Robust stream processing with Apache Flink
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Aljoscha Krettek offers a very short introduction to stream processing before diving into writing code and demonstrating the features in Apache Flink that make truly robust stream processing possible, with a focus on correctness and robustness in stream processing. All of this will be done in the context of a real-time analytics application that we’ll be modifying on the fly based on the topics we’re working though, as Aljoscha exercises Flink’s unique features, demonstrates fault recovery, clearly explains why event time is such an important concept in robust, stateful stream processing, and covers the features you need in a stream processor to do robust, stateful stream processing in production. We’ll also use a real-time analytics dashboard to visualize the results we’re computing in real time, allowing us to easily see the effects of the code we’re developing as we go along. Topics include: * Apache Flink * Stateful stream processing * Event time versus processing time * Fault tolerance * State management in the face of faults * Savepoints * Data reprocessing
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Flink Forward San Francisco 2019: Developing and operating real-time applicat...
Developing and operating real-time applications with Oceanus
The Tencent Data team (data.qq.com) is responsible to build a reliable and scalable infrastructure for various products at Tencent including Tencent Games, Tencent Video and WeChat. We have to process over 1.6 trillion events per day and the peak throughput reaches 210 million per second. Such a large scale exposes great challenges in the developing and operating of real-time applications. In this talk, we will give an introduction to Oceanus, a platform built at Tencent to facilitate the development of real-time applications. We will introduce typical user cases and how applications are developed, deployed and monitored with Oceanus. We will also introduce the management of Flink jobs in Oceanus, together with some experience gained in our operating practice. Finally, we will discuss some efforts we have done to enhance the reliability and efficiency, e.g. master failover and local keyed streams.
Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream p...
Stream Processing is a powerful paradigm, especially when backed by a system like Apache Flink. With each release and year, we see Flink being used for more challenging use case and applications.
But beyond the individual application (though it may be grand and challenging in itself), stream processing is a much broader building block: the foundational piece of a platform that brings together the different parts of a data architecture. A platform that integrates data analytics, data ingestion, SQL, Machine Learning, data provenance, databases, and other aspects of a data-driven infrastructure in a meaningful way.
In this keynote, we look at what goes into building a stream processing platform that is more than the sum of its parts.
Flink Forward SF 2017: Cliff Resnick & Seth Wiesman - From Zero to Streami...
Apache Flink provides powerful stream processing capabilities which can allow organizations to move directly from batch to real time analytics, skipping the lambda architecture entirely. However, getting to production is not always as simple as rewriting your job in a new API, but requires rethinking your application design with a stream first mindset. This talk will cover MediaMath’s journey in rebuilding its reporting infrastructure using Apache Flink. We will discuss high level architectural designs when building an extensible reporting platform as well as deep dive into specific technical hurdles. Topics will include managing a Flink cluster on EC2 spot instances, reconciling Flink’s consistency model with S3’s, handling massive data skew as well as tools and techniques for building performant, fault tolerant streaming applications.
A Marriage of Lambda and Kappa: Supporting Iterative Development of an Event ...
With increasing data volumes typically comes a corresponding increase in (non-windowed) batch processing times, and many companies have looked to streaming as a way of delivering data processing results faster and more reliably. Event Driven Architectures further enhance these offerings by breaking centralised Data Platforms into loosely coupled and distributed solutions, supported by linearly scalable technologies such as Apache Kafka(TM) and Apache Kafka Streams(TM).
However, there remains a problem of how to handle changes to operational systems: if a record is the result of business logic, and that business logic changes, what do we do? Do we recalculate everything on the fly, adding in additional latencies for all data requests and potentially breaching non-functional requirements? Or do we run a batch job, risking that incorrect data will be served whilst the job is running?
This talk covers how 6point6 leveraged Kafka and Kafka Streams to transition a customer from a traditional business flow onto an Event Driven Architecture, with business logic triggered directly by real-time events across over 3000 loosely coupled business services, whilst ensuring that the active development of these services (and their containing logic and models) would not affect components which relied on data served by the platform.
Learn how we:
– Used versioning of topics, data and business logic to facilitate iterative development, ensuring that the reprocessing of large volumes of data would not result in incorrect or stale data being delivered.
– Handled distributed versioning of JSON event messages between separate teams/services, using discovery, automated contract negotiation and version porting.
– Developed technical patterns and libraries to allow rapid development and deployment of new event driven services using Kafka Streams.
– Developed functionality and approaches for deploying defensive services, including strategies for event retry and failure.
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
As many industries, banking is undergoing a fundamental change because of the software revolution. No longer are banks competing only on interest rates and having the best traders, these days customer experience and having the best engineers are the focus. In this changing world, banks compete with new start-ups, the so-called Fintechs, and with large platform organisations such as Google, Facebook and Apple. At ING, we believe that staying ahead of the game means changing how we interact with our customers, no longer a traditional model of waiting for the customers to come to the bank through our website or apps, but to actively reach out to the customer with information that is relevant to him or her in order to make their financial life frictionless. Many of these changes are driven by reacting to all events that are relevant to the customer, and using streaming analytics to be able to reach out to the customer in milliseconds after the event occurs. Apache Flink is key for ING to achieve this. This presentation addresses how ING approaches the challenge, the role that Apache Flink plays, and the consequences regulations have on how we work with Open Source in general, and with Apache Flink (and data Artisans) in particular. This keynote takes place at Kino 3.
Streaming in the Wild with Apache Flink
This talk is an application-driven walkthrough to modern stream processing, exemplified by Apache Flink, and how this enables new applications and makes old applications easier and more efficient. In this talk, we will walk through several real-world stream processing application scenarios of Apache Flink, highlighting unique features in Flink that make these applications possible. In particular, we will see (1) how support for handling out of order streams enables real-time monitoring of cloud infrastructure, (2) how the ability handle high-volume data streams with low latency SLAs enables real-time alerts in network equipment, (3) how the combination of high throughput and the ability to handle batch as a special case of streaming enables an architecture where the same exact program is used for real-time and historical data processing, and (4) how stateful stream processing can enable an architecture that eliminates the need for an external database store, leading to more than 100x performance speedup, among many other benefits.
Flink Forward Berlin 2017: Till Rohrmann - From Apache Flink 1.3 to 1.4
The document summarizes updates and new features in Apache Flink versions 1.3 to 1.4, and previews what is planned for 1.5 and beyond. Key points include: improved APIs, side outputs and state handling in 1.3; event-driven I/O, flow control and deployment changes in 1.4; and planned additions of side inputs, state management evolution, and state replication in 1.5. The document encourages attendees to learn more about Flink's internals by attending related talks at the event.
Flink Forward Berlin 2017: Steffen Hausmann - Build a Real-time Stream Proces...
The increasing number of available data sources in today's application stacks created a demand to continuously capture and process data from various sources to quickly turn high volume streams of raw data into actionable insights. Apache Flink addresses many of the challenges faced in this domain as it's specifically tailored to distributed computations over streams. While Flink provides all the necessary capabilities to process streaming data, provisioning and maintaining a Flink cluster still requires considerable effort and expertise. We will discuss how cloud services can remove most of the burden of running the clusters underlying your Flink jobs and explain how to build a real-time processing pipeline on top of AWS by integrating Flink with Amazon Kinesis and Amazon EMR. We will furthermore illustrate how to leverage the reliable, scalable, and elastic nature of the AWS cloud to effectively create and operate your real-time processing pipeline with little operational overhead.
Kafka Tiered Storage | Satish Duggana and Sriharsha Chintalapani, Uber
Kafka is a vital part of data infrastructure in many organizations. When the Kafka cluster grows and more data is stored in Kafka for a longer duration, several issues related to scalability, efficiency, and operations become important to address. Kafka cluster storage is typically scaled by adding more broker nodes to the cluster. But this also adds needless memory and CPUs to the cluster making overall storage cost less efficient compared to storing the older data in external storage.
Tiered storage is introduced to extend Kafka's storage beyond the local storage available on the Kafka cluster by retaining the older data in cheaper stores, such as HDFS, S3, Azure or GCS with minimal impact on the internals of Kafka.
We will talk about
- How tiered storage addresses the above problems and also brings several other advantages.
- High level architecture of tiered storage
- Future work planned as part of tiered storage.
Aljoscha Krettek - Portable stateful big data processing in Apache Beam
Apache Beam's new State API brings scalability and consistency to fine-grained stateful processing while remaining portable to any Beam runner. Aljoscha Krettek introduces the new state and timer features in Beam and shows how to use them to express common real-world use cases in a backend-agnostic manner.
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2018-04 Kafka Summit London: Stephan Ewen - "Apache Flink and Apache Kafka fo...
2018-04 Kafka Summit London: Stephan Ewen - "Apache Flink and Apache Kafka fo...
Stream Processing with Apache Flink (Flink.tw Meetup 2016/07/19)
Stream Processing with Apache Flink (Flink.tw Meetup 2016/07/19)
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by Flink
Flink Forward Berlin 2017: Hao Wu - Large Scale User Behavior Analytics by Flink
Kostas Tzoumas - Apache Flink®: State of the Union and What's Next
Kostas Tzoumas - Apache Flink®: State of the Union and What's Next
Flink Forward San Francisco 2019: High cardinality data stream processing wit...
Flink Forward San Francisco 2019: High cardinality data stream processing wit...
Flink Forward SF 2017: Srikanth Satya & Tom Kaitchuck - Pravega: Storage Rei...
Flink Forward SF 2017: Srikanth Satya & Tom Kaitchuck - Pravega: Storage Rei...
Flink Forward Berlin 2017: Jörg Schad, Till Rohrmann - Apache Flink meets Apa...
Flink Forward Berlin 2017: Jörg Schad, Till Rohrmann - Apache Flink meets Apa...
Flink Forward San Francisco 2019: Developing and operating real-time applicat...
Flink Forward San Francisco 2019: Developing and operating real-time applicat...
Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream p...
Flink Forward San Francisco 2018 keynote: Stephan Ewen - "What turns stream p...
Flink Forward SF 2017: Cliff Resnick & Seth Wiesman - From Zero to Streami...
Flink Forward SF 2017: Cliff Resnick & Seth Wiesman - From Zero to Streami...
A Marriage of Lambda and Kappa: Supporting Iterative Development of an Event ...
A Marriage of Lambda and Kappa: Supporting Iterative Development of an Event ...
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
Flink Forward Berlin 2017: Till Rohrmann - From Apache Flink 1.3 to 1.4
Flink Forward Berlin 2017: Till Rohrmann - From Apache Flink 1.3 to 1.4
Flink Forward Berlin 2017: Steffen Hausmann - Build a Real-time Stream Proces...
Flink Forward Berlin 2017: Steffen Hausmann - Build a Real-time Stream Proces...
Kafka Tiered Storage | Satish Duggana and Sriharsha Chintalapani, Uber
Kafka Tiered Storage | Satish Duggana and Sriharsha Chintalapani, Uber
Aljoscha Krettek - Portable stateful big data processing in Apache Beam
Aljoscha Krettek - Portable stateful big data processing in Apache Beam
Similar to Robust stream processing with Apache Flink
Flink Community Update 2015 June
This document summarizes Apache Flink community updates from June 2015. It discusses the 0.9.0 release of Apache Flink, an open source platform for scalable batch and stream data processing. Key points include the addition of two new committers, blog posts and workshops promoting Flink, and various conference and meetup talks about Flink occurring that month. It encourages registration for the Flink Forward conference in October 2015.
Apache Fink 1.0: A New Era for Real-World Streaming Analytics
These are the slides of my talk at the Chicago Apache Flink Meetup on April 19, 2016. This talk explains how Apache Flink 1.0 announced on March 8th, 2016 by the Apache Software Foundation, marks a new era of Real-Time and Real-World streaming analytics. The talk will map Flink's capabilities to streaming analytics use cases.
Unified Batch and Real-Time Stream Processing Using Apache Flink
This talk was given at Capital One on September 15, 2015 at the launch of the Washington DC Area Apache Flink Meetup. Apache flink is positioned at the forefront of 2 major trends in Big Data Analytics:
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In these slides, we will also find answers to the burning question: Why Apache Flink? You will also learn more about how Apache Flink compares to Hadoop MapReduce, Apache Spark and Apache Storm.
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El Stream processing es un requisito previo de la pila de data streaming, que impulsa aplicaciones y pipelines en tiempo real.
Permite una mayor portabilidad de datos, una utilización optimizada de recursos y una mejor experiencia del cliente al procesar flujos de datos en tiempo real.
En nuestro taller práctico híbrido, aprenderás cómo filtrar, unir y enriquecer fácilmente datos en tiempo real dentro de Confluent Cloud utilizando nuestro servicio Flink sin servidor.
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This introductory level talk is about Apache Flink: a multi-purpose Big Data analytics framework leading a movement towards the unification of batch and stream processing in the open source.
With the many technical innovations it brings along with its unique vision and philosophy, it is considered the 4 G (4th Generation) of Big Data Analytics frameworks providing the only hybrid (Real-Time Streaming + Batch) open source distributed data processing engine supporting many use cases: batch, streaming, relational queries, machine learning and graph processing.
In this talk, you will learn about:
1. What is Apache Flink stack and how it fits into the Big Data ecosystem?
2. How Apache Flink integrates with Hadoop and other open source tools for data input and output as well as deployment?
3. Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark.
4. Who is using Apache Flink?
5. Where to learn more about Apache Flink?
Apache Flink Online Training
http://www.learntek.org/product/apache-flink/
Apache Flink is an open source stream processing framework developed by the Apache Software Foundation. The core of Apache Flink is a distributed streaming dataflow engine written in Java and Scala. Apache Flink’s dataflow programming model provides event-at-a-time processing on both finite and infinite datasets. At a basic level, Flink programs consist of streams and transformations. Conceptually, a stream is a (potentially never-ending) flow of data records, and a transformation is an operation that takes one or more streams as input, and produces one or more output streams as a result. Programs can be written in Java, Scala, Python, and SQL and are automatically compiled and optimized into dataflow programs that are executed in a cluster or cloud environment.
http://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses. We are dedicated to designing, developing and implementing training programs for students, corporate employees and business professional.
Apache flink
https://www.learntek.org/apache-flink/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Apache flink
Apache Flink Training
https://www.learntek.org/apache-flink/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Liveperson DLD 2015
In this meetup, Kobi Salant - Data Platform Technical Lead & Vladi Feigin - Data System Architect, both from Liveperson will talk about : Making scale a non-issue for real-time Data apps.
Have you ever tried to build a system processing in real-time hundreds of thousands events per second and servicing more than 1M concurrent visitors?
We're going to talk about the LivePerson real-time stream processing solution doing exactly that. Learn how we empower digital call centers with insights for their critical decision making processes and never-ending efficiency goals.
Overview of Apache Flink: Next-Gen Big Data Analytics Framework
These are the slides of my talk on June 30, 2015 at the first event of the Chicago Apache Flink meetup. Although most of the current buzz is about Apache Spark, the talk shows how Apache Flink offers the only hybrid open source (Real-Time Streaming + Batch) distributed data processing engine supporting many use cases: Real-Time stream processing, machine learning at scale, graph analytics and batch processing.
In these slides, you will find answers to the following questions: What is Apache Flink stack and how it fits into the Big Data ecosystem? How Apache Flink integrates with Apache Hadoop and other open source tools for data input and output as well as deployment? What is the architecture of Apache Flink? What are the different execution modes of Apache Flink? Why Apache Flink is an alternative to Apache Hadoop MapReduce, Apache Storm and Apache Spark? Who is using Apache Flink? Where to learn more about Apache Flink?
Overview of Apache Flink: the 4G of Big Data Analytics Frameworks
This document provides an overview of Apache Flink and discusses why it is suitable for real-world streaming analytics. The document contains an agenda that covers how Flink is a multi-purpose big data analytics framework, why streaming analytics are emerging, why Flink is suitable for real-world streaming analytics, novel use cases enabled by Flink, who is using Flink, and where to go from here. Key points include Flink innovations like custom memory management, its DataSet API, rich windowing semantics, and native iterative processing. Flink's streaming features that make it suitable for real-world use include its pipelined processing engine, stream abstraction, performance, windowing support, fault tolerance, and integration with Hadoop.
Overview of Apache Fink: the 4 G of Big Data Analytics Frameworks
Slides of my talk at the Hadoop Summit Europe in Dublin, Ireland on April 13th, 2016. The talk introduces Apache Flink as both a multi-purpose Big Data analytics framework and real-world streaming analytics framework. It is focusing on Flink's key differentiators and suitability for streaming analytics use cases. It also shows how Flink enables novel use cases such as distributed CEP (Complex Event Processing) and querying the state by behaving like a key value data store.
Overview of Apache Fink: The 4G of Big Data Analytics Frameworks
This document provides an overview of Apache Flink and discusses why it is suitable for real-world streaming analytics. The document contains an agenda that covers how Flink is a multi-purpose big data analytics framework, why streaming analytics are emerging, why Flink is suitable for real-world streaming analytics, novel use cases enabled by Flink, who is using Flink, and where to go from here. Key points include Flink innovations like custom memory management, its DataSet API, rich windowing semantics, and native iterative processing. Flink's streaming features that make it suitable for real-world use include its pipelined processing engine, stream abstraction, performance, windowing support, fault tolerance, and integration with Hadoop.
Cloud lunch and learn real-time streaming in azure
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Apache pulsar is an open source, cloud-native distributed messaging and streaming platform.
Apache flink
The document outlines the topics that will be covered in an Apache Flink online training, including: what Apache Flink is; why use Apache Flink; its architecture, features, and deployment; its streaming, batch processing, and table APIs; complex event processing; graph processing; and integration with Hadoop. The training will cover Apache Flink's stream processing engine, fault tolerance, state management, and support for stream, batch, and iterative processing using its dataflow model.
Hybrid Kafka, Taking Real-time Analytics to the Business (Cody Irwin, Google ...
Apache Kafka users who want to leverage Google Cloud Platform's (GCPs) data analytics platform and open source hosting capabilities can bridge their existing Kafka infrastructure on-premise or in other clouds to GCP using Confluent's replicator tool and managed Kafka service on GCP. Using actual customer examples and a reference architecture, we'll showcase how existing Kafka users can stream data to GCP and use it in popular tools like Apache Beam on Dataflow, BigQuery, Google Cloud Storage (GCS), Spark on Dataproc, and Tensorflow for data warehousing, data processing, data storage, and advanced analytics using AI and ML.
Budapest Data/ML - Building Modern Data Streaming Apps with NiFi, Flink and K...
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Timothy Spann
Principal Developer Advocate
Cloudera
Data in Motion
https://budapestdata.hu/2023/en/speakers/timothy-spann/
Timothy Spann
Principal Developer Advocate
Cloudera (US)
LinkedIn · GitHub · datainmotion.dev
June 8 · Online · English talk
Building Modern Data Streaming Apps with NiFi, Flink and Kafka
In my session, I will show you some best practices I have discovered over the last 7 years in building data streaming applications including IoT, CDC, Logs, and more.
In my modern approach, we utilize several open-source frameworks to maximize the best features of all. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Kafka. From there we build streaming ETL with Apache Flink SQL. We will stream data into Apache Iceberg.
We use the best streaming tools for the current applications with FLaNK. flankstack.dev
BIO
Tim Spann is a Principal Developer Advocate in Data In Motion for Cloudera. He works with Apache NiFi, Apache Pulsar, Apache Kafka, Apache Flink, Flink SQL, Apache Pinot, Trino, Apache Iceberg, DeltaLake, Apache Spark, Big Data, IoT, Cloud, AI/DL, machine learning, and deep learning. Tim has over ten years of experience with the IoT, big data, distributed computing, messaging, streaming technologies, and Java programming.
Previously, he was a Developer Advocate at StreamNative, Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton & NYC on Big Data, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science.
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https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
Apache kafka
https://www.learntek.org/
https://www.learntek.org/blog/apache-kafka/
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Apache Fink 1.0: A New Era for Real-World Streaming Analytics
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More from Aljoscha Krettek
Apache Flink(tm) - A Next-Generation Stream Processor
In diesem Vortrag wird es zunächst einen kurzen Überblick über den aktuellen Stand im Bereich der Streaming-Datenanalyse geben. Danach wird es mit einer kleinen Einführung in das Apache-Flink-System zur Echtzeit-Datenanalyse weitergehen, bevor wir tiefer in einige der interessanten Eigenschaften eintauchen werden, die Flink von den anderen Spielern in diesem Bereich unterscheidet. Dazu werden wir beispielhafte Anwendungsfälle betrachten, die entweder direkt von Nutzern stammen oder auf unserer Erfahrung mit Nutzern basieren. Spezielle Eigenschaften, die wir betrachten werden, sind beispielsweise die Unterstützung für die Zerlegung von Events in einzelnen Sessions basierend auf der Zeit, zu der ein Ereignis passierte (event-time), Bestimmung von Zeitpunkten zum jeweiligen Speichern des Zustands eines Streaming-Programms für spätere Neustarts, die effiziente Abwicklung bei sehr großen zustandsorientierten Streaming-Berechnungen und die Zugänglichkeit des Zustandes von außerhalb.
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Flink is a great stream processor, Python is a great programming language, Apache Beam is a great programming model and portability layer. Using all three together is a great idea! We will demo and discuss writing Beam Python pipelines and running them on Flink. We will cover Beam's portability vision that led here, what you need to know about how Beam Python pipelines are executed on Flink, and where Beam's portability framework is headed next (hint: Python pipelines reading from non-Python connectors)
The Evolution of (Open Source) Data Processing
Beginning with MapReduce and its first popular open-source implementation in Apache Hadoop the data processing landscape has evolved quite a bit. Since then we have seen several paradigm shifts and open-source systems evolved to support new types of applications and to attract new audiences. We will follow developments using the example of the open-source stream processing system Apache Flink and in the end we will see how expressive APIs, support for event-driven applications, Flink SQL for seamless batch and stream processing, and a powerful runtime enable a wide range of applications.
Apache Flink and what it is used for
Aljoscha Krettek is the PMC chair of Apache Flink and Apache Beam, and co-founder of data Artisans. Apache Flink is an open-source platform for distributed stream and batch data processing. It allows for stateful computations over data streams in real-time and historically. Flink supports batch and stream processing using APIs like DataSet and DataStream. Data Artisans originated Flink and provides an application platform powered by Flink and Kubernetes for building stateful stream processing applications.
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Flink has evolved from a batch processor to a unified stream and batch processing framework. It now supports event-time processing, state, and low-level streaming with ProcessFunction. Looking ahead, Flink aims to improve elasticity, fault tolerance, SQL support, and handling large state through incremental snapshots. It also plans to offer more control over resource allocation and scaling through both active and reactive modes.
(Past), Present, and Future of Apache Flink
This document discusses the past, present, and future of Apache Flink. It describes how Flink enables stateful computations over data streams, as well as batch and stream processing. New features in Flink 1.5 include improved integration with resource managers, localized failure recovery, and a 50% rewritten network stack. Upcoming features in Flink 1.6 involve autoscaling, hot-standby replication for high availability, and zero-downtime upgrades. The document concludes that Flink is already the best open-source stream processing system and more exciting features are planned for the future.
Python Streaming Pipelines with Beam on Flink
Python is popular amongst data scientists and engineers for data processing tasks. The big data ecosystem has traditionally been rather JVM centric. Often Java (or Scala) are the only viable option to implement data processing pipelines. That sometimes poses an adoption barrier for organizations that have already invested in other language ecosystems. The Apache Beam project provides a unified programming model for data processing and its ongoing portability effort aims to enable multiple language SDKs (currently Java, Python and Go) on a common set of runners. The combination of Python streaming on the Apache Flink runner is one example. Let’s take a look how the Flink runner translates the Beam model into the native DataStream (or DataSet) API, how the runner is changing to support portable pipelines, how Python user code execution is coordinated with gRPC based services and how a sample pipeline runs on Flink.
The Past, Present, and Future of Apache Flink
Stream processing still evolves and changes at a speed that can make it hard to keep up with the developments. Being at the forefront of stream processing technology, the evolution of Apache Flink has mirrored many of these developments and continues to do so.
We will take you on a journey through the major milestones of stream processing technology in past years, diving into the latest additions that Apache Flink and other communities introduced to the stream processing landscape, such as Streamng SQL, Time Versioned Tables, cluster-library-duality, language portability, etc.
We will take a sneak peek into our crystal ball and present in what the Flink community is working on next.
Unified stateful big data processing in Apache Beam (incubating)
Apache Beam lets you process unbounded, out-of-order, global-scale data with portable high-level pipelines, but not all use cases are pipelines of simple “map” and “combine” operations. Aljoscha Krettek introduces Beam’s new State API, which brings scalability and consistency to fine-grained stateful processing while interoperating with Beam’s other features such as consistent event-time windowing and windowed side inputs—all while remaining portable to any Beam runner, including Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Aljoscha covers the new state and timer features in Beam and shows how to use them to express common real-world use cases in a backend-agnostic manner.
Examples of new use cases unlocked by Beam’s new mutable state and timers include:
* Microservice-like streaming applications such as new user account verification and digital ordering
* Complex aggregations that cannot easily be expressed as an efficient associative combiner
* Output based on customized conditions, such as limiting to only “significant” changes in a learned model (resulting in potentially large cost savings in subsequent processing)
* Fine control over retrieval and storage of intermediate values during aggregation
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Aljoscha Krettek offers an overview of the modern stream processing space, details the challenges posed by stateful and event-time-aware stream processing, and shares core archetypes ("application blueprints”) for stream processing drawn from real-world use cases with Apache Flink.
Topics include:
* Aggregating IoT event data, in which event-time-aware processing, handling of late data, and state are important
* Data enrichment, in which a stream of real-time events is “enriched” with data from a slowly changing database of supplemental data points
* Dynamic stream processing, in which a stream of control messages and dynamically updated user logic is used to process a stream of events for use cases such as alerting and fraud detection
Advanced Flink Training - Design patterns for streaming applications
The document describes requirements for a platform to detect suspicious behavior in an organization. It involves three patterns:
1) Time-based aggregations to detect behaviors like many login failures within a short time. Windowing and aggregating events is needed.
2) Data enrichment to report details of alerts, like fetching user profiles to identify users. Side inputs allow querying external databases during event processing.
3) Dynamic processing since rules change over time. Broadcast state stores evolving rules and connects them to user event streams for continuous checking.
Apache Flink - A Stream Processing Engine
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Apache Flink is an open source project that offers both batch and stream processing on top of a common runtime and exposing a common API.
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Apache Flink Hands-On
Apache Flink is an open source stream processing framework that originated from a research project at TU Berlin in 2009. It is undergoing incubation at the Apache Software Foundation and allows for fast and reliable processing of big data. Flink provides high-level APIs for the Java and Scala programming languages to define and execute dataflow programs on top of distributed datasets.
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Apache Flink(tm) - A Next-Generation Stream Processor
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Overview
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Key Topics Covered
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Robust stream processing with Apache Flink
- 1. 1 Aljoscha Krettek @aljoscha Strata + Hadoop World Singapore December 2016 Robust Stream Processing with Apache Flink®
- 2. 2 Original creators of Apache Flink® Providers of the dA Platform, a supported Flink distribution
- 3. Agenda 3 What is Apache Flink®? What is stateful Stream Processing? Windowed Computation over Streams Robust Time Handling Robust Failure Handling Robust Planned Downtime Handling
- 4. What is Apache Flink? 4 Apache Flink is an open source platform for distributed stream and batch data processing.
- 5. What is Apache Flink? 5 Cluster Standalone, YARN Cloud GCE, EC2 Local Single JVM Runtime Distributed Streaming Dataflow DataStream API Stream Processing DataSet API Batch Processing CEP EventProcessing Table Relational Table Relational FlinkML MachineLearning Gelly GraphProcessing DeployCoreAPI&Librarie
- 6. Stream Processing 6 Data Stream Data Stream Your code
- 7. Stateful Stream Processing 7 State Data Stream Data Stream Your code
- 8. A More Complex Example 8 Kafka Files Rabbit MQ Filter Map Join/Su m Influx DB C*
- 10. Robust Stream Processing with Apache Flink® 10
- 11. Code Example! 11Based on http://data-artisans.com/robust-stream-processing-flink-walkthrough/
- 12. Windowing 12
- 13. Processing Time vs. Event Time 13
- 14. What Is Event-Time Processing 14 1977 1980 1983 1999 2002 2005 2015 Processing Time Episode IV Episode V Episode VI Episode I Episode II Episode III Episode VII Event Time
- 15. What Is Event-Time Processing 15 1312735961112 1234567891011121314 Processing Time Event timestamp Message Queue
- 16. What’s The Problem? 16 13 12 735961112 1234567891011121314 Processing Time Processing-Time Windows 137356 12 137 356Event-Time Windows 12 1112 Mismatch between event time and processing time.
- 17. Sources of Time Mismatch Big Mismatch • Network disconnects • Slow network Small Mismatch • The nature of distributed systems • Differing system clock time 17
- 18. 18 Processing Time ~ Errors!
- 19. 19 Event Time ~ Accuracy
- 22. Closing 22
- 23. Flink community > 240 contributors, 95 contributors in Flink 1.1 42 meetups around the world with > 15,000 members 2x-3x growth in 2015, similar in 2016 23
- 24. Powered by Flink 24 Zalando, one of the largest ecommerce companies in Europe, uses Flink for real- time business process monitoring. King, the creators of Candy Crush Saga, uses Flink to provide data science teams with real-time analytics. Bouygues Telecom uses Flink for real-time event processing over billions of Kafka messages per day. Alibaba, the world's largest retailer, built a Flink-based system (Blink) to optimize search rankings in real time. See more at flink.apache.org/poweredby.html
- 25. 30 Flink applications in production for more than one year. 10 billion events (2TB) processed daily Complex jobs of > 30 operators running 24/7, processing 30 billion events daily, maintaining state of 100s of GB with exactly-once guarantees Largest job has > 20 operators, runs on > 5000 vCores in 1000-node cluster, processes millions of events per second 25
- 29. Software Engineer • Take on a dual role in both the open source Apache Flink® project as well as data Artisans' products. • Take responsibility for designs and features and be able to work both self-supervised and in cross- functional teams. • Be in direct touch with Flink users and participate in the day-to-day open source work. Sr. Data Engineer Openings at • Use your extensive experience to solve data engineering and stream processing problems for our customers. • Meet with customers, understand their requirements, and help guide them towards best-of-breed architectures. • Provide guidance and coding assistance to ensure successful production deployments.
- 30. 30 Flink Forward 2017 San Francisco Berlin • April 10-11, 2017 • The first Flink Forward event outside of Berlin • Register at sf.flink-forward.org/ • September 11-13 • Over 350 attendees at the last event • Registration opening soon!