events and configuration data. Performing correlation and finding patterns, not just to get to root causes but also to predict failures and reduce risk requires tools that go beyond current solutions.
In the prototype developed by Red Hat and KEEDIO (keedio.com), we managed to address the above challenges with the use of Big Data tools like Apache NiFi, Apache Kafka and Apache Flink, that enabled us to process the constant stream of syslog messages (RFC5424) produced by the Infrastructure as a Service, provided by OpenStack services, and also detect common failure patterns that could arise and generate alerts as needed.
This session is an (Intermediate) talk in our Apache Nifi and Data Science track. It focuses on Apache Flink, Apache Nifi, Apache Kafka and is geared towards Architect, Data Scientist, Data Analyst, Developer / Engineer audiences.
Speaker
Miguel Perez Colino, Senior Design Product Manager, Red Hat
Suneel Marthi, Senior Principal Engineer, Red Hat
Serverless architectures are one of the hottest trends in cloud computing this year, and for good reason. There are several technical capabilities and business factors coming together to make this approach compelling from both an application development and deployment cost perspective. The new OpenWhisk project provides an open source platform to enable these cloud-native, event-driven applications.
This talk will lay out the technical and business drivers behind the rise of serverless architectures, provide an introduction to the OpenWhisk open source project (and describe how it differs from other services like AWS Lambda), and give a demonstration showing how to start developing with this new cloud computing model using the OpenWhisk implementation available on IBM Bluemix.
Presented on October 12, 2016 at the NYC Bluemix meetup
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.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
Flink meetup
1. Get your hands on implementing a Flink app: A
tutorial
Christos Hadjinikolis & Satyasheel | DataReply.uk
2. Tutorial Overview:
What is Apache Flink?
Why Flink?
Processing both bounded and un-bounded data!
Anatomy of a Flink App
Windowing in Flink
Event time & Process time in Flink
2/22/17C. Hadjinikolis & Satyasheel | DataReply 2
3. What is Apache Flink?
“A distributed data processing platform…”
2/22/17C. Hadjinikolis & Satyasheel | DataReply 3
4. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 4
Flink is a distributed stream- & batch- data
processing platform
Stream processing
…the real-time processing of data continuously, concurrently, and in a record-by-record
fashion, where data is not static.
Batch processing
…the execution of a series of programs each on a set or "batch" of static inputs, rather
than a single input (which would instead be a custom job).
5. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 5
…distributed processing dataset types
Unbounded
Infinite datasets that are appended to continuously:
End users interacting with mobile or web applications
Physical sensors providing measurements
Financial markets
Machine log data
Surveillance camera frames
7. Why Flink?
“The world is turning more and more towards stream processing…”
2/22/17C. Hadjinikolis & Satyasheel | DataReply 7
8. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 8
Opt for Flink because it:
Provides results that are accurate
Is stateful and fault-tolerant and can seamlessly
recover from failures
Performs at large scale
9. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 9
…exactly-once semantics
Statefull
… apps can maintain summaries of
processed data.
Checkpointing
… a mechanism that ensures that in the
event of failure no duplicate re-
computation of an event will take place.
10. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 10
…event time semantics
…event-time-based windowing
Event time makes it easy to compute accurate results over streams where events arrive out
of order and where events may arrive delayed.
11. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 11
… flexible windowing
Windows can be customized with flexible triggering conditions to
support sophisticated streaming patterns based on:
Time;
Count, and;
Sessions.
12. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 12
… lightweight fault tolerance
Recovers from failures with zero
data loss while the tradeoff
between reliability and latency is
negligible.
13. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 13
… lightweight fault tolerance
Savepoints
Provide a state versioning mechanism.
Applications can update and reprocess historic
data with no lost state.
14. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 14
… Scalable
Designed to run on large
scale clusters with many
thousands on nodes.
15. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 15
So, in summary…
Flink is an open-source stream processing framework, which:
Eliminates the “performance vs. reliability” problem and;
Performs consistently in both categories.
16. Processing both
bounded & un-bouded data!
“Unbounding the boundaries…”
2/22/17C. Hadjinikolis & Satyasheel | DataReply 16
17. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 17
…the streaming model & bounded datasets
DataStream API un-bounded data
DataSet API bounded data
A bounded dataset is handled inside of Flink
as a “finite stream”, with only a few minor
differences in how Flink manages un-
bounded datasets.
18. Anatomy of a Flink App
“Let’s get this started…”
2/22/17C. Hadjinikolis & Satyasheel | DataReply 18
19. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 19
…Flink programs transform collections of data
Each program consists of the same basic parts:
Obtain an execution environment,
Load/create the initial data,
Specify transformations on this data,
Specify where to put the results of your computations
Trigger the program execution
21. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 21
…Lazy evaluation
When the program’s main method is
executed:
Each operation is created and added to the
program’s plan.
execution is explicitly triggered by
an execute() call.
This helps with constructing an optimised
data-flow as a holistically planned unit.
22. Lets take 15 mins
…
2/22/17C. Hadjinikolis & Satyasheel | DataReply 22
23. Windowing in Flink
“…a simple word count app.”
2/22/17C. Hadjinikolis & Satyasheel | DataReply 23
24. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 24
…so what is a window?
A window is a way to get a {snapshot} of the streaming data.
A {snapshot} can be based on time or other variables.
One can define the window based on no of records or other stream
specific variables.
25. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 25
…enough with theory! Give us some code!
A streaming word count example with no windowing
26. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 26
…updating states
Flink automatically updates its states without
the user explicitly doing so.
To better appreciate this, it is worth
contrasting Flink with Spark.
Spark relies on micro-batches:
This means one has to define the batch size either in
terms of time or size
Flink, does not require defining a batch size.
It can process each and every new event individually
(it is true stream processing!)
27. Lets see an example
…
2/22/17C. Hadjinikolis & Satyasheel | DataReply 27
28. Windowing in Flink
“Don't waste a minute not being happy. If one window closes, run to the next window - or break down a door. …”
2/22/17C. Hadjinikolis & Satyasheel | DataReply 28
29. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 29
…so why use windowing at all?
Aggregation on DataStream is different from aggregation
Dataset.
One cannot count all records on infinite stream.
DataStream aggregation makes sense on window stream.
30. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 30
…what types of windowing can you use?
Tumbling Windows :
Aligned, fixed length, non-overlapping window.
Sliding Windows :
Aligned, fixed length, overlapping window.
Session Windows :
Non aligned, variable length window.
Count Windows :
Fixed number of records/events, non-overlapping window.
31. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 31
…anatomy of the window API
3 window functions:
Window Assigner:
Responsible for assigning given element to window.
Depending upon the definition of window, one element can belong to one or more windows at a
time.
Trigger:
Defines the condition for triggering window evaluation.
This function controls when a given window created by window assigner is evaluated.
Evictor:
An optional function which defines the preprocessing before firing window operations.
32. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 32
…understanding count window
Window Assigner (for count-based window user-defined)
No start or end to the window, therefore the window is non-time based.
For these windows we use the GlobalWindows window assigner.
For a given key, all key-values are filled into the same window.
keyValue.window(GlobalWindows.create())
The window API allows us to add the window assigner to the window.
Every window assigner has a default trigger.
for global windows that trigger is NeverTrigger which never triggers.
so, this window assigner has to be used with a custom trigger.
33. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 33
…understanding count window
Count trigger
Once we have the window assigner, we have to define when the window needs to be
trigger-ed, for example:
trigger(CountTrigger.of(2))
This results in the window being evaluated every two records.
Evictor
In addition to these, an evictor can be used for further preprocessing tasks before firing a
window operation, e.g. to remove the every 3rd element of all window.
Some default evictors:
CountEvictor , DeltaEvictor , TimeEvictor
34. The anatomy of a
window API
…
2/22/17C. Hadjinikolis & Satyasheel | DataReply 34
37. Lets take 15 mins
…
2/22/17C. Hadjinikolis & Satyasheel | DataReply 37
38. Timing in Flink
“The two most powerful warriors are patience and time.
2/22/17C. Hadjinikolis & Satyasheel | DataReply 38
39. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 39
…the time concept in streaming
A streaming application is an always running application.
..we need to take snapshots of the stream at various points.
..these points can be defined using a time component.
..we can group, correlate, different events happening in the stream.
Some of the constructs like window, heavily use the time component.
Most of the streaming frameworks support a single meaning of time, which
is mostly tied to the processing time.
40. 2/22/17C. Hadjinikolis & Satyasheel | DataReply 40
…time in Flink
When we say, last “t” seconds, what do we mean exactly? Well in Flink
it’s one of three things:
Processing Time
“…the records arrived in last "t" seconds for the processing.”
Event Time
“… all the records generated in those last "t" seconds at the source.”
Ingestion Time
The time when events ingested into the system.
This time is in between of the event time and processing time.
Second, 2 types of execution models
Streaming: Processing that executes continuously as long as data is being produced
Batch: Processing that is executed and runs to completeness in a finite amount of time, releasing computing resources when finished
It’s possible, though not necessarily optimal, to process either type of dataset with either type of execution model. For instance, batch execution has long been applied to unbounded datasets despite potential problems with windowing, state management, and out-of-order data.
Flink relies on a streaming execution model, which is an intuitive fit for processing unbounded datasets: streaming execution is continuous processing on data that is continuously produced. And alignment between the type of dataset and the type of execution model offers many advantages with regard to accuracy and performance.
Before we go into detail about Flink, let’s review at a higher level the types of datasets you’re likely to encounter when processing data as well as types of execution models you can choose for processing. These two ideas are often conflated, and it’s useful to clearly separate them.
First, 2 types of datasets
Unbounded: Infinite datasets that are appended to continuously
Bounded: Finite, unchanging datasets
Many real-word data sets that are traditionally thought of as bounded or “batch” data are in reality unbounded datasets. This is true whether the data is stored in a sequence of directories on HDFS or in a log-based system like Apache Kafka.
Examples of unbounded datasets include but are not limited to:
End users interacting with mobile or web applications
Physical sensors providing measurements
Financial markets
Machine log data
We have all interacted with bounded dataset on our machines, like: picturesm or documents of any kind, database tables etc.
Earlier, we discussed aligning the type of dataset (bounded vs. unbounded) with the type of execution model (batch vs. streaming). Many of the Flink features listed below–state management, handling of out-of-order data, flexible windowing–are essential for computing accurate results on unbounded datasets and are enabled by Flink’s streaming execution model.
Flink guarantees exactly-once semantics for stateful computations. ‘Stateful’ means that applications can maintain an aggregation or summary of data that has been processed over time, and Flink’s checkpointing mechanism ensures exactly-once semantics for an application’s state in the event of a failure.
Flink guarantees exactly-once semantics for stateful computations. ‘Stateful’ means that applications can maintain an aggregation or summary of data that has been processed over time, and Flink’s checkpointing mechanism ensures exactly-once semantics for an application’s state in the event of a failure.
Flink supports stream processing and windowing with event time semantics. Event time makes it easy to compute accurate results over streams where events arrive out of order and where events may arrive delayed.
Flink supports flexible windowing based on time, count, or sessions in addition to data-driven windows. Windows can be customized with flexible triggering conditions to support sophisticated streaming patterns. Flink’s windowing makes it possible to model the reality of the environment in which data is created.
… allows the system to maintain high throughput rates and provide exactly-once consistency guarantees at the same time. Flink recovers from failures with zero data loss while the tradeoff between reliability and latency is negligible.
Flink’s savepoints provide a state versioning mechanism, making it possible to update applications or reprocess historic data with no lost state and minimal downtime.
Flink is designed to run on large-scale clusters with many thousands of nodes, and in addition to a standalone cluster mode, Flink provides support for YARN and Mesos
In summary, Apache Flink is an open-source stream processing framework that eliminates the “performance vs. reliability” tradeoff often associated with open-source streaming engines and performs consistently in both categories.
Earlier in this write-up, we introduced the streaming execution model (“processing that executes continuously, an event-at-a-time”) as an intuitive fit for unbounded datasets. So how do bounded datasets relate to the stream processing paradigm?
In Flink’s case, the relationship is quite natural. A bounded dataset can simply be treated as a special case of an unbounded one, so it’s possible to apply all of the same streaming concepts that we’ve laid out above to finite data.
This is exactly how Flink’s DataSet API behaves. A bounded dataset is handled inside of Flink as a “finite stream”, with only a few minor differences in how Flink manages bounded vs. unbounded datasets.
And so it’s possible to use Flink to process both bounded and unbounded data, with both APIs running on the same distributed streaming execution engine–a simple yet powerful architecture.
Lazy Evaluation
All Flink programs are executed lazily: When the program’s main method is executed, the data loading and transformations do not happen directly. Rather, each operation is created and added to the program’s plan. The operations are actually executed when the execution is explicitly triggered by an execute() call on the execution environment. Whether the program is executed locally or on a cluster depends on the type of execution environment.
The lazy evaluation lets you construct sophisticated programs that Flink executes as one holistically planned unit.
For example, if we create a window for 5 seconds then it will be all the records which arrived in the that time frame.
Why do we need windowing?
Aggregation on DataStream is different from aggregation dataset, One cannot count all records on infinite stream.
DataStream aggregation makes sense on window stream.
In spark, after each batch, the state has to be updated explicitly if you want to keep track of wordcount across batches. But in flink the state is up-to-dated as and when new records arrive implicitly.
Most of the window operations are encouraged to be used on KeyedDataStream. A KeyedDataStream is a datastream which is partitioned by the key. This partitioning by key allows window to be distributed across machines resulting in good performance.
Trigerring:Most of the window operations are encouraged to be used on KeyedDataStream. A KeyedDataStream is a datastream which is partitioned by the key. This partitioning by key allows window to be distributed across machines resulting in good performance.Evictor:Like removing the third element in a count window of 10 elements…
Most of the window operations are encouraged to be used on KeyedDataStream. A KeyedDataStream is a datastream which is partitioned by the key. This partitioning by key allows window to be distributed across machines resulting in good performance.
**CountEvictor:** keeps up to a user-specified number of elements from the window and discards the remaining ones from the beginning of the window buffer.
**DeltaEvictor:** takes a DeltaFunction and a threshold, computes the delta between the last element in the window buffer and each of the remaining ones, and removes the ones with a delta greater or equal to the threshold.
**TimeEvictor:** takes as argument an interval in milliseconds and for a given window, it finds the maximum timestamp max_ts among its elements and removes all the elements with timestamps smaller than max_ts - interval.
**Note:** All evictors apply their logic before the window function.
In Flink it depends and it could be one of three following.
Processing TimeMost of the streaming application uses this concept and this is one of the most familiar concept users. This time is tracked using a clock run by the processing engine. So, last "t" seconds means the records arrived in last "t" seconds for the processing.
Processing time is very good way of keeping track of time, but not always helpful. Let's say we want to measure the state of sensor at a given point of time so, we want to collect the event at that time. But if the events arrive lately to processing system due to various reasons, we may miss some of the events as processing clock does not care about the actual time of events. To address this, Flink support another kind of time called event time.
Event TimeThis time is embedded in data. Means this time comes with the data. So here last "t" seconds means, all the records generated in those last "t" seconds at the source. These may come out of order to processing. This time is independent of the clock that is kept by the processing engine.Event time is extremely useful for handling the late arrival events.
Ingestion TimeIngestion time is the time when events ingested into the system. This time is in between of the event time and processing time. Normally in processing time, each machine in cluster is used to assign the time stamp to track events. This may result in little inconsistent view of the data, as there may be delays in time across the cluster. But ingestion time, timestamp is assigned in ingestion so that all the machines in the cluster have exact same view. These are useful to calculate results on data that arrive in order at the level of ingestion.